CN113470833A - Person tracking method, person tracking device, electronic device, and storage medium - Google Patents
Person tracking method, person tracking device, electronic device, and storage medium Download PDFInfo
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Abstract
The present application relates to a person tracking method, a person tracking apparatus, an electronic apparatus, and a storage medium, wherein the person tracking method includes: acquiring perception data of various main bodies acquired by various acquisition devices in a target area, wherein the types of the acquisition devices comprise: the system comprises MAC acquisition equipment, an RFID device, bayonet equipment and face acquisition equipment; establishing a first appearance model and a last appearance model of each type of main body according to the acquired sensing data, and regularly updating the first appearance model and the last appearance model; the first-last-occurrence model comprises a date of occurrence of the subject in the target region; and searching for the persons appearing in a specified area and at a specified time based on the first-time appearance model, wherein the specified area is positioned in the target area. By the method and the device, the problem that the close contact person using the non-own telephone number cannot be accurately positioned in the related technology is solved.
Description
Technical Field
The present application relates to the field of big data analysis technologies, and in particular, to a method and an apparatus for tracking people, an electronic apparatus, and a storage medium.
Background
Infectious diseases are diseases caused by pathogens and can be transmitted from person to animal or from person to animal. For example, the novel coronavirus pneumonia has long latent period, the infected patients can not present symptoms within a certain period of time, but the patients who are closely contacted with the latent patients can easily infect the novel coronavirus pneumonia, so that the search of the close contact person with the diagnosed patients is particularly important for the prevention and control of the epidemic situation during the epidemic situation. Nowadays, the traffic is developed, the mobility of people is high, and the tracking difficulty of close contact persons is very high.
For the judgment and tracking of close contact people of infected people, the current main technical means is to analyze and locate the passing area of the user based on the action track of the mobile phone carried by the user. The action track of a person is judged according to the user corresponding to the mobile phone, and a relevant model is established and tracked through the mobile phone number of the user and user track data provided by a relevant mobile operator, so that most suspected close contacts can be determined. However, although the real-name system of the mobile phone number has been popularized for a long time at present, a lot of people still use the phone cards for opening accounts in batches in real life, and the real-name verification is not one-to-one. And part of family users open a plurality of auxiliary cards for other people to use. Therefore, there is a case where the telephone number does not correspond to the person actually used. The part of people cannot be accurately tracked by the prior art. When the epidemic situation is outbreak in a large area, due to the large number of infection sources, one or two close contacts in the latent period are missed, which can cause the virus to spread in a large range, so that how to completely track all the close contacts of patients diagnosed during the epidemic situation is particularly important.
Aiming at the problem that the close contact person using the phone number of the non-self cannot be accurately positioned in the related technology, no effective solution is provided at present.
Disclosure of Invention
The embodiment provides a person tracking method, a person tracking device, an electronic device and a storage medium, so as to solve the problem that the related art cannot accurately locate the close contact person using the non-own telephone number.
In a first aspect, there is provided in this embodiment a people tracking method, comprising:
acquiring perception data of various main bodies acquired by various acquisition devices in a target area, wherein the types of the acquisition devices comprise: the system comprises MAC acquisition equipment, an RFID device, bayonet equipment and face acquisition equipment;
establishing a first appearance model and a last appearance model of each type of main body according to the acquired sensing data, and regularly updating the first appearance model and the last appearance model; the first-last-occurrence model comprises a date of occurrence of the subject in the target region;
and searching for the persons appearing in a specified area and at a specified time based on the first-time appearance model, wherein the specified area is positioned in the target area.
In some of these embodiments, the method further comprises:
dividing the target area into a plurality of area blocks through an address coding algorithm, and storing the first and last appearance models of various types of the main bodies according to the area blocks in a classified mode.
In some embodiments, the finding persons who appear in a specified area and at a specified time based on the first-last-occurrence model includes:
searching a target subject appearing in the designated area and the designated time according to the first-time appearance model and the last-time appearance model;
and searching the individual related to the target subject in a database related to various subjects.
In some of these embodiments, 0 and 1 are used in the first and last occurrence model to represent the occurrence of the subject on the current day.
In some of these embodiments, the first and last occurrence models are stored in the form of a monthly table including occurrence data for all days of the subject that occur in the zone block in the corresponding month.
In some of these embodiments, said periodically updating said first-last-occurrence model comprises:
updating the occurrence data of the subject on a previous day in the monthly table on a daily basis, including:
if the date of the previous day is the beginning of the month, establishing a new month table, and updating the appearance data of the main body on the previous day in the new month table; if the date of the previous day is not the beginning of the month, updating the occurrence data of the subject on the previous day in a current month table;
and if the main body does not exist in the monthly table, adding the main body to the monthly table, and updating the appearance data of the main body.
In some embodiments, the establishing a first-time appearance model of the subject according to the acquired perception data includes:
and cleaning the acquired perception data of various main bodies in the target area, and establishing the first and last appearance models of the main bodies according to the cleaned data.
In some of these embodiments, the method further comprises:
classifying the personnel searched by the various types of main bodies into different risk grades according to the types of the main bodies, wherein the risk grade of the personnel searched by the MAC acquisition equipment and the face acquisition equipment is a high risk grade, and the risk grade of the personnel searched by the RFID device and the bayonet equipment is a low risk grade.
In a second aspect, the present embodiment provides a tracking apparatus for closely contacting persons in an epidemic situation, including a data acquisition module, a data storage module, and a person search module;
the data acquisition module is used for acquiring perception data of various main bodies acquired by various acquisition devices in a target area, and the types of the acquisition devices comprise: the system comprises MAC acquisition equipment, an RFID device, bayonet equipment and face acquisition equipment;
the data storage module is used for establishing first and last appearance models of various main bodies according to the acquired sensing data and regularly updating the first and last appearance models; the first-last-occurrence model comprises a date of occurrence of the subject in the target region;
and the personnel searching module is used for searching personnel appearing in a designated area and designated time based on the first-time appearance model and the last-time appearance model, wherein the designated area is positioned in the target area.
In a third aspect, in this embodiment, an electronic device is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the person tracking method according to the first aspect is implemented.
In a fourth aspect, in the present embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which when executed by a processor, implements the person tracking method according to the first aspect described above.
Compared with the prior art, the personnel tracking method, the personnel tracking device, the electronic device and the storage medium have the advantages that the first-time occurrence model is established according to the daily snapshot and acquired sensing data of the security equipment, the main body appearing in the designated area and the designated time is searched according to the first-time occurrence model, the person related to the main body is searched according to the main body, and the problem that the prior art cannot accurately position the close contact person using the phone number of the non-self is solved.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a block diagram of a hardware structure of an application server of a person tracking method according to an embodiment of the present disclosure;
FIG. 2 is a flow chart of a person tracking method according to an embodiment of the present application;
FIG. 3 is a schematic diagram illustrating steps of a process for periodically updating a first-time appearance model in a person tracking method according to an embodiment of the present application;
fig. 4 is a block diagram of a person tracking device according to an embodiment of the present disclosure.
Detailed Description
For a clearer understanding of the objects, aspects and advantages of the present application, reference is made to the following description and accompanying drawings.
Unless defined otherwise, technical or scientific terms used herein shall have the same general meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The use of the terms "a" and "an" and "the" and similar referents in the context of this application do not denote a limitation of quantity, either in the singular or the plural. The terms "comprises," "comprising," "has," "having," and any variations thereof, as referred to in this application, are intended to cover non-exclusive inclusions; for example, a process, method, and system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or modules, but may include other steps or modules (elements) not listed or inherent to such process, method, article, or apparatus. Reference throughout this application to "connected," "coupled," and the like is not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Reference to "a plurality" in this application means two or more. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. In general, the character "/" indicates a relationship in which the objects associated before and after are an "or". The terms "first," "second," "third," and the like in this application are used for distinguishing between similar items and not necessarily for describing a particular sequential or chronological order.
The method embodiments provided in the present embodiment may be executed in a terminal, a server, or a similar computing device. For example, the method is executed on a server, and fig. 1 is a hardware configuration block diagram of the server of the person tracking method according to the embodiment. As shown in fig. 1, the server may include one or more (only one shown in fig. 1) processors 102 and a memory 104 for storing data, wherein the processors 102 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA. The server may also include a transmission device 106 for communication functions and an input-output device 108. It will be understood by those of ordinary skill in the art that the configuration shown in fig. 1 is merely illustrative and is not intended to limit the configuration of the server described above. For example, the server may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
The memory 104 may be used for storing computer programs, for example, software programs and modules of application software, such as a computer program corresponding to the person tracking method in the present embodiment, and the processor 102 executes various functional applications and data processing by running the computer programs stored in the memory 104, so as to implement the above-mentioned method. The memory 104 may also be used for storing data used for executing a computer program, such as a first and last occurrence model in the person tracking method in the present embodiment. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to a server over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used to receive or transmit data via a network, for example, to acquire perception data of the subject of the person tracking method of the present embodiment. The network described above includes a wireless network provided by a communication provider of the server. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
The input/output device 108 is used to realize the interaction between the server and the user, for example, to display the person tracked by the person tracking method in the present embodiment to the user.
In this embodiment, a person tracking method is provided, in which person tracking is performed through data collected by various types of collection equipment installed in a city, including MAC collection equipment, an RFID device, a bayonet device, and face collection equipment. Different collection devices collect data of different subjects, wherein the subjects can be network devices, labels, license plates and human body characteristics. For example, the MAC acquisition device acquires a MAC address of a network device (such as a mobile phone), the RFID device acquires a number of an RFID tag of a non-motor vehicle, the gate device captures a license plate number of a vehicle, and the face acquisition device acquires a feature ID of a face. According to the information, the individuals related to various data can be found by combining the database of the related system.
Specifically, the related system has a database established for the association and relationship between different elements, and associates various elements such as a national identification number, a telephone number, a license plate number, an MAC address, an RFID tag number, a home address, an IMSI number, and the like. For example, the RFID tag number is a unique identifier of a non-motor vehicle, and is equivalent to the license plate number of the motor vehicle, and according to the RFID tag number, the owner of the non-motor vehicle corresponding to the RFID tag number can be located according to the existing non-motor vehicle registration information of the related system. The MAC address is an address used to identify the location of network devices (e.g., handsets), each having a fixed MAC address. When a user buys a mobile phone, the user can register personal information, the related system can acquire the mobile phone corresponding to the MAC address and find the collected belonging information, and the person to which the mobile phone belongs is positioned according to the MAC address of the mobile phone. The bayonet device is a road traffic on-site monitoring device which is used for shooting, recording and processing all motor vehicles passing through the bayonet point by depending on the special places on the road, such as the toll stations, traffic or security inspection stations and the like. The card gate equipment can collect the license plate number of the vehicle, and can search the vehicle owner corresponding to the license plate number in the existing vehicle management information database of the vehicle management station according to the license plate number.
The data acquired by various acquisition devices in the application can also comprise information such as device numbers, acquisition time, longitude and latitude during acquisition and the like corresponding to the acquisition devices, and the information can be used for searching for main bodies appearing in designated time and designated areas.
In epidemic situations, when a patient is diagnosed, the passing area and the moving range of the patient in the last few days can be clearly determined by means of oral inquiry, mobile phone track tracking and the like. When the close contact person needs to be tracked, the main bodies appearing in the area and the time range, including the mobile phone device, the captured non-motor vehicle and the captured human face, can be searched according to the data of various main bodies collected by different collection devices in the designated area and time, and the searched main bodies are corresponding to specific individuals through information retrieval of a resource library of a related system, so that the close contact person can be comprehensively and accurately found. The following describes the flow of the person tracking method provided in this embodiment specifically:
fig. 2 is a flowchart of the person tracking method provided in this embodiment, and as shown in fig. 2, the flow includes the following steps:
step S201, obtaining perception data of various types of main bodies collected by various types of collection equipment in a target area, wherein the types of the collection equipment comprise: MAC collection equipment, RFID device, bayonet socket equipment and people's face collection equipment.
The above sensing data refers to various types of data collected for the temporal and spatial activities of the subject, and here refers to data collected by various types of collection devices adopted in this embodiment.
As described above, sensing data of various subjects can be acquired by various types of acquisition devices installed in a city, including MAC acquisition devices, RFID devices, bayonet devices, and face acquisition devices. The main body can be network equipment, a label, a license plate, human body characteristics and the like. For example, the MAC acquisition device acquires a MAC address of a network device (such as a mobile phone), the RFID device acquires a number of an RFID tag of a non-motor vehicle, the gate device captures a license plate number of a vehicle, and the face acquisition device acquires a feature ID of a face.
Step S202, establishing first and last appearance models of various main bodies according to the acquired sensing data, and regularly updating the first and last appearance models, wherein the first and last appearance models comprise the date of the main bodies appearing in the target area.
The statistical analysis of the acquired perception data can be realized by various existing statistical methods. Because the perception data in this application comes from multiclass main part, and time factors such as date must be considered in personnel's pursuit moreover, based on this, this application utilizes the first appearance model to record the perception data that all kinds of main parts were gathered, and the realization data classification that can be clear is counted and is taken into account time information. According to the first and last appearance model, the main body appearing in the designated area and the designated time can be quickly searched.
Specifically, the first-time and last-time appearance model records the appearance of the subject every day, that is, whether the data of the subject is acquired by the acquisition device on the current day. When a main body is detected in a certain area for the first time, a first-time and last-time appearance model of the main body is established, then whether the main body appears in the area on the same day or not is stored in the first-time and last-time appearance model in a data mode every day, and the main body which appears in the area on the same day can be known through the first-time and last-time appearance model of the main body.
And S203, searching the personnel appearing in the designated area and the designated time based on the first and last appearance model, wherein the designated area is positioned in the target area.
The designated area may be the area and range of motion that a patient found to be diagnosed has passed in the last few days, and the designated time may be a period of time based on consideration of the latency of the infectious disease during which there may be close contacts in latency, such as during a new coronary pneumonia epidemic, which is typically 14 days. Whether the main body appears in the designated time and the designated area can be known through the data in the first-time appearance model of the main body and the last-time appearance model of the main body, all the main bodies appearing in the designated time and the designated area can be found through searching, and then the individuals related to the main bodies can be found in the database of the related system.
The embodiment provides a personnel tracking method, wherein a first-time and last-time appearance model is established according to sensing data acquired by daily snapshot and acquisition of security equipment, the first-time and last-time appearance model comprises every day appearance information of various main bodies in a target area, the main bodies appearing in a specified area and a specified time are searched according to the first-time and last-time appearance model, and individuals related to the main bodies are searched according to the main bodies. The personnel tracking method provided by the embodiment provides effective supplement on the basis of the prior art that people are positioned only by the mobile phone, and solves the problem that the prior art cannot accurately position the close contact person using the non-own phone number. In addition, the personnel tracking method provided by the embodiment determines the action track of the personnel more comprehensively through the fusion judgment of the bayonet vehicle passing, the face snapshot, the MAC track acquisition and the RFID track acquisition.
In some embodiments, the person tracking method provided by the application may further divide the target area into a plurality of area blocks through an address coding algorithm, and store the first and last appearance models of various subjects according to the area blocks in a classified manner, so as to facilitate data management and geographic positioning, thereby facilitating subsequent statistical analysis.
The division of the regions may be achieved by various address encoding algorithms, such as the GeoHash algorithm, Morton code, and Google's S2 algorithm, etc. The present embodiment is described by taking a GeoHash algorithm as an example: and coding each region block in the geographic space by adopting a GeoHash algorithm, wherein each region block corresponds to one GeoHash block, and the size of the geographic space division region block can be adjusted by adjusting the accurate digit of the GeoHash block. And storing the models appearing at the first time and the last time of various main bodies according to the region blocks in a classified manner, and screening the region blocks contained in a specified region when people passing through the specified region need to be searched, and then searching in the region blocks.
Further, in step S203, searching for people appearing in a designated area and at a designated time based on the first-time appearance model specifically includes:
step S2031, searching for target subjects appearing in the designated area and the designated time according to the first and last appearance model. It should be noted that the target subject herein refers to all the subjects found to be present in the designated area and at the designated time.
Step S2032, searching the individual related to the target subject in the database related to various subjects. In particular, the database here may be a database of a correlation system.
In some embodiments, the person tracking method provided herein uses 0 and 1 in the first and last occurrence model to represent the occurrence of the subject on the current day. For example, 1 indicates that the subject appears on the current day, 0 indicates that the subject does not appear on the current day, and 100100000 indicates that the subject appears only on days 1 and 4 and does not appear on any other day within 10 days of statistics. This representation greatly reduces the amount of data stored.
In some of these embodiments, the code composed of 0 and 1 is stored in the form of a monthly table, for example, if the subject only appears in a certain area block in the month's 3 th and 5 th, the corresponding appearance code is 00101000 … … (the specific number of bits is consistent with the day of the month). Optionally, the monthly table is stored in a year classification.
Further, in the above step S202, the first-and-last-occurrence model may be periodically updated by:
updating occurrence data of the subject on the previous day in the month table every day, if the date of the previous day is the beginning of the month, establishing a new month table, and updating the occurrence data of the subject on the previous day in the new month table; if the date of the previous day is not the beginning of the month, the appearance data of the subject on the previous day is updated in the current month table. If the agent does not exist in the month table, the agent is added to the month table, and the occurrence data of the agent is updated.
Specifically, as shown in fig. 3, the process of periodically updating the first and last occurrence models includes the following steps:
in step S301, it is determined whether the previous day is the beginning of the month, and if so, steps S302 and S303 are executed, and if not, step S305 is executed.
Step S302: a new monthly table is established.
Step S303: the occurrence data of the subject on the previous day is updated in the new monthly table.
Step S304: it is determined whether the subject exists in the monthly table. If yes, go to step S305, otherwise go to step S306.
Step S305: and updating the occurrence data of the subject on the previous day in the monthly table.
Step S306: and adding the main body in the monthly table, and updating the appearance data of the main body.
The update method may be such that the code of the corresponding bit of all dates of the month table is initialized to 0 in advance, and the code of the corresponding bit of the previous day of the subject detected on the previous day is modified to 1 every day. Optionally, when the epidemic situation is serious, the updating frequency may also be increased, for example, the updating frequency is increased every hour, that is, the data of the occurrence of the current day in the lunar surface of the subject detected in the previous hour is updated every other hour, so that the traceable time of the subject can be advanced to the previous hour.
In some embodiments, in step S202, the process of establishing the first-time appearance model and the last-time appearance model of each type of subject according to the acquired sensing data specifically includes:
and cleaning the acquired perception data of various main bodies in the target area, and establishing a first-time appearance model of the main bodies according to the cleaned data. The aim of the step is to filter out invalid data, and simultaneously realize normalization of a data storage mode to obtain a piece of valid data which can be used for subsequent unified analysis and calculation.
Taking the MAC acquisition device as an example, the MAC acquisition device may frequently acquire data of the same MAC device. If a person holds a mobile phone and is always within the collected range, a lot of redundant data can be generated, so that repeated data filtering, such as duplicate removal, false removal and the like, needs to be performed on the data collected by the MAC device, so as to reduce the data storage amount and ensure the validity of the stored data. The processing of the data of the other three types of acquisition equipment is similar to the processing of MAC data.
In some embodiments, a person tracking method is provided, which is based on the foregoing embodiments, and after step S203, further includes:
and classifying the personnel searched by various main bodies into different risk grades according to the types of the main bodies, wherein the risk grade of the personnel searched by the MAC acquisition equipment and the face acquisition equipment is a high risk grade, and the risk grade of the personnel searched by the RFID device and the bayonet equipment is a low risk grade.
During an epidemic situation, the personnel searched by the vehicle captured by the bayonet device and the personnel searched by the non-motor vehicle data collected by the RFID device are classified into low risk levels because the personnel are in the vehicle, the speed of passing through a designated area is high, and the infection possibility is low. For the personnel searched by the face acquisition equipment and the MAC acquisition equipment, the speed of the personnel passing through the designated area is low, the infection probability is high, and therefore the personnel are classified into high risk levels. For persons with different risk levels, different measures can be taken, for example, for persons with low risk registration, the persons can be reminded by short messages, and for persons with high risk levels, the persons can be checked by isolation or nucleic acid detection.
The embodiment provides a personnel tracking method, different risk levels are set according to different sources of personnel, and accurate epidemic prevention and control are facilitated.
For the personnel tracking method provided by any embodiment, a mainstream Hadoop distributed system framework and Spark, hive and other big data analysis frameworks can be used for calculating and storing large-scale data, and the overall data analysis efficiency is improved.
The present embodiment is described and illustrated below by means of preferred embodiments.
The preferred embodiment provides a person tracking method, including:
step S401, a GeoHash algorithm is used to divide the target area into a plurality of area blocks.
Step S402, acquiring perception data of various main bodies acquired by various acquisition devices in a target area, wherein the types of the acquisition devices comprise: MAC collection equipment, RFID device, bayonet socket equipment and people's face collection equipment.
And S403, cleaning the perception data acquired by various acquisition devices.
Step S404, establishing first and last appearance models of various main bodies according to the cleaned data, and storing the first and last appearance models according to the region block classification; specifically, the first and last appearance model is stored in the form of a month table, and the month table is stored by year classification. Wherein, the occurrence of the subject in the current day is recorded by 0 and 1 in the monthly table, 0 represents no occurrence, and 1 represents occurrence.
For the established first and last appearance model, the maintenance can be updated regularly in the following way:
updating occurrence data of a subject of a previous day in the month table every day, if the date of the previous day is the beginning of the month, establishing a new month table, and updating occurrence data of the subject on the previous day in the new month table, specifically, initializing each bit of the month code to 0 first, and if the subject appears on the first day of the month, modifying the first bit of the code to 1; if the date of the previous day is not the beginning of the month, the appearance data of the subject on the previous day is updated in the current month table, specifically, if the subject appears on the previous day, the code of the date correspondence bit is modified to 1. And if the body does not exist in the month table, adding the body to the month table, and updating the appearance data of the body, specifically, setting the code of the corresponding bit of the date to be 1, and setting other bits to be 0.
Step S405, searching a target subject appearing in a designated area and a designated time according to the first and last appearance model; wherein the designated area is located within the target area.
Step S406, the individual associated with the target subject is searched in the database associated with each type of subject.
Step S407, determining the risk level of the searched personnel, wherein the risk level of the personnel searched by the MAC acquisition equipment and the face acquisition equipment is a high risk level, and the risk level of the personnel searched by the RFID device and the gate equipment is a low risk level.
The embodiment also provides a tracking method of the close contact persons in the epidemic situation, which comprises the following steps:
step S501, according to a GeoHash algorithm, a city is divided into a plurality of GeoHash blocks from a geographic space, and data information collected by various collection devices in the city is managed uniformly. The acquisition equipment comprises bayonet equipment, MAC acquisition equipment, an RFID device and face acquisition equipment.
Step S502, establishing a month table and a year table for recording the first and last appearance of the main body aiming at each GeoHash block, and summarizing the flow information of all the personnel in the statistical area.
Step S503, collecting data acquired by the four types of devices, and performing unified storage management.
And step S504, acquiring the perception data acquired by various acquisition devices, and performing data governance on the perception data. The data management is mainly used for cleaning, filtering and the like of data, and aims to reduce the storage capacity of the data and improve the quality of the data.
And step S505, analyzing the sensing data acquired on the previous day every day, performing data classification calculation according to a specific rule, counting the first and last occurrence time corresponding to each main body, and updating the data into a result table. The specific process is as follows:
initializing each bit of the month code to 0, and modifying the first bit of the code to 1 if the main body appears on the first day of the month; if the date of the previous day is not the beginning of the month, the appearance data of the subject on the previous day is updated in the current month table, specifically, if the subject appears on the previous day, the code of the date correspondence bit is modified to 1. And if the body does not exist in the month table, adding the body to the month table, and updating the appearance data of the body, specifically, setting the code of the corresponding bit of the date to be 1, and setting other bits to be 0.
Step S506, when the confirmed patient is found, finding out the area which the patient passes through within 14 days, drawing out the corresponding GeoHash area range, and determining the date range and the GeoHash area range for searching the close contact person.
And step S507, screening the GeoHash region block to be searched from the summarized first-time appearance model and searching all main bodies appearing in the corresponding GeoHash region block according to the specified date range.
Step S508, searching in the database of the relevant system, and locating each subject found in step S507 to a specific individual.
Step S509, determining risk levels of the searched personnel, where the risk level of the personnel searched through the MAC acquisition device and the face acquisition device is a high risk level, and the risk level of the personnel searched through the RFID device and the gate device is a low risk level.
According to the method for tracking the close contacts in the epidemic situation, a first-time and last-time appearance model is established according to the daily snapshot and collected sensing data of the security equipment, the first-time and last-time appearance model comprises every day appearance information of various main bodies in a target area, the main bodies appearing in a specified area and a specified time are searched according to the first-time and last-time appearance model, and then the individuals related to the main bodies are searched according to the main bodies. The personnel tracking method provided by the embodiment provides effective supplement on the basis of the prior art that people are positioned only by the mobile phone, and solves the problem that the prior art cannot accurately position the close contact person using the non-own phone number. . In addition, according to the personnel tracking method, the target area is divided into the area blocks through the GeoHash algorithm, the main bodies appearing in the target area are stored in a classified mode according to the area blocks, the daily appearance condition of the main bodies is represented in a 0 and 1 mode, the storage space is saved, and meanwhile the searching efficiency is improved. Furthermore, the personnel tracking method provided by the application also sets different risk levels according to different sources of personnel, and is favorable for realizing accurate epidemic prevention and control.
In this embodiment, a person tracking device is further provided, and the person tracking device is used for implementing the above embodiments and preferred embodiments, which have already been described and are not described again. The terms "module," "unit," "subunit," and the like as used below may implement a combination of software and/or hardware for a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 4 is a block diagram of a person tracking device according to this embodiment, and as shown in fig. 4, the device includes: data acquisition module 10, data storage module 20 and personnel search module 30, wherein:
the data acquisition module 10 is configured to acquire sensing data of various types of main bodies acquired by various types of acquisition devices in a target area, where the types of the acquisition devices include: MAC collection equipment, RFID device, bayonet socket equipment and people's face collection equipment.
The data storage module 20 is configured to establish a first-time and last-time appearance model of each type of subject according to the acquired sensing data, and periodically update the first-time and last-time appearance model; the first and last appearance model includes the date the subject appeared in the target region.
The person searching module 30 is configured to search for persons who appear in a designated area and at a designated time based on the first-time appearance model, where the designated area is located in the target area.
In one embodiment, a person tracking apparatus is provided, which includes all the modules shown in fig. 4, and further includes an area dividing module, where the area dividing module is configured to divide the target area into a plurality of area blocks through an address coding algorithm, and store first and last appearance models of various types of subjects according to the area blocks in a classified manner, so as to facilitate data management and geographic positioning, thereby facilitating subsequent statistical analysis.
Further, the process of the person searching module 30 searching for persons appearing in the designated area and at the designated time based on the first-time appearance model specifically includes:
step S2031, searching for target subjects appearing in the designated area and the designated time according to the first and last appearance model. It should be noted that the target subject herein refers to all the subjects found to be present in the designated area and at the designated time.
Step S2032, searching the individual related to the target subject in the database related to various subjects. In particular, the database here may be a database of a correlation system.
Further, 0 and 1 are used in the above-described first-and-last-occurrence model to represent the occurrence of the subject on the present day to reduce the amount of stored data.
The above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
There is also provided in this embodiment an electronic device comprising a memory having a computer program stored therein and a processor arranged to run the computer program to perform the steps of any of the above method embodiments.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
It should be noted that, for specific examples in this embodiment, reference may be made to the examples described in the foregoing embodiments and optional implementations, and details are not described again in this embodiment.
In addition, in combination with the person tracking method provided in the above embodiment, a storage medium may also be provided to implement the method in this embodiment. The storage medium having stored thereon a computer program; the computer program, when executed by a processor, implements any of the person tracking methods in the above embodiments.
It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to be limiting. All other embodiments, which can be derived by a person skilled in the art from the examples provided herein without any inventive step, shall fall within the scope of protection of the present application.
It is obvious that the drawings are only examples or embodiments of the present application, and it is obvious to those skilled in the art that the present application can be applied to other similar cases according to the drawings without creative efforts. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
The term "embodiment" is used herein to mean that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the present application. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is to be expressly or implicitly understood by one of ordinary skill in the art that the embodiments described in this application may be combined with other embodiments without conflict.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the patent protection. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.
Claims (11)
1. A person tracking method, comprising:
acquiring perception data of various main bodies acquired by various acquisition devices in a target area, wherein the types of the acquisition devices comprise: the system comprises MAC acquisition equipment, an RFID device, bayonet equipment and face acquisition equipment;
establishing a first appearance model and a last appearance model of each type of main body according to the acquired sensing data, and regularly updating the first appearance model and the last appearance model; the first-last-occurrence model comprises a date of occurrence of the subject in the target region;
and searching for the persons appearing in a specified area and at a specified time based on the first-time appearance model, wherein the specified area is positioned in the target area.
2. The person tracking method according to claim 1, further comprising:
dividing the target area into a plurality of area blocks through an address coding algorithm, and storing the first and last appearance models of various types of the main bodies according to the area blocks in a classified mode.
3. The people tracking method of claim 1, wherein said finding people who appear in a specified area and at a specified time based on said first-last-occurrence model comprises:
searching a target subject appearing in the designated area and the designated time according to the first-time appearance model and the last-time appearance model;
and searching the individual related to the target subject in a database related to various subjects.
4. The people tracking method of claim 1, wherein 0 and 1 are used in the first and last occurrence model to represent the occurrence of the subject on the current day.
5. The person tracking method according to claim 2, wherein the first-time occurrence model is stored in the form of a monthly table including occurrence data of all days of the subject that occur in the area block in the corresponding month.
6. The people tracking method of claim 5, wherein said periodically updating the first-last-occurrence model comprises:
updating the occurrence data of the subject on a previous day in the monthly table on a daily basis, including:
if the date of the previous day is the beginning of the month, establishing a new month table, and updating the appearance data of the main body on the previous day in the new month table; if the date of the previous day is not the beginning of the month, updating the occurrence data of the subject on the previous day in a current month table;
and if the main body does not exist in the monthly table, adding the main body to the monthly table, and updating the appearance data of the main body.
7. The person tracking method according to any one of claims 1 to 6, wherein the establishing a first-last occurrence model of the subject according to the acquired perception data comprises:
and cleaning the acquired perception data of various main bodies in the target area, and establishing the first and last appearance models of the main bodies according to the cleaned data.
8. The person tracking method according to any one of claims 1 to 6, characterized in that the method further comprises:
classifying the personnel searched by the various types of main bodies into different risk grades according to the types of the main bodies, wherein the risk grade of the personnel searched by the MAC acquisition equipment and the face acquisition equipment is a high risk grade, and the risk grade of the personnel searched by the RFID device and the bayonet equipment is a low risk grade.
9. A personnel tracking device is characterized by comprising a data acquisition module, a data storage module and a personnel searching module;
the data acquisition module is used for acquiring perception data of various main bodies acquired by various acquisition devices in a target area, and the types of the acquisition devices comprise: the system comprises MAC acquisition equipment, an RFID device, bayonet equipment and face acquisition equipment;
the data storage module is used for establishing first and last appearance models of various main bodies according to the acquired sensing data and regularly updating the first and last appearance models; the first-last-occurrence model comprises a date of occurrence of the subject in the target region;
and the personnel searching module is used for searching personnel appearing in a designated area and designated time based on the first-time appearance model and the last-time appearance model, wherein the designated area is positioned in the target area.
10. An electronic device comprising a memory and a processor, wherein the memory has stored therein a computer program, and the processor is configured to execute the computer program to perform the tracking method of any one of claims 1 to 8.
11. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the tracking method according to any one of claims 1 to 8.
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