Close contact person identification method, device, equipment and medium

Abstract

The application provides a close contact person identification method, device, equipment and medium. The method comprises: acquiring a first space-time information matrix and a second space-time information matrix in a preset time period, the first space-time information matrix corresponding to a first user, and the second space-time information matrix being composed of space-time information matrices of a plurality of second users; determining a close contact value between the first user and each of the second users according to the first space-time information matrix and the second space-time information matrix; determining a preliminary close contact person of the first user from the plurality of second users according to the close contact value; determining a new close contact value according to a preset weight and a close contact value corresponding to the preliminary close contact person; and determining a final close contact person of the first user from the preliminary close contact person according to the new close contact value. The application can quickly and accurately identify close contact persons and secondary close contact persons, thereby providing conditions for precise prevention and control of the epidemic.

Description

Technical Field

[0001] This application relates to the field of epidemic prevention and control technology, and in particular to a method, device, equipment and medium for identifying close contacts. Background Technology

[0002] With the recurring outbreaks of the epidemic, timely and accurate identification of close contacts and secondary close contacts is particularly important for epidemic prevention and control.

[0003] Currently, the identification of close contacts and secondary close contacts is primarily achieved through telephone inquiries or manual verification. This method is not only lacking in real-time performance and accuracy, but also involves a huge workload, severely impacting the efficiency of close contact identification and easily leading to risks such as the spread or spillover of the epidemic. Summary of the Invention

[0004] This application provides a method, device, equipment, and medium for identifying close contacts, which can quickly and accurately identify close contacts and secondary close contacts, providing conditions for precise epidemic prevention and control.

[0005] In a first aspect, embodiments of this application provide a method for identifying close contacts, including:

[0006] Obtain a first spatiotemporal information matrix and a second spatiotemporal information matrix within a preset time period. The first spatiotemporal information matrix corresponds to the first user, and the second spatiotemporal information matrix is ​​composed of the spatiotemporal information matrices of multiple second users.

[0007] Based on the first spatiotemporal information matrix and the second spatiotemporal information matrix, determine the close contact value between the first user and each of the second users;

[0008] Based on the close contact value, the initial close contacts of the first user are determined from among a plurality of second users;

[0009] A new close contact value is determined based on the preset weights and the close contact values ​​corresponding to the initial close contacts.

[0010] Based on the new close contact value, the final close contacts of the first user are determined from the initial close contacts.

[0011] Secondly, embodiments of this application provide a close contact identification device, comprising:

[0012] The matrix acquisition module is used to acquire a first spatiotemporal information matrix and a second spatiotemporal information matrix within a preset time period. The first spatiotemporal information matrix corresponds to a first user, and the second spatiotemporal information matrix is ​​composed of multiple spatiotemporal information matrices of second users.

[0013] The first determining module is used to determine the close contact value between the first user and each of the second users based on the first spatiotemporal information matrix and the second spatiotemporal information matrix.

[0014] The second determining module is used to determine the preliminary close contacts of the first user from among a plurality of second users based on the close contact value;

[0015] The third determining module is used to determine a new close contact value based on a preset weight and the close contact value corresponding to the initial close contact person;

[0016] The fourth determining module is used to determine the final close contacts of the first user from the preliminary close contacts based on the new close contact value.

[0017] Thirdly, embodiments of this application provide a close contact identification device, comprising:

[0018] A processor and a memory, the memory being used to store a computer program, and the processor being used to call and run the computer program stored in the memory to perform the close contact identification method described in the first aspect embodiment.

[0019] Fourthly, embodiments of this application provide a computer-readable storage medium for storing a computer program that causes a computer to perform the close contact identification method described in the first aspect embodiment.

[0020] Fifthly, embodiments of this application provide a computer program product, including a computer program / instructions, which, when executed by a processor, implement the close contact identification method described in the first aspect embodiment.

[0021] The technical solutions disclosed in this application have the following beneficial effects:

[0022] By using a first spatiotemporal information matrix and a second spatiotemporal information matrix within a preset time period, the close contact value between the first user and each second user is determined. Based on this close contact value, the initial close contacts of the first user are identified from among the multiple second users. Then, based on preset weights and the close contact values ​​corresponding to the initial close contacts, a new close contact value is determined, from which the final close contacts of the first user are identified. Thus, a single identification process can determine whether multiple individuals are close contacts of confirmed cases or secondary close contacts of close contacts, enabling rapid and accurate identification of close contacts and secondary close contacts, providing conditions for precise epidemic prevention and control. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a method for identifying close contacts according to an embodiment of this application;

[0025] Figure 2 This is a flowchart illustrating another method for identifying close contacts provided in an embodiment of this application;

[0026] Figure 3 This is a schematic block diagram of a close contact identification device provided in an embodiment of this application;

[0027] Figure 4 This is a schematic block diagram of a close contact identification device provided in an embodiment of this application. Detailed Implementation

[0028] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0030] This application addresses the challenges of identifying close contacts and secondary close contacts in epidemic prevention and control scenarios. Methods such as telephone inquiries or manual verification suffer from poor real-time performance, low accuracy, and high workload, leading to low efficiency in identification and increasing the risk of epidemic spread or spillover. Therefore, this application designs a method for identifying close contacts and secondary close contacts to achieve automatic identification, thereby improving the speed and accuracy of identification and providing conditions for precise epidemic prevention and control.

[0031] The following describes in detail, with reference to the accompanying drawings, a method for identifying close contacts provided in this application.

[0032] Figure 1 This is a schematic flowchart illustrating a method for identifying close contacts according to an embodiment of this application. The method for identifying close contacts provided in this application can be executed by a close contact identification device to control the close contact identification process. This close contact identification device can consist of hardware and / or software and can be integrated into a close contact identification equipment. Figure 1 As shown, this method for identifying close contacts includes the following steps:

[0033] S101, obtain a first spatiotemporal information matrix and a second spatiotemporal information matrix within a preset time period. The first spatiotemporal information matrix corresponds to the first user, and the second spatiotemporal information matrix is ​​composed of the spatiotemporal information matrices of multiple second users.

[0034] In this embodiment, the preset time period can be adaptively set according to the needs of close contact identification. For example, the preset time period can be 24 hours, 42 hours, or 72 hours, etc., and there is no limitation on it here.

[0035] Among them, when the first user is a confirmed case, the second user is a person who may have had close contact with the confirmed case; when the first user is a close contact, the second user is a person who may have had close contact with the close contact.

[0036] Considering that identifying individuals who have had close contact with confirmed cases or close contacts primarily relies on spatiotemporal association, which can be summarized as "three similarities": "same household registration," "same residence," and "same travel." These three similarities include at least: resident registration information, resident current address information, resident movement trajectory information, and resident transportation information. Therefore, in order to identify individuals who have had close contact with confirmed cases or close contacts based on the above information, this application can define information about locations within a certain time period as spatiotemporal information. This allows for the identification of close contacts of confirmed cases, or secondary close contacts of close contacts, based on the overlap of spatiotemporal information between individuals.

[0037] Since determining close contacts and secondary close contacts requires spatiotemporal information, this application can first obtain the first spatiotemporal information matrix of the first user within a preset time period, and the second spatiotemporal information matrix composed of the spatiotemporal information matrices of multiple second users within the preset time period.

[0038] like Figure 2 As shown, obtaining the first and second spatiotemporal information matrices may include the following steps:

[0039] S11, acquire all first time information and all first spatial information of the first user within the preset time period, and all second time information and all second spatial information of each second user.

[0040] Specifically, all time information and all first spatial information of the first user within a preset time period, as well as all time information and all second spatial information of each second user, can be obtained from systems such as the public security household registration system, the epidemiological investigation system, and the mobile operator system.

[0041] For example, if the preset time period is 24 hours, then all first-time information and all first-space information of the first user within 24 hours, as well as all second-time information and all second-space information of each second user within 24 hours, can be obtained from systems such as the public security household registration system, the epidemiological investigation system, and the mobile operator system.

[0042] It should be noted that if any user travels from one location to another using transportation within a preset time period, this application, when obtaining all time and spatial information of that user within that time period, considers that the user is in a confined space during the transportation journey, with minimal personnel movement and minimal change. In contrast, the origin and destination stations experience significant personnel movement, greatly increasing the possibility of close contact. Therefore, this embodiment may only obtain the user's time and spatial information at the origin and destination stations.

[0043] S12, based on all the first time information and all the first space information of the first user, obtain the first spatiotemporal information matrix of the first user.

[0044] Optionally, obtaining the first spatiotemporal information matrix of the first user may specifically include the following steps: dividing all first time information of the first user into multiple first sub-time information and dividing all first spatial information of the first user into multiple first sub-space information according to a preset time interval; encoding and converting each first sub-time information and each first sub-space information respectively; splicing new first sub-time information and new first sub-space information belonging to the same time interval, and constructing the first spatiotemporal information matrix based on the obtained multiple first spatiotemporal information.

[0045] The preset time interval can be adaptively set based on the accuracy of close contact identification. That is, if high accuracy is required, the preset time interval can be set in minutes. For example, the selectable preset time interval can be set to 20 minutes, 30 minutes, or 40 minutes. If lower accuracy is required, the preset time interval can be set in hours. For example, the selectable preset time interval can be set to 1 hour, 1.5 hours, or 2 hours.

[0046] Continuing with the example in step S11, assuming the preset time interval is 1 hour, then all the first time information of the first user within 24 hours can be divided into 24 first sub-time information, and all the first space information of the first user within 24 hours can be divided into 24 first sub-space information.

[0047] After obtaining multiple first sub-time information and multiple first sub-space information, this application can encode and convert each first sub-time information to obtain multiple new first sub-time information. Specifically, multiple new first sub-time information can be obtained by performing binary encoding conversion on each first sub-time information. For example, if there are 24 first sub-time information, specifically: 1 o'clock, 2 o'clock, 3 o'clock, ..., 24 o'clock, then performing binary encoding conversion on these 24 first sub-time information will yield 24 new first sub-time information, specifically: 00001|00010|00011|00100|……|10111|11000.

[0048] Furthermore, the information in each first subspace can be encoded and converted to obtain multiple new first subspace information. Specifically, the latitude and longitude values ​​of each first subspace information can be obtained firstly. Then, based on the GeoHash algorithm, each latitude and longitude value can be binary encoded and converted to obtain multiple new first subspace information.

[0049] For example, if the latitude and longitude of a certain first subspace information is (39.923201, 116.390705), then the range of latitude is first determined to be (-90, 90), with a midpoint of 0. Based on Table 1, latitude 39.923201 falls within the interval (0, 90) of division 1, thus yielding a 1. Since the midpoint of the interval (0, 90) is 45 degrees, and latitude 39.923201 is less than 45, latitude 39.923201 falls within the interval (0, 45) of division 0, thus yielding a 0. This process continues to obtain the binary representation of latitude 39.923201, as shown in Table 1 below.

[0050] Table 1

[0051] Latitude range Divide the interval 0 Divide the interval 1 39.9232 1 (-90，90) (-90，0.0) (0.0，90) 1 2 (0.0，90) (0.0，45.0) (45.0，90) 0 3 (0.0，45.0) (0.0，22.5) (22.5，45.0) 1 4 (22.5，45.0) (22.5，33.75) (33.75，45.0) 1 5 (33.75，45.0) (33.75，39.375) (39.375，45.0) 1 6 (39.375，45.0) (39.375，42.1875) (42.1875，45.0) 0 7 (39.375，42.1875) (39.375，40.7812) (40.7812，42.1875) 0 8 (39.375，40.7812) (39.375，40.0781) (40.0781，40.7812) 0 9 (39.375，40.0781) (39.375，39.7265) (39.7265，40.0781) 1 10 (39.7265，40.0781) (39.7265，39.9023) (39.9023，40.0781) 1 11 (39.9023，40.0781) (39.9023，39.9902) (39.9902，40.0781) 0 12 (39.9023，39.9902) (39.9023，39.9462) (39.9462，39.9902) 0 13 (39.9023，39.9462) (39.9023，39.9243) (39.9243，39.9462) 0 14 (39.9023，39.9243) (39.9023，39.9133) (39.9133，39.9243) 1 15 (39.9133，39.9243) (39.9133，39.9188) (39.9188，39.9243) 1

[0052] Based on Table 1, the binary representation of latitude 39.923201 is: 10111 00011 00011 11001. Similarly, the binary representation of longitude 116.390705 is: 11010 01011 00010 00100. Then, by merging the binary longitude and latitude according to the odd and even bits, the new first subspace information is: 11100 11101 00100 01111 0000001101 01011 00001.

[0053] Since spatiotemporal information is composed of time information and spatial information, after encoding and converting each first sub-time information and each first sub-space information respectively, this embodiment can splice together the new first sub-time information and the new first sub-space information belonging to the same time interval to obtain multiple first spatiotemporal information, and then construct a spatiotemporal information matrix based on the multiple first spatiotemporal information.

[0054] As an optional implementation, the new first sub-time information and the new first sub-space information belonging to the same time interval can be concatenated according to the format of new first sub-time information + new first sub-space information to obtain multiple first spatiotemporal information. Then, the multiple first spatiotemporal information are arranged into a 1×N matrix, i.e., a row matrix, in chronological order. N is determined based on the number of first sub-time information or first sub-space information. For example, if the number of first sub-time information is 24, then N is 24.

[0055] The following example illustrates how to concatenate new first sub-time information and new first sub-space information within the same time interval to obtain first spatiotemporal information. Assume the new first sub-time information within a certain time interval is 00100, and the new first sub-space information is 11100 11101 00100 01111 00000 01101 01011 00001. Then, concatenating the new first sub-time information and new first sub-space information within this time interval according to the format "new first sub-time information + new first sub-space information" yields the following first spatiotemporal information: 00100 + 11100 11101 00100 01111 0000001101 01011 00001.

[0056] It should be noted that, in this embodiment, after splicing the new first sub-time information and the new first sub-space information that belong to the same time interval, the splicing result can optionally be converted to decimal and the converted splicing result can be used as the first spatiotemporal information. The first spatiotemporal information is made simpler through decimal conversion, thereby providing convenience for the subsequent determination of close contact value.

[0057] S13, based on all second time information and all second spatial information of each second user, obtain the spatiotemporal information matrix of each second user, and construct the second spatiotemporal information matrix based on the spatiotemporal information matrix of all second users.

[0058] In this embodiment of the application, obtaining the spatiotemporal information matrix of each second user may specifically include the following steps: dividing all second time information of each second user into multiple second sub-time information and dividing all second spatial information of each second user into multiple second sub-space information according to a preset time interval; encoding and converting each second sub-time information and each second sub-space information respectively; splicing new second sub-time information and new second sub-space information belonging to the same time interval, and constructing the spatiotemporal information matrix of the second user based on the obtained multiple second spatiotemporal information.

[0059] It should be noted that the construction process of the spatiotemporal information matrix of each second user is the same as the principle of constructing the first spatiotemporal information matrix. For details, please refer to the process of constructing the first spatiotemporal information matrix mentioned above. It will not be elaborated on here.

[0060] After obtaining the spatiotemporal information matrix of each second user, this embodiment can construct a second spatiotemporal information matrix based on the spatiotemporal information matrices of all second users according to a preset method. Specifically, this can be implemented in the following ways:

[0061] Method 1

[0062] The spatiotemporal matrix of each of the second users is used as a row vector to construct the initial second spatiotemporal information matrix;

[0063] The initial second spatiotemporal information matrix is ​​transposed to obtain the second spatiotemporal information matrix.

[0064] For example, if the number of second users is 5, specifically second user 1, second user 2, second user 3, second user 4, and second user 5. Furthermore, the spatiotemporal information matrices of second user 1, second user 2, second user 3, second user 4, and second user 5 are all 1×N row matrices, specifically: [b 11 b 12 … b 1N ]、[b 21 b 22 … b 2N ]、[b 31 b 32 … b 3N ]、[b 41 b 42 … b 4N ] and [b 51 b 52 … b 5N Using the spatiotemporal information matrices of the aforementioned five second users as row vectors, the initial second spatiotemporal information matrix is ​​constructed as follows:

[0065]

[0066] Then, by transposing the initial second spatiotemporal information matrix, the second spatiotemporal information matrix can be obtained, specifically as follows:

[0067]

[0068] Method 2

[0069] The second spatiotemporal information matrix is ​​constructed by using the spatiotemporal matrix of each second user as a column vector.

[0070] For example, if the number of second users is 5, specifically second user 1, second user 2, second user 3, second user 4, and second user 5. Furthermore, the spatiotemporal information matrices of second user 1, second user 2, second user 3, second user 4, and second user 5 are all 1×N row matrices, specifically: [b 11 b 12 … b 1N ]、[b 21 b 22 … b 2N ]、[b 31 b 32 … b3N ]、[b 41 b 42 … b 4N ] and [b 51 b 52 … b 5N Using the spatiotemporal information matrices of the aforementioned five second users as column matrices, the constructed second spatiotemporal information matrix is ​​as follows:

[0071]

[0072] S102, determine the close contact value between the first user and each of the second users based on the first spatiotemporal information matrix and the second spatiotemporal information matrix.

[0073] Optionally, the close contact value between the first user and each of the second users is determined by performing the following steps: determining the third spatiotemporal information matrix and the fourth spatiotemporal information matrix based on the first spatiotemporal information matrix and the second spatiotemporal information matrix; and determining the close contact value between the first user and each of the second users based on the third spatiotemporal information matrix and the fourth spatiotemporal information matrix.

[0074] In this embodiment of the application, when determining the third spatiotemporal information matrix, the first spatiotemporal information matrix and the second spatiotemporal information matrix can be multiplied together, and the product can be used as the third spatiotemporal information matrix.

[0075] For example, if the first spatiotemporal information matrix is ​​a 1×N row matrix [a x1 a x2 … a xN The second spatiotemporal information matrix is ​​an N×y matrix. Multiplying the first and second spatiotemporal information matrices together, we obtain the third spatiotemporal information matrix 1×y as follows:

[0076]

[0077] Where y is the yth second user, and y is an integer greater than or equal to 2.

[0078] Furthermore, the determination of the fourth spatiotemporal information matrix can be achieved through the following steps: determine the modulus of the row vectors in the first spatiotemporal information matrix and the modulus of the column vectors in the second spatiotemporal information matrix; construct the first modulus matrix based on the modulus of the row vectors in the first spatiotemporal information matrix, and construct the second modulus matrix based on the modulus of the column vectors in the second spatiotemporal information matrix; multiply the first modulus matrix and the second modulus matrix, and use the product as the fourth spatiotemporal information matrix.

[0079] Modulo operation is a conventional technique in this field, and will not be elaborated on here.

[0080] For example, the first spatiotemporal information matrix is ​​a 1×N row matrix [a x1 a x2 … a xN The second spatiotemporal information matrix is ​​an N×y matrix. Then, by taking the modulus of the row vectors in the first spatiotemporal information matrix, we obtain a first modulus matrix [A] of size 1×1, and by taking the modulus of the column vectors in the second spatiotemporal information matrix, we obtain a second modulus matrix [B1 B2 ... B] of size 1×y. y Then, multiplying the first and second modulo matrices, we obtain the fourth spatiotemporal information matrix of 1×y as follows:

[0081] [A×B1 A×B2 ... A×B y ].

[0082] After obtaining the third and fourth spatiotemporal information matrices, this embodiment can determine the close proximity value between the first user and each of the second users based on the third and fourth spatiotemporal information matrices. Specifically, the cosine similarity between the column vectors in the third and fourth spatiotemporal information matrices can be calculated, and the calculated cosine similarity can be determined as the close proximity value between the first user and each of the second users.

[0083] S103, based on the close contact value, determine the preliminary close contacts of the first user from among the plurality of second users.

[0084] Optionally, in this embodiment, the close contact values ​​between the first user and each second user can be compared with a first threshold to determine whether each close contact value is greater than the first threshold. If any close contact value is greater than the first threshold, it indicates that the second user corresponding to the close contact value may have spatiotemporal association with the first user, and therefore the second user corresponding to the close contact value can be identified as a preliminary close contact of the first user. If any close contact value is less than or equal to the first threshold, it indicates that the second user corresponding to the close contact value does not have spatiotemporal association with the first user, and in this case, the second user corresponding to the close contact value can be excluded to reduce interference with the subsequent determination of the final close contact.

[0085] The first threshold can be adaptively set according to the accuracy of close contact identification; no specific restrictions are imposed here. For example, the first threshold can be set to 0.8 or 0.85, etc.

[0086] S104. Determine a new close contact value based on the preset weight and the close contact value corresponding to the initial close contact person.

[0087] S105, Based on the new close contact value, determine the final close contact of the first user from the preliminary close contact personnel.

[0088] Considering the identification of close contacts and secondary close contacts, the method also considers the area of ​​the person's location and their social relationships with other individuals. Therefore, this embodiment needs to obtain the area of ​​the person's location and their social relationships with other individuals. Based on the weights determined by the location area and social relationships, and the close contact value corresponding to the initial close contacts, a new close contact value is jointly determined between the first user and the initial close contacts. Then, based on this new close contact value, the final close contacts who had close contact with the first user are identified from the initial close contacts.

[0089] In practice, each calculated new close contact value is compared with a second threshold to determine whether each new close contact value is greater than the second threshold. If any new close contact value is greater than the second threshold, it indicates that the initial close contact person corresponding to the new close contact value has spatiotemporal accompaniment with the first user, and in this case, the initial close contact person corresponding to the new close contact value can be identified as the final close contact person of the first user. If any new close contact value is less than or equal to the second threshold, it indicates that the initial close contact person corresponding to the new close contact value does not have spatiotemporal accompaniment with the first user, and in this case, the initial close contact person corresponding to the new close contact value is determined not to be the final close contact person of the first user.

[0090] The second threshold can be adaptively set according to the accuracy of close contact identification; no specific restrictions are imposed here. For example, the second threshold can be set to 0.90 or 0.95, etc., without any limitations.

[0091] The close contact identification method provided in this application determines the close contact value between a first user and each second user based on a first spatiotemporal information matrix and a second spatiotemporal information matrix within a preset time period. Based on this close contact value, preliminary close contacts of the first user are identified from among the multiple second users. Then, based on a new close contact value determined by a preset weight and the close contact value corresponding to the preliminary close contacts, the final close contacts of the first user are identified from among the preliminary close contacts. Thus, a single identification process can determine whether multiple individuals are close contacts of confirmed cases or secondary close contacts of close contacts, thereby achieving rapid and accurate identification of close contacts and secondary close contacts, providing conditions for precise epidemic prevention and control.

[0092] As described above, in this embodiment of the application, the close contact value is calculated based on the spatiotemporal information matrix of the first user and the spatiotemporal information matrices of multiple second users. The initial close contacts of the first user are determined in a coarse-grained manner from multiple second users. Then, based on preset weights and close contact values, the final close contacts of the first user are determined in a fine-grained manner from the initial close contacts.

[0093] As an optional implementation of this application's embodiments, since the preset weights may include social relationship weights and location weights, based on the above embodiments, the determination of new close contact values ​​according to preset weights and the close contact values ​​corresponding to initial close contacts will be further explained, as follows: Figure 2 As shown.

[0094] Figure 2 This is a flowchart illustrating another method for identifying close contacts provided in an embodiment of this application. Figure 2 As shown, the method may include the following steps:

[0095] S201, obtain a first spatiotemporal information matrix and a second spatiotemporal information matrix within a preset time period. The first spatiotemporal information matrix corresponds to the first user, and the second spatiotemporal information matrix is ​​composed of the spatiotemporal information matrices of multiple second users.

[0096] S202, based on the first spatiotemporal information matrix and the second spatiotemporal information matrix, determine the close contact value between the first user and each of the second users.

[0097] S203, based on the close contact value, determine the preliminary close contacts of the first user from among the plurality of second users.

[0098] S204, determine the sum of the social relationship weight and the location weight.

[0099] Considering that the identification of close contacts and secondary close contacts is linearly correlated with the area of ​​the location where the person is located—that is, the smaller the area, the greater the likelihood of close contact—the location weight can be determined based on the area of ​​the location of the first person. Specifically, the reciprocal of the area of ​​the location of the first person can be used as the location weight. For example, if the location area is 10 square meters, then the location weight is 1 / 10 = 0.1.

[0100] Furthermore, the identification of close contacts and secondary close contacts is linearly correlated with their social relationships with other individuals. That is, the closer the social relationship, the greater the likelihood of being a close contact. Therefore, the weight of social relationships can be determined based on the social relationships between the primary contact and each secondary contact.

[0101] Considering that social relationships include three types: primary relationships, secondary relationships, and no relationships. Primary relationships refer to interpersonal relationships with emotional connotations that encompass the individual's entire personality and diverse role behaviors, such as immediate family relationships and romantic / marital relationships. Secondary relationships refer to interpersonal relationships without emotional connotations, where the individual only partially participates in specific role behaviors, such as colleague relationships, teacher-student relationships, and sales relationships. No relationships refer to relationships where the two parties have no social connection whatsoever. Therefore, in determining the weight of social relationships in this application embodiment, three weights—high, medium, and low—can be set according to the priority order of primary relationships, secondary relationships, and no relationships. The high weight can be set to 1, the medium weight to 0.5, and the low weight to 0.1. Of course, the above three weights can be adaptively adjusted according to actual needs, and no specific restrictions are imposed here.

[0102] After determining the social relationship weight and location weight between the first user and each initial close contact, this application can add the social relationship weight and location weight to obtain the sum of the social relationship weight and location weight between the first user and each initial close contact.

[0103] S205, determine the new close contact value based on the close contact value corresponding to the initial close contact person and the sum value.

[0104] After determining the sum of the social relationship weights and location weights between the first user and each initial close contact, this embodiment can calculate the new close contact value between the first user and each initial close contact based on the sum and the close contact value corresponding to the initial close contact.

[0105] For example, if the social relationship weight between the first user and a certain initial close contact is 1, and the location weight is 0.1, then the sum of the social relationship weight and the location weight is 1 + 0.1 = 1.1. If the close contact value corresponding to this initial close contact is 0.85, then based on the sum of the social relationship weight and the location weight, and the close contact value corresponding to this initial close contact, the new close contact value can be determined as: 1.1 * 0.85 = 0.935.

[0106] S206, determine whether the new close contact value is greater than the second threshold. If yes, proceed to S207; otherwise, proceed to S208.

[0107] S207, if any of the new close contact values ​​is greater than the second threshold, then the preliminary close contact person corresponding to the new close contact value is determined as the final close contact person of the first user.

[0108] S208, if all the new close contact values ​​are less than or equal to the second threshold, then it is determined that the initial close contact person corresponding to the new close contact value is not the final close contact person of the first user.

[0109] The second threshold is set adaptively based on the accuracy of close contact identification. For example, the second threshold can be set to 0.90 or 0.95, etc., and there is no restriction on it here.

[0110] Specifically, the new close contact values ​​between the first user and each initial close contact can be compared with a second threshold to determine whether each new close contact value is greater than the second threshold. If any new close contact value is greater than the second threshold, it indicates that the initial close contact corresponding to the new close contact value has spatiotemporal association with the first user, and in this case, the initial close contact corresponding to the new close contact value can be identified as the first user's final close contact. If any new close contact value is less than or equal to the second threshold, it indicates that the initial close contact corresponding to the new close contact value does not have spatiotemporal association with the first user, and in this case, the initial close contact corresponding to the new close contact value can be determined not to be the first user's final close contact.

[0111] The close contact identification method provided in this application determines the close contact value between a first user and each second user based on a first spatiotemporal information matrix and a second spatiotemporal information matrix within a preset time period. Based on this close contact value, preliminary close contacts of the first user are identified from among the multiple second users. Then, based on a new close contact value determined by a preset weight and the close contact value corresponding to the preliminary close contacts, the final close contacts of the first user are identified from among the preliminary close contacts. Thus, a single identification process can determine whether multiple individuals are close contacts of confirmed cases or secondary close contacts of close contacts, thereby achieving rapid and accurate identification of close contacts and secondary close contacts, providing conditions for precise epidemic prevention and control.

[0112] The following is a reference to the appendix. Figure 3 This application describes a close contact identification device proposed in its embodiments. Figure 3 This is a schematic block diagram of a close contact identification device provided in an embodiment of this application.

[0113] The close contact identification device 300 includes: a matrix acquisition module 310, a first determination module 320, a second determination module 330, a third determination module 340, and a fourth determination module 350.

[0114] The matrix acquisition module 310 is used to acquire a first spatiotemporal information matrix and a second spatiotemporal information matrix within a preset time period. The first spatiotemporal information matrix corresponds to the first user, and the second spatiotemporal information matrix is ​​composed of multiple spatiotemporal information matrices of the second users.

[0115] The first determining module 320 is used to determine the close contact value between the first user and each of the second users based on the first spatiotemporal information matrix and the second spatiotemporal information matrix.

[0116] The second determining module 330 is used to determine the preliminary close contacts of the first user from among a plurality of second users based on the close contact value;

[0117] The third determining module 340 is used to determine a new close contact value based on a preset weight and the close contact value corresponding to the initial close contact person;

[0118] The fourth determining module 350 is used to determine the final close contact of the first user from the preliminary close contact personnel based on the new close contact value.

[0119] In one optional implementation of this application embodiment, the first determining module 320 is specifically used for:

[0120] Based on the first spatiotemporal information matrix and the second spatiotemporal information matrix, determine the third spatiotemporal information matrix and the fourth spatiotemporal information matrix;

[0121] Based on the third spatiotemporal information matrix and the fourth spatiotemporal information matrix, the close contact value between the first user and each of the second users is determined.

[0122] In one optional implementation of this application embodiment, the second determining module 330 is specifically used for:

[0123] Determine whether the close contact value is greater than a first threshold;

[0124] If any of the close contact values ​​is greater than the first threshold, then the second user corresponding to the close contact value is determined as the first user's preliminary close contact.

[0125] In one optional implementation of this application, the preset weights include: social relationship weights and location weights;

[0126] Correspondingly, the third determining module 340 is specifically used for:

[0127] Determine the sum of the social relationship weight and the location weight;

[0128] The new close contact value is determined based on the close contact value corresponding to the initial close contact and the sum value.

[0129] In one optional implementation of this application embodiment, the fourth determining module 350 is specifically used for:

[0130] Determine whether the new close contact value is greater than the second threshold;

[0131] If any of the new close contact values ​​is greater than the second threshold, then the preliminary close contact person corresponding to the new close contact value is determined as the final close contact person of the first user.

[0132] In one optional implementation of this application embodiment, the matrix acquisition module 310 includes:

[0133] The first acquisition unit is used to acquire all first time information and all first space information of the first user within the preset time period, as well as all second time information and all second space information of each second user.

[0134] The second acquisition unit is used to acquire the first spatiotemporal information matrix of the first user based on all first time information and all first spatial information of the first user.

[0135] The second acquisition unit is used to acquire the spatiotemporal information matrix of each second user based on all second time information and all second spatial information of each second user, and to construct the second spatiotemporal information matrix based on the spatiotemporal information matrix of all second users.

[0136] In one optional implementation of this application embodiment, the second acquisition unit is specifically used for:

[0137] According to a preset time interval, all time information is divided into multiple sub-time information, and all spatial information is divided into multiple sub-spatial information;

[0138] Each of the sub-time information and each of the sub-space information are encoded and converted respectively;

[0139] The new sub-time information and new sub-space information belonging to the same time interval are spliced ​​together, and a spatiotemporal information matrix is ​​constructed based on the obtained multiple spatiotemporal information.

[0140] In an optional implementation of this application embodiment, the second acquisition unit is further configured to:

[0141] The spatiotemporal matrix of each of the second users is used as a row vector to construct the initial second spatiotemporal information matrix;

[0142] The initial second spatiotemporal information matrix is ​​transposed to obtain the second spatiotemporal information matrix;

[0143] or,

[0144] The second spatiotemporal information matrix is ​​constructed by using the spatiotemporal matrix of each second user as a column vector.

[0145] The close contact identification device provided in this application determines the close contact value between a first user and each second user based on a first spatiotemporal information matrix and a second spatiotemporal information matrix within a preset time period. Based on this close contact value, it identifies preliminary close contacts of the first user from among the multiple second users. Then, based on preset weights and the close contact value corresponding to the preliminary close contacts, it determines the final close contacts of the first user from among the preliminary close contacts. Thus, a single identification can determine whether multiple individuals are close contacts of confirmed cases or secondary close contacts of close contacts, achieving rapid and accurate identification of close contacts and secondary close contacts, providing conditions for precise epidemic prevention and control.

[0146] It should be understood that the embodiments of the close contact identification device and the embodiments of the close contact identification method can correspond to each other, and similar descriptions can be referred to the method embodiments. To avoid repetition, they will not be repeated here. Specifically, Figure 3 The close contact identification device 300 shown can perform... Figure 1 The corresponding method embodiments, and the foregoing and other operations and / or functions of each module in the close contact identification device 300, are respectively for implementing Figure 1 For the sake of brevity, the corresponding processes in each method are not described in detail here.

[0147] The close contact identification device 300 of this application embodiment has been described above from the perspective of functional modules in conjunction with the accompanying drawings. It should be understood that this functional module can be implemented in hardware, in software instructions, or in a combination of hardware and software modules. Specifically, each step of the close contact identification method embodiment in this application can be completed by the integrated logic circuits in the processor's hardware and / or by software instructions. The steps of the close contact identification method disclosed in this application embodiment can be directly manifested as execution by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. Optionally, the software module can be located in a mature storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps in the above method embodiment.

[0148] Figure 4 This is a schematic block diagram of a close contact identification device provided in an embodiment of this application.

[0149] like Figure 4 As shown, the close contact identification device 400 may include:

[0150] The system includes a memory 410 and a processor 420. The memory 410 stores computer programs and transfers the program code to the processor 420. In other words, the processor 420 can retrieve and run the computer program from the memory 410 to implement the close contact identification method in this embodiment.

[0151] For example, the processor 420 can be used to execute the above-described close contact identification method embodiment according to instructions in the computer program.

[0152] In some embodiments of this application, the processor 420 may include, but is not limited to:

[0153] General-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0154] In some embodiments of this application, the memory 410 includes, but is not limited to:

[0155] Volatile memory and / or non-volatile memory. Non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. Volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of RAM are available, such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDR SDRAM), Enhanced Synchronous DRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), and Direct Rambus RAM (DR RAM).

[0156] In some embodiments of this application, the computer program may be divided into one or more modules, which are stored in the memory 410 and executed by the processor 420 to perform the method provided in this application. The one or more modules may be a series of computer program instruction segments capable of performing specific functions, which describe the execution process of the computer program in the close contact identification device.

[0157] like Figure 4 As shown, the close contact identification device 400 may further include:

[0158] Transceiver 430, which can be connected to processor 420 or memory 410.

[0159] The processor 420 can control the transceiver 430 to communicate with other devices; specifically, it can send information or data to other devices or receive information or data sent by other devices. The transceiver 430 may include a transmitter and a receiver. The transceiver 430 may further include antennas, and the number of antennas may be one or more.

[0160] It should be understood that the various components in the close contact identification device 400 are connected through a bus system, which includes a data bus, a power bus, a control bus, and a status signal bus.

[0161] This application also provides a computer storage medium storing a computer program thereon, which, when executed by a computer, enables the computer to perform the close contact identification method of the above embodiments. Alternatively, embodiments of this application also provide a computer program product containing instructions that, when executed by a computer, cause the computer to perform the method of the above method embodiments.

[0162] When implemented using software, it can be implemented entirely or partially as a computer program product. This computer program product includes one or more computer instructions. When these computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital video disc (DVD)), or a semiconductor medium (e.g., solid-state disk (SSD)).

[0163] Those skilled in the art will recognize that the modules and algorithm steps of the various examples 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 implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0164] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0165] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. For example, the functional modules in the various embodiments of this application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0166] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A close contact person identification method characterized by, The method comprises the following steps: acquiring a first space-time information matrix and a second space-time information matrix in a preset time period, the first space-time information matrix corresponding to a first user, the second space-time information matrix being composed of space-time information matrices of a plurality of second users, wherein the first space-time information matrix is obtained by acquiring all first time information and all first space information of the first user in the preset time period; dividing all first time information of the first user into a plurality of first sub-time information according to a preset time interval, and dividing all first space information of the first user into a plurality of first sub-space information according to the preset time interval; respectively encoding and converting each first sub-time information and each first sub-space information to obtain a plurality of new first sub-time information and a plurality of new first sub-space information; splicing the new first sub-time information and the new first sub-space information belonging to the same time interval to obtain a plurality of first space-time information, and constructing the first space-time information matrix according to the plurality of first space-time information; the second space-time information matrix is obtained by acquiring all second time information and all second space information of each second user in the preset time period; dividing all second time information of each second user into a plurality of second sub-time information according to a preset time interval, and dividing all second space information of each second user into a plurality of second sub-space information according to the preset time interval; respectively encoding and converting each second sub-time information and each second sub-space information to obtain a plurality of new second sub-time information and a plurality of new second sub-space information; splicing the new second sub-time information and the new second sub-space information belonging to the same time interval to obtain a plurality of second space-time information, and constructing the space-time information matrix of each second user according to the plurality of second space-time information, and constructing the second space-time information matrix according to the space-time information matrix of all second users; determining a third space-time information matrix and a fourth space-time information matrix according to the first space-time information matrix and the second space-time information matrix, wherein the third space-time information is obtained by multiplying the first space-time information and the second space-time information matrix; the fourth space-time information matrix is obtained by determining the modulus of the row vector in the first space-time information matrix and the modulus of the column vector in the second space-time information matrix; constructing a first modulus matrix according to the modulus of the row vector in the first space-time information matrix, and constructing a second modulus matrix according to the modulus of the column vector in the second space-time information matrix; multiplying the first modulus matrix and the second modulus matrix to obtain; determining the close contact value between the first user and each second user according to the third space-time information matrix and the fourth space-time information matrix, specifically comprising: calculating the cosine similarity between the column vector in the third space-time information matrix and the column vector in the fourth space-time information matrix, and determining the calculated cosine similarity as the close contact value between the first user and each second user; determining the preliminary close contact person of the first user from a plurality of second users according to the close contact value; determining a new close contact value according to a preset weight and the close contact value corresponding to the preliminary close contact person; determining, according to the new contact values, final contact persons of the first user from the preliminary contact persons.

2. The method of claim 1, wherein, determining, according to the contact values, preliminary contact persons of the first user from a plurality of second users, comprising: determining whether the contact values are greater than a first threshold value; if any of the contact values is greater than the first threshold value, determining the second user corresponding to the contact value as a preliminary contact person of the first user.

3. The method of claim 1, wherein, The preset weight includes a social relationship weight and a location weight. Correspondingly, determining a new contact value according to the preset weight and the contact value corresponding to the preliminary contact person, comprising: determining a sum value of the social relationship weight and the location weight; determining the new contact value according to the contact value corresponding to the preliminary contact person and the sum value.

4. The method of claim 1, wherein, determining, according to the new contact values, final contact persons of the first user from the preliminary contact persons, comprising: determining whether the new contact values are greater than a second threshold value; if any of the new contact values is greater than the second threshold value, determining the preliminary contact person corresponding to the new contact value as a final contact person of the first user.

5. The method of claim 1, wherein, constructing the second spatio-temporal information matrix according to spatio-temporal information matrices of all second users, comprising: constructing an initial second spatio-temporal information matrix by taking the spatio-temporal matrix of each second user as a row vector; performing matrix transposition on the initial second spatio-temporal information matrix to obtain the second spatio-temporal information matrix; or, constructing the second spatio-temporal information matrix by taking the spatio-temporal matrix of each second user as a column vector.

6. A close contact person identification device characterized by comprising: comprising: a matrix acquisition module, configured to acquire a first spatio-temporal information matrix and a second spatio-temporal information matrix in a preset time period, the first spatio-temporal information matrix corresponding to a first user, the second spatio-temporal information matrix being composed of spatio-temporal information matrices of a plurality of second users, wherein the first spatio-temporal information matrix is obtained by acquiring all first time information and all first space information of the first user in the preset time period; dividing all first time information of the first user into a plurality of first sub-time information and all first space information of the first user into a plurality of first sub-space information according to a preset time interval; respectively performing encoding conversion on each first sub-time information and each first sub-space information to obtain a plurality of new first sub-time information and a plurality of new first sub-space information; The new first sub-time information and the new first sub-space information belonging to the same time interval are spliced to obtain a plurality of first space-time information, and a first space-time information matrix is constructed according to the plurality of first space-time information; the second space-time information matrix is obtained by acquiring all second time information and all second space information of each second user in the preset time period; according to a preset time interval, all second time information of each second user is divided into a plurality of second sub-time information, and all second space information of each second user is divided into a plurality of second sub-space information; each second sub-time information and each second sub-space information are respectively encoded and converted to obtain a plurality of new second sub-time information and a plurality of new second sub-space information; the new second sub-time information and the new second sub-space information belonging to the same time interval are spliced to obtain a plurality of second space-time information, and a space-time information matrix of each second user is constructed according to the plurality of second space-time information, and the second space-time information matrix is constructed according to the space-time information matrix of all second users; The first determining module is configured to determine a third space-time information matrix and a fourth space-time information matrix according to the first space-time information matrix and the second space-time information matrix, wherein the third space-time information is obtained by multiplying the first space-time information matrix and the second space-time information matrix; and the fourth space-time information matrix is obtained by determining a norm of a row vector in the first space-time information matrix and a norm of a column vector in the second space-time information matrix; The first norm matrix is constructed according to the norm of the row vector in the first space-time information matrix, and the second norm matrix is constructed according to the norm of the column vector in the second space-time information matrix; and the first norm matrix and the second norm matrix are multiplied to obtain the third space-time information matrix and the fourth space-time information matrix; The first determining module is further configured to determine a close contact value between the first user and each second user according to the third space-time information matrix and the fourth space-time information matrix, specifically including: calculating a cosine similarity between a column vector in the third space-time information matrix and a column vector in the fourth space-time information matrix, and determining the calculated cosine similarity as the close contact value between the first user and each second user; The second determining module is configured to determine a preliminary close contact person of the first user from the plurality of second users according to the close contact value; The third determining module is configured to determine a new close contact value according to a preset weight and the close contact value corresponding to the preliminary close contact person; The fourth determining module is configured to determine a final close contact person of the first user from the preliminary close contact person according to the new close contact value.

7. A close contact person identification apparatus characterized by comprising: The processor and the memory are included, the memory is used for storing a computer program, and the processor is used for calling and running the computer program stored in the memory to execute the close contact person identification method in any one of claims 1 to 5. The computer program is used for storing a computer program, and the computer program enables a computer to execute the close contact person identification method in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer program / instruction is executed by the processor to implement the close contact person identification method in any one of claims 1 to 5.

9. A computer program product comprising computer programs / instructions, characterized in that, ​

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