Information processing method, device, equipment and storage medium

Abstract

The embodiments of the present application relate to an information processing method, apparatus, device and storage medium. The method includes: obtaining the spatiotemporal feature data of each user; classifying each user based on the spatiotemporal feature data of every two users and a first preset trajectory length threshold, and determining at least two user trajectory categories; based on the resident address of each user contained in each user trajectory category, determining the target address of the subject to be selected corresponding to the corresponding user trajectory category. Through the above technical scheme, it is realized to determine the target address according to the actual situation of population flow, so that each user can conveniently go to the target address for information collection / sampling, thereby making the setting of the target address more reasonable, thereby improving the speed and comprehensiveness of information collection / sampling. In addition, for medical virus screening sampling, allowing each user with similar action trajectories to sample at the same target address can also reduce the probability of further spread of the virus.

Description

Technical Field

[0001] The present application relates to the technical field of big data analysis, and in particular to an information processing method, apparatus, device and storage medium. Background Art

[0002] For large-scale information screening / investigations, such as medical virus screening, screening points are often roughly set up in communities or streets based on population distribution, and surrounding users are notified to go to the screening point for information collection / sampling.

[0003] However, this information screening method cannot set more reasonable screening points according to actual conditions, resulting in the inability to collect / sampling information more quickly and comprehensively. Summary of the invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the present application provides an information processing method, apparatus, device and storage medium.

[0005] In a first aspect, an embodiment of the present application provides an information processing method, the method comprising:

[0006] Acquire spatiotemporal characteristic data of each user, where the spatiotemporal characteristic data is used to indicate the actual geographic location of the user at each moment;

[0007] Based on the spatiotemporal feature data of every two users and a first preset trajectory length threshold, classifying each of the users to determine at least two user trajectory categories;

[0008] Based on the residence address of each user contained in each user track category, a target address of the subject to be located corresponding to the corresponding user track category is determined.

[0009] In some embodiments, the first preset trajectory length threshold is determined based on the setting quantity limit of the entities to be sited, the capacity limit of each entity to be sited, and the number of users.

[0010] In some embodiments, classifying each of the users based on the spatiotemporal feature data of every two of the users and a first preset trajectory length threshold, and determining at least two user trajectory categories comprises:

[0011] Based on the spatiotemporal feature data of each two of the users, determining a first common action trajectory of the corresponding two of the users;

[0012] If the public trajectory length of any of the first public action trajectories is greater than the first preset trajectory length threshold, and the distance between the residence addresses of the users corresponding to the first public action trajectories is less than a preset distance threshold, the users corresponding to the first public action trajectories are classified into the same user trajectory category.

[0013] In some embodiments, after determining the target address of the subject to be selected corresponding to the corresponding user trajectory category based on the residence address of each user contained in each user trajectory category, the information processing method further includes: for each target address and the user trajectory category corresponding to the target address:

[0014] Determine the trajectory similarity between the spatiotemporal feature data of each of the users included in the user trajectory category and the target action trajectory;

[0015] Based on the outing frequency of each of the users included in the user trajectory category and the trajectory similarity corresponding to each of the users, the users are divided into at least two batches.

[0016] In some embodiments, determining the trajectory similarity between the spatiotemporal feature data of each user included in the user trajectory category and the target action trajectory includes:

[0017] Based on the spatiotemporal feature data of each user and the target action trajectory, determining a second common action trajectory between the corresponding user and the target action trajectory;

[0018] If the public trajectory length of any of the second public action trajectories is greater than a second preset trajectory length threshold, determining that the trajectory similarity corresponding to the user corresponding to the second public action trajectory is a first similarity;

[0019] If the public trajectory length of any of the second public action trajectories is less than or equal to the second preset trajectory length threshold, it is determined that the trajectory similarity corresponding to the user corresponding to the second public action trajectory is the second similarity.

[0020] In some embodiments, dividing each of the users into at least two batches based on the outing frequency of each of the users included in the user trajectory category and the trajectory similarity corresponding to each of the users comprises:

[0021] If the outing frequency of any of the users is the first frequency, and the trajectory similarity corresponding to the user is the first similarity, the users are divided into the first batch;

[0022] If the outing frequency of any of the users is the second frequency, and the trajectory similarity corresponding to the user is the first similarity, the users are divided into a second batch;

[0023] If the outing frequency of any of the users is the first frequency, and the trajectory similarity corresponding to the user is the second similarity, the users are divided into a third batch;

[0024] If the outing frequency of any of the users is the second frequency, and the trajectory similarity corresponding to the user is the second similarity, the users are divided into a fourth batch.

[0025] In some embodiments, after dividing the users into at least two batches based on the outing frequency of each user included in the user trajectory category and the trajectory similarity corresponding to each user, the information processing method further includes:

[0026] The target address and the preset arrival time corresponding to the corresponding batch are sent to each user corresponding to each batch.

[0027] In a second aspect, an embodiment of the present application further provides an information processing device, the device comprising:

[0028] A spatiotemporal feature data acquisition module, used to acquire the spatiotemporal feature data of each user, wherein the spatiotemporal feature data is used to indicate the actual geographical location of the user at each time;

[0029] A user trajectory category determination module, configured to classify each of the users based on the spatiotemporal feature data of every two of the users and a first preset trajectory length threshold, and determine at least two user trajectory categories;

[0030] The target address determination module is used to determine the target address of the subject to be selected corresponding to the corresponding user trajectory category based on the resident address of each user contained in each user trajectory category.

[0031] In some embodiments, the first preset trajectory length threshold is determined based on the setting quantity limit of the entities to be sited, the capacity limit of each entity to be sited, and the number of users.

[0032] In some embodiments, the user trajectory category determination module is specifically used to:

[0033] Based on the spatiotemporal feature data of each two of the users, determining a first common action trajectory of the corresponding two of the users;

[0034] If the public trajectory length of any of the first public action trajectories is greater than the first preset trajectory length threshold, and the distance between the residence addresses of the users corresponding to the first public action trajectories is less than a preset distance threshold, the users corresponding to the first public action trajectories are classified into the same user trajectory category.

[0035] In some embodiments, the information processing device further includes a user batch division module, and the user batch division module further includes a trajectory similarity determination submodule and a batch division submodule;

[0036] The trajectory similarity determination submodule is used to: after determining the target address of the subject to be selected corresponding to the corresponding user trajectory category based on the residence address of each user contained in each user trajectory category, determine the trajectory similarity between the spatiotemporal feature data of each user contained in the user trajectory category and the target action trajectory for each target address and the user trajectory category corresponding to the target address;

[0037] The batch division submodule is used to divide each of the users into at least two batches based on the outing frequency of each of the users contained in the user trajectory category and the trajectory similarity corresponding to each of the users, for each of the target addresses and the user trajectory category corresponding to the target address.

[0038] In some embodiments, the trajectory similarity determination submodule is specifically used to:

[0039] Based on the spatiotemporal feature data of each user and the target action trajectory, determining a second common action trajectory between the corresponding user and the target action trajectory;

[0040] If the public trajectory length of any of the second public action trajectories is greater than a second preset trajectory length threshold, determining that the trajectory similarity corresponding to the user corresponding to the second public action trajectory is a first similarity;

[0041] If the public trajectory length of any of the second public action trajectories is less than or equal to the second preset trajectory length threshold, it is determined that the trajectory similarity corresponding to the user corresponding to the second public action trajectory is the second similarity.

[0042] In some embodiments, the batch division submodule is specifically used to:

[0043] If the outing frequency of any of the users is the first frequency, and the trajectory similarity corresponding to the user is the first similarity, the users are divided into the first batch;

[0044] If the outing frequency of any of the users is the second frequency, and the trajectory similarity corresponding to the user is the first similarity, the users are divided into a second batch;

[0045] If the outing frequency of any of the users is the first frequency, and the trajectory similarity corresponding to the user is the second similarity, the users are divided into a third batch;

[0046] If the outing frequency of any of the users is the second frequency, and the trajectory similarity corresponding to the user is the second similarity, the users are divided into a fourth batch.

[0047] In some embodiments, the information processing device further includes an information sending module, which is used to:

[0048] After dividing the users into at least two batches based on the outing frequency of each user included in the user trajectory category and the trajectory similarity corresponding to each user, the target address and the preset arrival time corresponding to the corresponding batch are sent to each user corresponding to each batch.

[0049] In a third aspect, an embodiment of the present application further provides an electronic device, the electronic device comprising:

[0050] Processor and memory;

[0051] The processor is used to execute the steps of the information processing method described in any embodiment of the present application by calling the program or instruction stored in the memory.

[0052] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a program or instructions, wherein the program or instructions enable a computer to execute the steps of the information processing method described in any embodiment of the present application.

[0053] The information processing method provided in the embodiment of the present application obtains spatiotemporal feature data within a certain spatial range and a certain time range that can characterize the movement trajectory of each user, and based on the spatiotemporal feature data of every two users and the first preset trajectory length threshold, divides the users with similar movement trajectories into the same user trajectory category, and sets the target address of the subject to be selected according to the permanent address of each user contained in each user trajectory category. It is realized that the target address is determined according to the actual situation of population flow (i.e., the movement trajectory and permanent address of the user), so that each user can conveniently go to the target address for information collection / sampling, thereby making the setting of the target address more reasonable, thereby improving the speed and comprehensiveness of information collection / sampling. In addition, for medical virus screening sampling, allowing each user with similar movement trajectories to be sampled at the same target address can also reduce the probability of further spread of the virus. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0056] Figure 1 A flowchart of an information processing method provided in an embodiment of the present application;

[0057] Figure 2 A flowchart of another information processing method provided in an embodiment of the present application;

[0058] Figure 3 A schematic diagram of the structure of an information processing device provided in an embodiment of the present application;

[0059] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] In order to more clearly understand the above-mentioned purposes, features and advantages of the present application, the scheme of the present application will be further described in detail below. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0061] In the following description, many specific details are set forth to facilitate a full understanding of the present application, but the present application may also be implemented in other ways different from those described herein; obviously, the embodiments in the specification are only part of the embodiments of the present application, rather than all of the embodiments.

[0062] At present, for large-scale information screening / investigation, such as medical virus screening, screening points are often roughly set up in communities or streets based on population distribution, and surrounding users are notified to go to the screening points for information collection / sampling. However, this information screening method cannot set more reasonable screening points based on actual conditions such as population mobility and virus transmission, resulting in the inability to collect / sample information more quickly and comprehensively.

[0063] Based on the above situation, the embodiments of the present application provide an information processing method, apparatus, device and storage medium to classify users according to the spatiotemporal feature data of each user, and determine the target address according to the permanent address of each user in each classification category, thereby improving the rationality of the selection of the target address and improving the efficiency and comprehensiveness of information collection / sampling.

[0064] The information processing method provided in the embodiment of the present application is mainly applicable to the situation of large-area information collection / sampling, for example, it can be applied to scenarios such as medical virus screening and population census that require users to go to designated locations to collect user-related information. In the subsequent embodiments of this application, medical virus screening sampling will be used as an example for explanation. The information processing method provided in the embodiment of the present application can be executed by an information processing device, which can be implemented by software and / or hardware, and the device can be integrated in an electronic device with a large amount of data computing capabilities, such as a laptop computer, a desktop computer, a server, a server cluster, etc.

[0065] Figure 1 is a flow chart of an information processing method provided by an embodiment of the present application. Figure 1 , the information processing method specifically includes:

[0066] S110: Obtain spatiotemporal feature data of each user.

[0067] Among them, the spatiotemporal feature data is used to indicate the user's actual geographic location at each moment, which can also be called the user's movement trajectory.

[0068] Specifically, when a medical virus screening is required for a certain area, the electronic device obtains the spatiotemporal feature data within a certain time range authorized by each user in the area. The time range can be determined based on the incubation period of the medical virus (such as 14 days), or can be set to a fixed time range based on experience. The embodiment of the present invention does not specifically limit the setting of the time range, and those skilled in the art can determine it based on actual conditions.

[0069] S120: Classify each user based on the spatiotemporal feature data of every two users and a first preset trajectory length threshold, and determine at least two user trajectory categories.

[0070] Among them, the first preset trajectory length threshold is a critical value of the trajectory length of a predetermined action trajectory, which is used to determine whether two action trajectories are similar. User trajectory category refers to the category obtained by classifying users according to their action trajectories. The number of user trajectory categories does not exceed the set quantity limit of the subject to be sited. The subject to be sited here refers to the subject to be sited, for example, it can be a sampling point for taking samples for medical virus screening of users, a registration point for collecting census information on users, etc. The set quantity limit refers to the upper limit of the number of subjects to be sited that need to be deployed.

[0071] Specifically, for any two users among all users, the electronic device compares the spatiotemporal feature data of the two users to determine whether there is an overlapping trajectory (i.e., a public action trajectory) in the action trajectories corresponding to the two spatiotemporal feature data. When the judgment result is that there is no public action trajectory, any two other users that have not been compared are re-determined, and the process is executed in a loop, that is, the spatiotemporal feature data of any two other users that have not been compared are continued to be compared. When the judgment result is that there is a public action trajectory, the trajectory length of the public action trajectory is compared with the first preset trajectory length threshold. If the comparison result is that the trajectory length of the public action trajectory is greater than the first preset trajectory length threshold, then the two users corresponding to the public action trajectory are divided into the same user trajectory category. If the comparison result is that the trajectory length of the public action trajectory is less than or equal to the first preset trajectory length threshold, then any two other users that have not been compared are re-determined, and the process is executed in a loop. After the above process, all users can be classified into multiple user trajectory categories, and any two users in each user trajectory category can be considered to have similar action trajectories.

[0072] In some embodiments, the first preset trajectory length threshold may be a length value preset based on experience, such as 10 kilometers.

[0073] In other embodiments, the first preset trajectory length threshold is determined based on the set number limit of the entities to be sited, the capacity limit of each entity to be sited, and the number of users.

[0074] The capacity limit refers to the upper limit of the number of users that a site selection entity can accommodate. The number of users refers to the total number of users in the above area.

[0075] Specifically, according to the above description, the first preset trajectory length threshold is used to determine whether the action trajectories of two users are similar, and then used for user classification, and each user trajectory category corresponds to a subject to be selected. Therefore, the value of the first preset trajectory length threshold will affect the number of settings and site selection of the subject to be selected. Based on this, the electronic device can use the setting number limit of the subject to be selected, the number of people to be accommodated by each subject to be selected, and the number of users as the value constraints of the first preset trajectory length threshold to determine a more reasonable first preset trajectory length threshold. For example, in at least one case where the setting number limit of the subject to be selected is large, the number of people to be accommodated by each subject to be selected is small, and the number of users is large, the value of the first preset trajectory length threshold can be small, so as to divide more user trajectory categories. In at least one case where the setting number limit of the subject to be selected is small, the number of people to be accommodated by each subject to be selected is large, and the number of users is small, the value of the first preset trajectory length threshold can be large, so as to divide fewer user trajectory categories. In this way, the first preset trajectory length threshold can be determined more flexibly and accurately according to the number of entities to be sited, the number of people accommodated by each entity to be sited, and the number of users in the actual scenario, thereby further improving the rationality of the site selection of the entity to be sited.

[0076] It should be noted that the embodiment of the present invention does not specifically limit the setting of the first preset trajectory length threshold, and those skilled in the art may determine it according to actual conditions.

[0077] In some embodiments, S120 can be implemented as follows: based on the spatiotemporal feature data of every two users, determining the first common action trajectory of the corresponding two users; if the common trajectory length of any first common action trajectory is greater than a first preset trajectory length threshold, and the distance between the residence addresses of each user corresponding to the first common action trajectory is less than a preset distance threshold, then the users corresponding to the first common action trajectory are classified into the same user trajectory category.

[0078] Among them, the first public action trajectory refers to at least one overlapping action trajectory between any two users. For example, there are three overlapping action trajectories between the action trajectories of user A and user B, then the first public action trajectory between user A and user B is the sum of the above three overlapping action trajectories. The preset distance threshold is a preset critical value of the distance, which is used to determine whether the action trajectories between two users are similar. The resident address refers to the user's permanent address, for example, it can be a permanent home address or a work address. The preset distance threshold can be a fixed value set according to experience, or it can be determined according to the set number limit of the subject to be selected, the number of people that can be accommodated by each subject to be selected, and the number of users. The embodiment of the present invention does not specifically limit the setting of the above-mentioned preset distance threshold, and those skilled in the art can determine it according to actual conditions.

[0079] Specifically, in the process of classifying each user in the area, in addition to obtaining the first public action trajectory between any two users based on the spatiotemporal feature data between the two users, and determining that the trajectory length of the first public action trajectory is greater than the first preset trajectory length threshold, the electronic device can further consider whether the distance between the resident addresses of the two users is less than the preset distance threshold. If the distance between the resident addresses of the two users is equal to or greater than the preset distance threshold, it means that although the two users have a first public action trajectory that meets the requirements, the distance between the resident addresses of the two users is far, and it is not suitable to be divided into the same address for sampling. If the distance between the resident addresses of the two users is less than the preset distance threshold, it means that there is a first public action trajectory that meets the requirements between the two users, and the resident addresses of the two users are close, then the two users can be classified into the same user trajectory category. In this way, it can ensure that users with similar action trajectories and close resident addresses are classified into the same user trajectory category, thereby obtaining more user trajectory categories, and providing a more reasonable data basis for the subsequent site selection of the subject to be selected.

[0080] It should be noted that in the process of classifying users, the same user may be classified into multiple different user track categories. In this case, the electronic device may classify users in the following manner:

[0081] In one example, the user can be classified into various user track categories at the same time, so that the user can go to the site selection locations (i.e., target addresses) of multiple subject sites for sampling for medical virus screening.

[0082] In another example, on the basis that the length of the public trajectory of the first public action trajectory and the distance between the resident addresses are satisfied, the user can be classified into a certain user trajectory category based on the first public action trajectory between the user and other users. For example, the user can be classified into the user trajectory category corresponding to the longest public trajectory length based on the length of the public trajectory of the first public action trajectory between the user and each other user. For another example, the user can be classified into the user trajectory category corresponding to the least number of segments based on the number of overlapping trajectory segments in the first public action trajectory between the user and each other user. In this way, the user can be classified into a user trajectory category with a more similar action trajectory.

[0083] In another example, when the resident address includes multiple addresses such as a permanent home address and a work address, on the basis that the length of the public trajectory of the first public action trajectory and the distance between the resident addresses are satisfied, the user can be classified into the user trajectory category corresponding to the resident address when the stay period and the sampling period coincide according to the user's stay period at each resident address and the sampling period of medical virus sampling. If the user can still be classified into multiple user trajectory categories under the condition of overlapping time periods, then the user can be further classified into the user trajectory category corresponding to the resident address with a longer stay time according to the length of time the user stays at each resident address. This makes it easier for users to quickly reach the target address for sampling for medical virus screening.

[0084] S130: Based on the resident address of each user included in each user trajectory category, determine the target address of the subject to be located corresponding to the corresponding user trajectory category.

[0085] Specifically, each user trajectory category can set a subject to be selected. For any subject to be selected, the electronic device performs distance calculation based on the resident addresses of each user included in the user trajectory category to determine a location with approximately the same distance from each resident address as the target address of the subject to be selected. In this way, each user belonging to the same user trajectory category can more conveniently and quickly go to the target address for sampling for medical virus screening.

[0086] The information processing method provided by the above embodiments of the present application obtains spatiotemporal feature data within a certain spatial range and a certain time range that can characterize the movement trajectory of each user, and based on the spatiotemporal feature data of every two users and the first preset trajectory length threshold, divides the users with similar movement trajectories into the same user trajectory category, and sets the target address of the subject to be selected according to the permanent address of each user contained in each user trajectory category. It is realized that the target address is determined according to the actual situation of population flow (i.e., the movement trajectory and permanent address of the user), so that each user can conveniently go to the target address for information collection / sampling, thereby making the setting of the target address more reasonable, thereby improving the speed and comprehensiveness of information collection / sampling. In addition, for medical virus screening sampling, allowing each user with similar movement trajectories to sample at the same target address can also reduce the probability of further spread of the virus.

[0087] Figure 2 1 is a flowchart of another information processing method provided by an embodiment of the present application. Based on the above embodiments, the information processing method adds the steps of dividing the users corresponding to the same target address into batches. The explanations of the terms that are the same or corresponding to the above embodiments are not repeated here. Figure 2 , the information processing method specifically includes:

[0088] S210: Obtain spatiotemporal feature data of each user.

[0089] S220: Classify each user based on the spatiotemporal feature data of every two users and a first preset trajectory length threshold, and determine at least two user trajectory categories.

[0090] S230: Based on the resident address of each user included in each user trajectory category, determine the target address of the subject to be located corresponding to the corresponding user trajectory category.

[0091] S240 . For each target address and the user trajectory category corresponding to the target address, determine the trajectory similarity between the spatiotemporal feature data of each user contained in the user trajectory category and the target action trajectory.

[0092] The target action trajectory refers to the action trajectory of a specific user that has been determined, such as the action trajectory of a confirmed case of a medical virus. Trajectory similarity is the degree of overlap between two action trajectories. Trajectory similarity can be measured based on the total length of the overlapping action trajectories. The longer the total length, the higher the trajectory similarity. Trajectory similarity can also be measured based on the proportion of the overlapping action trajectories to the target action trajectory. The higher the proportion, the higher the trajectory similarity.

[0093] Specifically, according to the above description, users in the same user trajectory category have similar action trajectories, but when the target action trajectory appears, there are still some differences in the trajectory similarity between the action trajectories of these users and the target action trajectory. Based on this, in order to prevent the further spread of medical viruses, in the embodiment of the present application, users in the same user trajectory category are further classified to divide the users into different batches.

[0094] In a specific implementation, for each target address and each user in its corresponding user trajectory category, the electronic device compares the spatiotemporal feature data of the user with the target action trajectory and calculates the trajectory similarity between the two.

[0095] In some embodiments, the calculation of trajectory similarity can be implemented as follows: based on the spatiotemporal feature data of each user and the target action trajectory, determining the second common action trajectory between the corresponding user and the target action trajectory; if the common trajectory length of any second common action trajectory is greater than the second preset trajectory length threshold, determining the trajectory similarity corresponding to the user corresponding to the second common action trajectory as the first similarity; if the common trajectory length of any second common action trajectory is less than or equal to the second preset trajectory length threshold, determining the trajectory similarity corresponding to the user corresponding to the second common action trajectory as the second similarity.

[0096] Among them, the second public action trajectory refers to at least one overlapping action trajectory between the action trajectory of any user and the target action trajectory. The second preset trajectory length threshold is the critical value of the trajectory length of another predetermined action trajectory, which is used to determine whether the two action trajectories are similar. In one example, the second preset trajectory length threshold can be set as a fixed value based on experience. For example, the second preset trajectory length threshold can be set as the first preset trajectory length threshold. For another example, the second preset trajectory length threshold can refer to the first preset trajectory length threshold and take a value within its surrounding numerical range. In another example, the second preset trajectory length threshold can be determined based on the characteristics of medical virus transmission. For example, the transmission ability of medical viruses is weak, and the contact time between users is long enough to spread, then the value of the second preset trajectory length threshold is large; the transmission ability of medical viruses is strong, and the contact time between users is very short to spread, then the value of the second preset trajectory length threshold is small. In this way, the flexibility of the value of the second preset trajectory length threshold and its fit with the actual situation can be improved, and a more accurate data basis can be provided for subsequent users in batches. The embodiment of the present invention does not specifically limit the setting of the above-mentioned second preset trajectory length threshold, and those skilled in the art can determine it according to the actual situation. The first similarity and the second similarity are both preset similarity thresholds, and the first similarity is greater than the second similarity.

[0097] Specifically, in this embodiment, the total length of the public action trajectory is used to measure the trajectory similarity. The electronic device first compares the spatiotemporal feature data of each user with the target action trajectory, and determines the second public action trajectory between the action trajectory of each user and the target action trajectory. Then, the electronic device compares each second public action trajectory with the second preset trajectory length threshold, and determines the trajectory similarity between the action trajectory of each user and the target action trajectory according to the comparison result. If the comparison result is that the public trajectory length of the second public action trajectory is greater than the second preset trajectory length threshold, it means that the trajectory similarity between the action trajectory of the user corresponding to the second public action trajectory and the target action trajectory is large, then, the trajectory similarity corresponding to the user is determined to be the first similarity. If the comparison result is that the public trajectory length of the second public action trajectory is less than or equal to the second preset trajectory length threshold, it means that the trajectory similarity between the action trajectory of the user corresponding to the second public action trajectory and the target action trajectory is small, then, the trajectory similarity corresponding to the user is determined to be the second similarity. In this way, the trajectory similarity corresponding to each user can be determined simply and quickly.

[0098] S250 . For each target address and the user trajectory category corresponding to the target address, based on the outing frequency of each user contained in the user trajectory category and the trajectory similarity corresponding to each user, divide each user into at least two batches.

[0099] Among them, the frequency of going out refers to the frequency of the user leaving his permanent address, which is used to characterize the activeness of the user's outdoor activities. The outing here can include the user's outing within the area to which he belongs, and it can also include the user's outing outside the area to which he belongs (such as leaving the province, going abroad, etc.). In the scenario of medical virus screening, considering that the larger the span of the user's activity area, the greater the probability of being infected with the virus, when calculating the frequency of going out, the weight of the user's outing outside his area is larger. For example, for user A and user B, both have the same number of outings in the same time period, but user A has more outings outside his area, then user A's outing frequency is higher than user B's outing frequency.

[0100] Specifically, considering that the higher the user's outing frequency and the higher the trajectory similarity between his or her action trajectory and the target action trajectory, the greater the influence of the user, such as the greater the probability that the user is infected with the virus. Therefore, in an embodiment of the present application, the electronic device processes users in batches for each user trajectory category according to the outing frequency corresponding to each user and the trajectory similarity calculated above. The number of batches divided into each user trajectory category can be determined according to actual conditions. For example, if the transmission of medical viruses in the area is relatively serious, then in order to further avoid the occurrence of clustered virus transmission during the virus screening process, each user trajectory category can be divided into more batches to reduce the number of people in each batch; conversely, each user trajectory category can be divided into fewer batches.

[0101] In some embodiments, the above-mentioned user batch processing can be implemented as follows: if the out-of-town frequency of any user is the first frequency, and the corresponding trajectory similarity of the user is the first similarity, the user is divided into the first batch; if the out-of-town frequency of any user is the second frequency, and the corresponding trajectory similarity of the user is the first similarity, the user is divided into the second batch; if the out-of-town frequency of any user is the first frequency, and the corresponding trajectory similarity of the user is the second similarity, the user is divided into the third batch; if the out-of-town frequency of any user is the second frequency, and the corresponding trajectory similarity of the user is the second similarity, the user is divided into the fourth batch.

[0102] Specifically, in this embodiment, the users under each user trajectory category are divided into four batches. The first batch includes users in the user trajectory category with a high frequency of going out (i.e., the first frequency) and a high corresponding trajectory similarity (first similarity). The second batch includes users in the user trajectory category with a high corresponding trajectory similarity (first similarity) but a low frequency of going out (i.e., the second frequency). The third batch includes users in the user trajectory category with a frequency of going out of the first frequency but a low corresponding trajectory similarity (i.e., the second similarity). The fourth batch includes users in the user trajectory category with a frequency of going out of the second frequency and a corresponding trajectory similarity of the second similarity. In this way, users with high user activity and high similarity between the user action trajectory and the target action trajectory can be divided into the first batch for medical virus screening sampling first, thereby sampling separately from users in the second, third and fourth batches, which can further reduce the probability of virus spread, and give priority to screening a group of users with the highest virus exposure rate, thereby improving the efficiency of virus screening.

[0103] S260: Send the target address and the preset arrival time corresponding to the corresponding batch to each user corresponding to each batch.

[0104] Specifically, after the above steps, the electronic device can determine the target address corresponding to each user and the corresponding batch, and each batch can preset the time for the user to arrive at the target address (i.e., the preset arrival time). In this way, the electronic device can send the corresponding target address and preset arrival time to each user to notify the corresponding user to go to the target address at the preset arrival time for medical virus screening sampling.

[0105] The information processing method provided by the above embodiment of the present application is to determine the trajectory similarity between the spatiotemporal feature data of each user contained in the user trajectory category and the target action trajectory for each target address and the user trajectory category corresponding to the target address; and divide each user into at least two batches based on the outing frequency of each user contained in the user trajectory category and the trajectory similarity corresponding to each user. It is achieved that each user in the user trajectory category corresponding to each target address, with different user outing frequencies and different trajectory similarities between the user action trajectory and the target action trajectory, is divided into different batches for medical virus screening sampling, which can reduce the number of users who are simultaneously subjected to virus screening sampling, thereby further reducing the probability of virus spread, and can give priority to screening users with relatively high virus exposure rates, thereby improving virus screening efficiency.

[0106] Figure 3 This is a schematic diagram of the structure of an information processing device provided in an embodiment of the present application. Figure 3 As shown, the information processing device 300 includes:

[0107] The spatiotemporal characteristic data acquisition module 310 is used to acquire the spatiotemporal characteristic data of each user, where the spatiotemporal characteristic data is used to indicate the actual geographical location of the user at each moment;

[0108] A user trajectory category determination module 320, configured to classify each user based on the spatiotemporal feature data of every two users and a first preset trajectory length threshold, and determine at least two user trajectory categories;

[0109] The target address determination module 330 is used to determine the target address of the subject to be selected corresponding to the corresponding user trajectory category based on the residence address of each user contained in each user trajectory category.

[0110] The above-mentioned information processing device provided by the present application obtains the spatiotemporal feature data that can characterize the movement trajectory of each user within a certain spatial range and a certain time range, and divides each user with similar movement trajectory into the same user trajectory category based on the spatiotemporal feature data of every two users and the first preset trajectory length threshold, and sets the target address of the subject to be selected according to the permanent address of each user contained in each user trajectory category. It is realized that the target address is determined according to the actual situation of population flow (i.e., the movement trajectory and permanent address of the user), so that each user can conveniently go to the target address for information collection / sampling, thereby making the setting of the target address more reasonable, thereby improving the speed and comprehensiveness of information collection / sampling. In addition, for medical virus screening sampling, allowing each user with similar movement trajectory to be sampled at the same target address can also reduce the probability of further spread of the virus.

[0111] In some embodiments, the first preset trajectory length threshold is determined based on the set number limit of the entities to be sited, the capacity limit of each entity to be sited, and the number of users.

[0112] In some embodiments, the user trajectory category determination module 320 is specifically used to:

[0113] Based on the spatiotemporal feature data of every two users, determining a first common action trajectory of the corresponding two users;

[0114] If the public trajectory length of any first public action trajectory is greater than the first preset trajectory length threshold, and the distance between the residence addresses of each user corresponding to the first public action trajectory is less than the preset distance threshold, the users corresponding to the first public action trajectory are classified into the same user trajectory category.

[0115] In some embodiments, the information processing device 300 further includes a user batch division module, which further includes a trajectory similarity determination submodule and a batch division submodule;

[0116] The trajectory similarity determination submodule is used to: after determining the target address of the subject to be selected corresponding to the corresponding user trajectory category based on the residence address of each user contained in each user trajectory category, determine the trajectory similarity between the spatiotemporal feature data of each user contained in the user trajectory category and the target action trajectory for each target address and the user trajectory category corresponding to the target address;

[0117] The batch division submodule is used to divide each user into at least two batches based on the outing frequency of each user contained in the user trajectory category and the trajectory similarity corresponding to each user for each target address and the user trajectory category corresponding to the target address.

[0118] In some embodiments, the trajectory similarity determination submodule is specifically used to:

[0119] Based on the spatiotemporal feature data of each user and the target action trajectory, determining a second common action trajectory between the corresponding user and the target action trajectory;

[0120] If the public trajectory length of any second public action trajectory is greater than a second preset trajectory length threshold, determining that the trajectory similarity corresponding to the user corresponding to the second public action trajectory is the first similarity;

[0121] If the public trajectory length of any second public action trajectory is less than or equal to the second preset trajectory length threshold, the trajectory similarity corresponding to the user corresponding to the second public action trajectory is determined to be the second similarity.

[0122] In some embodiments, the batch division submodule is specifically used to:

[0123] If the outgoing frequency of any user is the first frequency and the trajectory similarity corresponding to the user is the first similarity, the user is divided into the first batch;

[0124] If the outing frequency of any user is the second frequency and the trajectory similarity corresponding to the user is the first similarity, the user is divided into the second batch;

[0125] If the outing frequency of any user is the first frequency and the trajectory similarity corresponding to the user is the second similarity, the user is divided into the third batch;

[0126] If the outing frequency of any user is the second frequency and the trajectory similarity corresponding to the user is the second similarity, the user is divided into a fourth batch.

[0127] In some embodiments, the information processing device 300 further includes an information sending module, which is used to:

[0128] After dividing the users into at least two batches based on the outgoing frequency of each user included in the user trajectory category and the trajectory similarity corresponding to each user, a target address and a preset arrival time corresponding to the corresponding batch are sent to each user corresponding to each batch.

[0129] The information processing device provided in the embodiments of the present application can execute the information processing method provided in any embodiment of the present application, and has the corresponding functional modules and beneficial effects of the execution method.

[0130] It is worth noting that in the embodiment of the above-mentioned information processing device, the various modules and sub-modules included are only divided according to functional logic, but are not limited to the above-mentioned division, as long as the corresponding functions can be achieved; in addition, the specific names of the functional modules / sub-modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application.

[0131] Figure 4 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 4 As shown, the electronic device 400 includes one or more processors 401 and a memory 402 .

[0132] The processor 401 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 400 to perform desired functions.

[0133] The memory 402 may include one or more computer program products, and the computer program product may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory (cache), etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on a computer-readable storage medium, and the processor 401 may run the program instructions to implement the information processing method described in any embodiment of the present application and / or other desired functions. Various contents such as the first / second preset track length threshold, the preset distance threshold, etc. may also be stored in the computer-readable storage medium.

[0134] In one example, the electronic device 400 may further include: an input device 403 and an output device 404, which are interconnected via a bus system and / or other forms of connection mechanisms (not shown). The input device 403 may include, for example, a keyboard, a mouse, etc. The output device 404 may output various information to the outside, including a target address, a preset arrival time corresponding to each batch, etc. The output device 404 may include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0135] Of course, to simplify, Figure 4 Only some of the components related to the present application in the electronic device 400 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application conditions, the electronic device 400 may also include any other appropriate components.

[0136] In addition to the above-mentioned methods and devices, the embodiments of the present application may also be a computer program product, which includes computer program instructions. When the computer program instructions are executed by a processor, the processor executes the steps of the information processing method provided by any embodiment of the present application.

[0137] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, etc., and conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as a separate software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0138] In addition, the embodiments of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the information processing method provided by the embodiments of the present application.

[0139] Computer readable storage media can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can include, for example, but is not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0140] It should be noted that the terms used in this application are only for describing specific embodiments, rather than limiting the scope of this application. As shown in the specification and claims of this application, unless the context clearly indicates an exception, the words "one", "a", "a kind of" and / or "the" do not specifically refer to the singular, but may also include the plural. The term "and / or" includes any and all combinations of one or more related listed items. The term "includes", "comprising" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such process, method or device. Relational terms such as "first", "second", etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply that there is any such actual relationship or order between these entities or operations. In the absence of more restrictions, the elements defined by the sentence "including one..." do not exclude the existence of other identical elements in the process, method or device including the elements.

[0141] The above description is only a specific implementation of the present application, so that those skilled in the art can understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments described herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An information processing method, characterized in that: include: Acquire spatiotemporal characteristic data of each user, where the spatiotemporal characteristic data is used to indicate the actual geographic location of the user at each moment; Based on the spatiotemporal feature data of every two users and a first preset trajectory length threshold, classify each user to determine at least two user trajectory categories; wherein the first preset trajectory length threshold is determined based on the setting quantity limit of the subject to be sited, the capacity limit of each subject to be sited, and the number of users; Based on the residence address of each of the users included in each of the user track categories, a target address of the subject to be located corresponding to the corresponding user track category is determined.

2. The method according to claim 1, characterized in that The classifying each of the users based on the spatiotemporal feature data of every two of the users and the first preset trajectory length threshold to determine at least two user trajectory categories comprises: Based on the spatiotemporal feature data of each two of the users, determining a first common action trajectory of the corresponding two of the users; If the public trajectory length of any of the first public action trajectories is greater than the first preset trajectory length threshold, and the distance between the residence addresses of the users corresponding to the first public action trajectories is less than a preset distance threshold, the users corresponding to the first public action trajectories are classified into the same user trajectory category.

3. The method according to claim 1, characterized in that After determining the target address of the subject to be located corresponding to the corresponding user trajectory category based on the resident address of each user included in each user trajectory category, the method further includes: For each of the target addresses and the user track category corresponding to the target address: Determine the trajectory similarity between the spatiotemporal feature data of each of the users included in the user trajectory category and the target action trajectory; Based on the outing frequency of each of the users included in the user trajectory category and the trajectory similarity corresponding to each of the users, the users are divided into at least two batches.

4. The method according to claim 3, characterized in that Determining the trajectory similarity between the spatiotemporal feature data of each user included in the user trajectory category and the target action trajectory includes: Based on the spatiotemporal feature data of each user and the target action trajectory, determining a second common action trajectory between the corresponding user and the target action trajectory; If the public trajectory length of any of the second public action trajectories is greater than a second preset trajectory length threshold, determining that the trajectory similarity corresponding to the user corresponding to the second public action trajectory is a first similarity; If the public trajectory length of any of the second public action trajectories is less than or equal to the second preset trajectory length threshold, it is determined that the trajectory similarity corresponding to the user corresponding to the second public action trajectory is the second similarity.

5. The method according to claim 3, characterized in that: The dividing each of the users into at least two batches based on the outgoing frequency of each of the users included in the user trajectory category and the trajectory similarity corresponding to each of the users comprises: If the outing frequency of any of the users is the first frequency, and the trajectory similarity corresponding to the user is the first similarity, the users are divided into the first batch; If the outing frequency of any of the users is the second frequency, and the trajectory similarity corresponding to the user is the first similarity, the users are divided into a second batch; If the outing frequency of any of the users is the first frequency, and the trajectory similarity corresponding to the user is the second similarity, the users are divided into a third batch; If the outing frequency of any of the users is the second frequency, and the trajectory similarity corresponding to the user is the second similarity, the users are divided into a fourth batch.

6. The method according to claim 3, characterized in that After dividing the users into at least two batches based on the outing frequency of each user included in the user trajectory category and the trajectory similarity corresponding to each user, the method further includes: The target address and the preset arrival time corresponding to the corresponding batch are sent to each user corresponding to each batch.

7. An information processing device, characterized in that: include: A spatiotemporal feature data acquisition module, used to acquire the spatiotemporal feature data of each user, wherein the spatiotemporal feature data is used to indicate the actual geographical location of the user at each time; A user trajectory category determination module is used to classify each of the users based on the spatiotemporal feature data of every two of the users and a first preset trajectory length threshold, and determine at least two user trajectory categories; wherein the first preset trajectory length threshold is determined based on the setting quantity limit of the subject to be sited, the accommodating number limit of each subject to be sited, and the number of users; The target address determination module is used to determine the target address of the subject to be selected corresponding to the corresponding user trajectory category based on the resident address of each user contained in each user trajectory category.

8. An electronic device, characterized in that: The electronic device comprises: Processor and memory; The processor is used to execute the steps of the information processing method according to any one of claims 1 to 6 by calling the program or instruction stored in the memory.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program or an instruction, and the program or the instruction enables a computer to execute the steps of the information processing method according to any one of claims 1 to 6.

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