Method and device for determining individual risk

Abstract

The present application provides a method and device for determining individual risk, which relates to the field of epidemic prevention and control technology, wherein the method includes obtaining the current first drug purchase data of an individual and the identity information of the individual, obtaining the second drug purchase data and travel data corresponding to the individual within the first time period closest to the current time in history based on the identity information, and determining the risk data of the individual based on the first drug purchase data, the second drug purchase data and the travel data. The technical solution provided by the present application can improve the accuracy of determining individual risk and improve the early warning effect.

Description

Technical Field

[0001] The present application relates to the technical field of epidemic prevention and control, and in particular to a method and device for determining individual risks. Background Art

[0002] The epidemic spreads widely, has strong infectiousness and spreads quickly, which will have a great impact on social security and people's lives. When patients have symptoms similar to those of the epidemic, they usually go to pharmacies and other medical places to buy medicine or check up, which may bring risks to the personnel of medical places.

[0003] In the existing technology, medical personnel can determine whether an individual is at risk by checking whether the individual has obvious characteristics related to the epidemic, such as whether the body temperature is too high, etc. However, this assessment method has low accuracy and is difficult to achieve a good early warning effect. Summary of the invention

[0004] In view of this, the present application provides a method and device for determining individual risks, which can improve the accuracy of determining individual risks and improve the warning effect.

[0005] In order to achieve the above purpose, this application adopts the following technical solutions:

[0006] In a first aspect, an embodiment of the present application provides a method for determining individual risk, comprising:

[0007] Obtaining current first drug purchase data of an individual and identity information of the individual;

[0008] Based on the identity information, obtain second drug purchase data and travel data corresponding to the individual within a first time period closest to the current time in history;

[0009] Based on the first drug purchasing data, the second drug purchasing data, and the travel data, risk data of the individual is determined.

[0010] In an embodiment of the present application, by obtaining the individual's current first drug purchasing data and the individual's identity information, and based on the identity information obtaining the second drug purchasing data and travel data corresponding to the individual within a first time period closest to the current time in history, the individual's risk data is determined based on the first drug purchasing data, the second drug purchasing data, and the travel data, thereby accurately determining the individual's risk data based on the individual's current drug purchasing status indicated by the first drug purchasing data, the individual's drug purchasing status in a recent period of time indicated by the second drug purchasing data, and the individual's travel status in a recent period of time indicated by the travel data.

[0011] Optionally, determining the risk data of the individual based on the first drug purchasing data, the second drug purchasing data and the travel data includes:

[0012] Determining a risk coefficient corresponding to the first drug purchasing data, a risk coefficient corresponding to the second drug purchasing data, and a risk coefficient corresponding to the travel data;

[0013] The risk data is determined based on a risk coefficient corresponding to the first drug purchasing data, a risk coefficient corresponding to the second drug purchasing data, and a risk coefficient corresponding to the travel data.

[0014] Optionally, the first drug purchasing data includes at least one of a first drug type, a first drug quantity, a first drug purchasing point location, and a first body temperature of the individual.

[0015] Optionally, determining the risk coefficient corresponding to the second drug purchasing data includes:

[0016] Determine an outlier factor corresponding to the second drug purchasing data based on a local outlier factor (LOF) algorithm;

[0017] Based on the outlier factor, a risk coefficient corresponding to the second drug purchasing data is determined.

[0018] Optionally, the second drug purchasing data includes at least one of a second drug type, a second drug quantity, a second drug purchasing point location, and a second body temperature of the individual.

[0019] Optionally, the second drug purchasing data corresponding to the individual includes drug purchasing data of the individual and other individuals at the same residential address as the individual.

[0020] Optionally, the travel data includes travel locations and travel modes corresponding to the travel locations, and determining the risk coefficient corresponding to the travel data includes:

[0021] Based on at least one of the travel locations, construct a travel trajectory vector and a location risk vector;

[0022] constructing a travel location type risk amount based on a travel mode corresponding to at least one of the travel locations;

[0023] Based on the travel trajectory vector, the location risk vector and the travel location type risk amount, a risk coefficient corresponding to the travel data is determined.

[0024] Optionally, the method further comprises:

[0025] Based on the identity information of the individual, obtaining at least one of the individual's occupational information, the individual's contact record within a second time period closest to the current time in history, the individual's infection detection record within a third time period closest to the current time in history, the individual's isolation record within a fourth time period closest to the current time in history, and the number of times the individual purchased medicine within a fifth time period closest to the current time in history;

[0026] The determining the risk data of the individual based on the first drug purchasing data, the second drug purchasing data and the travel data includes:

[0027] The risk data of the individual is determined based on at least one of the occupational information, the contact record, the infection detection record, the isolation record and the number of medicine purchases, as well as the first medicine purchase data, the second medicine purchase data and the travel data.

[0028] In a second aspect, an embodiment of the present application provides a device for determining individual risk, including:

[0029] An acquisition module, used to acquire the current first drug purchase data of an individual and the identity information of the individual; based on the identity information, acquire the second drug purchase data and travel data corresponding to the individual within a first time period closest to the current time in history;

[0030] A determination module is used to determine the risk data of the individual based on the first drug purchase data, the second drug purchase data and the travel data.

[0031] In a third aspect, an embodiment of the present application provides a server, comprising: a memory and a processor, the memory being used to store a computer program; the processor being used to execute the method described in the first aspect or any one of the implementations of the first aspect when calling the computer program.

[0032] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the method described in the first aspect or any one of the embodiments of the first aspect is implemented.

[0033] In a fifth aspect, an embodiment of the present application provides a computer program product, which, when executed on a server, enables the server to execute any of the methods described in the first aspect.

[0034] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Figure 1A schematic diagram of the structure of a system for determining individual risk provided in an embodiment of the present application;

[0036] Figure 2 A flowchart of a method for determining individual risk provided in an embodiment of the present application;

[0037] Figure 3 A schematic diagram of a data point set provided in an embodiment of the present application;

[0038] Figure 4 A schematic diagram of a vector corresponding to travel data provided in an embodiment of the present application;

[0039] Figure 5 A tree diagram of individual risk warning data provided in an embodiment of the present application;

[0040] Figure 6 A schematic diagram of the structure of a device for determining individual risk provided in an embodiment of the present application;

[0041] Figure 7 A structural block diagram of a server provided in an embodiment of the present application. DETAILED DESCRIPTION

[0042] In order to facilitate understanding of the technical solutions in the embodiments of the present application, the application scenarios of the embodiments of the present application are first introduced below.

[0043] Embodiments of the present application provide a system and method for determining individual risk.

[0044] Please refer to Figure 1 , is a system for determining individual risk provided by an embodiment of the present application, the system comprising a plurality of first terminals 110 ( Figure 1 Only 3 are shown) and server 120.

[0045] The first terminal 110 can be set in a medical place such as a pharmacy, a community clinic, a private clinic or a hospital. The first terminal 110 can be an electronic device such as a computer or a mobile phone. The first device 110 can interact with the server 120, including providing the server 120 with a variety of data required for the server 120 to determine individual risks, such as the name of the individual and other related information and various drug information. It also includes obtaining information such as the assessment result of the individual risk from the server 120.

[0046] The server 120 may be used to perform risk assessment on an individual specified by any first terminal 110, and to feed back corresponding assessment results to the first terminal 110. In some embodiments, assessment strategies for risk assessment, such as risk assessment models, may be stored in the server 120. In some embodiments, other data for risk assessment may be stored in the server 120.

[0047] In some embodiments, the system may further include a second terminal 130. The second terminal 130 may be a terminal held by a reviewer (such as a district administrator, police, or doctor) who tracks the evaluation results. Accordingly, the server 120 may send the evaluation results to the second terminal 130, and the second terminal 130 may display the evaluation results to the reviewer so that the reviewer can determine whether to conduct a home visit or further test on the individual corresponding to the evaluation result. The second terminal 130 may be an electronic device such as a computer or a mobile phone.

[0048] In some embodiments, the first terminal 110 and the second terminal 130 may be the same device, or, in other embodiments, the first terminal 110 may have at least some of the functions of the second terminal 130 , and the second terminal 130 may have at least some of the functions of the first terminal 110 .

[0049] The technical solution of the present application is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0050] Please refer to Figure 2 , is a flow chart of a method for determining individual risk provided in an embodiment of the present application. It should be noted that the following only applies this method to Figure 1 Taking the server 120 shown in FIG. 1 as an example, the method for determining individual risk provided in the embodiment of the present application is described. It can be understood that the method can also be applied to Figure 1 The first terminal 110 or the second terminal 130 shown in the figure, or at least two of the first terminal 110, the server 120 and the second terminal 130 can be collaboratively executed. It should be noted that the method is not based on Figure 2 The specific order described below is a limitation, and it should be understood that in other embodiments, the order of some steps in the method can be interchanged according to actual needs, or some steps can be omitted or deleted. The method includes the following steps:

[0051] S201, obtaining the current first drug purchasing data of an individual and the identity information of the individual.

[0052] When an individual has abnormal symptoms such as fever, he or she may go to a medical facility such as a pharmacy to buy medicines to eliminate these symptoms. However, if the individual has been infected with the epidemic, it may bring risks to the medical facility and other individuals. Therefore, the individual’s current first drug purchase data and identity information can be obtained to determine the individual’s risk.

[0053] In some embodiments, when an individual enters a medical facility such as a pharmacy, the individual or the staff of the medical facility may submit the individual's current first drug purchase data and identity information to the first terminal, and the first terminal may send the received first drug purchase data and identity information to the server.

[0054] The first drug purchase data is data related to the current drug purchase behavior of the individual. In some embodiments, the first drug purchase data may include at least one of the first drug type, the first drug quantity, the first drug purchase point location, and the first body temperature of the individual. Among them, the first drug type is the drug type currently purchased by the individual, and each drug type can be divided in advance by professional and technical personnel; the first drug quantity can be the number of drugs currently purchased by the individual; the first drug purchase point location can be a medical place or the location of the first terminal; the first body temperature is the current body temperature of the individual, and the first body temperature can be measured by a measuring instrument such as a temperature measuring panel machine.

[0055] The identity information of an individual can be used to identify the individual. In some embodiments, the identity information may include at least one of a name, an ID number, a mobile phone number, and a residential address.

[0056] S202, based on the identity information, obtaining second drug purchase data and travel data corresponding to the individual within a first time period closest to the current time in history.

[0057] The second drug purchase data is data related to the drug purchase behavior of an individual in the first time period closest to the current time in history, and can be used to indicate the individual's most recent drug purchase behavior in history, while the travel data can be data related to the individual's travel, and can be used to indicate the individual's most recent travel behavior in history. When an individual is infected with an epidemic, the individual's drug purchase behavior may change, such as frequently purchasing certain drugs, and the user's travel behavior may also lead to infection or spread of the epidemic, such as entering and leaving a city where the epidemic has broken out, etc. Therefore, in order to accurately determine the individual's risk, the second drug purchase data and travel data corresponding to the individual can be obtained.

[0058] The first duration can be determined in advance by relevant technical personnel based on various factors such as the severity of the epidemic and the prevention and control needs. For example, the first duration can be 14 days. Of course, in practical applications, the first duration can also be other durations, and the embodiment of the present application does not limit the duration of the first duration.

[0059] In some embodiments, the second drug purchase data may include at least one of a second drug type, a second drug quantity, a second drug purchase point location, and a second body temperature of the individual.

[0060] In some embodiments, since there is a high possibility that multiple individuals living in the same space are infected at the same time, the second drug purchasing data corresponding to the individual may include drug purchasing data of the individual and other individuals at the same residential address as the individual.

[0061] In some embodiments, the travel data may include travel time, travel location, and travel modes corresponding to each travel location. The travel location may include various types of travel locations such as departure location, destination, and stopover location. The stopover location may be a temporary stop due to transfer, passing through, or temporary stop; the travel mode may include car, train, or airplane.

[0062] Based on the identity information corresponding to the individual, the server can obtain the medicine purchasing data corresponding to the individual within the first time period closest to the current moment from the stored medicine purchasing data, thereby obtaining the second medicine purchasing data; based on the identity information, the server can obtain the travel data corresponding to the individual within the first time period closest to the current moment from the stored travel data.

[0063] Among them, for drug purchase data, when an individual goes to a medical facility, the drug purchase data can be submitted to the first terminal of the medical facility, and the first terminal sends the received drug purchase data to the server. The server can receive and store drug purchase data sent by multiple first terminals of multiple medical facilities. For travel data, the server can obtain and store travel data of multiple individuals from databases belonging to transportation departments such as railways and aviation. Of course, in actual applications, the server can also obtain and store multiple drug purchase data and multiple travel data in advance through other methods. The embodiment of the present application does not limit the method of obtaining multiple drug purchase data or travel data.

[0064] It should be noted that in other embodiments, it is not necessary to obtain multiple travel data in advance. Instead, in S202, based on the individual's identity information, the travel data corresponding to the identity information is obtained from a database belonging to transportation departments such as railways and aviation.

[0065] In some embodiments, the individual's itinerary card data and ticket purchase data can be obtained based on the individual's identity information, and the travel data corresponding to the identity information can be determined based on the itinerary card data and ticket purchase data. In other words, travel data can be obtained from more than one data source, reducing the problem of inaccurate travel data caused by hidden travel behaviors such as individuals purchasing tickets through multiple mobile phones.

[0066] S203, determining individual risk data based on the first medicine purchasing data, the second medicine purchasing data and the travel data.

[0067] The first medicine purchasing data can illustrate the individual's current medicine purchasing behavior, the second medicine purchasing data can be used to illustrate the individual's medicine purchasing behavior in the recent period, and the travel data can be used to illustrate the individual's travel behavior in the recent period. Therefore, based on the first medicine purchasing data, the second medicine purchasing data and the travel data, the individual's current medicine purchasing behavior, medicine purchasing behavior and travel behavior in the recent period can be analyzed to accurately determine the individual's risk data.

[0068] Risk data can be used to indicate the risk level of an individual. Risk data can include risk values, such as a value between 0 and 1, and can also include risk levels, such as low risk, medium risk, and high risk. Of course, in practical applications, the representation of risk data is not limited to the two types shown above, and the embodiments of the present application do not limit the representation of risk data.

[0069] Among them, when the first drug category or the second drug category includes a specific drug category corresponding to the epidemic, the individual's risk is greater; when the first drug quantity or the second drug quantity is larger, the individual's risk is greater; when the preset distance range around the first drug purchase point location or the second drug purchase point location includes specific places, such as ports, transportation hubs, farmers' markets, and cross-border business places, the individual's risk is greater; when the first body temperature or the second body temperature is in a specific temperature range, such as less than 36 degrees or greater than 37.3 degrees, the individual's risk is greater. When the individual's travel location includes a high-risk location, the individual's risk is greater. The more data types included in the first drug purchase data and the second drug purchase data, the more comprehensive the individual's risk can be.

[0070] It should be noted that the above-mentioned specific types of drugs, preset distance ranges, specific locations, preset temperature ranges and high-risk locations can be determined in advance.

[0071] In some embodiments, the risk coefficient corresponding to the first drug purchase data, the risk coefficient corresponding to the second drug purchase data, and the risk coefficient corresponding to the travel data can be determined, and then the risk data can be determined based on the risk coefficient corresponding to the first drug purchase data, the risk coefficient corresponding to the second drug purchase data, and the risk coefficient corresponding to the travel data. That is, by respectively determining the risk coefficients corresponding to the first drug purchase data, the second drug purchase data, and the travel data, each risk data can more accurately describe the risk size corresponding to the first drug purchase data, the second drug purchase data, or the travel data, and then the risk data corresponding to the individual can be more accurately determined by each risk coefficient.

[0072] In some embodiments, risk data corresponding to the first drug purchasing data may be determined based on each data included in the first drug purchasing data and a preset weight value corresponding to each data.

[0073] In some embodiments, the first drug purchase data includes the first drug category, the first drug purchase point location and the first body temperature of the individual, then the risk data corresponding to the first drug purchase data can be expressed as: a*0.05+b*0.05+c*0.1. Among them, the weight value corresponding to the first drug category and the weight value corresponding to the first drug purchase point location are both 0.05; the weight value corresponding to the first body temperature is 0.1; when the first drug category includes a specific drug category, a=1, otherwise a=0; when the preset distance range of the first drug purchase point location includes a specific place, b=1, otherwise b=0; when the first body temperature is within a specific temperature range, c=1, otherwise c=0.

[0074] In some embodiments, an outlier factor corresponding to the second drug purchasing data may be determined based on a LOF algorithm, and a risk coefficient corresponding to the second drug purchasing data may be determined based on the outlier factor.

[0075] The LOF algorithm is a distance-based unsupervised anomaly detection algorithm that achieves anomaly detection by calculating the local density deviation of a given data point relative to its neighborhood. Multiple data points can be Figure 3 As shown, the data points included in set W have uniform overall spacing, density and dispersion, so it can be considered that the data points included in set W are normal. Although the density of the data points included in set X is inconsistent with that of the data points included in set W, the overall spacing, density and dispersion of each data point are also uniform, so it can be considered that the data points included in set X are also normal. Point Y and point Z are far away from the data points included in set W and set X, and are relatively isolated, so they can be considered as abnormal points.

[0076] The outlier factor of a given data point can be calculated by the LOF algorithm. The outlier factor represents the average ratio of the local reachable density of other data points in the neighborhood of the given data point to the local reachable density of the given data point. If the outlier factor is closer to 1, it means that the local reachable density of the given data point is closer to that of other data points in its neighborhood. Figure 3 For example, the given data point may be a data point included in set W or set X. If the outlier factor is less than 1, it means that the local reachability density of the given data point is higher than the local reachability density of other data points in its neighborhood. Figure 3 For example, the given data point may be a data point in a relatively dense area in set W or set X. If the outlier factor is greater than 1, it means that the local reachable density of the given data point is lower than the local reachable density of other data points in its neighborhood, or it can be said that the neighborhood of the given data point includes fewer data points, that is, the possibility that the given data point is an abnormal outlier is greater. Figure 3 For example, the given data point may be point Y or point Z.

[0077] As can be seen from the foregoing, the second drug purchase data may include at least one of the second drug type, the second drug quantity, the second drug purchase point location, and the second body temperature. Therefore, a one-dimensional or multi-dimensional feature vector may be generated based on at least one data included in the second drug purchase data. The feature vector corresponds to the second drug purchase data, and the value of each dimension in the feature vector may correspond to a data in the second drug purchase data. The feature vector may be used as a data point. Based on the LOF algorithm, the outlier factor of the feature vector corresponding to the second drug purchase data in the preset sample set is determined. Among them, the preset sample set may include feature vectors corresponding to multiple third drug purchase data. Therefore, the outlier factor corresponding to the second drug purchase data may represent the degree of outlier of the second drug purchase data relative to multiple third drug purchase data. The third drug purchase data has the same data dimension as the second drug purchase data, and the third drug purchase data is normal drug purchase data. When the outlier factor is greater than 1, it can be explained that the more isolated the second drug purchase data is relative to the multiple third drug purchase data, the greater the possibility that the second drug purchase data is abnormal data.

[0078] In some embodiments, the third drug purchase data may be drug purchase data of multiple time slices in history for the same individual as the second drug purchase data, so as to indicate the drug purchase behavior of the individual in each time slice, and the duration of each time slice may be the first duration. Accordingly, the obtained outlier factor may indicate the degree of abnormality of the drug purchase behavior of the individual in the time slice closest to the current one compared with the drug purchase behavior in the previous earlier time slice. If the outlier factor is greater than 1, the possibility that the drug purchase behavior of the individual in the time slice closest to the current one is abnormal is greater, and the possibility that the individual may have symptoms related to the epidemic recently is higher. Alternatively, in some embodiments, the third drug purchase data may be drug purchase data of multiple normal individuals who are not infected with the epidemic in the same time slice or multiple time slices. Accordingly, the obtained outlier factor may indicate the degree of abnormality of the drug purchase behavior of the individual corresponding to the second drug purchase data compared with the drug purchase behavior of other normal individuals. If the outlier factor is greater than 1, the possibility that the drug purchase behavior of the individual in the time slice closest to the current one is abnormal is greater, and the possibility that the individual may have symptoms related to the epidemic recently is higher.

[0079] When the outlier factor (i.e., A) of the feature vector relative to the sample set is determined by the LOF algorithm, the risk coefficient corresponding to the second drug purchasing data can be determined based on the outlier factor, and the risk coefficient corresponding to the second drug purchasing data can be positively correlated with the outlier factor. In some embodiments, the difference obtained by subtracting 1 from the outlier factor (i.e., A-1) can be multiplied by the weight value corresponding to the second drug purchasing data (e.g., 0.05), and the resulting product is the risk coefficient corresponding to the second drug purchasing data.

[0080] It should be noted that, in practical applications, the risk coefficient corresponding to the second drug purchasing data may also be determined by other anomaly detection algorithms or other methods.

[0081] In some embodiments, the risk coefficient corresponding to the travel data may be determined based on each travel location, the risk coefficient corresponding to each travel location, and the risk coefficient corresponding to each travel mode.

[0082] Among them, the higher the risk of the travel location and the higher the risk of the travel mode, the higher the risk indicated by the risk coefficient corresponding to the travel data.

[0083] In some embodiments, a travel trajectory vector and a location risk vector can be constructed based on at least one travel location, a travel destination type risk amount can be constructed based on the travel mode corresponding to at least one travel location, and a risk coefficient corresponding to the travel data can be determined based on the travel trajectory vector, the location risk vector and the travel destination type risk amount.

[0084] The travel trajectory vector can be used to intuitively and accurately represent the individual's travel location. The length of the travel trajectory vector is the total number of multiple locations. The value range of each element in the travel trajectory vector can include 0 and 1. 0 indicates that the individual has not traveled to the location, and 1 indicates that the individual has traveled to the location. In some embodiments, the elements corresponding to the departure and destination in the travel trajectory vector can be set to 1, and the elements corresponding to other locations can be set to 0. In some embodiments, due to the different lengths of stay of individuals at locations under different travel modes, the risks of individuals spreading the epidemic or infection are also different. Therefore, the travel trajectory vector can be determined based on the travel location and travel mode included in the travel mode travel data, thereby improving the accuracy of the travel trajectory vector. For example, for travel by airplane, the elements corresponding to the departure point, destination and transfer point in the travel trajectory vector can be set to 1; for travel by train or car, the elements corresponding to the departure point, destination and stopover points such as the place on the way in the travel trajectory vector are all set to 1.

[0085] The location risk vector can be used to intuitively and accurately represent the risk level of each location. The length of the location wind direction vector is the total number of multiple locations. In some embodiments, the value range of each element included in the location risk vector may include 0-1, and the closer the element is to 1, the greater the risk shown. In some embodiments, the value range of each element of the location risk vector may include 0, 0.1, 0.2 and 0.3, where 0.1, 0.2 and 0.3 represent low risk, medium risk and high risk, respectively, and 0 represents no risk. Of course, in actual applications, each element in the location risk vector may be a value within other numerical ranges.

[0086] The travel destination type risk amount can be used to intuitively and accurately represent the risk level of different types of travel locations. The length of the travel destination type risk amount is the total number of multiple locations. In some embodiments, the value range of each element in the travel destination type risk amount is multiple values ​​of different sizes, and each value can correspond to a travel destination type. For example, the travel destination types include the departure point, the destination, and the pause point, and the value range of each element in the travel destination type risk amount can include 0, 0.1 and 1, 0 means no risk, corresponding to other locations, 0.1 means a small risk, corresponding to the pause point, and 1 means a large risk, corresponding to the departure point or destination. If any location is the departure point or destination, the element corresponding to the location in the travel destination type risk amount is set to 1; if any location is a pause point, the element corresponding to the location in the travel destination type risk amount is set to 0.1, and the elements corresponding to other locations are set to 0.

[0087] It should be noted that the lengths of the above travel trajectory vector, location risk vector and travel location type risk amount may be the same.

[0088] In some embodiments, the travel trajectory vector may be a row vector, and the location risk vector and the travel location type risk amount may be a column vector, which may be as follows: Figure 3 As shown, the product of the travel trajectory vector, the location risk vector and the travel location type risk amount is determined as the risk coefficient corresponding to the travel data.

[0089] For example, Figure 3 Take the first three elements of each vector in as an example. The first element in the travel trajectory vector is 0, indicating that the individual has not traveled to the place corresponding to the first element; the first element in the location risk vector is 0, indicating that the place corresponding to the first element is risk-free, and the first element in the travel location type risk amount is 0, indicating that the user has no risk in traveling to the place. The second element in the travel trajectory vector is 1, indicating that the individual has traveled to the place corresponding to the second element; the second element in the location risk vector is 0.3, indicating that the place corresponding to the second element is high-risk, and the second element in the travel location type risk amount is 1, indicating that the place is the departure or destination. The third element in the travel trajectory vector is 1, indicating that the individual has traveled to the place corresponding to the second element; the second element in the location risk vector is 0.2, indicating that the place corresponding to the second element is medium-risk, and the second element in the travel location type risk amount is 0.1, indicating that the user has temporarily stayed at the place and has a small risk.

[0090] In some embodiments, the product of the travel trajectory vector, the location risk vector, the travel location type risk amount, and the weight value corresponding to the travel data (such as 0.3) can be determined as the risk coefficient corresponding to the travel data.

[0091] In some embodiments, in order to be able to determine the risks of an individual more comprehensively from more dimensions and further improve the accuracy of determining the risks of an individual, it is possible to obtain at least one of the individual's identity information, the individual's occupational information, the individual's contact records within the second time period closest to the current time period in history, the individual's infection detection records within the third time period closest to the current time period in history, the individual's isolation records within the fourth time period closest to the current time period in history, and the individual's number of drug purchases within the fifth time period closest to the current time period in history. Accordingly, the individual's risk data can be determined based on at least one of the occupational information, contact records, infection detection records, isolation records and number of drug purchases, as well as the first drug purchase data, the second drug purchase data and the travel data.

[0092] People engaged in certain specific occupations may be more likely to be exposed to or spread germs, that is, they are more likely to be infected or spread the epidemic. Therefore, when an individual's occupation is a specific occupation, the individual's risk is higher. In some embodiments, specific occupations may include at least one of farmers' market services, cold chain services, quarantine hotel services, and cross-border transportation services. It should be noted that specific occupations can be determined in advance by relevant technical personnel based on the epidemic. The embodiment of the present application does not specifically limit the method for determining a specific occupation and the type of specific occupation.

[0093] In some embodiments, the occupation information of the individual can be obtained based on the identity information of the individual, and the occupation information can be compared with the stored specific occupation information. If the occupation information is the same as any specific occupation information, it is determined that the individual is engaged in a specific occupation. Alternatively, in other embodiments, the identity information of the individual can be compared with the identity information of multiple individuals of a specific occupation stored. If the identity information of the individual is the same as the identity information of any of the multiple individuals stored, it is determined that the individual is engaged in a specific occupation.

[0094] The contact record may include whether the individual has a specific contact history. When an individual has a specific contact history in the recent period of time, the individual is more likely to be infected or spread the epidemic, and accordingly, the individual will also have a higher risk. In some embodiments, the specific contact history may include at least one of a history of overseas travel, a history of travel to medium- and high-risk areas, and a history of contact with confirmed cases or suspected cases.

[0095] Among them, the staff of the medical place can ask the individual, or the individual can actively report the contact record, and the first terminal receives the contact record submitted by the relevant staff or individual, and sends the contact record to the server. Of course, in actual applications, the contact record can also be determined by the server in other ways. For example, for overseas travel history and travel history in medium- and high-risk areas, the individual's travel data mentioned above can be used to determine whether the individual has a history of overseas travel or a history of travel in medium- and high-risk areas.

[0096] Infection testing is an effective means to identify whether an individual is infected. The more times an individual undergoes infection testing, the greater the possibility that the area where the individual is located may be infected by the epidemic, and therefore the greater the risk for the individual.

[0097] Isolation records can be used to indicate whether an individual has been isolated due to an epidemic. If an individual has been isolated due to an epidemic, it is possible that the individual is more likely to be infected and therefore has a higher risk.

[0098] The more times an individual purchases medicine, the more severe the symptoms of the individual are, and therefore the higher the risk of the individual is.

[0099] It should be noted that the second duration, the third duration, the fourth duration, and the fifth duration may be determined in advance, and the durations of two or more of the first duration, the second duration, the third duration, the fourth duration, and the fifth duration may be the same. The embodiment of the present application does not limit the manner of determining the second duration, the third duration, the fourth duration, and the fifth duration, and the durations of the second duration, the third duration, the fourth duration, and the fifth duration.

[0100] In some embodiments, at least one of the risk coefficients corresponding to occupational information, the risk coefficients corresponding to contact records, the risk coefficients corresponding to infection detection records, the risk coefficients corresponding to quarantine records, and the risk coefficients corresponding to the number of drug purchases can be determined, and the risk data of an individual can be determined based on the risk coefficients corresponding to occupational information, the risk coefficients corresponding to contact records, the risk coefficients corresponding to infection detection records, the risk coefficients corresponding to quarantine records, and the risk coefficients corresponding to the number of drug purchases, as well as the risk coefficients corresponding to the first drug purchase data, the risk coefficients corresponding to the second drug purchase data, and the risk coefficients corresponding to the travel data. In some embodiments, the risk data of an individual can be determined by summing up at least one of the risk coefficients corresponding to occupational information, the risk coefficients corresponding to contact records, the risk coefficients corresponding to infection detection records, the risk coefficients corresponding to quarantine records, and the risk coefficients corresponding to the number of drug purchases, as well as the risk coefficients corresponding to the first drug purchase data, the risk coefficients corresponding to the second drug purchase data, and the risk coefficients corresponding to the travel data.

[0101] The risk factor corresponding to the occupation information can be determined based on the occupation information and the weight value corresponding to the occupation information. In some embodiments, the risk factor of the occupation information can be expressed as d*0.3, where d=1 if the occupation indicated by the occupation information is a specific occupation, otherwise d=0, and 0.3 is the weight value corresponding to the occupation information.

[0102] The risk factor corresponding to the contact record can be determined based on the individual's contact record and the weight value corresponding to the contact record. In some embodiments, the risk factor corresponding to the contact record can be expressed as e*0.3, where if the contact record includes any specific contact history, then e=1, otherwise e=0, and 0.3 is the weight value corresponding to the contact record.

[0103] The number of infection detections of an individual can be determined based on the infection detection record, and the risk coefficient corresponding to the infection detection record can be determined based on the number of infection detections and the weight value corresponding to the infection detection record. In some embodiments, the risk coefficient corresponding to the infection detection record can be expressed as f*0.2, where f is the number of infection detections and 0.2 is the weight value corresponding to the infection detection record.

[0104] The risk coefficient corresponding to the quarantine record can be determined based on the quarantine record and the weight value corresponding to the quarantine record. In some embodiments, the risk record corresponding to the quarantine record can be expressed as g*0.3, where if the individual has been quarantined, such as staying in a quarantine hotel, g=1, otherwise g=0, and 0.3 is the weight value corresponding to the quarantine record.

[0105] The risk coefficient corresponding to the number of drug purchases can be determined based on the number of drug purchases and the weight value corresponding to the number of drug purchases. In some embodiments, the risk coefficient corresponding to the number of drug purchases can be expressed as h*0.1, where h is the number of drug purchases and 0.1 is the weight value corresponding to the number of drug purchases.

[0106] It should be noted that, in practical applications, the above-mentioned weight values ​​may be other values, and the embodiments of the present application do not limit the numerical values ​​of the weight values.

[0107] In some embodiments, the risk data corresponding to an individual can be expressed as: a*0.05+b*0.05+c*0.1+d*0.3+e*0.3+f*0.2+g*0.3+h*0.1+i*0.3+j*0.05. The meanings of a, b, c, d, e, f, g and h can be found in the relevant descriptions above; i corresponds to the product of the travel trajectory vector, the location risk vector and the travel and location type risk amount mentioned above; j=A-1, A represents the outlier factor corresponding to the second drug purchase data mentioned above.

[0108] Through the above steps, a risk value representing individual risk can be obtained, and in other embodiments, the risk data may include a risk level, and the determined value can be compared with a plurality of preset risk thresholds of different sizes to determine whether the individual's risk level is low risk, medium risk or high risk.

[0109] It should be noted that the embodiment of the present application does not limit the method for determining the risk value and the numerical value of the risk threshold. For example, multiple risk thresholds may include 0.2 and 0.4. If the risk value representing an individual is less than 0.2, the individual is determined to be at low risk. If the risk value representing an individual is greater than or equal to 0.2 and less than 0.4, the individual is determined to be at medium risk. If the risk value representing an individual is greater than or equal to 0.4, the individual is determined to be at high risk.

[0110] In some embodiments, the risk data may be fed back to at least one of the first terminal and the second terminal so that the individual and relevant staff can determine the risk level of the individual and take corresponding countermeasures.

[0111] In some embodiments, the reviewer can re-verify the individual based on the risk data of the individual displayed by the second terminal, including at least one verification method such as home visits, testing the individual at designated medical places such as fever clinics, or infection testing of the individual. The second terminal can receive the review result submitted by the reviewer and send the review result to the server. Correspondingly, the server can store the review result or send the review result to the first terminal, so that the staff of the medical place where the first terminal is located can further determine the risk of the individual.

[0112] In some embodiments, relevant staff can adjust the aforementioned weight values ​​or thresholds (such as risk thresholds related to risk levels) and other parameters based on the review results and / or the development status of the epidemic, can receive new weight values ​​or thresholds and other parameters submitted by relevant staff, and determine individual risk data based on the new parameters, thereby forming a closed loop, realizing dynamic adaptive adjustment of the method for determining individual risks, and can further improve the accuracy of determining individual risks.

[0113] In some embodiments, the server may feed back individual risk warning data to the first terminal and / or the second terminal, and accordingly, the first terminal or the second terminal may display the individual risk warning data, wherein the individual risk warning data includes risk data and may also include other data related to the individual or individual risk. In some embodiments, the data structure of the individual risk warning data may be as follows: Figure 5As shown, it includes individual identity information, risk value, risk details and review results. Identity information includes the individual's name, ID number, mobile phone number and residential address. Risk details include occupational information, contact records in the past 14 days, current body temperature, the most recent infection test record, isolation records in the past month, drug purchase records in the past 21 days and trajectory distribution in the past 21 days. Occupational information includes whether it is a specific occupation such as farmers' market service industry, cold chain service industry, quarantine hotel service industry or cross-border transportation service industry; contact records in the past 14 days include whether there is a history of travel to medium- and high-risk areas, whether there is a history of travel abroad, and whether there is a history of contact with confirmed cases or suspected cases; drug purchase records in the past 21 days include whether specific drugs are purchased and the number of drug purchases corresponding to the same residential address; the trajectory distribution in the past 21 days includes whether there are specific places such as transportation hubs, farmers' markets or logistics companies near the pharmacies reached. The review results include door-to-door visit results, fever clinic diagnosis results and infection test results.

[0114] In an embodiment of the present application, by obtaining the individual's current first drug purchasing data and the individual's identity information, and based on the identity information obtaining the second drug purchasing data and travel data corresponding to the individual within a first time period closest to the current time in history, the individual's risk data is determined based on the first drug purchasing data, the second drug purchasing data, and the travel data, thereby accurately determining the individual's risk data based on the individual's current drug purchasing status indicated by the first drug purchasing data, the individual's drug purchasing status in a recent period of time indicated by the second drug purchasing data, and the individual's travel status in a recent period of time indicated by the travel data.

[0115] Based on the same inventive concept, as an implementation of the above method, an embodiment of the present application provides a device 600 for determining individual risks. The device embodiment corresponds to the above method embodiment. For ease of reading, the device embodiment will no longer repeat the details of the above method embodiment one by one, but it should be clear that the device in this embodiment can correspond to all the contents of the above method embodiment.

[0116] Please refer to Figure 6 , is a schematic diagram of the structure of an apparatus 600 for determining individual risk provided in an embodiment of the present application, such as Figure 6 As shown, the device 600 provided in this embodiment includes:

[0117] The acquisition module 601 is used to acquire the current first drug purchase data of the individual and the identity information of the individual; based on the identity information, acquire the second drug purchase data and travel data corresponding to the individual within the first time period closest to the current time in history;

[0118] The determination module 602 is used to determine individual risk data based on the first drug purchase data, the second drug purchase data and the travel data.

[0119] Optionally, the determination module 602 is specifically configured to:

[0120] Determine a risk coefficient corresponding to the first drug purchase data, a risk coefficient corresponding to the second drug purchase data, and a risk coefficient corresponding to the travel data;

[0121] The risk data is determined based on the risk coefficient corresponding to the first medicine purchasing data, the risk coefficient corresponding to the second medicine purchasing data, and the risk coefficient corresponding to the travel data.

[0122] Optionally, the first drug purchasing data includes at least one of a first drug type, a first drug quantity, a first drug purchasing point location, and a first body temperature of the individual.

[0123] Optionally, the determination module 602 is specifically configured to:

[0124] Based on the LOF algorithm, determine the outlier factor corresponding to the second drug purchase data;

[0125] Based on the outlier factor, a risk coefficient corresponding to the second drug purchasing data is determined.

[0126] Optionally, the second drug purchasing data includes at least one of a second drug type, a second drug quantity, a second drug purchasing point location, and a second body temperature of the individual.

[0127] Optionally, the travel data includes travel locations and travel modes corresponding to each travel location, and the determination module 602 is specifically used to:

[0128] Based on at least one travel location, construct a travel trajectory vector and a location risk vector;

[0129] Based on the travel mode corresponding to at least one travel location, construct a travel location type risk amount;

[0130] Based on the travel trajectory vector, the location risk vector and the travel location type risk amount, the risk coefficient corresponding to the travel data is determined.

[0131] Optionally, the acquisition module 601 is further used to acquire, based on the identity information of the individual, at least one of the individual's occupational information, the individual's contact record within a second time period closest to the current time in history, the individual's infection detection record within a third time period closest to the current time in history, the individual's isolation record within a fourth time period closest to the current time in history, and the individual's drug purchase frequency within a fifth time period closest to the current time in history;

[0132] The determination module 602 is specifically used for:

[0133] The risk data of the individual is determined based on at least one of occupational information, contact records, infection detection records, quarantine records and number of medicine purchases, as well as first medicine purchase data, second medicine purchase data and travel data.

[0134] The device 600 provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0135] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated in a processing unit, or each unit can exist physically separately, or two or more units can be integrated in one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, which will not be repeated here.

[0136] Based on the same inventive concept, an embodiment of the present application also provides a server. Figure 7 A schematic diagram of the structure of the server 700 provided in the embodiment of the present application is shown in FIG. Figure 7 As shown, the server 700 provided in this embodiment includes: a memory 710 and a processor 720, the memory 710 is used to store computer programs; the processor 720 is used to execute the method described in the above method embodiment when calling the computer program.

[0137] The server 700 provided in this embodiment can execute the above method embodiment, and its implementation principle and technical effect are similar, which will not be repeated here.

[0138] The embodiment of the present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described in the above method embodiment is implemented.

[0139] The embodiment of the present application also provides a computer program product. When the computer program product runs on a server, the server implements the method described in the above method embodiment when executing.

[0140] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present application implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned various method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable storage medium may at least include: any entity or device that can carry the computer program code to the camera / terminal device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, USB flash drive, mobile hard disk, disk or optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0141] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0142] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0143] In the embodiments provided in the present application, it should be understood that the disclosed devices / equipment and methods can be implemented in other ways. For example, the device / equipment embodiments described above are merely schematic, for example, the division of the modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0144] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, wholes, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or combinations thereof.

[0145] It should also be understood that the term “and / or” used in the specification and appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0146] As used in the specification and appended claims of this application, the term "if" can be interpreted as "when" or "uponce" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" can be interpreted as meaning "uponce it is determined" or "in response to determining" or "uponce [described condition or event] is detected" or "in response to detecting [described condition or event]", depending on the context.

[0147] In addition, in the description of the present application specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0148] References to "one embodiment" or "some embodiments" etc. described in the specification of this application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit it. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for determining individual risk, characterized in that include: Obtaining current first drug purchase data of an individual and identity information of the individual; Based on the identity information, obtain second drug purchase data and travel data corresponding to the individual within a first time period closest to the current time in history; Determining a risk coefficient corresponding to the first drug purchasing data, a risk coefficient corresponding to the second drug purchasing data, and a risk coefficient corresponding to the travel data; determining the risk data based on a risk coefficient corresponding to the first drug purchasing data, a risk coefficient corresponding to the second drug purchasing data, and a risk coefficient corresponding to the travel data; The travel data includes travel locations and travel modes corresponding to the travel locations, and the determining of the risk coefficient corresponding to the travel data includes: Based on at least one of the travel locations, construct a travel trajectory vector and a location risk vector, wherein the travel trajectory vector includes at least one element, one element in the travel trajectory vector corresponds to one of the travel locations, and when the element in the travel trajectory vector is 0, it indicates that the individual has not traveled to the corresponding travel location, and when the element in the travel trajectory vector is 1, it indicates that the individual has traveled to the corresponding travel location, and the location risk vector includes at least one element, one element in the location risk vector corresponds to one of the travel locations, and one element in the location risk vector indicates the risk level of the corresponding travel location; Based on the travel mode corresponding to at least one of the travel locations, a travel location type risk amount is constructed, wherein the travel location type risk vector includes at least one element, wherein one element in the travel location type risk vector corresponds to one of the travel locations, and one element in the travel location type risk vector represents a risk level corresponding to a travel location type of the corresponding travel location, wherein the travel location type is a departure location, a destination, or a stopover location; The product of the travel trajectory vector, the location risk vector and the travel location type risk amount is determined as the risk coefficient corresponding to the travel data.

2. The method according to claim 1, characterized in that The first drug purchasing data includes at least one of a first drug type, a first drug quantity, a first drug purchasing point location, and a first body temperature of the individual.

3. The method according to claim 1, characterized in that The determining the risk coefficient corresponding to the second drug purchasing data includes: Determine an outlier factor corresponding to the second drug purchasing data based on a local outlier factor LOF algorithm; Based on the outlier factor, a risk coefficient corresponding to the second drug purchasing data is determined.

4. The method according to any one of claims 1 to 3, characterized in that: The second drug purchasing data includes at least one of a second drug type, a second drug quantity, a second drug purchasing point location, and a second body temperature of the individual.

5. The method according to claim 1, characterized in that The method further comprises: Based on the identity information of the individual, obtaining at least one of the individual's occupational information, the individual's contact record within a second time period closest to the current time in history, the individual's infection detection record within a third time period closest to the current time in history, the individual's isolation record within a fourth time period closest to the current time in history, and the number of times the individual purchased medicine within a fifth time period closest to the current time in history; The determining the risk data of the individual based on the first drug purchasing data, the second drug purchasing data and the travel data includes: The risk data of the individual is determined based on at least one of the occupational information, the contact record, the infection detection record, the isolation record and the number of times of purchasing medicines, as well as the first medicine purchasing data, the second medicine purchasing data and the travel data.

6. A device for determining individual risk, characterized in that include: An acquisition module, used to acquire the current first drug purchase data of an individual and the identity information of the individual; Based on the identity information, obtain second drug purchase data and travel data corresponding to the individual within a first time period closest to the current time in history; a determination module, configured to determine a risk coefficient corresponding to the first drug purchasing data, a risk coefficient corresponding to the second drug purchasing data, and a risk coefficient corresponding to the travel data; determining the risk data based on a risk coefficient corresponding to the first drug purchasing data, a risk coefficient corresponding to the second drug purchasing data, and a risk coefficient corresponding to the travel data; The travel data includes travel locations and travel modes corresponding to the travel locations, and the determination of the risk coefficient corresponding to the travel data includes: constructing a travel trajectory vector and a location risk vector based on at least one of the travel locations, the travel trajectory vector includes at least one element, one element in the travel trajectory vector corresponds to one of the travel locations, and when the element in the travel trajectory vector is 0, it indicates that the individual has not traveled to the corresponding travel location, and when the element in the travel trajectory vector is 1, it indicates that the individual has traveled to the corresponding travel location, the location risk vector includes at least one element, one element in the location risk vector corresponds to one of the travel locations, and one element in the location risk vector indicates the risk level of the corresponding travel location; constructing a travel location type risk amount based on the travel mode corresponding to at least one of the travel locations, the travel location type risk vector includes at least one element, one element in the travel location type risk vector corresponds to one of the travel locations, and one element in the travel location type risk vector indicates the risk level corresponding to the travel location type of the corresponding travel location, and the travel location type is a departure location, a destination, or a pause location; and determining the product of the travel trajectory vector, the location risk vector, and the travel location type risk amount as the risk coefficient corresponding to the travel data.

7. A server, characterized in that: include: A memory and a processor, wherein the memory is used to store a computer program; The processor is configured to execute the method according to any one of claims 1 to 5 when calling the computer program.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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