CN114286280A - Big data analysis method for checking close contact crowd based on mobile phone signaling - Google Patents
Big data analysis method for checking close contact crowd based on mobile phone signaling Download PDFInfo
- Publication number
- CN114286280A CN114286280A CN202010976119.8A CN202010976119A CN114286280A CN 114286280 A CN114286280 A CN 114286280A CN 202010976119 A CN202010976119 A CN 202010976119A CN 114286280 A CN114286280 A CN 114286280A
- Authority
- CN
- China
- Prior art keywords
- data
- signaling
- close contact
- big data
- mro
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000011664 signaling Effects 0.000 title claims abstract description 51
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000007405 data analysis Methods 0.000 title claims abstract description 23
- 238000004458 analytical method Methods 0.000 claims abstract description 15
- 230000008569 process Effects 0.000 claims abstract description 7
- 230000000007 visual effect Effects 0.000 claims abstract description 6
- 238000012098 association analyses Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 238000010219 correlation analysis Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000013024 troubleshooting Methods 0.000 claims 2
- 238000011841 epidemiological investigation Methods 0.000 abstract description 3
- 230000009471 action Effects 0.000 abstract description 2
- 238000005259 measurement Methods 0.000 description 13
- 238000005516 engineering process Methods 0.000 description 4
- 230000000737 periodic effect Effects 0.000 description 4
- CSRZQMIRAZTJOY-UHFFFAOYSA-N trimethylsilyl iodide Substances C[Si](C)(C)I CSRZQMIRAZTJOY-UHFFFAOYSA-N 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 230000007123 defense Effects 0.000 description 2
- 238000002955 isolation Methods 0.000 description 2
- 230000002265 prevention Effects 0.000 description 2
- 238000007789 sealing Methods 0.000 description 2
- 206010035664 Pneumonia Diseases 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000903 blocking effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 230000002498 deadly effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000003745 diagnosis Methods 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
Images
Landscapes
- Telephonic Communication Services (AREA)
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a big data analysis method for checking closely contacted people based on mobile phone signaling, which comprises the following processes: reading user and signaling data; reading MRO data; carrying out terminal positioning analysis according to the MR data; carrying out big data analysis; a visual presentation of the end user; and (4) judging and early warning of close contact people. The invention provides a big data analysis method and equipment for investigating close contact population based on mobile phone signaling. The invention can realize the longitude positioning of 30 meters of the terminal user and objectively present the action trail of the patient diagnosed in the epidemic situation period, thereby achieving comprehensive and objective epidemiological tracking and further finding all close contacts for epidemiological investigation at the first time.
Description
Technical Field
The invention relates to the technical field of wireless communication and core networks, in particular to a big data analysis method for checking closely contacted people based on mobile phone signaling.
Background
At present, the main blocking measure of the new coronary pneumonia is the traditional strict defense and deadly defense isolation and sealing, although the sealing isolation is effective, the efficiency is relatively low, and the close contact crowd cannot be actively discovered and early warned.
The prior technical scheme mainly adopts two modes to discover and early warn the close contact person:
1. the method mainly comprises the steps that if a confirmed case is found, a professional epidemiological investigator analyzes the confirmed case epidemiologically, the forming track of the confirmed case is cleared up, and the contact personnel are judged whether the close contact person exists or not, so that the close contact person is further isolated. The method is easy to judge the information of the fellow passengers in real-name bills of airplanes, trains, coaches and the like, but a checking funnel exists for the close contacts in subways, buses and the like which do not need real-name taking in short-distance traveling, and if suspected cases and confirmed cases go to a supermarket, a restaurant, a meeting place, a cinema and other dense places of people and contact potential close contacts, the judgment is difficult to be made.
2. The method for positioning the infected person by using the base station positioning method is used for positioning by using the base station signaling, and mainly positions and judges which base station the terminal user is positioned in, so that the specific position of the terminal user is difficult to accurately judge. .
In the method of 'epidemiological investigation' adopted by the prior art scheme, on one hand, the method has lower efficiency, on the other hand, the method has higher service capability requirement on investigators, and a certain requirement is provided for the backtracking capability of the inquirers, so that once the inquirers are maliciously concealed, the efficiency and the effect of epidemic prevention are influenced; on the other hand, the adopted base station positioning method has relatively large positioning error, even the coverage range of the base station can reach about 500 meters, and the judgment and the response of a person who is in close contact can be seriously influenced by the large positioning error. Therefore, the technical personnel in the field provide a big data analysis method for checking the close contact crowd based on the mobile phone signaling, so as to solve the problems in the background technology.
Disclosure of Invention
The invention aims to provide a big data analysis method for checking closely contacted people based on mobile phone signaling, so as to solve the problems in the background technology.
In order to achieve the purpose, the invention provides the following technical scheme:
a big data analysis method for checking close contact crowd based on mobile phone signaling is characterized by comprising the following processes:
(1) reading user and signaling data;
(2) reading MRO data;
(3) carrying out terminal positioning analysis according to the MR data;
(4) carrying out big data analysis;
(5) a visual presentation of the end user;
(6) and (4) judging and early warning of close contact people.
As a further scheme of the invention: the user and signaling data reading comprises: and generating a (KEY, VALUE) KEY VALUE pair (mobile phone number, longitude and latitude) of the mobile phone number and the position according to KEY data such as the signaling data, the HSS data and the like. When the epidemic situation occurs, an emergency corresponding flow is started, and the signaling log and the core network data are read, so that the early warning of sensitive contact people is generated. Read S1-MME interface signaling. When 33 changes such as UE request for attaching to a network, paging, cell updating, S1 handover, UE state release and the like occur, the requests all trigger S1-MME signaling. S1-original XDR data of MME interface signaling contains 58 fields, where the 4 th field is longitude, the 5 th field is latitude (but the longitude and latitude is eNodeB or downlink BBU and RRU location information), the 17 th field is MmeUeS1apId, and the 25 th field is TMSI. The true phone number of the user can be obtained from the HSS by associating the 25 th field with the TMSI in the HSS. Currently, the analysis of the user location by the big data platform of three operators is mainly based on the S1-MME interface data. However, the analysis error of the S1-MME interface signaling is relatively large, and the maximum analysis error can reach 500 meters, so that the purpose of accurate positioning cannot be achieved. The user's trajectory cannot be accurately restored. And the introduction of MR report data can accurately calculate the position of the user.
As a still further scheme of the invention: reading the MRO data includes: and reading main parameters such as TA (timing advance), AOA (antenna arrival angle) and the like of the MRO data.
As a still further scheme of the invention: the terminal positioning analysis according to the MR data comprises the following steps: and calculating the relative accurate position of the UE according to the TA data and the AOA data in the MR report. Longitude difference [ TA × sin (aoa) ]/the circumference of the latitude of the earth at the position of the base station/360 °. Latitude difference [ TA × cos (aoa) ]/circumference of the earth's equator/360 °. The MRO also has an MmeUeS1apId field, and the longitude and latitude of the user can be calculated by performing association analysis on the MmeUeS1apId field in the S1-MME original XDR by the time node through the MmeUeS1apId field, so that the purpose of accurate positioning is achieved.
As a still further scheme of the invention: the big data analysis is mainly carried out by extracting S1-MME signaling data and MRO data from a large amount of log information and carrying out management analysis on the S1-MME signaling data and the MRO data based on an MmeUeS1apId field and a time point. And performing correlation analysis by adopting a Hadoop cluster or a database cluster based on MPP (Massively Parallel processing). Firstly, formatting data of massive S1-MME signaling data and MRO data, and performing extraction (extract), conversion (transform) and loading (load) of the data.
As a still further scheme of the invention: and the visual presentation of the terminal user comprises the step of visually presenting the calculated longitude, latitude and time nodes of the terminal user on a digital map. And S1-the longitude and latitude calculated by associating the MME signaling with the MRO, the user telephone number information inquired by associating the S1-MME signaling with the HSS, and the system time information are visually presented on the geographic information system. The digital map can show the specific position of a user at a certain moment, and the positions of nodes at different times are linked to obtain a relatively accurate track.
As a still further scheme of the invention: the judgment and early warning of the close contact crowd comprises the steps of setting contact parameters, and screening data with the longitude and latitude difference distance smaller than meters in the same time from a database to judge the close contact crowd. And highlighting the pre-judged close contact person on a map, and triggering an early warning and disposal process.
Compared with the prior art, the invention has the beneficial effects that:
1. aiming at the defects of the prior art, the invention provides a big data analysis method and equipment for checking close contact people based on mobile phone signaling.
2. The invention can relatively accurately position the accurate longitude and latitude of the user by combining the signal measurement report sent to the mobile phone by the eNodeB with the signaling data. The time points and the longitude and latitude are attached to the digital map, so that the personal activity track can be accurately restored and presented. The track of the suspected case and the confirmed case is restored as a hot spot on the map, and the close contact persons on the occasions with dense people and trips are found out, so that the prejudgment on the close contact persons is realized, and the help is provided for the prevention and control of the epidemic situation.
3. The invention provides a big data analysis method and equipment for investigating close contact population based on mobile phone signaling. The invention can realize the longitude positioning of 30 meters of the terminal user and objectively present the action trail of the patient diagnosed in the epidemic situation period, thereby achieving comprehensive and objective epidemiological tracking and further finding all close contacts for epidemiological investigation at the first time.
The technical advantages of the present proposal mainly include two aspects:
on one hand, the objectivity of 'epidemiology' tracing is realized by utilizing MR big data geographic presentation and related positioning technology, the situations of malicious report hiding, report missing and the like of patients with confirmed diagnosis are avoided, and the social safety is guaranteed to the maximum extent.
In the second aspect, the technology improves the accuracy of user positioning by utilizing the relevant analysis of parameters such as AOA, TA and the like. The prior art positions to a base station level, the accuracy of the base station level is usually about 500 meters, and the positioning longitude of a terminal user is improved to a 30 meter level through the proposal, so that the aim of accurate positioning is fulfilled.
Drawings
Fig. 1 is a schematic flow chart of a method for analyzing big data for examining a closely contacted person based on a mobile phone signaling.
Fig. 2 is a general structure table of 58 fields of XDR data of S1-MME in a big data analysis method for investigating close-contact population based on mobile phone signaling.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
MRO data defines: measurement is an important function of TD-LTE systems. Statistical analysis of large amounts of measurement data may also be used for discovering network problems. Mr (measurement report) measurement report refers to measurement data sent by eNodeB to UE, and reports the environmental data of UE in fixed format, and these data reports can be used for network evaluation and optimization. MR (measurement report) in a TD-LTE system, the output comprises three parts: MRS, MRE and MRO. Wherein MRE is a measure of event statistics; the MRO/MRS data is based on periodic measurement statistics, where MRS is the cell level average statistics performed on MRO, which is the raw statistics of each periodic measurement event for each user, and what can be used to calculate the accurate location of a user is the MRO file. Currently, the main operators have turned on the periodic measurement function and the Network Management System (NMS) collects the Measurement Reports (MR). The periodic measurement data is stored in an MRO (sample data file representing a measurement report) file, the MRO data volume is large, the information is complete, and the MRO data is the preferred data for positioning the user position.
Referring to fig. 1-2, in an embodiment of the present invention, a big data analysis method for checking a close-contact crowd based on a mobile phone signaling includes the following processes:
(1) reading user and signaling data; and generating a (KEY, VALUE) KEY VALUE pair (mobile phone number, longitude and latitude) of the mobile phone number and the position according to KEY data such as the signaling data, the HSS data and the like. When the epidemic situation occurs, an emergency corresponding flow is started, and the signaling log and the core network data are read, so that the early warning of sensitive contact people is generated. Read S1-MME interface signaling. When 33 changes such as UE request for attaching to a network, paging, cell updating, S1 handover, UE state release and the like occur, the requests all trigger S1-MME signaling. S1-original XDR data of MME interface signaling contains 58 fields, where the 4 th field is longitude, the 5 th field is latitude (but the longitude and latitude is eNodeB or downlink BBU and RRU location information), the 17 th field is MmeUeS1apId, and the 25 th field is TMSI. The true phone number of the user can be obtained from the HSS by associating the 25 th field with the TMSI in the HSS. Currently, the analysis of the user location by the big data platform of three operators is mainly based on the S1-MME interface data. However, the analysis error of the S1-MME interface signaling is relatively large, and the maximum analysis error can reach 500 meters, so that the purpose of accurate positioning cannot be achieved. The user's trajectory cannot be accurately restored. And the introduction of MR report data can accurately calculate the position of the user.
(2) Reading the MRO data mainly includes main parameters such as TA (timing advance), AOA (antenna arrival angle) and the like for reading the MRO data.
(3) Carrying out terminal positioning analysis according to the MR data; and calculating the relative accurate position of the UE according to the TA data and the AOA data in the MR report. Longitude difference [ TA × sin (aoa) ]/the circumference of the latitude of the earth at the position of the base station/360 °. Latitude difference [ TA × cos (aoa) ]/circumference of the earth's equator/360 °. The MRO also has an MmeUeS1apId field, and the longitude and latitude of the user can be calculated by performing association analysis on the MmeUeS1apId field in the S1-MME original XDR by the time node through the MmeUeS1apId field, so that the purpose of accurate positioning is achieved.
(4) And carrying out big data analysis to extract S1-MME signaling data and MRO data from a large amount of log information, and carrying out management analysis on the S1-MME signaling data and the MRO data based on the MmeUeS1apId field and the time point. And performing correlation analysis by adopting a Hadoop cluster or a database cluster based on MPP (Massively Parallel processing). Firstly, formatting data of massive S1-MME signaling data and MRO data, and performing extraction (extract), conversion (transform) and loading (load) of the data.
(5) And the visual presentation of the terminal user mainly comprises the step of visually presenting the longitude, the latitude and the time node of the terminal user obtained by calculation on a digital map. And S1-the longitude and latitude calculated by associating the MME signaling with the MRO, the user telephone number information inquired by associating the S1-MME signaling with the HSS, and the system time information are visually presented on the geographic information system. The specific effect achieved is that the digital map can show the specific position of a certain user at a certain moment, and the positions of nodes at different times are linked to obtain a relatively accurate track
(6) The judgment and early warning of the close contact crowd comprises the steps of setting contact parameters, and screening data with the longitude and latitude difference distance smaller than meters in the same time from a database to judge the close contact crowd. And highlighting the pre-judged close contact person on a map, and triggering an early warning and disposal process.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (5)
1. A big data analysis method for checking close contact crowd based on mobile phone signaling is characterized by comprising the following processes:
(1) reading user and signaling data;
(2) reading MRO data;
(3) carrying out terminal positioning analysis according to the MR data;
(4) carrying out big data analysis;
(5) a visual presentation of the end user;
(6) and (4) judging and early warning of close contact people.
2. The big data analysis method for troubleshooting close-contact people based on handset signaling as claimed in claim 1 wherein performing terminal location analysis based on MR data includes calculating the relative accurate location of the UE based on TA data and AOA data in the MR report. Longitude difference [ TA × sin (aoa) ]/the circumference of the latitude of the earth at the position of the base station/360 °. Latitude difference [ TA × cos (aoa) ]/circumference of the earth's equator/360 °. The MRO also has an MmeUeS1apId field, and the longitude and latitude of the user can be calculated by performing association analysis on the MmeUeS1apId field in the S1-MME original XDR by the time node through the MmeUeS1apId field, so that the purpose of accurate positioning is achieved.
3. The big data analysis method for checking the close contact crowd based on the mobile phone signaling as claimed in claim 1, wherein the big data analysis is performed to extract the S1-MME signaling data and MRO data from a large amount of log information, and the S1-MME signaling data and the MRO data are managed and analyzed based on the MmeUeS1apId field and the time point. And performing correlation analysis by adopting a Hadoop cluster or a database cluster based on MPP (Massively Parallel processing). Firstly, formatting data of massive S1-MME signaling data and MRO data, and performing extraction (extract), conversion (transform) and loading (load) of the data.
4. The big data analysis method for troubleshooting close-contact people according to claim 1 and characterized in that the visual presentation of the end user mainly comprises the step of visually presenting the calculated longitude, latitude and time nodes of the end user on a digital map. And S1-the longitude and latitude calculated by associating the MME signaling with the MRO, the user telephone number information inquired by associating the S1-MME signaling with the HSS, and the system time information are visually presented on the geographic information system. The digital map can show the specific position of a user at a certain moment, and the positions of nodes at different times are linked to obtain a relatively accurate track.
5. The big data analysis method for investigating close contact population based on mobile phone signaling according to claim 1, characterized in that the judgment and early warning of close contact population comprises setting contact parameters, and screening out data with the longitude and latitude difference distance less than meters in the same time from a database to judge as close contact. And highlighting the pre-judged close contact person on a map, and triggering an early warning and disposal process.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010976119.8A CN114286280A (en) | 2020-09-17 | 2020-09-17 | Big data analysis method for checking close contact crowd based on mobile phone signaling |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010976119.8A CN114286280A (en) | 2020-09-17 | 2020-09-17 | Big data analysis method for checking close contact crowd based on mobile phone signaling |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114286280A true CN114286280A (en) | 2022-04-05 |
Family
ID=80867399
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010976119.8A Pending CN114286280A (en) | 2020-09-17 | 2020-09-17 | Big data analysis method for checking close contact crowd based on mobile phone signaling |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114286280A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115662650A (en) * | 2022-09-02 | 2023-01-31 | 深圳市名通科技股份有限公司 | Tight-lock user fishing method based on big data accurate positioning |
-
2020
- 2020-09-17 CN CN202010976119.8A patent/CN114286280A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115662650A (en) * | 2022-09-02 | 2023-01-31 | 深圳市名通科技股份有限公司 | Tight-lock user fishing method based on big data accurate positioning |
| CN115662650B (en) * | 2022-09-02 | 2024-04-26 | 深圳市名通科技股份有限公司 | Close-contact user salvaging method based on big data accurate positioning |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN114189807B (en) | Aggregation crowd tracking system and method based on mobile phone positioning algorithm | |
| US8521191B2 (en) | System and method for determining a location for a mobile device | |
| CN103997757B (en) | Pseudo-base station localization method, equipment and information uploading method, equipment | |
| CN111885502A (en) | Epidemic situation prevention and control early warning and tracing system and method for protecting privacy | |
| CN103648096A (en) | Method for rapidly detecting and positioning illegal base station intrusion | |
| EP1762113A1 (en) | Method of providing alarm service upon movement out of safety zone | |
| CN103796241A (en) | Method for judging and positioning pseudo base station based on reported information of terminal | |
| CN105307119A (en) | Pseudo base station positioning method based on RSSI base station signal estimation | |
| US20140274149A1 (en) | System and Method for Localizing Wireless Devices | |
| CN109831742B (en) | Monitoring method and system based on terminal detection | |
| EP2755433B1 (en) | Mobile communication system | |
| CN104601716A (en) | Earthquake cloud monitoring and early-warning network system based on mobile phones | |
| CN105101399B (en) | Pseudo-base station mobile route acquisition methods, device and pseudo-base station localization method, device | |
| CN105792209B (en) | A kind of method and system detecting pseudo-base station using mobile terminal | |
| CN114286280A (en) | Big data analysis method for checking close contact crowd based on mobile phone signaling | |
| EP3001215A1 (en) | Method for determining the relative position of user equipment in a wireless telecommunication network, a node and a computer program product | |
| CN113645625B (en) | Pseudo base station positioning method, pseudo base station positioning device, electronic equipment and readable medium | |
| CN107682100A (en) | A kind of method and device of specific region intelligent terminal information gathering positioning | |
| Mangla et al. | A tale of three datasets: characterizing mobile broadband access in the US | |
| CN104683982A (en) | False base station determination method based on data mining of signaling system | |
| US9900736B1 (en) | Associating a mobile station with an audio incident | |
| CN113161003B (en) | System and method for tracking and registering activity track and contact of infectious disease patient | |
| CN104900021A (en) | Security and protection processing method and device, security and protection terminal and security and protection processing server | |
| CN106231588A (en) | A kind of mobile network cell identification information correction method | |
| CN110087254A (en) | A kind of identification system merged with communication network and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WD01 | Invention patent application deemed withdrawn after publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20220405 |