JP6906816B1 - Infectious disease infection risk assessment program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To automatically and highly accurately determine an infection risk. SOLUTION: In an infection risk determination program for determining an infection risk of an infectious disease, an information acquisition step of acquiring the position information of a user to be determined and the distance information regarding the distance between the other party obtained from the position information of the other party. The distance information acquired in the above information acquisition step by referring to the reference distance information regarding the distance between one user detected in the past and the other party and the degree of association between the infection risk of the infectious disease and the three or more stages. Based on the above, the computer is made to execute the determination step of determining the infection risk of the user to be determined. [Selection diagram] Fig. 4

Description

The present invention relates to an infection risk determination program for determining the infection risk of an infectious disease.

In recent years, there has been an increasing social demand for risk assessment of infectious disease transmission. Among them, the new coronavirus, whose number of infected people is increasing rapidly in recent years, is extremely infectious, so in order to contain the spread of the infection, each individual should be aware of it and take actions to prevent the spread of the infection. It is required to take.

Among them, if it is possible to determine in real time the degree of infection risk of one's own behavior and one's own partner's behavior, it is possible to raise awareness of one's own behavior and that partner's behavior, and eventually infection. It is thought that this can lead to prevention of expansion. However, the current situation is that a technique for determining the infection risk in real time from the behavior of oneself or the behavior of one's own partner has not yet been proposed.

Therefore, the present invention has been devised in view of the above-mentioned problems, and an object of the present invention is an infectious disease capable of determining the risk of infection in real time from the behavior of oneself or the behavior of one's own partner. To provide an infection risk assessment program.

In order to solve the above-mentioned problems, the present invention presents the distance between the position information of the user to be determined and the distance between the other party obtained from the position information of the other party in the infection risk determination program for determining the infection risk of infectious disease. Refer to the information acquisition step for acquiring the distance information regarding the infectious disease, the reference distance information regarding the distance between one user detected in the past and the other party, and the degree of association of three or more levels with the infection risk of the above-mentioned infectious disease. Based on the three or more levels of association between the reference distance information according to the distance information acquired in the information acquisition step and the infection risk of the infectious disease, the determination step for determining the infection risk of the user to be determined is determined. It is characterized by having a computer execute it.

Even if you do not have any special skills or experience, you can judge the risk of infection in real time from your own actions and the actions of your partner.

It is a block diagram which shows the whole structure of the system to which this invention is applied. It is a figure which shows the specific configuration example of a search device. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention. It is a figure for demonstrating the operation of this invention.

Hereinafter, the 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 an 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 implemented. 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 who uses this system to input various commands and information, and specifically, is composed of a keyboard, buttons, a touch panel, a mouse, a switch, and the like. 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 image pickup device capable of capturing an image of a camera or the like. The information acquisition unit 9 may be configured by a scanner having a function of recognizing a character string from a paper-based document. Further, the information acquisition unit 9 may be integrated with the discrimination device 2 described later. The information acquisition unit 9 outputs the detected information to the determination device 2. Further, the information acquisition unit 9 may be configured by means for specifying the position information by scanning the map information. Further, the information acquisition unit 9 may be composed of an illuminance sensor for measuring a temperature sensor, a humidity sensor, and a wind direction sensor. Further, the information acquisition unit 9 may be composed of a communication interface for acquiring data about the weather from the Japan Meteorological Agency or a private weather forecast company. Further, the information acquisition unit 9 may be composed of a body sensor that is attached to the body to detect body data, and this body sensor detects, for example, body temperature, heart rate, blood pressure, number of steps, walking speed, and acceleration. It may be composed of a sensor for the purpose. Further, the body sensor may acquire biological data of animals as well as humans. Further, the information acquisition unit 9 may be configured as a device that acquires information such as drawings by scanning or reading it from a database. In addition to these, the information acquisition unit 9 may be configured by an odor sensor that detects an odor or scent.

Database 3 stores various information necessary for determining the risk of infectious disease infection. Information necessary for determining the risk of infectious disease infection includes reference distance information regarding the distance between one user detected in the past and the other party, and the other party with respect to the direction of the face of one user detected in the past. Face direction information for reference regarding the direction of the face, conversation time information for reference between one user detected in the past and the other party, attribute information for reference of one user detected in the past, one user detected in the past, and / Or reference infection history information regarding the infection history of the infectious disease in the other party, one user detected in the past and / or reference awareness information regarding the subjective symptoms of the infectious disease in the other party, one user detected in the past Reference image information that captures the face or body of the other person and / or reference living information regarding the lifestyle of one user and / or the other person detected in the past to prevent infection, increase in infectious diseases in each region A data set of reference increasing trend information regarding trends and infection risk of the user to be determined is stored.

That is, in addition to such reference distance information, the database 3 contains reference face direction information, reference conversation time information, reference attribute information, reference infection history information, reference awareness information, and reference image information. Any one or more of the reference living information and the reference increasing tendency information and the infection risk determined in the past are stored in association with each other. By the way, the infection risk determined in the past is not only the actual data from each user actually determined in the past, but also a fictitious persona is set, and experts such as institutions, hospitals, and doctors have determined this. Infection risk may be included in the learning data.

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

FIG. 2 shows a specific configuration example of the search device 2. The search device 2 performs wired communication or wireless communication with a control unit 24 for controlling the entire search device 2 and an operation unit 25 for inputting various control commands via operation buttons, a keyboard, or the like. A communication unit 26 for the purpose, an estimation unit 27 for making various judgments, and a storage unit 28 for storing a program for performing a search to be executed represented by a hard disk or the like are connected to the internal bus 21, respectively. .. Further, a display unit 23 as a monitor that actually displays information is connected to the internal bus 21.

The control unit 24 is a so-called central control unit for controlling each component mounted in the search device 2 by transmitting a control signal via the internal bus 21. Further, the control unit 24 transmits various control commands via the internal bus 21 in response to the operation via the operation unit 25.

The operation unit 25 is embodied by a keyboard or a touch panel, and an execution command for executing a program is input from the user. When the execution command is input by the user, the operation unit 25 notifies the control unit 24 of the execution command. Upon receiving this notification, the control unit 24, including the estimation unit 27, executes a desired processing operation in cooperation with each component. The operation unit 25 may be embodied as the information acquisition unit 9 described above.

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

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

When the storage unit 28 is composed of a hard disk, predetermined information is written to each address based on the control by the control unit 24, and is read out as needed. Further, the storage unit 28 stores a program for executing the present invention. This program is read and executed by the control unit 24.

The operation of the infectious disease infection risk determination system 1 having the above-described configuration will be described.

In the infectious disease infection risk determination system 1, for example, as shown in FIG. 3, it is premised that the reference distance information and the degree of association between the infection risk and the infection risk are set and acquired in advance. The reference distance information is information on the distance between one user detected in the past and the other party. This reference distance information may be obtained from the position information of the user to be determined and the distance between the other parties obtained from the position information with the other party. In such a case, the position information of the user and the other party may be obtained by a well-known position information detecting means such as GPS, and the distance between the users may be calculated. Further, the reference distance information may be used to measure the distance between each other by a well-known distance sensor. Further, the reference distance information may be obtained by using a well-known distance detection application between conversation partners that can be installed on a smartphone or the like.

One user here does not have to be the same as the user whose infection risk is determined, and may be any other person who has previously determined the infection risk. Further, the other party to this one user is premised on the person who is actually specified as the conversation partner with the one user, but is not limited to this, and is not the conversation partner with the one user. Is also included. Furthermore, this person includes a person who is passing by without any acquaintance with one user, or a person who is nearby in a train or an elevator.

The risk of infection indicates the degree of risk of infection and may be expressed by two values of whether or not there is a risk, and it is very high risk, slightly dangerous, usually slightly safe. The risk may be evaluated in five stages such as safety and safety. Further, the infection risk may be composed of, for example, a score out of 1000 points, and may be evaluated so that the higher the score, the higher the risk. For example, the infection risk A in FIG. 3 may be evaluated as having the above score of 844 points, the infection risk B as being evaluated as 424 points, the infection risk C as being evaluated as 501 points, and the like.

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

In other words, through this reference distance information and the infection risk data set, it is possible to know what kind of infection risk was actually evaluated for each distance shown in the reference distance information. In other words, the distance specified in the reference distance information and the infection risk are the data set. Therefore, by collecting reference distance information and infection risk data set, it is possible to know how much the infection risk was when the distance between one user detected in the past and the other party was. It becomes possible.

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

The reference distance information is associated with each other through three or more levels of association with the infection risk as the output solution. The reference distance information is arranged on the left side through this degree of association, and the infection risk is arranged on the right side through this degree of association. The degree of association indicates the degree of which infection risk is highly relevant to the reference distance information arranged on the left side. In other words, this degree of association is an index showing what kind of infection risk each reference distance information is likely to be associated with, and is used to select the most probable infection risk from the reference distance information. It shows the accuracy. In the example of FIG. 3, w13 to w19 are shown as the degree of association. 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 relevance of each combination as an intermediate node to the infection risk as an output. On the contrary, the closer to one point, the lower the degree of association between each combination as an intermediate node and the risk of infection as an output.

The search device 2 acquires in advance the degree of association w13 to w19 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference distance information and the infection risk in that case is adopted in determining the actual search solution, and analyzes and analyzes these. By doing so, the degree of association shown in FIG. 3 is created.

For example, it is assumed that a certain reference distance information has an infection risk of 916 at a certain distance (60 cm). In such a situation, it is assumed that the reference distance information consisting of (60 cm) has a similar infection risk of 916. In such a case, the degree of association of infection risk 916 becomes stronger at that distance (60 cm). On the other hand, in the exact same reference distance information (60 cm) pattern (classification), it is assumed that the infection risk was determined to be 412 in many cases, and 916 was determined in few cases. In such a case, the infection risk is 412, which is more relevant, and 916, which is less relevant.

In fact, such a data set may be created by actually measuring the distance between the user and the other party and investigating whether or not the user is actually infected with the infectious disease accordingly. In addition, this data set may be obtained from the data of research results and survey results obtained at each research institution or hospital. In addition to the actual data itself, the infection risk data may be created by adding the views of experts.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of reference distance information P01, analysis is performed from past infection risk data. This may be extracted from past infection risk data stored in, for example, at each institution or hospital.

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

Further, the degree of association shown in FIG. 3 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In such a case, as shown in FIG. 4, reference distance information is input as input data, each infection risk is output as output data, and at least one or more hidden layers are provided between the input node and the output node. , Machine learning may be made. On the contrary, the infection risk may be input and the reference distance information may be output. Such a degree of association is what is called learned data in artificial intelligence.

After creating such learned data, the position information of the user to be determined and the distance information regarding the distance between the other parties obtained from the position information with the other party are acquired. The method of acquiring the distance information is the same as the method of acquiring the reference distance information described above. Further, it is assumed that the other party to the user to be determined is a person who is actually identified as a conversation partner with the user, but the present invention is not limited to this, and includes a person other than the conversation partner with the user. Is done. Further, this partner includes a person passing by who has no acquaintance with the user who is actually judged, and a person who is nearby in a train or an elevator.

Based on the newly acquired timing information in this way, the risk of infection is searched for. In such a case, the corresponding reference distance information is specified from the acquired distance information. When the reference distance information is the same as or similar to P02, the increase / decrease rate B is associated with w15 and the increase / decrease rate C is associated with the association degree w16 via the degree of association. In such a case, the rate of increase / decrease B having 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 increase / decrease rate C in which the degree of association is low but the association itself is recognized may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In this way, it is possible to search for the infection risk to be determined from the newly acquired distance information and display it to the user, various institutions, and the operator. By looking at the results of this search, users can be aware that they will take actions to reduce the risk of infection based on the risk of infection searched, and each institution and operator will be able to take future policies. Can be considered. By the way, in the process of outputting this infection risk, in addition to simply displaying the searched infection risk, the specific action policy itself based on this infection risk may be displayed, or more specifically. The user may be strongly encouraged to take such an action policy. For example, "Please talk at a distance" may be displayed on the screen.

In the example of FIG. 5, it is assumed that the input data is, for example, reference distance information P01 to P03 and reference face direction information P14 to 17. The intermediate node shown in FIG. 5 is a combination of reference distance information and reference face direction information as such input data. Each intermediate node is further linked to the output. In this output, each infection risk as an output solution is displayed.

In the example of FIG. 5, it is premised that a combination of the reference distance information and the reference face direction information is formed. The reference face direction information is information regarding the direction of the other person's face with respect to the direction of the face of one user detected in the past. That is, this reference face direction information indicates whether or not the face directions of the other party face each other in a 180 ° relationship with respect to the face direction of one user, and if they do not face each other, the face directions of each other. It may indicate a relative angle. In addition, the reference face direction information indicates a specific direction, whether the face direction of one user is north and the face direction of the other party is south or northwest, and the relative angular relationship with each other from there. May be asked.

The direction of the face here indicates the direction in which the eyes, nose, and mouth are actually facing. Such reference face direction information is not limited to the case of detecting an accurate face direction in 1 ° increments, and the face direction of the other party faces each other with respect to the face direction of about one user. It suffices as long as it indicates whether or not the face is present, and the orientation of the face of the user or the other party may be detected through a well-known glasses-type terminal or wearable terminal. Further, a well-known technique capable of detecting the line of sight of a person in real time from a camera installed in a city or a store may be utilized to obtain face direction information for reference. In addition, the direction and line of sight of the face may be detected by capturing images of each other's faces with each other through an application that can be implemented on a smartphone, acquiring the images in real time, and analyzing the images.

In the example of FIG. 5, it is assumed that the input data is, for example, reference distance information P01 to P03 and reference face direction information P14 to 17. The intermediate node shown in FIG. 5 is a combination of reference distance information and reference face direction information as such input data. Each intermediate node is further linked to the output. In this output, the risk of infection as an output solution is displayed.

Each combination (intermediate node) of the reference distance information and the reference facial direction information is associated with each other through three or more levels of association with the infection risk as the output solution. The reference distance information and the reference facial direction information are arranged on the left side through this degree of association, and the infection risk is arranged on the right side through this degree of association. The degree of association indicates the degree of relevance to each infection risk with respect to the reference distance information and the reference facial direction information arranged on the left side. In other words, this degree of association is an index indicating what kind of infection risk each reference distance information and reference face direction information is likely to be associated with, and is a reference distance information and reference face direction information. It shows the accuracy in selecting each infection risk that is most probable from. In addition to the distance information, the degree to which droplets actually fly differs depending on the direction of the face, and the risk of infection changes.

In the example of FIG. 5, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference distance information, the reference face direction information, and the infection risk in that case was suitable for determining the actual search solution, and these By analyzing and analyzing the above, the degree of association shown in FIG. 5 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, in the case of the reference distance information P01 and the reference face direction information P16, the infection risk is analyzed from the past data. If there are many cases of infection risk A, the degree of association that leads to A is set higher, and if there are many cases of B and there are few cases of A, the degree of association that leads to B is increased to A. Set the degree of connection to be low. For example, in the example of the intermediate node 61a, the output of A and B is linked, but from the previous case, the degree of association of w13 connected to A is set to 7 points, and the degree of association of w14 connected to B is set to 2 points. There is.

Further, the degree of association shown in FIG. 5 may be composed of the nodes of the neural network in artificial intelligence. That is, the weighting coefficient for the output of the node of this neural network corresponds to the above-mentioned degree of association. Further, the network is not limited to a neural network, and may be composed of all decision-making factors constituting artificial intelligence.

In the example of the degree of association shown in FIG. 5, the node 61b is a node in which the reference distance information P01 is combined with the reference face direction information P14, the degree of association of the infection risk C is w15, and the association of the infection risk E. The degree is w16. The node 61c is a node in which the reference face direction information P15 and P17 are combined with the reference distance information P02, and the degree of association of the infection risk B is w17 and the degree of association of the infection risk D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such learned data, the above-mentioned learned data will be used in the actual search for determining the risk of infectious disease infection. In such a case, the face direction information is acquired in addition to the distance information from the user who newly determines the infection risk and the other party. The method of acquiring the face direction information is the same as the method of acquiring the reference face direction information.

Based on the distance information newly acquired in this way, the optimum infection risk is searched for. In such a case, the degree of association shown in FIG. 5 (Table 1) acquired in advance is referred to. For example, when the newly acquired distance information is the same as or similar to P02, is the reference face direction information consisting of the reservation status according to the time specified in the distance information the same as P17? In a similar case, the node 61d is associated with the degree of association through the degree of association, and the node 61d is associated with the infection risk C by w19 and the infection risk D by the degree of association w20. In such cases, the infection risk C with the highest degree of association is selected as the optimal 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 the lowest degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

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

The intermediate node 61 may be selected based on the degree of association w1 to w12 extending from this input. That is, the larger the degree of association w1 to w12, the heavier the weighting in the selection of the intermediate node 61 may be. However, the degrees of association w1 to w12 may all have the same value, and the weights in the selection of the intermediate node 61 may all be the same.

FIG. 6 shows an example in which a combination of the above-mentioned reference distance information and reference conversation time information and an infection risk for the combination are set to three or more levels of association.

The reference conversation time information is information on the conversation time between one user and the other party detected in the past. The conversation time referred to here indicates a time interval from the start time to the end time of each other's conversation when talking alternately without a gap between them. If the conversation progresses intermittently with intervals in the middle, the interval is specified in advance, and if the conversation pause period is longer than the specified interval, the previous conversation is over. The time is set as the above end point. The user side or the system side may freely decide how many seconds and minutes the predetermined period should be. This reference conversation time information indicates a time interval from the start time to the end time of the talk when one user or the other party talks unilaterally.

In the example of FIG. 6, it is assumed that the input data is, for example, reference distance information P01 to P03 and reference conversation time information P18 to 21. The intermediate node shown in FIG. 6 is a combination of the reference distance information and the reference conversation time information as such input data. Each intermediate node is further linked to the output. In this output, the risk of infection as an output solution is displayed.

Each combination (intermediate node) of the reference distance information and the reference conversation time information is associated with each other through three or more levels of association with the infection risk as this output solution. The reference distance information and the reference conversation time information are arranged on the left side through this degree of association, and the infection risk is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the infection risk with respect to the reference distance information and the reference conversation time information arranged on the left side. In other words, this degree of association is an index indicating what kind of infection risk each reference distance information and reference conversation time information is likely to be associated with, and is based on the reference distance information and reference conversation time information. It shows the accuracy in selecting each of the most probable infection risks. In addition to the distance information, the longer the actual conversation time, the higher the risk of droplets from the other party entering the body, and the risk of infection changes. Therefore, the optimum infection risk is searched for by combining the reference distance information and the reference conversation time information.

In the example of FIG. 6, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which is more suitable for the reference distance information, the reference conversation time information, and the infection risk in that case in determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 6 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, when the reference distance information P01 is the reference conversation time information P20, the infection risk is analyzed from the past data. For example, in the example of the intermediate node 61a, the output of infection risk A and infection risk B is linked, but from the previous case, the degree of association of w13 leading to infection risk A is set to 7 points, and the association of w14 leading to infection risk B is set to 7. 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 in which the reference distance information P01 is combined with the reference conversation time information P18, the infection risk C is associated with w15, and the infection risk E is associated with E. The degree is w16. The node 61c is a node in which the reference conversation time information P19 and P21 are combined with the reference distance information P02, and the degree of association of the infection risk B is w17 and the degree of association of the infection risk D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, the conversation time information is acquired in addition to the distance information from the user who newly determines the infection risk and the other party. The method of acquiring the conversation time information is the same as the method of acquiring the reference conversation time information.

Based on the distance information and conversation time information newly acquired in this way, the infection risk is searched for. In such a case, the degree of association shown in FIG. 6 (Table 1) acquired in advance is referred to. For example, when the newly acquired distance information is the same as or similar to P02, and the reference conversation time information corresponding to the newly acquired conversation time information is P21, the degree of association is The node 61d is associated with the node 61d, and the infection risk C is associated with w19 and the infection risk D is associated with the association degree w20. In such cases, the infection risk C with the highest degree of association is selected as the optimal 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 the lowest degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

FIG. 7 shows an example in which the combination of the above-mentioned reference distance information and the reference attribute information and the infection risk for the combination are set to three or more levels of association.

The reference attribute information is all information related to the attribute of one user detected in the past. The attributes here are, for example, age, gender, illness other than occupation, lifestyle restricted by a doctor, overtime hours per day, amount of exercise per day, sleep time per day, and going to bed after dinner. This includes health information such as time to time, frequency of smoking and drinking.

In the example of FIG. 7, it is assumed that the input data is, for example, reference distance information P01 to P03 and reference attribute information P18 to 21. The intermediate node shown in FIG. 7 is a combination of reference attribute information and reference distance information as such input data. Each intermediate node is further linked to the output. In this output, the risk of infection as an output solution is displayed.

Each combination (intermediate node) of the reference distance information and the reference attribute information is associated with each other through three or more levels of association with the infection risk as this output solution. The reference distance information and the reference attribute information are arranged on the left side through this degree of association, and the infection risk is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the infection risk with respect to the reference distance information and the reference attribute information arranged on the left side. In other words, this degree of association is an index showing what kind of infection risk each reference distance information and reference attribute information is likely to be associated with, and is the most reliable from the reference distance information and the reference attribute information. It shows the accuracy in selecting each likely infection risk. Infection risk changes according to the actual attribute information in addition to the distance information. Especially in the case of the new coronavirus, the risk of infection changes because the older the person, the higher the mortality rate, and the older the person, the more thorough risk management is required. In addition, in the case of the new coronavirus, it has been reported that smokers are more likely to become ill, so the risk of infection changes depending on the presence or absence of smoking. As described above, since the infection risk differs depending on the type of attribute information, the degree of association may be formed so that the higher the infection risk, the higher the infection risk.

In the example of FIG. 7, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. 7. That is, the search device 2 accumulates past data on which of the reference distance information, the reference attribute information, and the infection risk in that case was suitable for determining the actual search solution, and these By analyzing and analyzing the above, the degree of association shown in FIG. 7 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, when the reference distance information P01 is 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 output of infection risk A and infection risk B is linked, but from the previous case, the degree of association of w13 leading to infection risk A is set to 7 points, and the association of w14 leading to infection risk B is set to 7. The degree is set to 2 points.

In the example of the degree of association shown in FIG. 7, the node 61b is a node in which the reference attribute information P18 is combined with the reference distance information P01, the infection risk C association degree is w15, and the infection risk E association degree. Is w16. The node 61c is a node in which the reference attribute information P19 and P21 are combined with respect to the reference distance information P02, and the degree of association of the infection risk B is w17 and the degree of association of the infection risk D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, the attribute information is acquired in addition to the distance information from the user who newly determines the infection risk and the other party. The method of acquiring the attribute information is the same as the method of acquiring the reference attribute information.

Based on the distance information and attribute information newly acquired in this way, the infection risk is searched for. In such a case, the degree of association shown in FIG. 7 (Table 1) acquired in advance is referred to. For example, when the newly acquired distance information is the same as or similar to P02, and the reference attribute information corresponding to the newly acquired attribute information is P21, the degree of association is used. The node 61d is associated with the node 61d, and the infection risk C is associated with w19 and the infection risk D is associated with the association degree w20. In such cases, the infection risk C with the highest degree of association is selected as the optimal 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 the lowest degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

FIG. 8 shows an example in which the combination of the above-mentioned reference distance information, the reference infection history information, and the infection risk for the combination are set to three or more levels of association.

The reference infection history information is all information regarding the infection history of the above-mentioned infectious disease in one user or the other party detected in the past. The information regarding this infection history may be from either one user or the other party detected in the past, or may be extracted from both.
The reference infection history information is information on the past infection history of the infectious disease to be determined. This reference infection history information may be obtained by each user or the other party by inputting it by the information acquisition unit 9, or may be obtained from a medical institution or the like. Reference infection history information includes all information regarding the infection history, such as when, where, how many times the infectious disease took, how it was treated and cured, and the time to complete cure.

In the example of FIG. 8, it is assumed that the input data is, for example, reference distance information P01 to P03 and reference infection history information P18 to 21. The intermediate node shown in FIG. 8 is a combination of reference infection history information and reference distance information as such input data. Each intermediate node is further linked to the output. In this output, the risk of infection as an output solution is displayed.

Each combination (intermediate node) of the reference distance information and the reference infection history information is associated with each other through three or more levels of association with the infection risk as this output solution. The reference distance information and the reference infection history information are arranged on the left side through this degree of association, and the infection risk is arranged on the right side through this degree of association. The degree of association indicates the degree of high relevance to the infection risk with respect to the reference distance information and the reference infection history information arranged on the left side. In other words, this degree of association is an index showing what kind of infection risk each reference distance information and reference infection history information is likely to be associated with, and is based on the reference distance information and the reference infection history information. It shows the accuracy in selecting each of the most probable infection risks. In addition to distance information, the risk of infection varies depending on the actual infection history information. The risk of infection is reduced, especially if you have a previous history of infection and are immune. On the other hand, in the case of an infectious disease in which the possibility of infection does not change even with immunity, the risk of infection does not change in particular. 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 more the history of infection, the higher the risk of infection.

In the example of FIG. 8, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference distance information, the reference infection history information, and the infection risk in that case was suitable for determining the actual search solution. By analyzing and analyzing these, the degree of association shown in FIG. 8 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, when the reference distance information P01 is 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 output of infection risk A and infection risk B is linked, but from the previous case, the degree of association of w13 leading to infection risk A is set to 7 points, and the association of w14 leading to infection risk B is set to 7. The degree is set to 2 points.

In the example of the degree of association shown in FIG. 8, the node 61b is a node in which the reference infection history information P18 is combined with the reference distance information P01, the infection risk C association degree is w15, and the infection risk E association. The degree is w16. The node 61c is a node in which the reference infection history information P19 and P21 are combined with the reference distance information P02, and the degree of association of the infection risk B is w17 and the degree of association of the infection risk D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, the infection history information is acquired in addition to the distance information from the user who newly determines the infection risk and the other party. The method of acquiring the infection history information is the same as the method of acquiring the reference infection history information.

Based on the distance information and infection history information newly acquired in this way, the infection risk is searched for. In such a case, the degree of association shown in FIG. 8 (Table 1) acquired in advance is referred to. For example, when the newly acquired distance 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 degree of association is The node 61d is associated with the node 61d, and the infection risk C is associated with w19 and the infection risk D is associated with the association degree w20. In such cases, the infection risk C with the highest degree of association is selected as the optimal 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 the lowest degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

FIG. 9 shows an example in which a combination of the above-mentioned reference distance information and reference awareness information and an infection risk for the combination are set to three or more levels of association.

The reference subjective information is all information regarding the subjective symptoms of the infectious disease in one user or the other party detected in the past. The information regarding this subjective symptom may be from either one user or the other party detected in the past, or may be extracted from both.

The reference awareness information indicates the symptoms that the person is actually aware of about the infectious disease to be judged, for example, runny nose, malaise, body temperature, coughing condition, suffocation of breathing, palpitation and shortness of breath. This includes all the symptoms that you are aware of. This reference awareness information may be obtained by each user or the other party inputting the information by himself / herself through the information acquisition unit 9, or an interview is conducted from each user or the other party, and the interviewer manually inputs the information. Alternatively, it may be obtained from a medical institution or the like.

In the example of FIG. 9, it is assumed that the input data is, for example, reference distance information P01 to P03 and reference awareness information P18 to 21. The intermediate node shown in FIG. 9 is a combination of the reference distance information and the reference awareness information as such input data. Each intermediate node is further linked to the output. In this output, the risk of infection as an output solution is displayed.

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

In the example of FIG. 9, w13 to w22 are shown as the degree of association. As shown in Table 1, these w13 to w22 are shown in 10 stages, and the closer to 10 points, the higher the degree of relevance of each combination as an intermediate node to the output, and conversely, 1 point. The closer they are, the less relevant each combination as an intermediate node is to the output.

The search device 2 acquires in advance the degree of association w13 to w22 of three or more stages shown in FIG. That is, the search device 2 accumulates past data as to which of the reference distance information, the reference awareness information, and the infection risk in that case was suitable for determining the actual search solution, and these By analyzing and analyzing the above, the degree of association shown in FIG. 9 is created.

This analysis and analysis may be performed by artificial intelligence. In such a case, for example, when the reference distance information P01 is the reference awareness information P20, the infection risk is analyzed from the past data. For example, in the example of the intermediate node 61a, the output of infection risk A and infection risk B is linked, but from the previous case, the degree of association of w13 leading to infection risk A is set to 7 points, and the association of w14 leading to infection risk B is set to 7. The degree is set to 2 points.

In the example of the degree of association shown in FIG. 9, the node 61b is a node in which the reference distance information P01 is combined with the reference awareness information P18, the degree of association of infection risk C is w15, and the degree of association of infection risk E. Is w16. The node 61c is a node in which the reference awareness information P19 and P21 are combined with respect to the reference distance information P02, and the degree of association of the infection risk B is w17 and the degree of association of the infection risk D is w18.

Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually giving advice from now on, the above-mentioned trained data will be used. In such a case, in addition to the distance information, the awareness information is acquired from the user who newly determines the infection risk and the other party. The method of acquiring the awareness information is the same as the method of acquiring the reference awareness information.

Based on the newly acquired distance information and awareness information in this way, the infection risk is searched for. In such a case, the degree of association shown in FIG. 9 (Table 1) acquired in advance is referred to. For example, when the newly acquired distance information is the same as or similar to P02, and the reference awareness information corresponding to the newly acquired awareness information is P21, the degree of association is used. The node 61d is associated with the node 61d, and the infection risk C is associated with w19 and the infection risk D is associated with the association degree w20. In such cases, the infection risk C with the highest degree of association is selected as the optimal 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 the lowest degree of association but is recognized as having the association itself, may be selected as the optimum solution. In addition to this, it goes without saying that an output solution to which the arrows are not connected may be selected, and any other output solution may be selected in any other priority as long as it is based on the degree of association.

In the present invention, as a substitute for the reference awareness information shown in FIG. 9, the reference image information obtained by capturing the face or body of one user and / or the other party detected in the past is a combination of the reference distance information. It may be learned in association with the degree of association.

The reference image information is information obtained by photographing 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, the droplets will be less likely to fly, and if gloves are worn on the user or the other party's hands, the possibility of preventing infection by contact will increase. .. Whether or not such masks and gloves are in a state where there is a high possibility of preventing infection is determined through images. If a mask is actually worn on the face of one user and / or the other person, it is read by image analysis, and if necessary, deep learning technology is used, and it is automatically based on the features of the analyzed image. It may be discriminated 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 expert opinions, on how much the risk of infection can be prevented by wearing the mask. May be designed.

In such a case, the combination of the reference distance information and the reference image information and the degree of association with the infection risk at three or more levels are acquired in advance. Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually performing a search from now on, the above-mentioned trained data will be used. In such a case, in addition to the distance information described above, image information obtained by capturing an image of the face or body of the user and / or the other party to be determined is acquired. This image information is composed of the same type of data as the above-mentioned reference image information. Then, with reference to the degree of association, the infection risk is determined in the same manner as described above based on the newly acquired distance information and image information.

In the present invention, as a substitute for the reference awareness information shown in FIG. 9, the reference lifestyle information regarding the lifestyle for preventing infection in one user and / or the other party detected in the past is the reference distance. It may be a combination of information that is learned in association with the degree of association.

The reference lifestyle information is information that typifies a lifestyle that is particularly effective in preventing infection in the lifestyle. Effective lifestyles to prevent infection include, for example, gargling, washing hands, the presence and frequency of bathing, whether or not you live a regular life, and the time to avoid commuting rush hours to prevent infection. It also includes information on whether or not you have chosen a band and whether or not you are actively vaccinated to prevent infection.

In addition, when learning through the degree of association, various public health data and expert opinions are included on how much the risk of infection can be prevented by taking an effective lifestyle to prevent infection. , The degree of association that leads to the risk of infection may be designed.

In such a case, the combination having the reference distance information and the reference lifestyle information and the degree of association with the infection risk at three or more levels are acquired in advance. Such a degree of association is what is called learned data in artificial intelligence. After creating such trained data, when actually performing a search from now on, the above-mentioned trained data will be used. In such a case, in addition to the distance information described above, the lifestyle information of the user and / or the other party to be determined is acquired. This lifestyle information is composed of the same kind of data as the above-mentioned reference lifestyle information. Then, after referring to the degree of association, the infection risk is determined in the same manner as described above based on the newly acquired distance information and lifestyle information.

In FIG. 10, in addition to the above-mentioned reference distance information and reference face direction information, a combination of reference increasing tendency information and an infection risk for the combination are set to three or more levels of association. An example is shown. The reference increasing trend information is information on the increasing trend of infectious diseases in each region. This reference increasing tendency information is information indicating whether the number of persons suffering from the infectious disease is increasing, stagnant, or decreasing, roughly classified. The reference increasing tendency information is not limited to the case where it is shown at such three levels, and the degree of increase and the degree of decrease may be shown at a finer pitch. Further, the reference increasing tendency information may be composed of data itself such as a bar graph showing an increasing tendency or a decreasing tendency of infectious diseases by prefecture or country. Such information on increasing trends for reference is associated with each region classified by prefecture, country, or the like.

In such a case, as shown in FIG. 10, the degree of association is such that the set of combinations of the reference distance information, the reference face direction information, and the reference increasing tendency information is set as the nodes 61a to 61e of the intermediate node as described above. It will be expressed.

For example, in FIG. 10, in the node 61c, the reference distance information P02 is associated with the association degree w3, the reference face direction information P15 is associated with the association degree w7, and the reference increasing tendency information P21 is associated with the association degree w11. Similarly, in the node 61e, the reference distance information P03 is associated with the association degree w5, the reference face direction information P15 is associated with the association degree w8, and the reference increasing tendency information P20 is associated with the association degree w10.

Similarly, when such a degree of association is set, in addition to the newly acquired distance information and face direction information, new area information is acquired. This area information is area information about the area corresponding to the position information of the user to be determined. If the location information of the user to be judged is Tokyo at the present time, the area information is Tokyo. In addition, the regional information consisting of a wide range is Japan.

Based on the reference increasing tendency information linked to the area specified in the acquired area information, the solution search is performed while referring to the degree of association shown in FIG.

In estimating this price, the degree of association shown in FIG. 10 acquired in advance is referred to. For example, the acquired distance information is the same as or similar to the reference distance information P02, the acquired face direction information corresponds to the reference face direction information P15, and the reference increasing tendency information corresponding to the area of the acquired area information In the case corresponding to P21, the combination is associated with the node 61c, in which the infection risk B is associated with the association degree w17 and the infection risk D is associated with the association degree w18. As a result of such a degree of association, a search solution is actually obtained based on w17 and w18.

When three or more types of such input parameters are combined, in addition to the reference increasing tendency information, the reference distance information, the reference face direction information, the reference conversation time information, the reference attribute information, and the reference infection It is applicable even if the combination is composed of any one or more of history information, awareness information for reference, image information for reference, and living information for reference.

In addition to the distance information, if any two or more of face direction information, conversation time information, attribute information, infection history information, awareness information, image information, living information, etc. are acquired, the two or more to be acquired. Two or more reference information (reference face direction information, reference conversation time information, reference attribute information, reference infection history information, reference awareness information, reference image information, reference living information, etc., according to the information. ) Is combined with the reference distance information, and the solution search can be performed in the same manner by creating learning data consisting of three or more levels of association with the infection risk for the combination.

In addition to distance information, in addition to any one or more of face direction information, conversation time information, attribute information, infection history information, awareness information, image information, living information, etc., other information may be acquired. Similarly, reference face direction information, reference conversation time information, reference attribute information, reference infection history information, reference awareness information, reference image information, reference living information, etc. according to the acquired information, and so on. A solution search can be performed in the same manner by creating learning data consisting of a combination with reference information according to other acquired information and a degree of association with the infection risk for the combination at three or more levels. ..

Further, as shown in FIG. 11, the present invention determines each infection risk based on the degree of association between two or more types of information, reference information U and reference information V. The reference information U is the reference distance information, and the reference information V is the reference face direction information, the reference conversation time information, the reference attribute information, the reference infection history information, the reference awareness information, and the reference image. It shall be information, living information for reference, etc. Alternatively, the reference information U is reference face direction information, reference conversation time information, reference attribute information, reference infection history information, reference awareness information, reference image information, reference life information, etc., and is for reference. It is assumed that the information V is the reference distance information.

At this time, as shown in FIG. 11, the output obtained for the reference information U may be used as the input data as it is, and may be associated with the output via the intermediate node 61 in combination with the reference information V. For example, for the reference information U, after the output solution is output, this may be used as an input as it is, and the output may be searched by using the degree of association with other reference information V.

In the above-mentioned degree of association, the degree of association is expressed by a 10-step evaluation, but it is not limited to this, and it may be expressed by a degree of association of 3 or more levels, and conversely, it may be expressed by 3 or more levels. 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 related to each other, either 1 or 0.

According to the present invention having the above-described configuration, anyone can easily search for the infection risk of a company considering financing without any special skill or experience. Further, according to the present invention, it is possible to determine the search solution with higher accuracy than that performed by a human being. Further, by configuring the above-mentioned degree of association with artificial intelligence (neural network or the like), it is possible to further improve the discrimination accuracy by learning this.

Since there are many cases where the input data and the output data described above do not exist exactly the same in the process of learning, the input data and the output data may be classified by type. That is, the information P01, P02, ... P15, 16, ... That constitute the input data are classified according to the criteria classified in advance on the system side or the user side according to the content of the information, and the classified inputs. A data set may be created between the data and the output data and trained.

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

By determining the infection risk with higher credibility and less misunderstanding based on the degree of association expressed by such numerical values of 3 or more levels, there are multiple possible candidates for the search solution. In, 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 judge without overlooking the discrimination result of the output having an extremely low degree of association such as 1%. It warns the user that even a judgment result with an extremely low degree of association is connected as a slight sign and may be useful as the judgment result once every tens or hundreds of times. be able to.

Further, according to the present invention, there is an advantage that the search policy can be determined by the method of setting the threshold value by performing the search based on the degree of association of three or more stages. If the threshold value is lowered, even if the above-mentioned degree of association is 1%, it can be picked up without omission, but it is unlikely that a more appropriate discrimination result can be detected favorably, and a lot of noise may be picked up. be. On the other hand, if the threshold value is raised, there is a high possibility that the optimum search solution can be detected with high probability, but the degree of association is usually low and it is passed through, but it is suitable that it appears once every tens or hundreds of times. Sometimes the solution is overlooked. It is possible to decide which one to prioritize based on the ideas of the user side and the system side, but it is possible to increase the degree of freedom in selecting the points to be emphasized.

Further, in the present invention, the above-mentioned degree of association may be updated. This update may reflect information provided via a public communication network such as the Internet. In addition to distance information, when knowledge, information, and data related to face direction information, conversation time information, attribute information, infection history information, awareness information, image information, living information, etc. are acquired, the degree of association is increased accordingly. Or lower it.

In other words, this update corresponds to learning in the sense of artificial intelligence. It can be said that it is a learning act because it acquires new data and reflects it in the learned data.

In addition, this update of the degree of association is not based on the information that can be obtained from the public communication network, but is also updated by 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 artificially or automatically. Artificial intelligence may be utilized in these update processes.

Further, the process of first creating the trained model and the above-mentioned update 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 reading and learning the data set of input data and output data, information corresponding to the input data is read and trained, and the degree of association related to the output data is self-formed from there. You may let it.

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 Estimate unit 28 Storage unit 61 node

Claims (10)

In the infection risk determination program that determines the infection risk of infectious diseases
An information acquisition step for acquiring the position information of the user to be determined and the distance information regarding the distance between the other party obtained from the position information with the other party.
Refer to the reference distance information regarding the distance between one user detected in the past and the other party, and the degree of association between the infection risk of the infectious disease in three or more stages, and respond to the distance information acquired in the information acquisition step. Infectious disease infection, which is characterized by having a computer execute a determination step for determining the infection risk of the user to be determined based on the degree of association between the reference distance information and the infection risk of the infectious disease in three or more stages. Risk assessment program. In the information acquisition step, face direction information regarding the face direction of the other party with respect to the face direction of the user to be determined is acquired.
In the determination step, the combination having the reference distance information and the reference face direction information regarding the direction of the other person's face with respect to the face direction of one user detected in the past, and the infection risk of the infectious disease The combination of the reference distance information according to the distance information acquired in the above information acquisition step and the reference face direction information according to the face direction information, and the infectious disease The infectious disease infection risk determination program according to claim 1, wherein the infection risk of the user to be determined is determined based on the degree of association with the infection risk in three or more stages. In the above information acquisition step, the conversation time information regarding the conversation time between the user to be determined and the other party is acquired.
In the determination step, the degree of association between the combination of the reference distance information, the reference conversation time information between one user and the other party detected in the past, and the infection risk of the infectious disease is determined. There are three or more stages of a combination having reference distance information according to the distance information acquired in the above information acquisition step and reference conversation time information according to the conversation time information, and the infection risk of the infectious disease. The infectious disease infection risk determination program according to claim 1, wherein the infection risk of the user to be determined is determined based on the degree of association. In the above information acquisition step, the attribute information of the user to be determined is acquired.
In the determination step, the combination having the reference distance information and the reference attribute information of one user detected in the past and the degree of association with the infection risk of the infectious disease in three or more stages are referred to, and the information is described. Based on the combination of the reference distance information according to the distance information acquired in the acquisition step and the reference attribute information according to the attribute information, and the degree of association with the infection risk of the infectious disease in three or more stages , the user. The infectious disease infection risk determination program according to claim 1, wherein the infection risk of the infectious disease is determined. In the information acquisition step, infection history information regarding the infection history of the infectious disease in the user and / or the other party to be determined is acquired.
In the determination step, the combination having the reference distance information and the reference infection history information regarding the infection history of the infectious disease in one user and / or the other party detected in the past, and the infection risk of the infectious disease The combination of the reference distance information according to the distance information acquired in the above information acquisition step and the reference infection history information according to the infection history information, and the infectious disease The infectious disease infection risk determination program according to claim 1, wherein the infection risk of the user is determined based on the degree of association with the infection risk in three or more stages. In the information acquisition step, the subjective symptom information regarding the subjective symptom related to the infectious disease in the user and / or the other party to be determined is acquired.
In the determination step, the combination of the reference distance information, the reference subjective information regarding the subjective symptom of the infectious disease in one user and / or the other party detected in the past, and the infection risk of the infectious disease. A combination of reference distance information according to the distance information acquired in the above information acquisition step and reference subjective symptom information according to the subjective symptom information by referring to three or more levels of association , and infection of the infectious disease. The infectious disease infection risk determination program according to claim 1, wherein the infection risk of the user to be determined is determined based on the degree of association with the risk in three or more stages. In the information acquisition step, image information of the face or body of the user and / or the other party to be determined is acquired.
In the determination step, the combination of the reference distance information, the reference image information of the face or body of one user and / or the other party detected in the past, and the infection risk of the infectious disease are 3 The combination of the reference distance information according to the distance information acquired in the above information acquisition step and the reference image information according to the image information by referring to the degree of association of the stage or higher, and the infection risk of the infectious disease. The infectious disease infection risk determination program according to claim 1, wherein the infection risk of the user to be determined is determined based on the degree of association of three or more stages. In the above information acquisition step, living information regarding a lifestyle for preventing infection in the user and / or the other party to be determined is acquired.
In the determination step, the combination of the reference distance information and the reference living information regarding the lifestyle for preventing infection in one user and / or the other party detected in the past, and the infection risk of the infectious disease The combination of the reference distance information according to the distance information acquired in the above information acquisition step and the reference living information according to the living information by referring to the three or more levels of association, and the infection risk of the infectious disease. The infectious disease infection risk determination program according to claim 1, wherein the infection risk of the user to be determined is determined based on the degree of association of three or more levels with. In the above information acquisition step, the area information about the area corresponding to the position information of the user to be determined is acquired.
In the above determination step, the above combination having the reference increasing tendency information regarding the increasing tendency of the infectious disease in each region and the degree of association with the infection risk of the infectious disease in three or more stages are referred to and acquired in the above information acquisition step. The infectious disease infection risk determination program according to any one of claims 2 to 8, wherein the infection risk of the user is determined based on the reference increasing tendency information corresponding to the regional information. The infectious disease infection risk determination according to any one of claims 1 to 9, wherein in the determination step, the degree of association corresponding to the weighting coefficient of each output of the node of the neural network in artificial intelligence is used. program.

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