JP2023009963A - Program for determining propriety of passage - Google Patents

Abstract

To automatically and highly accurately determine whether a subject can pass or not.SOLUTION: Disclosed is a program for determining the propriety of the passage, in which it is determined whether a subject can pass or not. In this program, a computer is caused to perform an information acquiring step for acquiring vaccination information for proving inoculation of a vaccine against an infectious disease from a person to be determined; and a determination step of determining whether the person to be determined can pass or not on the basis of the reference vaccination information for proving that a vaccine for an infectious disease is inoculated, and the reference vaccination information corresponding to the vaccination information acquired in the information acquisition step with reference to the association with the propriety of the passage.SELECTED DRAWING: Figure 14

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a passability determination program for determining whether or not a subject who may be infected with an infectious disease can pass.

In recent years, there has been an increasing social demand for risk assessment of infectious disease infection. In particular, the number of infected people has been increasing rapidly in recent years, and the new coronavirus is extremely contagious. required to take.

In such a case, there are so-called gates, such as gates and ticket gates at airports, stations, vehicles, entrances to venues, gates of houses, etc., which allow only specific persons to pass through and do not allow non-specific persons to pass through. If it is possible to detect an infected person or a person who may be infected at this gate portion, it may be possible to prevent the spread of infection by allowing the infected person to enter the inside.

Vaccination has been progressing in recent years in order to detect such infection risks and infected persons. In particular, for the new coronavirus, the movement to issue and utilize vaccination certificates is spreading around the world. It is also necessary to make judgments based on the assumption that those who have received this vaccine will have a lower risk of infection.

However, in the past, when detecting an infected person or an infection risk, such proof of vaccination was not taken into consideration, so the current situation was that more accurate detection and discrimination could not be performed.

Therefore, the present invention has been devised in view of the above-mentioned problems, and the purpose thereof is to determine whether or not a subject who may be infected with an infectious disease can pass or not, as well as a proof of vaccination. The present invention relates to a passage propriety judgment program for judging based on the above.

In order to solve the above-described problems, a passability determination program according to the present invention determines vaccination information proving that a subject has been vaccinated against an infectious disease in the passability determination program for determining whether or not a subject can pass through. By referring to the relationship between the information acquisition step acquired from the subject, the reference vaccination information that proves that the person has been vaccinated against infectious diseases, and whether or not the passage is permitted, the vaccination information acquired in the information acquisition step is obtained. and a determination step of determining whether or not the person to be determined can pass, based on the corresponding reference vaccination information.

Even if there is no special skill or experience, it is possible to determine the infection risk in real time from the user's current location.

1 is a block diagram showing the overall configuration of a system to which the present invention is applied; FIG. It is a figure which shows the specific structural example of a search apparatus. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention. It is a figure for demonstrating the operation|movement of this invention.

First Embodiment Hereinafter, an infectious disease infection risk determination program to which the present invention is applied will be described in detail with reference to the drawings.

FIG. 1 is a block diagram showing the overall configuration of an infectious disease infection risk determination system 1 in which an infectious disease infection risk determination program to which the present invention is applied is installed. The infectious disease infection risk determination system 1 includes an information acquisition unit 9 , a search device 2 connected to the information acquisition unit 9 , and a database 3 connected to the search device 2 .

The information acquisition unit 9 is a device for a person using this system to input various commands and information. The information acquisition unit 9 is not limited to a device for inputting text information, and may be configured by a device such as a microphone that can detect voice and convert it into text information. Further, the information acquisition unit 9 may be configured as an imaging device capable of capturing an image, such as a camera. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper document. Further, the information acquisition unit 9 may be integrated with the discriminating device 2 to be described later. The information acquisition unit 9 outputs the detected information to the discrimination device 2 . Further, the information acquisition unit 9 may be configured by means for specifying position information by scanning map information. Further, the information acquisition unit 9 may be configured with an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. The information acquisition unit 9 may also be configured with a communication interface that acquires weather data from the Meteorological Agency or a private weather forecast company. The information acquisition unit 9 may be composed of a body sensor worn on the body to detect body data, and the body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be configured with a sensor for Also, the body sensor may acquire biological data not only of humans but also of animals. The information acquisition unit 9 may be configured as a device that acquires information such as drawings by scanning or reading from a database. The information acquisition unit 9 may be configured by an odor sensor that detects odors and scents in addition to these.

The database 3 accumulates various information necessary to determine the risk of infectious disease infection. The information necessary to determine the risk of infectious disease infection includes reference congestion degree information regarding the degree of congestion of people in each area, reference attribute information of users detected in the past, and the above-mentioned infectious disease in users detected in the past. Reference infection history information on infection history, reference self-awareness information on subjective symptoms of infectious diseases detected in the past, reference image information of the face or body of the user detected in the past, information on the user detected in the past A data set of reference lifestyle information related to lifestyles for preventing infection and the infection risk of the user to be determined is stored.

In other words, in addition to the congestion degree information for reference, the database 3 stores attribute information for reference, infection history information for reference, self-awareness information for reference, image information for reference, life information for reference, and increase trend information for reference. Any one or more and the infection risk determined in the past are associated with each other and stored. By the way, the infection risk determined in the past is not only based on actual data from each user determined in the past, but also by setting a fictitious persona and determining it by experts such as institutions, hospitals, and doctors. Infection risk may be included in learning data.
In addition to the case where learning data is accumulated in the database 3, a so-called edge AI may be configured in which learning data is installed in edge-side terminals, apparatuses, devices, and the like.

The search device 2 is composed of an electronic device such as a personal computer (PC), for example, but in addition to the PC, it is embodied in any other electronic device such as a mobile phone, a smartphone, a tablet terminal, a wearable terminal, etc. It may be one that is made into. A user can obtain a search solution by this search device 2 .

FIG. 2 shows a specific configuration example of the search device 2. As shown in FIG. This searching device 2 performs wired or wireless communication with a control unit 24 for controlling the entire searching device 2 and an operation unit 25 for inputting various control commands via operation buttons, keyboards, etc. A communication unit 26 for searching, an estimating unit 27 for making various judgments, and a storage unit 28, represented by a hard disk, for storing a program for performing a search to be executed are connected to the internal bus 21, respectively. . Furthermore, the internal bus 21 is connected to a display unit 23 as a monitor for actually displaying information.

The controller 24 is a so-called central control unit for controlling each component implemented in the search device 2 by transmitting control signals via the internal bus 21 . In addition, the control unit 24 transmits commands for various controls through the internal bus 21 according to operations through the operation unit 25 .

The operation unit 25 is embodied by a keyboard or a touch panel, and a user inputs an execution command for executing a program. The operation unit 25 notifies the control unit 24 when the execution command is input by the user. Upon receiving this notification, the control unit 24 cooperates with each component including the estimation unit 27 to execute desired processing operations. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

The estimation unit 27 estimates a search solution. The estimation unit 27 reads various information stored in the storage unit 28 and various information stored in the database 3 as necessary information in executing the estimation operation. This estimation unit 27 may be controlled by artificial intelligence. This artificial intelligence may be based on any known artificial intelligence technology.

The display unit 23 is composed of a graphic controller that creates a display image based on control by the control unit 24 . The display unit 23 is implemented by, for example, a liquid crystal display (LCD).

When the storage unit 28 is configured with a hard disk, predetermined information is written to each address based on the control by the control unit 24, and this information is read as necessary. The storage unit 28 also stores a program for executing the present invention. This program is read and executed by the control unit 24 .

The operation of the contagious disease infection risk determination system 1 configured as described above will be described.

In the infectious disease infection risk determination system 1, as shown in FIG. 3, for example, it is assumed that three or more degrees of association between the reference congestion level information and the infection risk are preset and acquired. The reference congestion degree information is information indicating how crowded each area is with passers-by (traffic vehicles) and people staying there.

FIG. 4 is a diagram for explaining this reference congestion degree information. Each area is a concept including both outdoors and indoors. The number of people is different for each area, the area with the fewest people is L3, and the area with the most people is L4. They are L4, L2, L1, and L3 in descending order of the number of people, and the degree of congestion is also in that order. The reference congestion degree is information indicating such a congestion degree. That is, this reference congestion degree may be indicated by the number of people per unit area.

In order to obtain such reference congestion degree data, it may be obtained from location information transmitted from a device possessed by each person. The device here is an electronic device owned by each person, such as a mobile phone, a smart phone, or a wearable terminal, and each device is composed of a device capable of acquiring position information. As a method of acquiring position information, for example, GPS or the like may be used. In this way, the location information of each person can be acquired by collecting the location information acquired by the device possessed by each person. By collecting such position information from each person in each of the areas L1 to L4, the above-described degree of congestion can be obtained. By sequentially acquiring such position information of each person in chronological order, the degree of congestion can be sequentially obtained in chronological order. It is possible to update.

In order to obtain such reference congestion degree data, it may be obtained from image information obtained by capturing pedestrian traffic conditions from a camera installed in each area. A camera captures an image that can capture the situation of each area. Passers-by are extracted from the obtained image through image analysis technology, and the number of passers-by is counted. This extraction of passers-by may use deep learning technology as necessary, automatically discriminate based on the feature amount of the analysis image, and convert it into data. By executing such image analysis for each of the areas L1 to L4, the number of people in each of the areas L1 to L4 can be detected, and the degree of congestion described above can be obtained. By sequentially obtaining image information of pedestrian traffic conditions from cameras installed in each area in a time-series manner, it is possible to determine the degree of congestion in a time-series manner. The congestion degree information for reference can also be sequentially updated to the latest information in real time.

In order to obtain such reference congestion degree data, it may be obtained from voice information obtained by recording voices on the street with a microphone installed in each area. Record audio in each area with a microphone. Estimate the number of passers-by using speech recognition technology. A simple model may be used in which it is estimated that the louder the sound detected by the microphone, the more passers-by. By executing such voice recognition for each of the areas L1 to L4, the number of people in each of the areas L1 to L4 can be detected, and the congestion degree described above can be obtained. By sequentially acquiring the traffic conditions of pedestrians from the microphones installed in each area in chronological order, the degree of congestion can be obtained in chronological order. can be sequentially updated to the latest in real time.

Infection risk indicates the degree of risk of infection, and may be expressed as a binary value of whether or not there is danger, or very high risk, moderate risk, normal, and moderate safety. , safety, and the like. In addition, the risk of infection may be composed of, for example, a score out of 1000 points, and may be evaluated such that the higher the score, the higher the risk. For example, the infection risk A in FIG. 3 may be evaluated such that the score is 844 points, the infection risk B is 424 points, and the infection risk C is 501 points.

Contagious diseases for which the risk of infection is determined include all viruses and contagious diseases, such as common colds, influenza, and norovirus. Infectious diseases in particular include the novel coronavirus and severe acute respiratory syndrome (SARS).

In other words, through this reference congestion degree information and the infection risk data set, it is possible to know what kind of infection risk was actually evaluated with respect to the reference congestion degree information. In other words, the density of people and the risk of infection in the congestion degree information for reference form a data set. For this reason, by collecting reference congestion degree information and data sets of infection risk, it is possible to know how much the infection risk was when the distance between the user detected in the past and the other party was. becomes possible.

In the example of FIG. 3, it is assumed that the input data is, for example, reference congestion degree information P01 to P03. The reference congestion degree information as such input data is connected to the output. In this output, infection risk is displayed as an output solution.

The reference congestion level information is associated with the infection risk as the output solution through three or more levels of association. The congestion degree information for reference is arranged on the left side through this association degree, and the infection risk is arranged on the right side through the association degree. The degree of association indicates the degree of association with which infection risk the reference congestion degree information arranged on the left side is highly relevant. In other words, this degree of association is an index that indicates what kind of infection risk each reference congestion level information is likely to be associated with, and is used to select the most probable infection risk from the reference congestion level information. It shows the accuracy in In the example of FIG. 3, w13 to w19 are shown as association degrees. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of correlation between each combination as an intermediate node and the infection risk as an output. , conversely, the closer to 1 point, the lower the degree of correlation between each combination as an intermediate node and the infection risk as an output. This degree of association may be composed of nodes of a neural network in artificial intelligence, as shown in FIG. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence.

After creating such learned data, area information indicating the area where the user to be judged is actually located at the present time is acquired. Then, an increasing trend of regional epidemics in the acquired regional information is acquired. The increasing trend of this epidemic can be obtained from information on the number of infected people in each region that is released from time to time, information released by the government and health institutions, and news and newspaper articles released by each media outlet. The information may be obtained by reading the text data or by artificially inputting the information using a keyboard or the like. Then, reference increasing trend information indicating a trend similar to the increasing trend of epidemics thus obtained is specified. Then, the infection risk of the determination target user is determined based on the identified reference-use increasing trend information. The details of this infection risk determination are the same as in the first embodiment.

In this way, it is possible to search for the infection risk to be determined from the newly acquired increasing tendency information and display it to the user, various organizations, and the operator. By looking at this search result, users can be aware of taking actions to reduce the infection risk based on the detected infection risk. can be considered. By the way, in the process of outputting this infection risk, in addition to simply displaying the found infection risk, it is also possible to display a specific course of action based on this infection risk. The user may be strongly urged to take such a course of action, and for example, "Please move away from here" may be displayed on the screen.

FIG. 6 shows an example in which three or more levels of association are set between combinations of the above-described reference congestion degree information and reference attribute information, and the infection risks associated with the combinations.

The attribute information for reference is all information related to one user's attribute detected in the past. Attributes here include, for example, age, gender, occupation, chronic disease, life style actually restricted by a doctor, overtime hours per day, amount of exercise per day, sleeping hours per day, going to bed after dinner, etc. This includes health-related information such as how long until retirement and how often you smoke or drink alcohol.

In the example of FIG. 6, it is assumed that the input data are, for example, reference congestion degree information P01 to P03 and reference attribute information P18 to P21. An intermediate node shown in FIG. 7 is a combination of reference congestion degree information as such input data and reference attribute information. Each intermediate node is also connected to an output. In this output, infection risk is displayed as an output solution.

Each combination (intermediate node) of the reference congestion degree information and the reference attribute information is associated with the infection risk as the output solution through three or more degrees of association. The congestion degree information for reference and the attribute information for reference are arranged on the left side through the association degree, and the infection risk is arranged on the right side through the association degree. The degree of relevance indicates the degree of high relevance to the infection risk for reference congestion degree information and reference attribute information arranged on the left side. In other words, this degree of association is an index that indicates what kind of infection risk each reference congestion degree information and reference attribute information is likely to be associated with, and from the reference congestion degree information and reference attribute information It indicates the accuracy in selecting each of the most probable infection risks. In addition to congestion degree information, infection risk changes according to actual attribute information. Especially in the case of the new coronavirus, the higher the age, the higher the mortality rate. In the case of the new coronavirus, it has been reported that smokers are more likely to become seriously ill, so the risk of infection changes depending on whether or not they smoke. In this way, since the infection risk differs depending on the type of attribute information, the degree of association may be formed such that the higher the infection risk of an attribute, the higher the infection risk.

In the example of FIG. 6, w13 to w22 are shown as association degrees. These w13 to w22 are shown in 10 stages as shown in Table 1. The closer to 10 points, the higher the degree of correlation between each combination as an intermediate node and the output. The closer it is, the lower the degree of correlation between each combination as an intermediate node and the output.

The search device 2 acquires in advance three or more degrees of association w13 to w22 shown in FIG. In other words, the search device 2 accumulates past data as to whether the reference congestion level information, the reference attribute information, and the infection risk in that case were preferable when determining the actual search solution, By analyzing these, the degree of association shown in FIG. 6 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference congestion level information P01 and the reference attribute information P20, the infection risk is analyzed from the past data. For example, in the example of the intermediate node 61a, the outputs of infection risk A and infection risk B are linked. The degree is set to 2 points.

In the example of the degree of association shown in FIG. 6, the node 61b is a node that combines the reference congestion degree information P01 with the reference attribute information P18. degree is w16. The node 61c is a node that combines reference attribute information P19 and P21 with reference congestion degree information P02, and the degree of association for infection risk B is w17 and the degree of association for infection risk D is w18.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually giving advice. In such a case, attribute information is acquired in addition to congestion degree information from the user and the other party for whom the risk of infection is to be newly determined. The attribute information acquisition method is the same as the reference attribute information acquisition method.

Based on the congestion degree information and attribute information newly acquired in this way, the infection risk is searched for. In such a case, reference is made to the degrees of association shown in FIG. 6 (Table 1) that have been acquired in advance. For example, when the newly acquired congestion degree information is the same as or similar to P02 and the reference attribute information corresponding to the newly acquired attribute information is P21, the association degree is This node 61d is associated with the infection risk C with w19 and the infection risk D with the degree of association w20. In such a case, infection risk C with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the infection risk D, which has a low degree of association but is recognized as having an association itself, may be selected as the optimum solution. In addition, it is of course possible to select an output solution that is not connected by an arrow, and any other priority may be used as long as it is based on the degree of association.

Table 2 below shows examples of degrees of association w1 to w12 extending from the input.

Intermediate nodes 61 may be selected based on degrees of association w1 to w12 extending from this input. In other words, the greater the degree of association w1 to w12, the heavier the weight in selecting the intermediate node 61 may be. However, the degrees of association w1 to w12 may all have the same value, and the weighting in selecting the intermediate nodes 61 may all be the same.

FIG. 7 shows an example in which three or more levels of association are set between combinations of the above-described reference congestion degree information and reference infection history information, and the infection risks associated with the combinations.

The reference infection history information is all information related to the infection history of the infectious disease in one user or the other party detected in the past. This information about the history of infection may be either one of the user detected in the past or the other party, or may be extracted from both.

The reference infection history information is information about the past infection history of the infectious disease to be determined. This reference infection history information may be obtained by each user or his/her partner inputting it through the information acquisition unit 9, or may be obtained from a medical institution or the like. The reference infection history information includes all information about the infection history, such as when, where, how many times the infectious disease was contracted, how it was treated and cured, and the period until complete recovery.

In the example of FIG. 7, it is assumed that the input data are, for example, reference congestion level information P01 to P03 and reference infection history information P18 to P21. An intermediate node shown in FIG. 7 is a combination of reference congestion degree information and reference infection history information as such input data. Each intermediate node is also connected to an output. In this output, infection risk is displayed as an output solution.

Each combination (intermediate node) of the reference congestion degree information and the reference infection history information is associated with the infection risk as the output solution through three or more degrees of association. The congestion degree information for reference and the infection history information for reference are arranged on the left side through the degree of association, and the infection risk is arranged on the right side through the degree of association. The degree of association indicates the degree of high relevance to the infection risk for the reference congestion degree information and the reference infection history information arranged on the left side. In other words, this degree of association is an index indicating to what kind of infection risk each reference congestion degree information and reference infection history information is likely to be linked. It indicates the accuracy in selecting the most probable infection risk from the information. In addition to congestion degree information, infection risk changes according to actual infection history information. Especially if you have a history of previous infection and are immune, the risk of infection is reduced. On the other hand, in the case of communicable diseases that do not change the possibility of infection even with immunity, the risk of infection does not change significantly. In the case of an infectious disease in which the possibility of infection changes depending on the presence or absence of immunity, the degree of association may be formed so that the stronger the immunity, that is, the higher the history of infection, the higher the risk of infection.

In the example of FIG. 7, w13 to w22 are shown as association degrees. These w13 to w22 are shown in 10 stages as shown in Table 1. The closer to 10 points, the higher the degree of correlation between each combination as an intermediate node and the output. The closer it is, the lower the degree of correlation between each combination as an intermediate node and the output.

The search device 2 acquires in advance three or more degrees of association w13 to w22 shown in FIG. In other words, the search device 2 accumulates past data as to which of the reference congestion degree information, the reference infection history information, and the infection risk in that case was preferable when determining the actual search solution. , and by analyzing these, the degree of association shown in FIG. 7 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference congestion degree information P01 and the reference infection history information P20, the infection risk is analyzed from the past data. For example, in the example of the intermediate node 61a, the outputs of infection risk A and infection risk B are linked. The degree is set to 2 points.

In the example of the degree of association shown in FIG. 7, a node 61b is a node that combines reference infection history information P18 with reference congestion degree information P01. The association degree is w16. The node 61c is a node that combines reference infection history information P19 and P21 with reference congestion degree information P02, and the degree of association for infection risk B is w17, and the degree of association for infection risk D is w18. .

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually giving advice. In such a case, infection history information is acquired in addition to congestion degree information from the user and the partner for whom the risk of infection is to be newly determined. The acquisition method of the infection history information is the same as the acquisition method of the reference infection history information.

Based on the congestion degree information and infection history information newly acquired in this way, the infection risk is searched for. In such a case, reference is made to the degrees of association shown in FIG. 7 (Table 1) that have been acquired in advance. For example, when the newly acquired congestion degree information is the same as or similar to P02 and the reference infection history information corresponding to the newly acquired infection history information is P21, the relationship This node 61d is associated with the infection risk C at w19 and with the infection risk D at w20. In such a case, infection risk C with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the infection risk D, which has a low degree of association but is recognized as having an association itself, may be selected as the optimum solution. In addition, it is of course possible to select an output solution that is not connected by an arrow, and any other priority may be used as long as it is based on the degree of association.

FIG. 8 shows an example in which three or more levels of association are set between combinations of the above-described reference congestion degree information and reference self-awareness information, and the infection risks associated with the combinations.

The subjective information for reference is any information related to subjective symptoms of the infectious disease detected in the past in one user or his/her partner. The information about the subjective symptoms may be either one of the previously detected user or the other party, or may be extracted from both.

The self-awareness information for reference shows the symptoms that the person is actually aware of regarding the infectious disease to be judged. This includes any symptoms that you are aware of. This self-awareness information for reference may be obtained by inputting by each user or the other party through the information acquisition unit 9, or by interviewing each user or the other party and inputting it manually by the interviewer. Alternatively, it may be obtained from a medical institution or the like.

In the example of FIG. 8, it is assumed that the input data are, for example, reference congestion degree information P01 to P03 and reference self-awareness information P18 to P21. The intermediate node shown in FIG. 8 is a combination of the reference congestion degree information as input data and the reference self-awareness information. Each intermediate node is also connected to an output. In this output, infection risk is displayed as an output solution.

Each combination (intermediate node) of the reference congestion degree information and the reference self-awareness information is associated with the infection risk as the output solution through three or more levels of association. The congestion degree information for reference and the self-awareness information for reference are arranged on the left side through the degree of association, and the infection risk is arranged on the right side through the degree of association. The degree of association indicates the degree of high relevance to the infection risk with respect to the reference congestion degree information and the reference self-awareness information arranged on the left side. In other words, the degree of association is an index that indicates what kind of infection risk each reference congestion degree information and reference self-awareness information is likely to be associated with, and from the reference congestion degree information and reference self-awareness information It indicates the accuracy in selecting each of the most probable infection risks. In addition to congestion degree information, infection risk changes according to actual self-awareness information. In particular, if the subject fits the subjective symptoms categorized for each infectious disease, there is a possibility that the patient is afflicted with that infectious disease, and the risk given to the other party increases accordingly. In such a case, the degree of association may be formed such that the more subjective symptoms there are, the higher the risk of infection.

In the example of FIG. 8, w13 to w22 are shown as association degrees. These w13 to w22 are shown in 10 stages as shown in Table 1. The closer to 10 points, the higher the degree of correlation between each combination as an intermediate node and the output. The closer it is, the lower the degree of correlation between each combination as an intermediate node and the output.

The search device 2 acquires in advance the degrees of association w13 to w22 of three or more stages shown in FIG. In other words, the search device 2 accumulates past data as to whether the reference congestion degree information, the reference self-awareness information, and the infection risk in that case was preferable in determining the actual search solution, By analyzing these, the degree of association shown in FIG. 8 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference congestion degree information P01 and the reference self-awareness information P20, the infection risk is analyzed from the past data. For example, in the example of the intermediate node 61a, the outputs of infection risk A and infection risk B are linked. The degree is set to 2 points.

In the example of the degree of association shown in FIG. 8, a node 61b is a node of a combination of reference self-awareness information P18 with reference congestion degree information P01. degree is w16. The node 61c is a node of a combination of the reference self-awareness information P19 and P21 with respect to the reference congestion degree information P02, and the degree of relevance of the infection risk B is w17 and the degree of relevance of the infection risk D is w18.

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually giving advice. In such a case, awareness information is acquired in addition to the congestion level information from the user and the partner for whom the risk of infection is to be newly determined. The acquisition method of the self-awareness information is the same as the acquisition method of the self-awareness information for reference.

Based on the newly acquired congestion degree information and self-awareness information in this way, the risk of infection is searched for. In such a case, reference is made to the degrees of association shown in FIG. 8 (Table 1) that have been acquired in advance. For example, when the newly acquired congestion degree information is the same as or similar to P02, and the reference self-awareness information corresponding to the newly acquired self-awareness information is P21, the association degree is This node 61d is associated with the infection risk C with w19 and the infection risk D with the degree of association w20. In such a case, infection risk C with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the infection risk D, which has a low degree of association but is recognized as having an association itself, may be selected as the optimum solution. In addition, it is of course possible to select an output solution that is not connected by an arrow, and any other priority may be used as long as it is based on the degree of association.

In the present invention, instead of the reference self-awareness information shown in FIG. 8, reference image information obtained by imaging the face or body of one user and/or the other party detected in the past is the reference congestion degree information. It may be learned in association with the degree of relevance in combination.

Reference image information is information obtained by capturing an image of the face or body of one user and/or the other party with a camera. If the face of the user or the other party is actually masked, droplets are less likely to fly, and if the user or the other party is wearing gloves, the possibility of preventing infection due to contact increases. . Whether or not the person is in a state where there is a high possibility that infection can be prevented, such as wearing such a mask and gloves, is determined through the image. If a mask is actually worn on the face of one user and/or the other party, it is read by image analysis, and if necessary, deep learning technology is used, and automatic analysis is performed based on the feature amount of the analysis image. It may be determined and converted into data.

In addition, when learning through the degree of association, the degree of association that leads to the risk of infection, including various public health data and opinions of experts, such as how much infection risk can be prevented by wearing the mask may be designed.

In such a case, three or more degrees of association between a combination of reference congestion degree information and reference image information and infection risk are acquired in advance. Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually performing a search from now on. In such a case, in addition to the congestion degree information described above, image information obtained by capturing an image of the face or body of the user to be determined and/or the partner is acquired. This image information is composed of the same kind of data as the reference image information described above. Then, after referring to the association degree, the infection risk is determined in the same manner as described above based on the newly acquired congestion degree information and image information.

In the present invention, as an alternative to the reference self-awareness information shown in FIG. 8, the previously detected user's reference lifestyle information related to the lifestyle for preventing infection is associated with a combination of reference congestion degree information. It may be learned in association with degrees.

The reference lifestyle information is information that categorizes lifestyles that are particularly effective in preventing infection among lifestyles. Lifestyles that are effective in preventing infection include, for example, gargling, washing hands, whether or not you take a bath, how often, and whether you lead a regular life. It also includes information on whether they have chosen a belt and whether they are actively vaccinated to prevent infection.

In addition, when learning through the degree of association, we will include various public health data and expert opinions, etc., such as how much infection risk can be prevented by adopting a lifestyle that is effective in preventing infection. , the degree of association leading to infection risk may be designed.

In such a case, three or more degrees of association between a combination of reference congestion degree information and reference lifestyle information and infection risk are acquired in advance. Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually performing a search from now on. In such a case, in addition to the above-described congestion degree information, the lifestyle information of the user to be judged and/or his partner is acquired. This lifestyle information is composed of the same type of data as the reference lifestyle information described above. Then, after referring to the association degree, the infection risk is determined in the same manner as described above based on the newly acquired congestion degree information and lifestyle information.

In FIG. 9, in addition to the above-described reference congestion degree information and reference attribute information, a combination of reference increase trend information and three or more degrees of association with the infection risk for the combination are set. shows an example. The reference increasing trend information is information about the increasing trend of epidemics in each area. This reference-use increasing trend information is information that is broadly classified and indicates whether the number of patients with the infectious disease is increasing, stagnant, or decreasing. The increasing trend information for reference is not limited to the case where it is indicated by such three levels, and the degree of increase and the degree of decrease may be indicated by a finer pitch. Also, the reference increasing trend information may be composed of data itself such as bar graphs that actually show the increasing trend or decreasing trend of epidemics by prefecture or country. Such reference-use increasing trend information is associated with each region classified by prefecture, country, or the like. The reference increasing trend information may also consist of information regarding whether clusters of infection have occurred in any area and at any building level in any area.

In such a case, as shown in FIG. 9, the degree of relevance is obtained by combining a set of combinations of the reference congestion level information, the reference attribute information, and the reference increasing trend information as intermediate nodes 61a to 61e in the same manner as described above. to be expressed.

For example, in FIG. 9, the node 61c is associated with the reference congestion degree information P02 with the degree of association w3, with the reference attribute information P15 with the degree of association w7, and with the reference increasing trend information P21 with the degree of association w11. Similarly, the node 61e is associated with the reference congestion degree information P03 with the degree of association w5, with the reference attribute information P15 with the degree of association w8, and with the reference increasing trend information P20 with the degree of association w10.

Similarly, when such a degree of association is set, in addition to newly acquired congestion degree information and attribute information, new area information is acquired. This area information is area information related to the area corresponding to the location information of the user to be determined. If the location information of the user to be determined is currently Tokyo, the area information will be Tokyo. In addition, in regional information consisting of broad categories, it is Japan.

A solution search is performed while referring to the degree of association shown in FIG.

When estimating this price, reference is made to the degree of association shown in FIG. 10 that has been acquired in advance. For example, the acquired congestion level information is the same or similar to the reference congestion level information P02, the acquired attribute information corresponds to the reference attribute information P15, and the reference increase trend information corresponding to the area of the acquired area information is If it corresponds to P21, the combination is associated with node 61c, and this node 61c is associated with infection risk B with a degree of association w17 and with infection risk D with a degree of association w18. As a result of such degree of association, search solutions are actually obtained based on w17 and w18.

When three or more types of such input parameters are combined, in addition to the increasing trend information for reference, congestion degree information for reference, attribute information for reference, infection history information for reference, self-awareness information for reference, and image for reference Any one or more of the information and the life information for reference can be applied in combination.

In addition to the congestion degree information, when acquiring any two or more of attribute information, infection history information, subjective information, image information, living information, etc., two or more depending on the two or more information to be acquired Reference information (attribute information for reference, infection history information for reference, self-awareness information for reference, image information for reference, living information for reference, etc.) is combined with congestion degree information for reference, and the infection risk for that combination By preparing learning data having three or more degrees of association, it is possible to search for solutions in the same way.

In addition to the congestion degree information, in addition to any one or more of attribute information, infection history information, self-awareness information, image information, living information, etc. In addition, when acquiring other information, the information to be acquired combination of reference attribute information, reference infection history information, reference self-awareness information, reference image information, reference living information, etc., and reference information according to other acquired information, and for the combination By creating learning data consisting of three or more degrees of association with infection risk, it is possible to search for solutions in the same way.

Further, according to the present invention, as shown in FIG. 10, each infection risk is determined based on the degree of association between a combination of two or more kinds of information, namely reference information U and reference information V. FIG. This reference information U is reference congestion degree information, and reference information V is reference attribute information, reference infection history information, reference self-awareness information, reference image information, reference living information, and the like. do. Alternatively, the reference information U is reference attribute information, reference infection history information, reference self-awareness information, reference image information, reference living information, etc., and reference V is reference congestion degree information. shall be

At this time, as shown in FIG. 10, the output obtained for the reference information U may be directly used as input data and associated with the output via an intermediate node 61 in combination with the reference information V. FIG. For example, after providing an output solution for the reference information U, this may be used as an input as it is, and the degree of association with other reference information V may be used to search for the output.

In the above-mentioned degree of relevance, the degree of relevance is expressed by a 10-level evaluation, but it is not limited to this, and it may be expressed by a degree of relevance of 3 or more stages. For example, 100 steps or 1000 steps may be used. On the other hand, this degree of association does not include those expressed in two stages, that is, whether or not they are associated with each other, either 1 or 0.

According to the present invention configured as described above, anyone can easily search for the infection risk of a company that is considering financing, even without special skills or experience. Further, according to the present invention, it is possible to judge the search solution with a higher degree of accuracy than a human being does. Furthermore, by configuring the degree of association described above with artificial intelligence (neural network, etc.) and making it learn, it is possible to further improve the discrimination accuracy.

Since the above-described input data and output data may not be exactly the same in many cases during the learning process, information obtained by classifying these input data and output data according to type may be used. That is, the information P01, P02, . . . P15, 16, . A data set may be created between the data and the output data for learning.

Moreover, according to the present invention, it is characterized in that the optimum solution search is performed through the degree of association set to three or more stages. The degree of association can be described by a numerical value of, for example, 0 to 100% in addition to the 10 stages described above, but is not limited to this, and can be described by a numerical value of 3 or more stages. may be configured.

Based on such a degree of association represented by a numerical value of three or more levels, by determining the infection risk with high credibility and low misidentification, it is possible to consider multiple candidates for the possibility of a search solution. , it is also possible to search and display in descending order of the degree of association.

In addition to this, according to the present invention, it is possible to make a judgment without overlooking even a very low output discrimination result such as a correlation degree of 1%. Remind the user that even discrimination results with an extremely low degree of association are connected as slight signs, and that once in dozens or hundreds of times, the discrimination results may be useful. be able to.

Furthermore, according to the present invention, there is an advantage that a search policy can be determined by setting thresholds by performing a search based on three or more degrees of association. If the threshold value is lowered, even if the degree of association is 1%, it can be picked up without omission. be. On the other hand, if the threshold is set high, there is a high possibility that the optimal search solution can be detected with a high probability. Sometimes the solution is overlooked. It is possible to decide which one to place emphasis on based on the way of thinking of the user side and the system side, and it is possible to increase the degree of freedom in selecting such points to place emphasis on.

Furthermore, in the present invention, the degree of association described above may be updated. This update may reflect information provided via a public communication network such as the Internet, for example. In addition to congestion degree information, when knowledge, information, and data related to attribute information, infection history information, self-awareness information, image information, life information, etc. are acquired, the association degree is increased or decreased accordingly.

In other words, this update corresponds to learning in terms of artificial intelligence. Since new data is acquired and reflected in the learned data, it can be said that it is a learning act.

In addition to updating the degree of association based on information that can be obtained from the public communication network, the system side or the user side based on the contents of research data, papers, conference presentations, newspaper articles, books, etc. by experts It may be updated manually or automatically. Artificial intelligence may be used in these update processes.

Also, the process of initially building a trained model and the above-described updating may use not only supervised learning but also unsupervised learning, deep learning, reinforcement learning, and the like. In the case of unsupervised learning, instead of loading and learning datasets of input and output data, learning is performed by loading information corresponding to the input data, and from there self-forming the degree of association related to the output data. You can let it run.

Second Embodiment A second embodiment of the infectious disease infection risk determination system 1 to which the present invention is applied will be described below. In executing the second embodiment, the infectious disease infection risk determination system 1, the information acquisition unit 9, the discrimination device 2, and the database 3 used in the first embodiment are used in the same manner. Description of each of these configurations is omitted below by citing the description of the first embodiment.

In the epidemic infection risk determination system 1, for example, as shown in FIG. 11, it is premised that three or more degrees of association between the reference increasing trend information and the infection risk are preset and acquired. The details of the increasing trend information for reference are as described in the first embodiment.

In the example of FIG. 11, it is assumed that the input data are, for example, reference increasing trend information P01 to P03. The increasing trend information for reference as such input data is connected to the output. In this output, infection risk is displayed as an output solution.

The increasing trend information for reference is associated with the infection risk as the output solution through three or more degrees of association. The increasing trend information for reference is arranged on the left side through this degree of association, and the infection risk is arranged on the right side through the degree of association. The degree of association indicates the degree of association with which infection risk the reference increasing tendency information arranged on the left side is highly associated. In other words, this degree of association is an index that indicates to what kind of infection risk each piece of reference increasing trend information is likely to be linked, and is used to select the most probable infection risk from the reference increasing trend information. It shows the accuracy in In the example of FIG. 11, w13 to w19 are shown as association degrees. These w13 to w19 are shown in 10 stages as shown in Table 1 below, and the closer to 10 points, the higher the degree of correlation between each combination as an intermediate node and the infection risk as an output. , conversely, the closer to 1 point, the lower the degree of correlation between each combination as an intermediate node and the infection risk as an output.

The search device 2 acquires in advance the degrees of association w13 to w19 of three or more stages shown in FIG. In other words, the search device 2 accumulates past data on which of the reference increase trend information and the infection risk in that case was adopted in determining the actual search solution, and analyzes these. By doing so, the degree of association shown in FIG. 11 is created.

In the case of the reference increasing trend information P01, if there are many cases of infection risk A, the degree of association leading to this infection risk A is set higher, and if there are many cases of infection risk B, this infection risk Set a higher degree of association leading to B. For example, in the example of the increasing trend information for reference P01, it is linked to infection risk A and infection risk C, but from the previous case, the degree of association of w13 that leads to infection risk A is 7 points, and w14 that leads to infection risk C is 7 points. The degree of association is set to 2 points.

Further, the degree of association shown in FIG. 11 may be composed of nodes of a neural network in artificial intelligence. That is, the weighting coefficients for the outputs of the nodes of this neural network correspond to the degrees of association described above. Moreover, it is not limited to a neural network, and may be composed of all decision-making factors that constitute artificial intelligence.

After creating such learned data, information on the area (region) in which the user to be judged is actually located at the present time is acquired. Then, the infection risk of the determination target user is determined based on the obtained reference-use increasing trend information in the area where the user is located.

FIG. 12 shows an example in which three or more levels of association are set between combinations of the above-described reference-use increasing trend information and reference-use external environment information, and infection risks for the combinations.

Reference external environment information is various information related to external environment information. The external environment information here includes economic data (GDP, employment statistics, industrial production index, capital investment, labor force survey, etc.), household data (household consumption survey, household data, average working hours per week, savings amount, etc.). statistical data, annual income statistical data, etc.), real estate data (office vacancy rate, unit price per tsubo, rent market, land price, vacant house data, etc.), natural environment data (disaster data, temperature data, precipitation data, wind direction data, humidity data) etc.). Some or all of these data are reflected in the external environment information. The external environment information for reference may categorize the external environment itself. In other words, the external environment information for reference may be information classified by type, for example, it may be classified by dividing it by data in the employment statistics. In addition, they may be categorized according to patterns (for example, patterns such as whether the growth rate of GDP increases rapidly or gradually). Information about the external environment, that is, the economy, is information that should be fully considered when considering the risk of infection. If we stop the economy out of fear of the risk of infection, the number of unemployed people and suicides will increase. Therefore, in order not to set the risk of infection too high, it can be said that this information on the economy must be fully considered.

In the example of FIG. 12, the input data are, for example, reference increasing trend information P01-P03 and reference external environment information P18-21. The intermediate node shown in FIG. 12 is a combination of the increasing trend information for reference and the external environment information for reference as such input data. Each intermediate node is also connected to an output. In this output, infection risk is displayed as an output solution.

Each combination (intermediate node) of the increasing trend information for reference and the external environment information for reference is associated with the infection risk as the output solution through three or more degrees of association. The increasing trend information for reference and the external environment information for reference are arranged on the left side through the degree of association, and the infection risk is arranged on the right side through the degree of association.

In the example of FIG. 12, w13 to w22 are shown as association degrees. These w13 to w22 are shown in 10 stages as shown in Table 1. The closer to 10 points, the higher the degree of correlation between each combination as an intermediate node and the output. The closer it is, the lower the degree of correlation between each combination as an intermediate node and the output.

The search device 2 acquires three or more degrees of association w13 to w22 shown in FIG. 12 in advance. In other words, the search device 2 accumulates past data indicating which of the increase trend information for reference and the external environment information for reference and the infection risk in that case was preferable in determining the actual search solution. , and by analyzing these, the degree of association shown in FIG. 12 is created.

This analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference-use increasing trend information P01 and the reference-use external environment information P20, the infection risk is analyzed from the past data. For example, in the example of the intermediate node 61a, the outputs of infection risk A and infection risk B are linked. The degree is set to 2 points.

In the example of the degree of association shown in FIG. 12, the node 61b is a node that combines the external environment information for reference P18 with the increasing trend information for reference P01. The association degree is w16. The node 61c is a node that is a combination of the reference external environment information P19 and P21 with respect to the reference increasing trend information P02, and the degree of association for infection risk B is w17 and the degree of association for infection risk D is w18. .

Such a degree of association becomes learned data in terms of artificial intelligence. After creating such learned data, the above-described learned data will be used when actually giving advice. In such a case, the external environment information is acquired in addition to the increase trend information from the user for whom the risk of infection is to be newly determined and from the other party. The acquisition method of the external environment information is the same as the acquisition method of the external environment information for reference.

The infection risk is searched for based on the newly acquired increasing trend information and external environment information in this way. In such a case, reference is made to the degrees of association shown in FIG. 12 (Table 1) that have been acquired in advance. For example, when the newly acquired increasing trend information is the same as or similar to P02, and the reference external environment information corresponding to the newly acquired external environment information is P21, the relationship This node 61d is associated with the infection risk C at w19 and with the infection risk D at w20. In such a case, infection risk C with the highest degree of association is selected as the optimum solution. However, it is not essential to select the one with the highest degree of association as the optimum solution, and the infection risk D, which has a low degree of association but is recognized as having an association itself, may be selected as the optimum solution. In addition, it is of course possible to select an output solution that is not connected by an arrow, and any other priority may be used as long as it is based on the degree of association.

As an alternative to the external environment information, the degree of association may be formed by combining the reference market information and the reference increasing trend information. The market condition information for reference here is various information about the market conditions. Examples of this reference market information include interest rates, exchange rates, stock prices of various brands, crude oil, futures, precious metals, bitcoins, and other price movements. This reference market condition information may be displayed as a time-series chart, a line graph, or the like for these targets. Further, information such as Bollinger band, trading volume, MACD, moving average line, etc. may be attached. Also, this market information may be accompanied by information such as charts, Bollinger bands, MACDs, moving averages, etc. of each issue. Information such as charts, Bollinger bands, MACD, moving averages, etc. showing price movements between currencies may also be attached to exchange rates.

In such a case, a combination of the reference increase trend information and the reference market condition information related to the market conditions at the time when the reference increase trend information was acquired, and the degree of association of three or more levels with the infectious disease infection risk are formed in advance. Keep Then, when market information is newly acquired, this degree of association is referred to. The type of market information to be newly acquired is the same as the reference market information. The method of obtaining the market information is the same as the method of obtaining the reference market information. A user's infection risk is determined based on the reference increasing trend information in the region corresponding to the acquired regional information and the reference market condition information corresponding to the acquired market condition information. A specific method for determining the infection risk is the same as in the first embodiment.

As an alternative to the external environment information, the degree of association may be formed by combining reference congestion degree information and reference increasing trend information. The details of the reference congestion degree information and the congestion degree information are the same as those described in the first embodiment.

In such a case, three or more degrees of association are prepared for the infectious disease infection risk for a combination of reference increasing trend information and reference congestion degree information in each area. Then, in addition to the reference increase trend information in the area corresponding to the newly acquired area information, the user's infection risk is determined based on the reference congestion degree information according to the congestion degree in the area corresponding to the acquired area information. do. A specific method for determining the infection risk is the same as in the first embodiment.

As an alternative to the external environment information, the degree of association may be formed by combining reference attribute information and reference increasing trend information. Details of reference attribute information and attribute information are the same as those described in the first embodiment.

In such a case, three or more degrees of association between a combination of the increasing trend information for reference and the attribute information for reference of users detected in the past and the infection risk of the infectious disease are prepared. Then, the infection risk of the user is determined based on the reference attribute information corresponding to the attribute information obtained in addition to the reference increase trend information in the area corresponding to the newly obtained area information.

As an alternative to the external environment information, the degree of association may be formed by combining reference infection history information and reference increasing trend information. The details of the reference infection history information and the infection history information are the same as those described in the first embodiment.

In such a case, a combination of reference increasing trend information and reference infection history information related to the infection history of an infectious disease detected in the past for each user and a degree of association of three or more levels with the infection risk of an infectious disease be prepared. Then, the user's infection risk is determined based on the reference increase trend information in the area corresponding to the newly acquired area information and the reference infection history information corresponding to the infection history information acquired from the user.

As an alternative to the external environment information, the degree of association may be formed by combining reference health information information and reference increasing trend information. The reference health information and health information referred to here are all information related to the body detected from the user via the vital sensor. The vital sensor here is composed of thermometers and sensors for measuring body temperature, pulse, pulse wave, heart rate, blood pressure, and the like. Such health-related information such as body temperature, pulse, pulse wave, heart rate, and blood pressure is also extremely important information for determining the risk of infection.

In such a case, three or more levels of correlation between a combination of reference-use increasing trend information and reference-use health information of each user detected in the past via a vital sensor and the infection risk of the infectious disease are determined in advance. keep forming. Then, when health information is newly detected from the user via the vital sensor, based on the reference increase trend information in the area corresponding to the acquired area information and the reference health information according to the acquired health information , to determine the user's infection risk.

As an alternative to the external environment information, the degree of association may be formed by combining reference climate information and reference increasing trend information. Reference climatic information and climatic information as used herein refer to all kinds of information related to climate. The reference climatic information and climatic information herein refer to all kinds of climatic information such as temperature, humidity, weather (clear, cloudy, rainy, etc.), amount of precipitation, wind speed, and wind direction. In particular, viruses such as colds and influenza are more likely to spread during the dry season in winter, so the user's risk of infection is determined by referring to such reference climate information.

In such a case, three or more degrees of association are acquired for the infectious disease infection risk for a combination of the reference increasing trend information and the reference climate information regarding the climate in each region. Then, the infection risk of the user is determined based on the reference increase trend information in the area corresponding to the newly acquired area information and the reference climate information corresponding to the acquired climate information.

In addition, in the second embodiment, in addition to the increase trend information, when acquiring any two or more of congestion degree information, external environment information, market information, attribute information, infection history information, health information, climate information, etc. , Two or more reference information (reference congestion degree information, reference external environment information, reference market information, reference attribute information, reference infection history information, reference health Information, climate information for reference, etc.) is combined with increasing trend information for reference, and learning data consisting of three or more levels of association with the infection risk for the combination is created, and the same solution search is performed. be able to.

In addition, in addition to the increase trend information, in addition to any one or more of congestion degree information, external environment information, market information, attribute information, infection history information, health information, climate information, etc. In addition, when acquiring other information Similarly, reference congestion degree information, reference external environment information, reference market information, reference attribute information, reference infection history information, reference health information, reference climate information, etc. , By creating learning data consisting of a combination of reference information according to other information to be acquired and the degree of association with the infection risk for that combination at three or more levels, it is possible to search for solutions in the same way. can.

In addition, in the first embodiment, reference congestion degree information is used as a basis, and reference attribute information, reference infection history information, reference self-awareness information, reference image information, reference life information, etc. Examples of configuring degrees have been described. In the second embodiment, in addition to reference increasing trend information, reference congestion degree information, reference external environment information, reference market information, reference attribute information, reference infection history information, reference health information, reference An example of configuring the degree of association by combining climate information and the like has been described.

Both the first embodiment and the second embodiment are not limited to these, and any other reference information (reference attribute information, reference infection history information, reference self-awareness information, reference image information, reference living information, external environmental information for reference, market information for reference, health information for reference, climate information for reference), and the degree of relevance is calculated by combining this reference information with other different reference information. may be configured. In such a case, it is a matter of course that the solution search is performed by receiving input of information corresponding to the reference information constituting the combination.

Further, both the first embodiment and the second embodiment are not limited to the embodiments described above. For example, as shown in FIG. A degree of association may be used. In such a case, solution search is performed based on three or more degrees of association between the reference information and the infection risk according to the newly acquired information.

In these cases, similarly, when information corresponding to reference information used as learning data is input, solution search is performed based on the above-described method.

Bullying probabilities determined through association may also be modified or weighted differently based on other reference information.

The other information for reference here means the above-mentioned information for reference (congestion degree information for reference, increasing trend information for reference, attribute information for reference, infection history information for reference, self-awareness information for reference, image information for reference, reference living information, reference external environment information, reference market information, reference health information, reference climate information), any other information other than the reference information Corresponds to reference information.

For example, in the reference infection history information as one of the other reference information, it is assumed that there was a history of being infected with a virus whose infection risk is to be determined in the past. In such cases, the risk of infection is often low due to immunity. At this time, it is set in advance so that A, which has a high infection risk, is weighted down, in other words, a process that leads to a search solution with a low infection risk is performed.

For example, other reference information G is an analysis result that suggests a higher risk level of infection, and reference information F is an analysis result that suggests a lower risk level of infection. and After the setting with the reference information in this way, if the actually obtained information is the same as or similar to the reference information G, processing is performed to increase the weight of the level with a high risk of infection. On the other hand, when the actually acquired information is the same as or similar to the reference information F, processing is performed to increase the weighting of the level with the low risk of infection. In other words, the degree of association itself leading to the risk of infection may be controlled based on the reference information FH. Alternatively, after the risk of infection is determined only by the degree of association described above, the obtained search solution may be modified based on the reference information FH. In the latter case, how and with what weight the possibility of bullying as a search solution is modified based on the reference information F to H is to reflect what is designed on the system side each time.

The reference information is not limited to any one type, and the solution search may be performed based on two or more types of reference information. Similarly, in such cases, the higher the risk of infection suggested by the reference information is, the higher the risk of infection as the search solution obtained through the degree of association is corrected to be higher, and the reference information is suggested. A case that leads to a lower risk of infection as a search solution obtained via the degree of association may be modified to have a lower risk of infection as a search solution.

Third Embodiment In the third embodiment, it is determined whether or not a vehicle can pass through a so-called gate portion. At airports, boarding gates and security checkpoints, at stations, ticket gates and train boarding gates, at venues, entrances to venues, at homes, entrances and gates, and at buildings, entrances to lobbies. , etc., there is a so-called gate portion that allows only specific persons to pass and does not allow non-specific persons to pass. A specific person indicates a person who is permitted to pass through a gate. For example, in an airport, it is a person with a boarding pass, or a person who is permitted to board after being inspected by a metal detector. , the person holding the train ticket, the person holding the entrance ticket to the party if it is a venue, and the person holding the key to the house if it is a house. Non-specific persons are all persons other than specific persons. Incidentally, it does not matter whether the person is a specific person or not when passing through the gate portion. For example, if you just enter the lobby of a hotel, there is no special cost, but if a security guard stands at the entrance of the hotel, you can restrict entry to the lobby. In this way, the gate portion includes any part as long as it can restrict passage depending on whether it is a specific person or a non-specific person.

In the third embodiment, it is determined whether or not a person can pass through such a gate portion. In determining whether or not to allow passage, as described in the first and second embodiments, the risk of infection may be obtained, and whether or not to allow passage may be determined based on the obtained infection risk. In such a case, each infection risk is associated with whether or not passage is possible, and whether or not passage is associated with the detected infection risk may be output.

As reference information used in this third embodiment, there is a new reference vaccination information. The reference vaccination information referred to here is information regarding whether or not a person has been vaccinated against a certain infectious disease. If you have been vaccinated against the new coronavirus, we may issue a vaccination certificate as a method of proving the fact that you have been vaccinated. The vaccination information may be electronic data of such a vaccination certificate, or electronic information consisting of text data using OCR technology from a paper medium vaccination certificate. Also, if there is proof such as a vaccination certificate, it may simply be expressed as a binary value indicating whether or not the vaccination was given. In addition, when the residents and employees to be managed are organized by numbers or the like on electronic data, the information on the vaccination certificate may be linked to the numbers of the electronic data.

Such a vaccination certificate issues a password only to those who have been vaccinated, and the vaccinated person can, for example, enter the password along with their name and date of birth on the website to obtain a vaccination certificate using a two-dimensional code, etc. may be sent to the inoculator's smartphone or the like. By displaying the two-dimensional code of the vaccination certificate on the smartphone and holding it over various readers or the like provided at the gate, it is possible to obtain the vaccination certificate at the gate.

This reference vaccination information is simply information on whether or not a vaccine against one epidemic has been administered, as well as vaccination history information indicating the number of vaccinations and when any vaccine was administered against any infectious disease. may be configured.

Such reference vaccination information is similarly stored in advance in the database 3, the terminal on the edge side, or the like.

FIG. 14 shows an example of association degrees applied to this third embodiment. It is assumed that three or more degrees of correlation between the reference vaccination information and whether or not passage is possible are set and acquired in advance. Here, whether or not the subject can pass through the gate portion may be indicated by the degree of passage tolerance, in addition to allowing or not allowing the passage of the target person through the gate portion. The higher the passage tolerance, the more positive the subject will pass through the gate portion, and the lower the passage tolerance, the more negative the subject will pass through the gate portion. In the example of FIG. 14, it is assumed that the input data are, for example, reference vaccination information P01 to P03. Reference vaccination information as such input data is coupled to the output. In this output, whether or not passage is possible is displayed as an output solution. During the actual search for a solution, vaccination information is acquired from a subject who determines whether or not to pass through the gate portion. Vaccination information is information about whether or not a person has been vaccinated, and its specific contents are the same as the reference vaccination information. In order to obtain vaccination information from the subject, the person in charge who confirmed the paper-based vaccination certificate presented by the subject may enter the data manually, or the information may be given only to the person who received the vaccination. The identification may be made by prompting the user to enter a given password or by reading a given two-dimensional code. Further, the information may be extracted by reading information given to the vaccinated person on a smart phone or wearable terminal owned by the vaccinated person, a device embedded in the human body, or the like. Furthermore, attribute information such as the name, date of birth, and address of the vaccinated person may be obtained, and based on this, an inquiry may be made to the server to read whether the person is the vaccinated person.

In this way, the vaccination information is obtained and the search solution is searched. has been vaccinated twice against an infectious disease α. In such a case, among the reference vaccination information, if the reference vaccination information corresponding to two vaccinations is, for example, the reference vaccination information P02, a search solution having a higher degree of association with this will be obtained.

At this time, as shown in FIG. 15, as an alternative to the degree of association, the association simply indicated by a binary value indicating whether or not there is a connection with the output solution may be used. In FIG. 15, each of the reference vaccination information P01 to P03 is associated with whether or not passage is permitted. Then, when vaccination information is obtained, a search solution linked to the corresponding reference vaccination information is obtained.

As an alternative to the acceptance/rejection of passage, the acceptance/rejection of participation may be determined. Here, whether or not to allow participation indicates whether or not to allow participation in a certain event, project, meeting, or event, regardless of whether or not the person passes through the gate. Whether or not this participation is possible may be displayed in the degree of participation tolerance. The higher the participation tolerance, the more positive the subject's participation, and the lower the passage tolerance, the more negative the subject's participation.

FIG. 16 shows an example in which three or more degrees of association are set between combinations of the above-described reference vaccination information and other reference information and whether or not the combination is permitted to pass. All of the reference information shown in the first and second embodiments can be applied to the reference information here. The method of solution search when such a degree of association is set is the same as described above.

In the third embodiment, the following can be newly applied as other reference information.

The reference positional information may be positional information for constructing the above-described reference area information and area information. Also, this reference positional information may be positional information obtained via GPS or the like. If the degree of infection differs depending on the area, the permission or rejection of passage may be determined based on this reference position information.

A degree of association is formed by combining such reference positional information, the positional information of the person to be determined is newly acquired, and determination is performed based on the reference positional information corresponding to the acquired positional information.

The reference location information and body temperature information are information related to body temperature. The body temperature may be detected by a thermometer capable of measuring body temperature in a non-contact manner, a body temperature measurement system, or a body temperature measurement camera. This is added as an explanatory variable because body temperature may indicate whether or not a person is infected with an infectious disease. A degree of association is formed by combining such reference body temperature information, body temperature information of a person to be determined is newly acquired, and determination is made based on the reference body temperature information corresponding to the acquired body temperature information.

Similarly, in the third embodiment, as shown in FIG. 13, it is also possible to modify whether or not passage is permitted according to other reference information. In such a case, as the basic reference information, all the reference information in the first to third embodiments can be applied. All reference information in the form is applicable. In other words, the reference vaccination information, reference location information, reference body temperature information, and the like described in the third embodiment are applied not only as valuable reference information, but also as other reference information. can be anything.

In the third embodiment, the search solution may search for the risk of infection itself instead of whether or not passage is permitted. The details of the infection risk are the same as those described in the above-described first embodiment. Since the infection risk is also affected by the reference vaccination information, the reference position information, the reference body temperature information, etc. described in the third embodiment, these are added as explanatory variables. In particular, it is desirable to add this as an explanatory variable because the possibility of infection from the target person changes depending on whether or not they have been vaccinated.

1 infectious disease infection risk determination system 2 search device 21 internal bus 23 display unit 24 control unit 25 operation unit 26 communication unit 27 estimation unit 28 storage unit 61 node

Claims (10)

In the passage propriety determination program for judging the propriety of passage of the target person,
an information acquisition step of acquiring vaccination information proving that the person has been vaccinated against an infectious disease;
By referring to the relationship between the reference vaccination information that proves that the person has been vaccinated against an infectious disease and whether or not to pass, based on the reference vaccination information corresponding to the vaccination information acquired in the information acquisition step, and a determination step of determining whether or not the person to be determined can pass. In the participation decision program that decides whether the target person can participate,
an information acquisition step of acquiring vaccination information proving that the person has been vaccinated against an infectious disease;
Referring to the relationship between the reference vaccination information that proves that the vaccine against the infectious disease has been vaccinated and whether or not to participate, based on the reference vaccination information according to the vaccination information acquired in the information acquisition step, A program for judging participation, characterized by causing a computer to execute a judging step for judging whether the person to be judged can participate or not. 2. The pass propriety judging program according to claim 1, wherein said judging step refers to a relation consisting of degrees of relation of three or more levels. In the information acquisition step, the attribute information of the person to be judged is acquired,
In the determination step, the association between the combination having the reference vaccination information and the reference attribute information and the permission/denial of passage is referred to, and based on the attribute information acquired in the information acquisition step, the determination target 4. The pass propriety determination program according to claim 1 or 3, wherein it is determined whether or not a person can pass through. In the information acquisition step, the position information of the person to be judged is acquired,
In the determination step, the association between the combination having the reference vaccination information and the reference position information and the possibility of passage is referred to, and based on the position information acquired in the information acquisition step, the determination target 4. The pass propriety determination program according to claim 1 or 3, wherein it is determined whether or not a person can pass through. In the information acquisition step, the body temperature information of the person to be judged is acquired,
In the determination step, the association between the combination having the reference vaccination information and the reference body temperature information and the possibility of passage is referred to, and based on the body temperature information acquired in the information acquisition step, the determination target 4. The pass propriety determination program according to claim 1 or 3, wherein it is determined whether or not a person can pass through. In the information acquisition step, acquisition of infection history information regarding the infection history of the infectious disease of the determination subject,
In the determination step, the association between the combination having the reference vaccination information and the reference infection history information related to the infection history of the infectious disease and the possibility of passage is referred to, and acquired in the information acquisition step 4. The passability determination program according to claim 1 or 3, wherein it is determined whether or not the person to be determined can pass through based on infection history information. In the information acquisition step, image information obtained by imaging the face or body of the person to be judged is acquired,
In the determination step, the image information acquired in the information acquisition step by referring to the combination of the vaccination information for reference and the image information for reference obtained by imaging the face or body, and the relationship between the possibility of passage and the image information acquired in the information acquisition step. 4. The pass propriety determination program according to claim 1 or 3, wherein the pass propriety determination program determines whether or not the person to be determined can pass. In the passage propriety determination program for judging the propriety of passage of the target person,
an information acquisition step of acquiring vaccination information proving that the person has been vaccinated against an infectious disease from a person to be judged, and acquiring image information of the face or body of the person to be judged;
Referring to three or more levels of association between the reference image information obtained by imaging the face or the body and the permission/prohibition of passage, and determining whether or not the passage is permitted for the reference image information according to the image information acquired in the information acquisition step. while prioritizing information with a higher degree of correlation, and determining whether or not the person subject to determination can pass based on the obtained vaccination information. The passability determination program according to any one of claims 1 to 9, wherein the determination step uses the association corresponding to the weighting coefficient of each output of a neural network node in artificial intelligence.

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