CN111028955B - Epidemic situation area display method and system - Google Patents
Epidemic situation area display method and system Download PDFInfo
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- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
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Abstract
The invention relates to the technical field of internet data processing, in particular to a method and a system for displaying an epidemic situation area. The method comprises the steps of acquiring a tracking information sequence recorded by a user terminal of each epidemic situation related user, acquiring a behavior related area of each epidemic situation related user in each tracking time period according to behavior information of each epidemic situation related user in each tracking time period, taking an area located in a geographic boundary range of the behavior related area and an area located outside the geographic boundary range of the behavior related area and within a preset distance range from the geographic boundary of the behavior related area as epidemic situation related areas of each epidemic situation related user in the tracking time period, and finally summarizing the epidemic situation related areas of each epidemic situation related user in all tracking time periods and then displaying the epidemic situation related areas on an epidemic situation map. The epidemic situation area display method can more carefully determine the epidemic situation area for display according to the specific activity of the epidemic situation related personnel, so that the accuracy and the reference of the epidemic situation area are improved.
Description
Technical Field
The invention relates to the technical field of internet data processing, in particular to a method and a system for displaying an epidemic situation area.
Background
In the prevention and control process for various infectious diseases, how to effectively determine an epidemic situation area and display the epidemic situation area, so as to quickly and accurately prompt people, which is a technical problem to be solved in the field, generally speaking, in a conventional epidemic situation prevention and control scheme, large areas (such as XX areas of XX cities and XX cities, or XX communities and XX cells and the like) where people (such as confirmed people and suspected people) related to an epidemic situation reside are analyzed, and then the determined large areas are displayed on an epidemic situation map. However, the above display scheme can only realize very rough display, has low referential property, and cannot determine the epidemic situation area to display more carefully according to the specific activity of the epidemic situation related personnel.
Disclosure of Invention
In view of this, an object of the present application is to provide a method and a system for displaying an epidemic situation area, which can determine the epidemic situation area to be displayed in detail according to the specific activity of the epidemic situation related personnel, so as to improve the accuracy and the referential property of the epidemic situation area.
In a first aspect, the present application provides a method for displaying an epidemic situation area, which is applied to a server, and the method includes:
acquiring a tracking information sequence recorded by a user terminal of each epidemic situation related user, wherein the tracking information sequence comprises tracking information taking each tracking time period as a recording unit, and the tracking information comprises the starting time and the ending time of the tracking time period and behavior information in the tracking time;
aiming at each tracking time period, acquiring a behavior related area of each epidemic situation related user in the tracking time period according to the behavior information of each epidemic situation related user in the tracking time period;
taking an area which is positioned in the geographic boundary range of the behavior related area and an area which is positioned outside the geographic boundary range of the behavior related area and is within a preset distance range from the geographic boundary of the behavior related area as epidemic situation related areas of each epidemic situation related user in the tracking time period;
and summarizing the epidemic situation associated areas of each epidemic situation related user in all tracking time periods, and displaying on an epidemic situation map.
In a possible design of the first aspect, the step of obtaining the tracking information sequence recorded by the user terminal of each epidemic situation related user includes:
for each user terminal, when detecting that the user terminal is provided with an application program for recording the tracking information sequence and the application program is added as an authorized application program allowing recording of the tracking information sequence, recording the starting time and the ending time of the corresponding user in each tracking time period through the authorized application program;
within the tracking time range, acquiring tracking data information of the user from different types of detection processes of the user terminal;
and determining the behavior information of the user in the tracking time according to the tracking data information corresponding to each different type of detection process.
In a possible design of the first aspect, the step of determining behavior information of the user within the tracking time according to tracking data information corresponding to different types of detection processes includes:
extracting the confidence of each generated behavior of the tracking data information corresponding to each type of detection process in the tracking time period according to the pre-training behavior extraction model corresponding to each type of detection process;
determining the generation behavior with the confidence coefficient larger than the set confidence coefficient as the target generation behavior according to the confidence coefficient of each generation behavior of the tracking data information in the tracking time period, and determining the behavior duration and the behavior time interval of the target generation behavior in the tracking time period;
and determining the behavior information of the user in the tracking time according to each determined target generation behavior and the behavior duration and the behavior time interval of each target generation behavior in the tracking time period.
In a possible design of the first aspect, the step of obtaining the behavior related area of each epidemic situation related user in the tracking time period according to the behavior information of each epidemic situation related user in the tracking time period includes:
for each behavior recording segment in a plurality of behavior recording segments in the behavior information of each epidemic situation related user in the tracking time segment, determining whether each behavior recording point in the behavior recording segment is a candidate target behavior recording point or not according to the continuous change value of the behavior recording point in the behavior recording segment, wherein each behavior recording point corresponds to each generated behavior;
determining each candidate space region corresponding to the behavior record segment according to the number of candidate target behavior record points in the behavior record segment;
for each candidate space region, dividing the candidate space region into a plurality of subspaces, and determining whether the candidate space region is a target space region according to the variation degree of each behavior recording point in each subspace and a preset behavior characteristic variation trend;
and determining the behavior related region of each epidemic situation related user in the tracking time period according to the behavior characteristics of each behavior recording point in the target space region.
In a possible design of the first aspect, the step of determining whether each behavior record point in the behavior record segment is a candidate target behavior record point according to a continuously changing value of the behavior record point in the behavior record segment includes:
and if the continuous change value of the behavior record point in the behavior record segment is in the set confidence degree range, determining that each behavior record point in the behavior record segment is a candidate target behavior record point, and otherwise determining that each behavior record point in the behavior record segment is not a candidate target behavior record point.
In a possible design of the first aspect, the step of determining each candidate spatial region corresponding to the behavior record segment according to the number of candidate target behavior record points in the behavior record segment includes:
determining an area filling boundary corresponding to each candidate target behavior record point according to each candidate target behavior record point in the behavior record segment and a preset area filling range;
and obtaining a plurality of target areas according to the area filling boundary corresponding to each candidate target behavior record point, judging whether the number of the candidate target behavior record points in each target area is greater than a preset number threshold value or not aiming at each target area, and if so, determining the target area as a candidate space area in the behavior record segment.
In a possible design of the first aspect, the step of determining whether the candidate space region is the target space region according to the variation degree of each behavior recording point in each subspace and a preset behavior feature variation trend includes:
for each subspace, determining whether each subspace meets the condition or not according to the variation degree of each corresponding behavior recording point in the subspace in the plurality of behavior recording sections before the behavior recording section, the variation degree mean value and the variance of the corresponding behavior recording point in the subspace in the plurality of behavior recording sections before the behavior recording section, and a preset corresponding variation degree threshold range;
and if the number of the subspaces meeting the conditions in the behavior recording segment meets the preset behavior characteristic change trend, determining the candidate space region as the target space region.
In a possible design of the first aspect, the behavior recording points include behavior time recording points and behavior space recording points, and the step of determining the behavior related region of each epidemic situation related user in the tracking time period according to the behavior feature of each behavior recording point in the target space region includes:
acquiring behavior track information of each behavior recording point in a preset tracking strategy matching the target space region, wherein the behavior track information comprises behavior time track information and behavior space track information, and the preset tracking strategy comprises matching modes corresponding to different behavior track types;
determining behavior time characteristics of each behavior time recording point and behavior space characteristics of each behavior space recording point according to behavior track information of each behavior information of different tracking time periods in the tracking information sequence;
and determining the behavior related region of each epidemic situation related user in the tracking time period according to the behavior time characteristics of each behavior time recording point and the behavior space characteristics of each behavior space recording point in the target space region.
In a possible design of the first aspect, the step of summarizing the epidemic situation associated areas of each epidemic situation related user in all tracking time periods and then displaying the areas on the epidemic situation map includes:
aiming at each epidemic situation associated area, determining a target time range corresponding to the total duration of the tracking time period related in the epidemic situation associated area and a target number range corresponding to the number of epidemic situation related users;
and displaying the epidemic situation associated area in the epidemic situation associated area according to the first display special effect corresponding to the target time range and the second display special effect corresponding to the target quantity range.
In a second aspect, an embodiment of the present application further provides a display system for an epidemic situation area, which is applied to a server, and the system includes:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a tracking information sequence recorded by a user terminal of each epidemic situation related user, the tracking information sequence comprises tracking information taking each tracking time period as a recording unit, and the tracking information comprises the starting time and the ending time of the tracking time period and behavior information in the tracking time;
the second acquisition module is used for acquiring a behavior related area of each epidemic situation related user in each tracking time period according to the behavior information of each epidemic situation related user in the tracking time period aiming at each tracking time period;
the region determining module is used for taking a region which is positioned in the geographic boundary range of the behavior related region and a region which is positioned outside the geographic boundary range of the behavior related region and is within a preset distance range from the geographic boundary of the behavior related region as epidemic situation related regions of each epidemic situation related user in the tracking time period;
and the display module is used for summarizing the epidemic situation associated areas of each epidemic situation related user in all tracking time periods and then displaying the epidemic situation associated areas on the epidemic situation map.
In a third aspect, an embodiment of the present application further provides a server, where the server includes a processor, a machine-readable storage medium, and a network interface, where the machine-readable storage medium, the network interface, and the processor are connected through a bus system, the network interface is configured to be in communication connection with at least one user terminal, the machine-readable storage medium is configured to store a program, an instruction, or a code, and the processor is configured to execute the program, the instruction, or the code in the machine-readable storage medium, so as to perform the method for displaying the epidemic situation area in the first aspect or any one of the possible designs in the first aspect.
In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, where instructions are stored in the computer-readable storage medium, and when the instructions are executed, the method for displaying an epidemic situation area in any one of the above-mentioned first aspect or the possible designs of the first aspect is implemented.
According to any one of the aspects, the tracking information sequence recorded by the user terminal of each epidemic situation related user is obtained, the behavior related area of each epidemic situation related user in the tracking time period is obtained according to the behavior information of each epidemic situation related user in each tracking time period, therefore, an area located in the geographic boundary range of the behavior related area and an area located outside the geographic boundary range of the behavior related area and within a preset distance range from the geographic boundary of the behavior related area can be used as the epidemic situation related area of each epidemic situation related user in the tracking time period, and finally, the epidemic situation related areas of each epidemic situation related user in all tracking time periods are summarized and displayed on the epidemic situation map. Therefore, the epidemic situation region can be determined more carefully to aim at the specific activity of the epidemic situation related personnel for displaying, and the accuracy and the referential performance of the epidemic situation region are improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic view of an application scenario of an epidemic situation service system according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for displaying an epidemic situation area according to an embodiment of the present application;
fig. 3 is a functional module schematic diagram of a display system of an epidemic situation area provided in the embodiment of the present application;
fig. 4 is a block diagram schematically illustrating a structure of a server for implementing the above-mentioned epidemic situation area display method according to an embodiment of the present application.
Detailed Description
The present application will now be described in detail with reference to the drawings, and the specific operations in the method embodiments may also be applied to the apparatus embodiments or the system embodiments.
Fig. 1 is an interactive schematic diagram of an epidemic situation service system 10 according to an embodiment of the present application. The epidemic situation service system 10 can include a server 100 and a user terminal 200 communicatively connected to the server 100, and the server 100 can include a processor for executing instruction operations. The epidemic service system 10 shown in fig. 1 is only one possible example, and in other possible embodiments, the epidemic service system 10 may include only one of the components shown in fig. 1 or may also include other components.
In some embodiments, the server 100 may be a single server or a group of servers. The set of servers may be centralized or distributed (e.g., server 100 may be a distributed system). In some embodiments, the server 100 may be local or remote to the user terminal 200. For example, the server 100 may access information stored in the user terminal 200 and a database, or any combination thereof, via a network. As another example, the server 100 may be directly connected to at least one of the user terminal 200 and a database to access information and/or data stored therein. In some embodiments, the server 100 may be implemented on a cloud platform; by way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud (community cloud), a distributed cloud, an inter-cloud, a multi-cloud, and the like, or any combination thereof.
In some embodiments, the server 100 may include a processor. The processor may process information and/or data related to the service request to perform one or more of the functions described herein. A processor may include one or more processing cores (e.g., a single-core processor (S) or a multi-core processor (S)). Merely by way of example, a Processor may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an Application Specific Instruction Set Processor (ASIP), a Graphics Processing Unit (GPU), a Physical Processing Unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller Unit, a reduced Instruction Set computer (reduced Instruction Set computer), a microprocessor, or the like, or any combination thereof.
The network may be used for the exchange of information and/or data. In some embodiments, one or more components (e.g., server 100, user terminal 200, and database) in the epidemic service system 10 can send information and/or data to other components. In some embodiments, the network may be any type of wired or wireless network, or combination thereof. Merely by way of example, the Network may include a wired Network, a wireless Network, a fiber optic Network, a telecommunications Network, an intranet, the internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Wide Area Network (WAN), a Public Switched Telephone Network (PSTN), a bluetooth Network, a ZigBee Network, a Near Field Communication (NFC) Network, or the like, or any combination thereof. In some embodiments, the network may include one or more network access points. For example, the network can include wired or wireless network access points, such as base stations and/or network switching nodes, through which one or more components of the epidemic service system 10 can connect to the network to exchange data and/or information.
The aforementioned database may store data and/or instructions. In some embodiments, the database may store data assigned to the user terminal 200. In some embodiments, the database may store data and/or instructions for the exemplary methods described herein. In some embodiments, the database may include mass storage, removable storage, volatile Read-write Memory, or Read-Only Memory (ROM), among others, or any combination thereof. By way of example, mass storage may include magnetic disks, optical disks, solid state drives, and the like; removable memory may include flash drives, floppy disks, optical disks, memory cards, zip disks, tapes, and the like; volatile read-write memory may include Random Access Memory (RAM); the RAM may include Dynamic RAM (DRAM), Double data Rate Synchronous Dynamic RAM (DDR SDRAM); static RAM (SRAM), Thyristor-Based Random Access Memory (T-RAM), Zero-capacitor RAM (Zero-RAM), and the like. By way of example, ROMs may include Mask Read-Only memories (MROMs), Programmable ROMs (PROMs), Erasable Programmable ROMs (PERROMs), Electrically Erasable Programmable ROMs (EEPROMs), compact disk ROMs (CD-ROMs), digital versatile disks (ROMs), and the like. In some embodiments, the database may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, across clouds, multiple clouds, or the like, or any combination thereof.
In some embodiments, a database may be connected to the network to communicate with one or more components in the epidemic service system 10 (e.g., server 100, user terminal 200, etc.). One or more components in the epidemic services system 10 can access data or instructions stored in a database via a network. In some embodiments, the database may be directly connected to one or more components of the epidemic service system 10 (e.g., the server 100, the user terminal 200, etc.; or, in some embodiments, the database may be part of the server 100.
In this embodiment, the user terminal 200 may include, but is not limited to, a mobile device, a tablet computer, a laptop computer, or any combination of two or more thereof. In some embodiments, the mobile device may include, but is not limited to, a smart home device, a wearable device, a smart mobile device, a virtual reality device, an augmented reality device, or the like, or any combination thereof. In some embodiments, the smart home devices may include, but are not limited to, smart lighting devices, control devices for smart appliances, smart monitoring devices, smart televisions, smart cameras, or walkie-talkies, or the like, or any combination thereof. In some embodiments, the wearable device may include, but is not limited to, a smart bracelet, a smart lace, smart glass, a smart helmet, a smart watch, a smart garment, a smart backpack, a smart accessory, and the like, or any combination thereof. In some embodiments, the smart mobile device may include, but is not limited to, a smartphone, a Personal Digital Assistant (PDA), a gaming device, a navigation device, or a point of sale (POS) device, or the like, or any combination thereof. In some embodiments, the virtual reality device and/or the augmented reality device may include, but is not limited to, a virtual reality helmet, virtual reality glass, a virtual reality patch, an augmented reality helmet, augmented reality glass, an augmented reality patch, or the like, or any combination thereof. For example, virtual reality devices and/or augmented reality devices may include, but are not limited to, various virtual reality products and the like.
To solve the technical problem in the foregoing background art, fig. 2 is a schematic flow chart of a method for displaying an epidemic situation area according to an embodiment of the present application, and the method for displaying an epidemic situation area according to the present embodiment may be executed by the server 100 shown in fig. 1, and the method for displaying an epidemic situation area is described in detail below.
Step S110, obtaining a tracking information sequence recorded by the user terminal of each epidemic situation related user, where the tracking information sequence includes tracking information with each tracking time period as a recording unit.
Step S120, aiming at each tracking time period, acquiring the behavior related area of each epidemic situation related user in the tracking time period according to the behavior information of each epidemic situation related user in the tracking time period.
Step S130, using the area located within the geographic boundary range of the behavior-related area and the area located outside the geographic boundary range of the behavior-related area and within a preset distance range from the geographic boundary of the behavior-related area as epidemic situation-related areas of each epidemic situation-related user within the tracking time period.
Step S140, gathering epidemic situation associated areas of all epidemic situation related users in all tracking time periods, and displaying on an epidemic situation map.
In this embodiment, the trace information sequence may include trace information in a unit of one record per trace time period, where the trace information includes a start time and an end time of the trace time period, and behavior information within the trace time. The tracking time period may be determined by the behavior starting characteristics of the user, for example, the epidemic situation related user may pass through five main staying places on a certain day, the entering time point at each staying place is the starting time of a tracking time period, and the corresponding leaving time point when leaving the staying place is the ending time of the tracking time period. It can be understood that, for different epidemic situation related users, the tracking time period may not be fixed, and may be determined according to the actual detection situation. Of course, in other possible examples, the time period with higher activity frequency may be preset as the tracking time period by using the historical big data of the epidemic situation relevant users.
In this embodiment, the epidemic situation relevant user can be determined by the currently uploaded identification information of the user terminal of the epidemic situation relevant user. The epidemic situation related users can be understood as confirmed users, suspected users, users in close epidemic situation contact, and the like, and can be specifically determined according to actual needs, which is not limited in detail herein.
Based on the above design, in this embodiment, the tracking information sequence recorded by the user terminal of each epidemic situation related user is obtained, and the behavior related area of each epidemic situation related user in the tracking time period is obtained according to the behavior information of each epidemic situation related user in each tracking time period, so that an area located within the geographic boundary range of the behavior related area and an area located outside the geographic boundary range of the behavior related area and within a preset distance range from the geographic boundary of the behavior related area can be used as the epidemic situation related area of each epidemic situation related user in the tracking time period, and finally, the epidemic situation related areas of each epidemic situation related user in all tracking time periods are summarized and displayed on the epidemic situation map. Therefore, the epidemic situation region can be determined more carefully to aim at the specific activity of the epidemic situation related personnel for displaying, and the accuracy and the referential performance of the epidemic situation region are improved.
In one possible design, when it is detected that an application for recording the tracking information sequence is installed in the user terminal and the application is added as an authorized application that allows recording of the tracking information sequence, the start time and the end time of the corresponding user in each tracking period may be recorded by the authorized application for each user terminal.
That is, the user terminal actually installs various applications, and in order to protect the privacy of the user, only when the user turns on the authorization status for an application, the application has the authority to record the aforementioned tracking information series.
Next, tracking data information of the user may be obtained from various different types of detection processes of the user terminal within the tracking time range.
The detection process may be an application process that is started in a foreground or a background of the user terminal 200, for example, a detection process for detecting audio information output by an epidemic situation-related user, a detection process for detecting a posture change of the epidemic situation-related user, a detection process for detecting a position change of the epidemic situation-related user, a detection process for detecting a residence time of the epidemic situation-related user, or the like, and these detection processes may be operated by the authorization application program, may also be operated by other application programs, and provide a related data interface for the authorization application program to call.
On the basis, different types of detection processes are considered, the specific data types and the related behavior predictions of the detection processes are possibly different, and therefore the behavior information of the epidemic situation related users in the tracking time can be determined according to the tracking data information corresponding to the different types of detection processes.
For example, in one possible design of this embodiment, a confidence level of each generation behavior of the trace data information corresponding to each type of detection process in the tracking time period may be extracted according to a pre-trained behavior extraction model corresponding to each type of detection process, then, according to the confidence level of each generation behavior of the trace data information in the tracking time period, a generation behavior whose confidence level is greater than a set confidence level is determined as a target generation behavior, and a behavior duration and a behavior time interval of the target generation behavior in the tracking time period are determined. Therefore, the behavior information of the user in the tracking time can be determined according to each determined target generation behavior and the behavior duration and the behavior time interval of each target generation behavior in the tracking time period.
Exemplarily, taking the foregoing detection procedure for detecting audio information output by a user as an example, the audio tracking data information may be related in a supermarket a 12: 00-12-35, assuming a preset confidence of 60, the resulting behavior with a confidence greater than 60 is determined as the target resulting behavior, e.g., if the confidence of the face-to-face conversation behavior is greater than 60, the face-to-face conversation behavior may be determined as the target resulting behavior, while the face-to-face conversation behavior may be determined at 12: duration of behaviour within 00-12-35 was 35 minutes, duration of behaviour was 12: 00-12-35, from which behavioral information can be determined over an associated tracking period.
As a possible example, the above-mentioned pre-training behavior extraction model may be obtained by training in the following manner, which is described in detail below.
Firstly, the data sample characteristics of the behavior detection data samples corresponding to each type of detection process and the corresponding behavior detection labels can be extracted, then the data sample characteristics are used as the input characteristics of the behavior extraction model to be trained, the data sample characteristics are input into the behavior extraction model to be trained, the behavior extraction model to be trained is used for analyzing the characteristics to be learned of the data sample characteristics in the behavior label data segments corresponding to the behavior detection labels, and the characteristics to be learned can comprise a characteristic segment set to be learned. Then, the feature segment set to be learned is segmented according to a preset identifier to obtain a plurality of segmented feature segments, a plurality of first behavior vector learning parameters can be determined according to feature vectors corresponding to features to be learned, the first behavior vector learning parameters are behavior vector learning parameters of the segmented feature segments trained in a behavior extraction model to be trained respectively, the behavior extraction model to be trained is used for learning segmented feature segments after segmentation processing is performed on the feature segment set to be learned and behavior vector learning parameters of the segmented feature segments mapped in the behavior extraction model to be trained, and the feature segment set to be learned is a feature segment set to be learned included in the features to be learned acquired in the behavior tag data segment. The first behavior vector learning parameter can be obtained according to the characteristic parameter type represented by the characteristic vector and the preset behavior vector learning parameters corresponding to different characteristic parameter types.
Then, the plurality of first behavior vector learning parameters may be ranked according to an order from a high convergence degree to a low convergence degree of each of the plurality of first behavior vector learning parameters to obtain a behavior vector learning parameter sequence, and then, based on a preset similarity ratio threshold and the behavior vector learning parameter sequence, a behavior vector learning parameter mapped by a segmentation feature segment in the plurality of segmentation feature segments in the to-be-trained behavior extraction model is determined, where the preset similarity ratio threshold is used to indicate a proportion of the to-be-learned feature segment set and a similar portion of the to-be-learned feature segment set acquired in the behavior tag data segment in the to-be-learned feature segment set.
When the behavior vector learning parameters mapped by the segmented characteristic segments in the behavior extraction model to be trained are matched with preset behavior vector learning parameters, determining that the characteristic to be learned is a target characteristic to be learned, when the characteristic to be learned is determined to be the target characteristic to be learned, controlling the segmented characteristic segments obtained by segmenting a plurality of characteristic segment sets to be learned obtained in a behavior label data segment according to the first behavior vector learning parameters for each first behavior vector learning parameter in the plurality of first behavior vector learning parameters, generating corresponding prediction labels after training, updating the behavior vector learning parameters of the behavior extraction model to be trained according to the prediction labels of each behavior detection data sample and the behavior detection labels corresponding to each behavior detection data sample, and iterating the process, and obtaining a pre-training behavior extraction model when the iteration times reach the set times.
Further, in a possible design, for step S120, in order to accurately associate the epidemic situation related area with the activity behavior of the epidemic situation related users and remove a part of the behavior noise, this embodiment may determine, for each behavior recording segment of the behavior information of each epidemic situation related user in the tracking time period, whether each behavior recording point in the behavior recording segment is a candidate target behavior recording point according to a continuously changing value of the behavior recording point in the behavior recording segment, where each behavior recording point corresponds to each generated behavior.
For example, if the continuous variation value of the behavior record point in the behavior record segment is within the set confidence degree range, determining that each behavior record point in the behavior record segment is a candidate target behavior record point, otherwise determining that each behavior record point in the behavior record segment is not a candidate target behavior record point.
On the basis, each candidate space region corresponding to the behavior record segment can be determined according to the number of candidate target behavior record points in the behavior record segment.
For example, for each candidate target behavior record point in the behavior record segment and a preset region filling range, a region filling boundary corresponding to each candidate target behavior record point may be determined, then a plurality of target regions may be obtained according to the region filling boundary corresponding to each candidate target behavior record point, and for each target region, it is determined whether the number of candidate target behavior record points in the target region is greater than a preset number threshold, and if the number of candidate target behavior record points in the target region is greater than the preset number threshold, the target region is determined as a candidate space region in the behavior record segment.
Then, for each candidate space region, the candidate space region may be divided into a plurality of subspaces, and whether the candidate space region is the target space region is determined according to the degree of change of each behavior recording point in each subspace and a preset behavior feature change trend.
For example, for each subspace, according to the degree of change of each corresponding behavior record point in the subspace in a plurality of behavior record segments before the behavior record segment, the mean and variance of the degree of change of the corresponding behavior record point in the subspace in the plurality of behavior record segments before the behavior record segment, and a preset corresponding degree of change threshold range, determining whether each subspace satisfies a condition (e.g., the mean and variance of the degree of change of the corresponding behavior record point in the subspace in the plurality of behavior record segments before the behavior record segment are both in the corresponding preset corresponding degree of change threshold range), and if the number of the subspaces meeting the conditions in the behavior recording segment meets the preset behavior characteristic change trend, determining the candidate space region as the target space region.
And finally, determining the behavior related area of each epidemic situation related user in the tracking time period according to the behavior characteristics of each behavior recording point in the target space area.
For example, behavior trace information of each behavior recording point in a preset tracking policy matching target space region may be obtained, where the behavior trace information includes behavior time trace information and behavior space trace information, and the preset tracking policy includes matching modes corresponding to different behavior trace types (e.g., a behavior time trace type and a behavior space trace type).
In this embodiment, it should be noted that the above-mentioned behavior time trajectory information, that is, the time trajectory of the related tracking behavior, lasts for a certain period of time, and stops at a certain time point. The behavior space trajectory information, that is, the related space trajectory of the tracking behavior, for example, persists in a certain area range and leaves in a certain area range.
On the basis, the behavior time characteristic of each behavior time recording point and the behavior space characteristic of each behavior space recording point can be determined according to the behavior track information of each behavior information of different tracking time periods in the tracking information sequence, and the behavior related region of each epidemic situation related user in the tracking time period is determined according to the behavior time characteristic of each behavior time recording point and the behavior space characteristic of each behavior space recording point in the target space region.
Further, in a possible design, in order to facilitate visual display of the progress of the epidemic situation area, for each epidemic situation associated area, in step S140, a target time range corresponding to the total duration of the tracking time period involved in the epidemic situation associated area and a target number range corresponding to the number of the epidemic situation related users are determined.
For example, assuming that the total duration of the tracking time period involved in the epidemic situation associated area is 1.5 hours, the target time range corresponding to the total duration may be 0-2 hours, and the number of the epidemic situation related users in the epidemic situation associated area is 2, and the corresponding target number range may be 0-3.
On the basis, the epidemic situation associated area can be displayed according to the first display special effect corresponding to the target time range and the second display special effect corresponding to the target quantity range in the epidemic situation associated area. For example, different display special effects can be set for different target time ranges and target number ranges, and the related special effects can be enhanced along with the increase of the target time ranges and the target number ranges, so that the condition of the progress of the epidemic situation area can be displayed visually.
Fig. 3 is a schematic functional module diagram of a display system 300 of an epidemic situation area according to an embodiment of the present application, and in this embodiment, the display system 300 of the epidemic situation area may be divided into functional modules according to the above method embodiments. For example, the functional blocks may be divided for the respective functions, or two or more functions may be integrated into one processing block. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, the division of the modules in the present application is schematic, and is only a logical function division, and there may be another division manner in actual implementation. For example, in the case of dividing each function module according to each function, the display system 300 of the epidemic situation area shown in fig. 3 is only a schematic diagram of an apparatus. The display system 300 of an epidemic situation area may include a first obtaining module 310, a second obtaining module 320, an area determining module 330, and a display module 340, and the functions of the functional modules of the display system 300 of an epidemic situation area are described in detail below.
The first obtaining module 310 is configured to obtain a tracking information sequence recorded by a user terminal of each epidemic situation related user, where the tracking information sequence includes tracking information that takes each tracking time period as a recording unit, and the tracking information includes a start time and an end time of the tracking time period and behavior information in the tracking time.
The second obtaining module 320 is configured to, for each tracking time period, obtain, according to the behavior information of each epidemic situation related user in the tracking time period, a behavior related area of each epidemic situation related user in the tracking time period.
The area determining module 330 is configured to use an area located within the geographic boundary range of the behavior-related area and an area located outside the geographic boundary range of the behavior-related area and within a preset distance range from the geographic boundary of the behavior-related area as epidemic situation-related areas of each epidemic situation-related user in the tracking time period.
The display module 340 is configured to summarize epidemic situation associated areas of each epidemic situation related user in all tracking time periods, and then display the summarized epidemic situation associated areas on an epidemic situation map.
Further, fig. 4 is a schematic structural diagram of a server 100 for executing the method for displaying an epidemic situation area according to the embodiment of the present application. As shown in FIG. 4, the server 100 may include a network interface 110, a machine-readable storage medium 120, a processor 130, and a bus 140. The processor 130 may be one or more, and one processor 130 is illustrated in fig. 4 as an example. The network interface 110, the machine-readable storage medium 120, and the processor 130 may be connected by a bus 140 or otherwise, as exemplified by the connection by the bus 140 in fig. 4.
The machine-readable storage medium 120 is a computer-readable storage medium, and can be used to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the method for displaying an epidemic situation area in the embodiment of the present application (for example, the first obtaining module 310, the second obtaining module 320, the area determining module 330, and the displaying module 340 of the display system 300 for an epidemic situation area shown in fig. 3). The processor 130 executes various functional applications and data processing of the terminal device by detecting the software program, the instructions and the modules stored in the machine-readable storage medium 120, that is, the display method of the epidemic situation area is realized, and details are not repeated herein.
The machine-readable storage medium 120 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to the use of the terminal, and the like. Further, the machine-readable storage medium 120 may be either volatile memory or nonvolatile memory, or may include both volatile and nonvolatile memory. The non-volatile memory may be a Read-only memory (ROM), a Programmable ROM (PROM), an Erasable PROM (EPROM), an Electrically Erasable PROM (EEPROM), or a flash memory. Volatile Memory can be Random Access Memory (RAM), which acts as external cache Memory. By way of example, but not limitation, many forms of RAM are available, such as Static random access memory (Static RAM, SRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic random access memory (Synchronous DRAM, SDRAM), Double data rate Synchronous Dynamic random access memory (DDR SDRAM), Enhanced Synchronous SDRAM (ESDRAM), Synchronous link SDRAM (SLDRAM), and direct memory bus RAM (DR RAM). It should be noted that the memories of the systems and methods described herein are intended to comprise, without being limited to, these and any other suitable memory of a publishing node. In some examples, the machine-readable storage medium 120 may further include memory located remotely from the processor 130, which may be connected to the server 100 over a network. Examples of such networks include, but are not limited to, the internet, an intranet of items to be compiled, a local area network, a mobile communications network, and combinations thereof.
The processor 130 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method embodiments may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 130. The processor 130 may be a general-purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. The various methods, steps, and logic blocks disclosed in the embodiments of the present application may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of the method disclosed in connection with the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor.
The server 100 may interact with other devices (e.g., the user terminal 200) via the network interface 110. Network interface 110 may be a circuit, bus, transceiver, or any other device that may be used to exchange information. Processor 130 may send and receive information using network interface 110.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
Embodiments of the present application are described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the embodiments of the present application without departing from the spirit and scope of the application. Thus, to the extent that such expressions and modifications of the embodiments of the application fall within the scope of the claims and their equivalents, the application is intended to embrace such alterations and modifications.
Claims (9)
1. A display method of an epidemic situation area is applied to a server, and the method comprises the following steps:
acquiring a tracking information sequence recorded by a user terminal of each epidemic situation related user, wherein the tracking information sequence comprises tracking information taking each tracking time period as a recording unit, and the tracking information comprises the starting time and the ending time of the tracking time period and behavior information in the tracking time;
aiming at each tracking time period, acquiring a behavior related area of each epidemic situation related user in the tracking time period according to the behavior information of each epidemic situation related user in the tracking time period;
taking an area which is positioned in the geographic boundary range of the behavior related area and an area which is positioned outside the geographic boundary range of the behavior related area and is within a preset distance range from the geographic boundary of the behavior related area as epidemic situation related areas of each epidemic situation related user in the tracking time period;
gathering epidemic situation associated areas of each epidemic situation related user in all tracking time periods, and displaying the epidemic situation associated areas on an epidemic situation map;
the step of acquiring the behavior related area of each epidemic situation related user in the tracking time period according to the behavior information of each epidemic situation related user in the tracking time period comprises the following steps:
for each behavior recording segment in a plurality of behavior recording segments in the behavior information of each epidemic situation related user in the tracking time segment, determining whether each behavior recording point in the behavior recording segment is a candidate target behavior recording point or not according to the continuous change value of the behavior recording point in the behavior recording segment, wherein each behavior recording point corresponds to each generated behavior;
determining each candidate space region corresponding to the behavior record segment according to the number of candidate target behavior record points in the behavior record segment;
for each candidate space region, dividing the candidate space region into a plurality of subspaces, and determining whether the candidate space region is a target space region according to the variation degree of each behavior recording point in each subspace and a preset behavior characteristic variation trend;
and determining the behavior related region of each epidemic situation related user in the tracking time period according to the behavior characteristics of each behavior recording point in the target space region.
2. The method for displaying an epidemic situation area according to claim 1, wherein the step of obtaining the tracking information sequence recorded by the user terminal of each epidemic situation related user comprises:
for each user terminal, when detecting that the user terminal is provided with an application program for recording the tracking information sequence and the application program is added as an authorized application program allowing recording of the tracking information sequence, recording the starting time and the ending time of the corresponding user in each tracking time period through the authorized application program;
in the tracking time, tracking data information of the user is obtained from different types of detection processes of the user terminal;
and determining the behavior information of the user in the tracking time according to the tracking data information corresponding to each different type of detection process.
3. The method for displaying an epidemic situation area according to claim 2, wherein the step of determining the behavior information of the user in the tracking time according to the tracking data information corresponding to each different type of detection process comprises:
extracting the confidence of each generated behavior of the tracking data information corresponding to each type of detection process in the tracking time period according to the pre-training behavior extraction model corresponding to each type of detection process;
determining the generation behavior with the confidence coefficient larger than the set confidence coefficient as the target generation behavior according to the confidence coefficient of each generation behavior of the tracking data information in the tracking time period, and determining the behavior duration and the behavior time interval of the target generation behavior in the tracking time period;
and determining the behavior information of the user in the tracking time according to each determined target generation behavior and the behavior duration and the behavior time interval of each target generation behavior in the tracking time period.
4. The method for displaying an epidemic situation area according to claim 1, wherein the step of determining whether each behavior record point in the behavior record segment is a candidate target behavior record point according to the continuous variation value of the behavior record point in the behavior record segment comprises:
and if the continuous change value of the behavior record point in the behavior record segment is in the set confidence degree range, determining that each behavior record point in the behavior record segment is a candidate target behavior record point, and otherwise determining that each behavior record point in the behavior record segment is not a candidate target behavior record point.
5. The method for showing an epidemic situation area according to claim 1, wherein the step of determining each candidate space area corresponding to the behavior record segment according to the number of candidate target behavior record points in the behavior record segment comprises:
determining an area filling boundary corresponding to each candidate target behavior record point according to each candidate target behavior record point in the behavior record segment and a preset area filling range;
and obtaining a plurality of target areas according to the area filling boundary corresponding to each candidate target behavior record point, judging whether the number of the candidate target behavior record points in each target area is greater than a preset number threshold value or not aiming at each target area, and if so, determining the target area as a candidate space area in the behavior record segment.
6. The epidemic situation area showing method according to claim 1, wherein the step of determining whether the candidate space area is the target space area according to the variation degree of each behavior record point in each subspace and the preset behavior characteristic variation trend comprises:
for each subspace, determining whether each subspace meets the condition or not according to the variation degree of each corresponding behavior recording point in the subspace in the plurality of behavior recording sections before the behavior recording section, the variation degree mean value and the variance of the corresponding behavior recording point in the subspace in the plurality of behavior recording sections before the behavior recording section, and a preset corresponding variation degree threshold range;
and if the number of the subspaces meeting the conditions in the behavior recording segment meets the preset behavior characteristic change trend, determining the candidate space region as the target space region.
7. The method for displaying the epidemic situation area according to claim 1, wherein the behavior recording points comprise behavior time recording points and behavior space recording points, and the step of determining the behavior related area of each epidemic situation related user in the tracking time period according to the behavior characteristics of each behavior recording point in the target space area comprises the steps of:
acquiring behavior track information of each behavior recording point in a preset tracking strategy matching the target space region, wherein the behavior track information comprises behavior time track information and behavior space track information, and the preset tracking strategy comprises matching modes corresponding to different behavior track types;
determining behavior time characteristics of each behavior time recording point and behavior space characteristics of each behavior space recording point according to behavior track information of each behavior information of different tracking time periods in the tracking information sequence;
and determining the behavior related region of each epidemic situation related user in the tracking time period according to the behavior time characteristics of each behavior time recording point and the behavior space characteristics of each behavior space recording point in the target space region.
8. The method for displaying the epidemic situation area according to claim 1, wherein the step of summarizing the epidemic situation related areas of each epidemic situation related user in all tracking time periods and then displaying the areas on the epidemic situation map comprises:
aiming at each epidemic situation associated area, determining a target time range corresponding to the total duration of the tracking time period related in the epidemic situation associated area and a target number range corresponding to the number of epidemic situation related users;
and displaying the epidemic situation associated area in the epidemic situation associated area according to the first display special effect corresponding to the target time range and the second display special effect corresponding to the target quantity range.
9. The display system of the epidemic situation area is characterized by being applied to a server, and the system comprises:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a tracking information sequence recorded by a user terminal of each epidemic situation related user, the tracking information sequence comprises tracking information taking each tracking time period as a recording unit, and the tracking information comprises the starting time and the ending time of the tracking time period and behavior information in the tracking time;
the second acquisition module is used for acquiring a behavior related area of each epidemic situation related user in each tracking time period according to the behavior information of each epidemic situation related user in the tracking time period aiming at each tracking time period;
the region determining module is used for taking a region which is positioned in the geographic boundary range of the behavior related region and a region which is positioned outside the geographic boundary range of the behavior related region and is within a preset distance range from the geographic boundary of the behavior related region as epidemic situation related regions of each epidemic situation related user in the tracking time period;
the display module is used for summarizing epidemic situation associated areas of each epidemic situation related user in all tracking time periods and then displaying the epidemic situation associated areas on an epidemic situation map;
the second acquisition module is used for acquiring the behavior related area of each epidemic situation related user in the tracking time period in the following mode:
for each behavior recording segment in a plurality of behavior recording segments in the behavior information of each epidemic situation related user in the tracking time segment, determining whether each behavior recording point in the behavior recording segment is a candidate target behavior recording point or not according to the continuous change value of the behavior recording point in the behavior recording segment, wherein each behavior recording point corresponds to each generated behavior;
determining each candidate space region corresponding to the behavior record segment according to the number of candidate target behavior record points in the behavior record segment;
for each candidate space region, dividing the candidate space region into a plurality of subspaces, and determining whether the candidate space region is a target space region according to the variation degree of each behavior recording point in each subspace and a preset behavior characteristic variation trend;
and determining the behavior related region of each epidemic situation related user in the tracking time period according to the behavior characteristics of each behavior recording point in the target space region.
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| CN111538464B (en) * | 2020-05-10 | 2021-05-07 | 浙江智飨科技有限公司 | Data cleaning method and device based on Internet of things platform |
| CN111640515A (en) * | 2020-05-26 | 2020-09-08 | 深圳市通用互联科技有限责任公司 | Method and device for determining epidemic situation risk of region, computer equipment and storage medium |
| CN113762432A (en) * | 2020-06-01 | 2021-12-07 | 浙江大开信息技术有限公司 | Unit code based management and control method and management and control system thereof |
| CN113820040B (en) * | 2020-06-19 | 2023-12-19 | 青岛海尔空调器有限总公司 | Body temperature detection method based on air conditioner and cloud server |
| JP7396214B2 (en) * | 2020-06-26 | 2023-12-12 | トヨタ自動車株式会社 | Information processing system, information processing method, and program |
| CN111858825A (en) * | 2020-07-30 | 2020-10-30 | 平安国际智慧城市科技股份有限公司 | Epidemic situation prevention and control trip management method and device, terminal equipment and storage medium |
| CN111933298B (en) * | 2020-08-14 | 2024-02-13 | 医渡云（北京）技术有限公司 | Crowd relation determining method and device, electronic equipment and medium |
| CN112331358A (en) * | 2020-10-27 | 2021-02-05 | 中德（珠海）人工智能研究院有限公司 | Disease monitoring method, device, equipment and storage medium based on three-dimensional modeling |
| CN113593713A (en) * | 2020-12-30 | 2021-11-02 | 南方科技大学 | Epidemic situation prevention and control method, device, equipment and medium |
| CN112948516A (en) * | 2021-02-25 | 2021-06-11 | 中冶南方连铸技术工程有限责任公司 | Method and system for automatically tracking equipment, electronic equipment and computer storage medium |
| CN113312915B (en) * | 2021-05-28 | 2022-08-30 | 北京航空航天大学 | Intelligent epidemiology investigation system |
| CN113658719A (en) * | 2021-08-20 | 2021-11-16 | 平安国际智慧城市科技股份有限公司 | Epidemic situation supervision method and device for target person, electronic equipment and storage medium |
| CN115497639B (en) * | 2022-11-17 | 2023-05-05 | 上海维智卓新信息科技有限公司 | Epidemic prevention space-time region determining method and device |
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| US9262991B2 (en) * | 2014-02-24 | 2016-02-16 | Hewlett Packard Enterprise Development Lp | Displaying visual analytics of entity data |
| CN105740615B (en) * | 2016-01-28 | 2018-10-16 | 中山大学 | Utilize the method for the mobile phone trajectory track infection sources and prediction disease transmission trend |
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