Big data-based epidemic prevention and control precise trajectory confirmation system and working method thereof

Abstract

The present application relates to a kind of big data-based epidemic prevention and control precise track confirmation system and working method thereof, the system includes intelligent terminal, server system and database server, intelligent terminal, database server is connected with server system respectively;Server system includes WEB server, file server, read data server and write data server.The present application can confirm the objective accurate activity track of case or positive patient detection within a certain period of time in the first time, to confirm the accurate range of possible contact personnel and key area place in the first time, innovation launches track code, for the people to check, automatically compare whether there is time, spatial overlap with case or positive patient, make the timeliness of epidemic prevention and control stronger, accuracy higher, can realize with minimum cost to obtain maximum epidemic prevention and control effect, reduce the influence of epidemic on economic and social development, improve the social comprehensive management efficiency and ability in the process of epidemic prevention and control.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of big data processing, and particularly relates to a big data-based epidemic prevention and control precise trajectory confirmation system and a working method thereof. BACKGROUND

[0002] Currently, in the epidemic prevention and control scheme, personnel are mainly used to collect the geographical positions, that is, the activity trajectories of personnel to determine whether the personnel have infection risk, which plays an important role in joint prevention and control. Whether the personnel have arrived at a certain city or a certain region is also determined by the activity trajectory, which is currently the only determination method. However, the current activity trajectory is collected based on communication base station signaling data, and the range is too large. The user's mobile phone signaling data is captured by the base station, and theoretically, the user is determined to be in the circular area covered by the base station. In order to distinguish whether the user stays or passes through the place, it is generally determined that the user has a stay history in the region only when the signal is above a certain period of time, and it is determined that the user passes through the place within a certain period of time. Therefore, there is a large error. On the one hand, the base station coverage area is too large and not accurate. On the other hand, the definition of a certain period of time has a loophole. In addition, the phenomenon of "signal drift" may occur, that is, the user is defined as being in another city while being in a certain city (caused by the relationship between the communication base station signal strength of adjacent jurisdiction boundaries). The scheme is simple in technical implementation, and only the signaling data source of a communication base station is used as the basis for calculation and statistics. Therefore, the current activity trajectory collection method cannot accurately and effectively meet the requirements of inter-city epidemic prevention and control.

[0003] When a city suddenly has an epidemic, the first task is to quickly and accurately determine the activity trajectory of the "case" in a certain period of time and immediately find out the possible contact personnel. The area and place involved in the activity trajectory of the case in a certain period of time are disinfected, managed and controlled, and the possible contact personnel are taken preventive measures at the first time.

[0004] In addition, the case trajectory is generally published in a text description manner, which is not intuitive, not visual, and has no date and time and position. The purpose of publishing the activity trajectory of the case is to mobilize the public to consciously query and compare whether there is a time and space coincidence phenomenon with the case, so as to supplement the possible omissions in the flow survey work. From the objective point of view, it is difficult for ordinary people to effectively self-check and compare whether there is a coincidence phenomenon between the time and space behavior recall and the case trajectory place. In summary, it is currently impossible to form the activity trajectory of the user to effectively self-check and compare whether there is a coincidence phenomenon between the case trajectory place. SUMMARY

[0005] In view of this, the purpose of the present invention is to overcome the shortcomings of the prior art and provide a big data-based precise trajectory confirmation system for epidemic prevention and control and its working method, so as to solve the problem that the prior art cannot specifically form the activity trajectory of users.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: a big data-based precise trajectory confirmation system for epidemic prevention and control, comprising: a smart terminal, a server system, and a database server, wherein the smart terminal and the database server are respectively connected to the server system; the server system includes a web server, a file server, a read data server, and a write data server; the web server is respectively connected to the file server, the read data server, and the write data server;

[0007] The web server is used to receive various sets of electronic information acquired by the smart terminal; the electronic information includes the user's time behavior information, spatial behavior information, and device information of the user's smart terminal;

[0008] The write data server performs data preprocessing on each group of electronic information;

[0009] The data reading server is used to process the pre-processed electronic information to obtain motion trajectory data, and to determine whether there is a spatiotemporal co-occurrence with the positive patient based on the motion trajectory data; the motion trajectory data includes time points and the location information corresponding to the time points; wherein, spatiotemporal co-occurrence means that the device information of the user's smart terminal and the device information of the smart terminal of the positive patient stay in the same spatiotemporal grid for more than a preset time.

[0010] The file server is used to generate motion trajectory files based on motion trajectory data; the motion trajectory files include trajectory files, relation files, region files, and trajectory code overlay files;

[0011] The database server is used to store data information from the file server, read data server, and write data server.

[0012] Furthermore, the write data server includes:

[0013] The data acquisition module is used to acquire users' time behavior information, spatial behavior information, and device information of users' smart terminals;

[0014] The data cleaning module is used to clean and process abnormal data in the time behavior information, spatial behavior information, and device information of the user's smart terminal.

[0015] The data tagging module is used to tag the cleaned time behavior information, spatial behavior information, and device information of the user's smart terminal; the tags include dynamic tags and static tags, the static tags are used to mark data that remains static over time, and the dynamic tags are used to represent data that changes over time.

[0016] The data anonymization module is used to anonymize users' sensitive data.

[0017] Furthermore, the data read server includes:

[0018] The log module is used to generate corresponding log data for users in preset time periods, and to collect and classify the log data of all users.

[0019] The trajectory module is used to generate trajectory data based on time behavior information, spatial behavior information, and device information of the user's smart terminal, and to form a trajectory information database with corresponding log data based on the trajectory data;

[0020] The region module is used to acquire the user's stationary status information and form a region location information database for the user's corresponding log data; the stationary status information includes the user's dwell time and location information.

[0021] The overlay module is used to overlay user time behavior information and spatial behavior information to form the user's behavior information on a daily basis.

[0022] Furthermore, the file server includes:

[0023] The trajectory file module is used to output the user's daily behavior trajectory file within a preset time period;

[0024] The relationship file module is used to output a list of people whose temporal and spatial behavioral information overlaps with that of positive patients within a preset time period.

[0025] The regional file module is used to output the geographical location and place name involved in the positive patient;

[0026] The trajectory code overlay module is used to overlay the movement trajectories of individuals whose temporal and spatial behavioral information overlaps with that of positive patients within a preset time period, generating a self-check comparison code for epidemic prevention and control; the self-check comparison code for epidemic prevention and control is then identified as the trajectory code.

[0027] Furthermore, the file server, read data server, and write data server are synchronized and mutually readable.

[0028] Furthermore, receiving the sets of electronic information acquired by the smart terminal includes:

[0029] The user's smart terminal receives signaling data from macro base stations, micro base stations, pico base stations, and femto base stations, as well as IMEI data, IMEI SV data, ICCD data, MEID data, and S / N data.

[0030] O-domain data, which includes internet access behavior, signaling, and location;

[0031] B-domain data, which includes: real-name information, mobile phone number, IMEI, IMSI, and terminal model;

[0032] M-domain data, which includes: ERP, portal, and project management;

[0033] V-domain data, which includes the operator's value-added services and base station services.

[0034] Furthermore, the abnormal data undergoes cleaning processing, including:

[0035] Missing value cleaning, format content cleaning, logical error cleaning, attribute dependency conflict correction, and non-required data cleaning.

[0036] This application provides an epidemic prevention and control command center platform, including the big data-based precise trajectory confirmation system for epidemic prevention and control provided in any embodiment, a server cluster, a search engine cluster, a load balancer, a firewall, and...

[0037] Disaster recovery servers are used to back up all collected and processed data;

[0038] A caching server is used to cache user data.

[0039] The data warehouse is used to collect and process information from all relevant channels related to epidemic prevention and control.

[0040] This application provides a working method for a big data-based precise trajectory confirmation system for epidemic prevention and control, including:

[0041] The web server receives various sets of electronic information acquired by the smart terminal; the electronic information includes the user's time behavior information, spatial behavior information, and device information of the user's smart terminal;

[0042] The data server performs data preprocessing on each group of electronic information.

[0043] The data server processes the pre-processed electronic information to obtain motion trajectory data, and determines whether there is a spatiotemporal co-occurrence with the positive patient based on the motion trajectory data; the motion trajectory data includes time points and the location information corresponding to the time points; wherein, spatiotemporal co-occurrence means that the device information of the user's smart terminal and the device information of the positive patient's smart terminal stay in the same spatiotemporal grid for more than a preset time.

[0044] The file server is used to generate motion trajectory files based on motion trajectory data; the motion trajectory files include trajectory files, relation files, region files, and trajectory code overlay files;

[0045] The database server is used to store data information from the file server, read data server, and write data server.

[0046] The beneficial effects that can be achieved by adopting the above technical solution in this invention include:

[0047] 1. Quickly provide accurate reports on the activity trajectories of cases (including behavioral information based on time, means of transportation, place names, activities, duration of each node, etc., and intuitively display them on the corresponding paths and locations on the map);

[0048] 2. Quickly identify individuals who may have been in contact with the case;

[0049] 3. Provide a scientific and accurate "trajectory code" that the public can scan to check for themselves;

[0050] In summary, the technical solution provided in this application helps to confirm the objective and accurate activity trajectory of confirmed cases or positive patients within a certain time period in a timely manner, and to identify potential contacts and key areas, greatly improving the timeliness and accuracy of urban epidemic prevention and control. The "visualized" activity trajectory of cases is made public, and a "trajectory query code" (hereinafter referred to as "trajectory code") is also released to the public for scanning. This allows the public to automatically compare their activity with that of confirmed cases or positive patients in terms of time and space, greatly facilitating the convenience, scientific rigor, and accuracy of self-checks, and significantly improving the efficiency and capacity of comprehensive social governance during the epidemic prevention and control process. Attached Figure Description

[0051] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0052] Figure 1This is a schematic diagram of the structure of the big data-based precise trajectory confirmation system for epidemic prevention and control according to the present invention;

[0053] Figure 2 This is a schematic diagram of the server system provided by the present invention;

[0054] Figure 3 A schematic diagram of the structure of the epidemic prevention and control command center platform provided by the present invention.

[0055] Figure 4 This is a schematic diagram illustrating the working steps of the big data-based precise trajectory confirmation system for epidemic prevention and control according to the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0057] The following describes, with reference to the accompanying drawings, a specific big data-based precise trajectory confirmation system for epidemic prevention and control, and its working method, provided in an embodiment of this application.

[0058] This invention constructs a database using data such as SIM, IMEI, MEID, S / N, and GPS. The data is cleaned, tagged, and anonymized on read and write data servers. Then, the application server uses big data algorithms to instantly identify cases or positive cases. It can intelligently compare and generate lists of potential contacts in real time, facilitating prevention.

[0059] like Figure 1 As shown in the embodiment of this application, the big data-based precise trajectory confirmation system for epidemic prevention and control includes: a smart terminal 30, a server system 10, and a database server 20. The smart terminal 30 and the database server 20 are respectively connected to the server system 10. The server system 10 includes a web server 11, a file server 14, a read data server 13, and a write data server 12. The web server 11 is connected to the file server 14, the read data server 13, and the write data server 12.

[0060] The WEB server 11 is used to receive various sets of electronic information acquired by the smart terminal 30; the electronic information includes the user's time behavior information, spatial behavior information, and device information of the user's smart terminal 30;

[0061] The data writing server 12 performs data preprocessing on each group of electronic information; preprocessing involves importing the data into the processing tool. This is essentially the data extraction process, importing the data into the processing tool. This includes two parts: first, examining metadata, including field explanations, data sources, code tables, and all information describing the data; second, extracting a portion of the data for manual review to gain a direct understanding of the data itself and identify any initial issues, preparing for subsequent processing.

[0062] The data reading server 13 is used to process the pre-processed electronic information of each group to obtain motion trajectory data, and to determine whether there is a simultaneous spatiotemporal behavior with the positive patient based on the motion trajectory data; the motion trajectory data includes time points and the location information corresponding to the time points; wherein, simultaneous spatiotemporal behavior means that the device information of the user's smart terminal 30 and the device information of the smart terminal 30 of the positive patient stay in the same spatiotemporal grid for more than a preset time.

[0063] The file server 14 is used to generate motion trajectory files based on motion trajectory data; the motion trajectory files include trajectory files, relationship files, region files, and trajectory code overlay files;

[0064] The "simultaneous spatial-temporal trajectory" provided in this application defines boundaries within the "meter-level" (adjustable parameters of a few meters or tens of meters), rather than a large area of ​​800 meters. It can very accurately pinpoint a specific floor of a shopping mall, rather than the entire mall, or a particular restaurant on a street, rather than all stores within an 800-meter radius. Providing the public with a "simultaneous spatial-temporal trajectory query code" allows them to easily scan and self-check, providing a scientific and accurate way to "reassur" that they have not had contact with confirmed cases or positive cases, thus preventing social instability and allowing the public to live and work with peace of mind.

[0065] The database server 20 is used to store data information in the file server 14, the read data server 13, and the write data server 12.

[0066] The technical solution provided in this application aims to confirm the objective and accurate activity trajectory of cases or positive patients within a certain time period as soon as an outbreak occurs or a positive patient is identified. It also allows for the immediate identification of potential contacts and key areas, thus significantly improving the timeliness and accuracy of urban epidemic prevention and control. This technical solution creates a "visualized" case activity trajectory for public display, and simultaneously publishes a "simultaneous spatial-temporal trajectory query code" (also known as a "trajectory code") for the public to scan and automatically compare against cases or positive patients in terms of time and space. This greatly facilitates the convenience, scientific rigor, and accuracy of self-checking for the public. The innovative introduction of a rapid and accurate "trajectory code" (for public self-checking) significantly improves the efficiency and capability of comprehensive social governance during epidemic prevention and control.

[0067] Figure 1 In this system, WEB server 11 is an external network server capable of data interaction with smart terminal 30 and internal network server. Internal network server uses server system 10. It acquires various sets of electronic information from smart terminal 30 via WEB server 11, then preprocesses the electronic information via write data server 12 in server system 10, and processes the preprocessed electronic information via read data server 13 to obtain motion trajectory data. Based on the motion trajectory data, it determines whether there is a simultaneous spatiotemporal interaction with a positive patient. File server 14 generates a motion trajectory file based on the motion trajectory data. Database server 20 stores the data information in file server 14, read data server 13, and write data server 12. The users of smart terminal 30 constitute the user group.

[0068] In some embodiments, the write data server 12 includes:

[0069] Data acquisition module G1 is used to acquire the user's time behavior information, spatial behavior information, and device information of the user's smart terminal 30;

[0070] Specifically, the data acquisition module G1 can collect data from the smart terminal 30. It collects the time and space behavior information of the users involved through a dedicated data API interface and an extended data interface. This collection process may involve data transfer and storage to a local server or reading and executing command algorithms from the interface database. The information data involved includes, but is not limited to, signaling data from macro base stations, micro base stations, pico base stations, and femto base stations; IMEI, IMEI SV, ICCD, MEID, S / N, etc.; or can be described as O-domain data (OSS domain refers to internet access behavior, signaling, location, etc.), B-domain data (BSS domain mainly refers to real-name information, mobile phone number, IMEI, IMSI, terminal model, etc.), M-domain data (MSS domain refers to ERP, portal, project management, etc.), and V-domain data (VALUE domain refers to operator value-added services and base station services, etc.). This can be understood as all the dimensions of the data required by the big data algorithm of this application.

[0071] Data cleaning module G2 is used to clean abnormal data in the time behavior information, spatial behavior information, and device information of the user's smart terminal 30;

[0072] In some embodiments, the abnormal data undergoes cleaning processing, including:

[0073] Missing value cleaning, format content cleaning, logical error cleaning, attribute dependency conflict correction, and non-required data cleaning.

[0074] Specifically, data cleaning is the process of cleaning redundant or erroneous data in big data. It generally involves identifying, detecting, correcting, or deleting incorrect, incomplete, irrelevant, repetitive, or problematic "dirty" data portions that do not affect the results or have other issues. Data cleaning is an important and indispensable part of big data analysis. In practice, data cleaning usually accounts for more than 50% of the analysis time. It standardizes various data elements according to custom specifications, improves work efficiency, and reduces the complexity caused by data anomalies during data analysis.

[0075] Data cleaning includes several cleaning processes:

[0076] Missing value cleaning: Removing unnecessary fields: For certain field contents that may not be needed in the data analysis process, they need to be deleted. Use "delete" directly. It is recommended to make a backup for each step of cleaning to prevent accidental deletion. Filling in missing content: If the field is required but missing in the data, a preset default value or calculation result (such as mean, median, mode, etc.) needs to be provided to fill it in. Retrieving data again: If the data of certain fields is very important but the missing data is severe, then it is necessary to retrieve the data through other channels and fill it in.

[0077] Format content cleaning: For time and date, identity information, and other values where the full-width and half-width displays are inconsistent, etc.: Such problems are usually related to the input end and may also be encountered when integrating multi-source data. Just process them into a consistent format. For example: There are characters that should not exist in the content. Some content may only include part of the characters, such as the ID card number consisting of numbers + letters. The most typical is the spaces at the beginning, end, and in the middle. There may also be problems such as numbers or symbols in the name and Chinese characters in the ID card number. In this case, a semi-automatic verification and semi-manual method need to be used to find possible problems and remove unnecessary characters. Another example: The content does not match the content that the field should have. Generally, there will be front-end verification during data acquisition, but these problems may still occur. At this time, we need to process them again. Unimportant fields can be removed, and important fields need to be processed for missing values. The processing method is the missing value cleaning mentioned above.

[0078] Logical error cleaning: Duplicate removal: It is recommended to perform duplicate removal after format content cleaning. Example of the reason: Multiple spaces cause the tool to think that "Zhao Benshan" and "Zhao Benshan" are not the same person, resulting in duplicate removal failure. Moreover, not all duplicates can be removed so simply.

[0079] Removing unreasonable values: For example, if a non-11-digit phone number appears in the data, it can be directly deleted or processed as a missing value.

[0080] Correcting attribute dependency conflicts: Some fields can be mutually verified, such as Beijing City and its postal code, the user's ID card number and the user's age, place of origin, etc. At this time, a reliable field needs to be specified, and then the unreliable fields need to be removed or reconstructed. For example: The ID card number field shows that you are 20 years old, but the age field shows 28 years old. At this time, the user takes the information on the ID card as the reliable field and changes the age field to 20 years old.

[0081] Cleaning non-required data: Just delete the unnecessary fields but also make a backup.

[0082] Or verify the data, specifically including:

[0083] Data format validation: Verify that the data formats all conform to the standardized format.

[0084] Correlation verification: Since data may come from multiple sources, if the same information obtained from multiple sources is inconsistent, it is necessary to re-evaluate and adjust the reliable data value, or remove the data.

[0085] If many judgment problems arise during the data cleaning process, such as whether a piece of data meets the standard or contains errors, appropriate algorithms can be used to address these issues.

[0086] The data tagging module G3 is used to tag the cleaned time behavior information, spatial behavior information, and device information of the user's smart terminal 30; the tags include dynamic tags and static tags, the static tags are used to mark data that remains static over time, and the dynamic tags are used to represent data that changes over time.

[0087] Specifically, the process of labeling data of different types and dimensions aims to quickly and accurately retrieve the required data information. Tags can be labeled manually or by machine. Due to the massive amount of data involved in this invention, a machine algorithm model is used for labeling. Tags are divided into static tags (data that does not change over time, such as basic personnel information, city and region information, etc.) and dynamic tags (data information that changes over time, such as the location and different activities of personnel at different times, etc.). According to different analysis needs, data dimensions can be divided into several data tags. The overall arrangement rule is: the tag index is sorted in the "column" column, and the tag content information is in the "row" column.

[0088] The data desensitization module G4 is used to desensitize users' sensitive data.

[0089] Specifically, any processing technology or method that directly or without "decoding" or "keys" cannot reveal the specific numerical information or data. For example, if someone's mobile phone number consists of 11 Arabic numerals, it can be anonymized and replaced with a visible string (the original 11-digit number becomes invisible), and this anonymization does not affect caller ID functionality. The simplest anonymization method on the market typically displays something like: 139****1234. (All personal data involved in this invention undergoes similar anonymization processing.)

[0090] In some embodiments, the data read server 13 includes:

[0091] The G5 log module is used to generate corresponding log data for users in preset time periods, and to collect and classify the log data of all users.

[0092] Specifically, the logging module G5 generates separate log information for each user based on a daily time unit for all categories of information in all database servers 20, completing the collection and division of log data for all users in the database.

[0093] The trajectory module G6 is used to generate trajectory data based on time behavior information, spatial behavior information and device information of the user's smart terminal 30, and to form a trajectory information database with corresponding log data based on the trajectory data.

[0094] Specifically, the trajectory module G6 collects all users' epidemiological investigation behavior information, i.e., epidemiological investigation trajectory data, on a user-by-user basis, and forms a trajectory information database with corresponding log data for each user.

[0095] The area module G7 is used to acquire the user's stationary status information and form a regional location information database for the user's corresponding log data; the stationary status information includes the user's dwell time and location information.

[0096] Specifically, the system collects all users' static status information, including their dwell time and location, and creates a regional location information database for each user's corresponding log data.

[0097] The overlay module G8 is used to overlay user time behavior information and spatial behavior information to form the user's behavior information on a daily basis.

[0098] Specifically, the overlay module G8 overlays user time behavior information and spatial behavior information to form complete behavior information of the user on a daily basis, and completes the processing of all log information of all users in the database. The above functional modules have an interactive read-write relationship, and can read and write to each other and compare and verify by executing commands.

[0099] In some embodiments, the file server 14 includes:

[0100] The trajectory file module G9 is used to output the user's daily behavior trajectory file within a preset time period;

[0101] The relational file module G10 is used to output a list of people whose temporal and spatial behavioral information overlaps with that of positive patients within a preset time period.

[0102] The regional file module G11 is used to output the geographical location and place name involved in the positive patient;

[0103] The trajectory code overlay module G12 is used to overlay the movement trajectories of individuals whose temporal and spatial behavioral information overlaps with that of positive patients within a preset time period, generating a self-check comparison code for epidemic prevention and control; the self-check comparison code for epidemic prevention and control is then identified as the trajectory code.

[0104] Understandably, the function of file server 14 is to output results, including "trajectory files," "user spatiotemporal relationship files," "regional location files," and "trajectory code" scanning files for self-checking trajectory spatiotemporal overlap. The trajectory file module G9 can output a user's complete daily behavioral trajectory file for a specific date. Based on the region file module G11, which outputs the regions and locations involved in cases or positive cases, including geographical locations and location names, accurate meter-level boundary delineation of regions and locations can be provided. The trajectory code overlay module G12 summarizes and overlays the activity trajectories of all cases or positive cases and potential contacts in the region, forming the region's epidemic prevention and control self-check comparison code "trajectory code" application file, providing a tool for public self-checking and social management.

[0105] In some embodiments, the file server 14, the read data server 13, and the write data server 12 are synchronized and mutually readable.

[0106] In some embodiments, the server system 10 is connected to the database server 20 via API interfaces and extended interfaces.

[0107] Specifically, the write data server 12 is equipped with an API interface and an extension interface for connection. The API interface and the extension interface maintain real-time access and real-time data collection for a certain period of time. Data collection can be carried out using a fixed hardware processor or a software system to ensure that the database is updated dynamically in real time. Data is synchronized between the write data server 12 and the read data server 13. The file server 14 has adjustable command parameters. Each server has machine-readable storage media and machine-executable command functions. All servers can be local or remote on the other end of the data interface. The API interface and the extension interface can cooperate with any authorized database or application port.

[0108] like Figure 3 As shown, this application embodiment provides an epidemic prevention and control command center platform, including the big data-based precise trajectory confirmation system for epidemic prevention and control provided in any embodiment, a server cluster, a search engine cluster 700, a load balancer 900, a firewall 1000, and...

[0109] Disaster recovery server 500 is used to back up all collected and processed data;

[0110] Cache server 600 is used to cache user data;

[0111] Data Warehouse 800 is used to collect and process information from all relevant channels related to epidemic prevention and control.

[0112] The above describes a big data-based precise trajectory confirmation system for epidemic prevention and control. When an epidemic breaks out, the emergency response time of a region is a crucial factor determining the effectiveness of prevention and control. Figure 3 As shown, its function is to form a nationwide network, so as to more effectively leverage the joint control mechanism for preventing the spread of the virus between cities and regions.

[0113] Due to the large number of people involved, the server cluster can include servers 100, 200, 300, and 400, each of which can be considered a cluster. The number of servers can be increased as needed. Furthermore, it can be localized or remotely configured, using physical connections or cloud networks, etc., which will not be elaborated further. Figure 3 In practice, 500 disaster recovery servers were added to back up all collected and processed data; 600 cache servers were added to solve the problem of high response rate caused by a large number of simultaneous users; 700 search engine clusters and 900 load balancers were added to effectively solve the system's peak access capacity; 1000 firewalls are essential for ensuring the security of system data and information; and 800 centralized data warehouses were added to collect and process all related information from epidemic prevention and control channels.

[0114] like Figure 4 As shown in the figure, this application provides a working method for a big data-based precise trajectory confirmation system for epidemic prevention and control, including:

[0115] S101, the WEB server 11 receives the various sets of electronic information acquired by the smart terminal 30; the electronic information includes the user's time behavior information, spatial behavior information, and device information of the user's smart terminal 30;

[0116] S102, the data server 12 performs data preprocessing on the electronic information of each group;

[0117] S103, the data server 13 processes the pre-processed electronic information of each group to obtain motion trajectory data, and determines whether there is a spatiotemporal behavior with the positive patient based on the motion trajectory data; the motion trajectory data includes time points and location information corresponding to the time points; wherein, spatiotemporal coexistence means that the device information of the user's smart terminal 30 and the device information of the smart terminal 30 of the positive patient stay in the same spatiotemporal grid for more than a preset time.

[0118] S104, the file server 14 is used to generate a motion trajectory file based on the motion trajectory data; the motion trajectory file includes a trajectory file, a relation file, a region file, and a trajectory code overlay file;

[0119] S105, the database server 20 is used to store the data information in the file server 14, the read data server 13 and the write data server 12.

[0120] The technical solution provided in this application enables the immediate identification of potential contacts and key areas, significantly improving the timeliness and accuracy of urban epidemic prevention and control. It creates a "visualized" case activity trajectory for public viewing, allowing citizens to scan a code to automatically compare their activity with confirmed cases or positive patients in terms of time and space. This greatly enhances the convenience, scientific rigor, and accuracy of self-checking for the public. The innovative "trajectory code" (for public self-checking) significantly improves the efficiency and capacity of comprehensive social governance during epidemic prevention and control.

[0121] In summary, this invention provides a big data-based system for precise trajectory confirmation in epidemic prevention and control, along with its operating method. The system includes a smart terminal, a server system, and a database server. The smart terminal and database server are connected to the server system. The server system includes a web server, a file server, a read data server, and a write data server. The web server is connected to the file server, read data server, and write data server. The technical solution provided by this invention can confirm the objective and accurate activity trajectory of cases or positive patients within a specific time period, thereby quickly identifying the precise range of potential contacts and key areas. It innovatively introduces a trajectory code for public self-checking, automatically comparing whether there is temporal or spatial overlap with cases or positive patients, making epidemic prevention and control more timely and accurate, and improving the overall social governance capabilities during the epidemic prevention and control process.

[0122] It is understood that the system embodiments provided above correspond to the device embodiments described above, and the specific details can be referred to each other, which will not be repeated here.

[0123] Those skilled in the art will understand that embodiments of this application can be provided as software systems, devices, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage and optical storage) containing computer-usable program code.

[0124] This application is described with reference to flowchart illustrations and / or block diagrams of systems, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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 a mechanism for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0125] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including an instruction set implemented in a process. Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0126] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0127] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A big data-based system for precise trajectory confirmation in epidemic prevention and control, characterized in that, include: The system comprises a smart terminal, a server system, and a database server, wherein the smart terminal and the database server are respectively connected to the server system; the server system includes a web server, a file server, a read data server, and a write data server; the web server is respectively connected to the file server, the read data server, and the write data server. The web server is used to receive various sets of electronic information acquired by the smart terminal; the electronic information includes the user's time behavior information, spatial behavior information, and device information of the user's smart terminal; The write data server performs data preprocessing on each group of electronic information; The data reading server is used to process the preprocessed electronic information of each group to obtain motion trajectory data, and to determine whether there is a spatiotemporal behavior with the positive patient based on the motion trajectory data; the motion trajectory data includes time points and the location information corresponding to the time points; wherein, spatiotemporal coexistence means that the device information of the user's smart terminal and the device information of the smart terminal of the positive patient stay in the same spatiotemporal grid for more than a preset time. The file server is used to generate motion trajectory files based on motion trajectory data; the motion trajectory files include trajectory files, relation files, region files, and trajectory code overlay files; The database server is used to store data information from the file server, read data server, and write data server; The write data server includes: The data acquisition module is used to acquire users' time behavior information, spatial behavior information, and device information of users' smart terminals; The data cleaning module is used to clean and process abnormal data in the time behavior information, spatial behavior information, and device information of the user's smart terminal. The data tagging module is used to tag the cleaned time behavior information, spatial behavior information, and device information of the user's smart terminal; the tags include dynamic tags and static tags, the static tags are used to mark data that remains static over time, and the dynamic tags are used to represent data that changes over time. The data desensitization module is used to desensitize users' sensitive data information; The receipt of each set of electronic information acquired by the smart terminal includes: The user's smart terminal receives signaling data from macro base stations, micro base stations, pico base stations, and femto base stations, as well as IMEI data, IMEI SV data, ICCD data, MEID data, and S / N data. O-domain data, which includes internet access behavior, signaling, and location; B-domain data, which includes: real-name information, mobile phone number, IMEI, IMSI, and terminal model; M-domain data, which includes: ERP, portal, and project management; V-domain data, which includes the operator's value-added services and base station services.

2. The system according to claim 1, characterized in that, The data read server includes: The log module is used to generate corresponding log data for users in preset time periods, and to collect and classify the log data of all users. The trajectory module is used to generate trajectory data based on time behavior information, spatial behavior information, and device information of the user's smart terminal, and to form a trajectory information database with corresponding log data based on the trajectory data; The region module is used to acquire the user's stationary status information and form a region location information database for the user's corresponding log data; the stationary status information includes the user's dwell time and location information. The overlay module is used to overlay user time behavior information and spatial behavior information to form the user's behavior information on a daily basis.

3. The system according to claim 1, characterized in that, The file server includes: The trajectory file module is used to output the user's daily behavior trajectory file within a preset time period; The relationship file module is used to output a list of people whose temporal and spatial behavioral information overlaps with that of positive patients within a preset time period. The regional file module is used to output the geographical location and place name involved in the positive patient; The trajectory code overlay module is used to overlay the movement trajectories of individuals whose temporal and spatial behavioral information overlaps with that of positive patients within a preset time period, generating a simultaneous spatiotemporal self-check comparison code for epidemic prevention and control; the simultaneous spatiotemporal self-check comparison code for epidemic prevention and control is then identified as the trajectory code.

4. The system according to claim 1, characterized in that, The file server, read data server, and write data server are synchronized and can read and write data to each other.

5. The system according to claim 1, characterized in that, The abnormal data undergoes cleaning processing, including: Missing value cleaning, format content cleaning, logical error cleaning, attribute dependency conflict correction, and non-required data cleaning.

6. The system according to claim 1, characterized in that, The server system is connected to the database server via API and extended interfaces.

7. A COVID-19 prevention and control command center platform, characterized in that, Including the big data-based precise trajectory confirmation system for epidemic prevention and control as described in any one of claims 1 to 6, a server cluster, a search engine cluster, a load balancer, a firewall, and Disaster recovery servers are used to back up all collected and processed data; A caching server is used to cache user data. The data warehouse is used to collect and process information from all relevant channels related to epidemic prevention and control.

8. A working method for a big data-based precise trajectory confirmation system for epidemic prevention and control, characterized in that, The web server receives various sets of electronic information acquired by the smart terminal; the electronic information includes the user's time behavior information, spatial behavior information, and device information of the user's smart terminal; The data server performs data preprocessing on each group of electronic information. The data server processes the pre-processed electronic information to obtain motion trajectory data, and determines whether there is a spatiotemporal co-occurrence with the positive patient based on the motion trajectory data; the motion trajectory data includes time points and the location information corresponding to the time points; wherein, spatiotemporal co-occurrence means that the device information of the user's smart terminal and the device information of the positive patient's smart terminal stay in the same spatiotemporal grid for more than a preset time. The file server is used to generate motion trajectory files based on motion trajectory data; the motion trajectory files include trajectory files, relation files, region files, and trajectory code overlay files; The database server is used to store the data information in the file server, read data server and write data server; The write data server includes: The data acquisition module is used to acquire users' time behavior information, spatial behavior information, and device information of users' smart terminals; The data cleaning module is used to clean and process abnormal data in the time behavior information, spatial behavior information, and device information of the user's smart terminal. The data tagging module is used to tag the cleaned time behavior information, spatial behavior information, and device information of the user's smart terminal; the tags include dynamic tags and static tags, the static tags are used to mark data that remains static over time, and the dynamic tags are used to represent data that changes over time. The data desensitization module is used to desensitize users' sensitive data information; The receipt of each set of electronic information acquired by the smart terminal includes: The user's smart terminal receives signaling data from macro base stations, micro base stations, pico base stations, and femto base stations, as well as IMEI data, IMEI SV data, ICCD data, MEID data, and S / N data. O-domain data, which includes internet access behavior, signaling, and location; B-domain data, which includes: real-name information, mobile phone number, IMEI, IMSI, and terminal model; M-domain data, which includes: ERP, portal, and project management; V-domain data, which includes the operator's value-added services and base station services.

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