CN117221824A - Real-time traffic accurate positioning method based on communication big data - Google Patents
Real-time traffic accurate positioning method based on communication big data Download PDFInfo
- Publication number
- CN117221824A CN117221824A CN202311290017.0A CN202311290017A CN117221824A CN 117221824 A CN117221824 A CN 117221824A CN 202311290017 A CN202311290017 A CN 202311290017A CN 117221824 A CN117221824 A CN 117221824A
- Authority
- CN
- China
- Prior art keywords
- real
- time
- offline
- data
- base station
- 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
- 238000004891 communication Methods 0.000 title claims abstract description 87
- 238000000034 method Methods 0.000 title claims abstract description 51
- 238000009826 distribution Methods 0.000 claims abstract description 119
- 230000011664 signaling Effects 0.000 claims abstract description 40
- 238000012545 processing Methods 0.000 claims description 18
- 238000010606 normalization Methods 0.000 claims description 17
- 238000012360 testing method Methods 0.000 claims description 17
- 230000004807 localization Effects 0.000 claims description 2
- 238000010295 mobile communication Methods 0.000 abstract description 10
- 238000012544 monitoring process Methods 0.000 abstract description 7
- 238000010586 diagram Methods 0.000 description 4
- 238000005516 engineering process Methods 0.000 description 3
- 230000033001 locomotion Effects 0.000 description 3
- 238000005457 optimization Methods 0.000 description 3
- 230000002159 abnormal effect Effects 0.000 description 2
- 230000001133 acceleration Effects 0.000 description 2
- 230000006835 compression Effects 0.000 description 2
- 238000007906 compression Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 230000005484 gravity Effects 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000012423 maintenance Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000009827 uniform distribution Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Landscapes
- Mobile Radio Communication Systems (AREA)
Abstract
The invention discloses a real-time traffic accurate positioning method based on communication big data, and belongs to the technical field of mobile communication. The real-time traffic accurate positioning method based on the communication big data comprises the following steps: acquiring signaling data of a target base station cell to which a target area belongs, wherein the signaling data comprises user identification information, first time and a first base station cell identification of the target base station cell; and determining target offline people flow distribution corresponding to the target base station cell according to the first time and the preset offline people flow distribution model matched with the first base station cell identification. The method solves the technical problem of low accuracy of real-time traffic accurate positioning based on communication big data in the related art, and improves the accuracy of real-time traffic accurate positioning based on communication big data in application scenes such as traffic accurate monitoring, intelligent travel, public safety and the like.
Description
Technical Field
The invention relates to the field of mobile communication, in particular to a real-time traffic accurate positioning method based on communication big data.
Background
With the wide application of mobile communication technology and the gradual maturing of big data analysis, research on real-time traffic accurate positioning of users based on mobile communication networks based on communication big data has become a hotspot problem for obtaining user positions. The real-time traffic flow accurate positioning method based on the communication big data for the mobile state user has wide commercial application value, such as position navigation, path planning, traffic condition monitoring and the like.
At present, a common method for accurately positioning the real-time traffic based on the communication big data mainly comprises the steps of positioning users, determining the number of the users at the positioned positions, and accurately positioning the real-time traffic based on the communication big data. The common positioning methods include hundred-degree positioning and Goldpositioning, and the positioning method initiates a positioning request through a terminal side, and a location service provider performs network operation to give a positioning result.
However, the positioning method mainly depends on the user behavior of the terminal, the position of the user can be obtained only when the user terminal initiates a real-time traffic accurate positioning request based on communication big data in mobile phone application software or other modes, and the accuracy of performing real-time traffic accurate positioning based on communication big data in application scenes such as traffic accurate monitoring, intelligent travel, public safety and the like is low because the data of each mobile phone application software service provider is not open-shared, so that the number of users who can obtain the position information is greatly reduced.
Disclosure of Invention
The invention mainly aims to provide a real-time traffic accurate positioning method based on communication big data, which aims to solve the technical problem of low accuracy of real-time traffic accurate positioning based on communication big data in the related technology.
In order to achieve the above purpose, the present invention provides a method for precisely positioning real-time traffic based on communication big data, the method for precisely positioning real-time traffic based on communication big data includes the following steps:
acquiring signaling data of a target base station cell to which a target area belongs, wherein the signaling data comprises user identification information, first time and a first base station cell identification of the target base station cell;
and determining target offline people flow distribution corresponding to the target base station cell according to the first time and the preset offline people flow distribution model matched with the first base station cell identification.
Optionally, before the step of obtaining the signaling data of the target base station cell to which the target area belongs, the method further includes:
acquiring minimization of drive test data within a preset duration;
determining offline user position data according to the minimization of drive tests data;
and establishing a preset offline people flow distribution model according to the offline user position data.
Optionally, the step of establishing a preset offline people flow distribution model according to the offline user position data includes:
performing rasterization processing on the offline user position data to obtain rasterized data;
carrying out classified statistical treatment on the rasterized data to obtain a single-class model sample set;
and carrying out normalization processing on the single model sample set to obtain a preset offline people flow distribution model.
Optionally, the step of rasterizing the offline user position data to obtain rasterized data includes:
dividing the off-line user position data into each preset round grid based on a preset round grid dividing rule to obtain grid data 。
Optionally, the rasterized data includes a second time; the step of carrying out classified statistical processing on the rasterized data to obtain a single-class model sample set comprises the following steps:
based on the second time, the rasterized data is divided into at least one single-class model sample set by two time dimensions, a date and a time slice.
Optionally, the step of normalizing the single model sample set to obtain a preset offline people flow distribution model includes:
Determining the first sample number corresponding to each preset rounding grid in each single-type model sample set and the second sample number in each single-type model sample set;
based on a preset normalization formula, normalizing the first sample number according to the second sample number and a preset round grid number limit value to obtain the corresponding normalized sample number of each preset round grid;
and according to the normalized sample number, establishing a sample data sequence corresponding to each preset round grid to obtain a preset offline people flow distribution model.
Optionally, the step of determining the real-time traffic of the target area according to the user identification information and the target offline traffic distribution includes:
determining the number of the user identification information as a first number, and randomly generating a random number of the first number based on the total offline traffic in the target offline traffic distribution, wherein the random number is greater than or equal to zero, and the random number is smaller than the total offline traffic;
generating real-time people flow distribution according to the random numbers and the target offline people flow distribution;
And inquiring the real-time traffic of the target area based on the real-time traffic distribution.
Further, in order to achieve the above object, the present invention also provides a real-time traffic accurate positioning device based on communication big data, the real-time traffic accurate positioning device based on communication big data includes:
the information acquisition module is used for acquiring signaling data of a target base station cell to which a target area belongs, wherein the signaling data comprises user identification information, first time and a first base station cell identification of the target base station cell;
the target offline people flow distribution determining module is used for determining target offline people flow distribution corresponding to the target base station cell according to the first time and the preset offline people flow distribution model matched with the first base station cell identification;
and the real-time people flow determining module is used for determining the real-time people flow of the target area according to the user identification information and the target offline people flow distribution.
Further, in order to achieve the above object, the present invention also provides a real-time traffic accurate positioning device based on communication big data, the device comprising: the system comprises a memory, a processor and a real-time traffic accurate positioning program which is stored on the memory and can run on the processor and is based on communication big data, wherein the real-time traffic accurate positioning program based on the communication big data is configured to realize the steps of the method for realizing the real-time traffic accurate positioning based on the communication big data.
Further, in order to achieve the above object, the present invention also provides a computer readable storage medium, on which a real-time traffic accurate positioning program based on communication big data is stored, the real-time traffic accurate positioning program based on communication big data realizing the steps of the method for real-time traffic accurate positioning based on communication big data as described above when being executed by a processor.
The method for precisely positioning the traffic volume in real time based on the big communication data provided by the embodiment of the invention can acquire the signaling data reported by the user terminal as long as the user terminal is in a mobile communication network connection state by acquiring the signaling data of the target base station cell to which the target area belongs, wherein the signaling data comprises the user identification information, the first time and the first base station cell identification of the target base station cell, so that the current total number of users in the target base station cell can be determined; further, according to the first time and the first base station cell identification, a preset offline people flow distribution model is matched, and the target offline people flow distribution corresponding to the target base station cell is determined, so that the offline people flow distribution conditions of all areas including the target area in the target base station cell are determined; furthermore, by determining the real-time traffic of the target area according to the user identification information and the target offline traffic distribution, the target offline traffic distribution can be matched with the current total number of users, so that the real-time traffic of the target area can be determined. Therefore, compared with the mode of acquiring the position of the user when the user terminal initiates the real-time traffic accurate positioning request based on the communication big data in the mobile phone application software or in other modes, the mode of acquiring the signaling data only needs to acquire the signaling data as long as the user terminal in the target base station cell is in the mobile communication network connection state, the current total number of the target base station cell can be determined without depending on the user to execute specific operation, so that the detected current total number of the target base station cell is closer to the actual situation, the accuracy is higher, the real-time traffic accurate positioning error based on the communication big data can be reduced, and the accuracy of real-time traffic accurate positioning based on the communication big data is improved. The method overcomes the technical defect that in the related art, the position of a user can be obtained only when a user terminal initiates a real-time traffic accurate positioning request based on communication big data in mobile phone application software or in other modes, and because the data of each mobile phone application software service provider is not in open sharing, the number of users of which the communication service provider can obtain position information is greatly reduced, thereby the accuracy of performing the real-time traffic accurate positioning based on the communication big data in application scenes such as traffic accurate monitoring, intelligent traveling, public safety and the like is lower, and the accuracy of performing the real-time traffic accurate positioning based on the communication big data in application scenes such as traffic accurate monitoring, intelligent traveling, public safety and the like is improved.
Drawings
FIG. 1 is a schematic diagram of a device structure of a hardware operating environment involved in a method for precisely positioning traffic of people in real time based on communication big data in an embodiment of the present invention;
FIG. 2 is a flow chart of a first embodiment of a method for real-time accurate positioning of traffic based on communication big data according to the present invention;
FIG. 3 is a flow chart of another embodiment of the method of the present invention for accurate real-time traffic localization based on communicating big data;
FIG. 4 is a flow chart of a second embodiment of the method for precisely locating real-time traffic based on communication big data according to the present invention;
fig. 5 is a schematic structural diagram of an embodiment of the real-time traffic accurate positioning device based on communication big data according to the present invention.
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
As shown in fig. 1, fig. 1 is a schematic diagram of a terminal structure of a hardware running environment according to an embodiment of the present invention.
The terminal of the embodiment of the invention can be a PC, or can be a mobile terminal device with a display function, such as a smart phone, a tablet personal computer, an electronic book reader, an MP3 (Moving Picture Experts Group Audio Layer III, dynamic image expert compression standard audio layer 3) player, an MP4 (Moving Picture Experts Group Audio Layer IV, dynamic image expert compression standard audio layer 3) player, a portable computer and the like.
As shown in fig. 1, the terminal may include: a processor 1001, such as a CPU, a network interface 1004, a user interface 1003, a memory 1005, a communication bus 1002. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a Display, an input unit such as a Keyboard (Keyboard), and the optional user interface 1003 may further include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory (non-volatile memory), such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
Optionally, the terminal may also include a camera, an RF (Radio Frequency) circuit, a sensor, an audio circuit, a WiFi module, and so on. Among other sensors, such as light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor that may adjust the brightness of the display screen according to the brightness of ambient light, and a proximity sensor that may turn off the display screen and/or the backlight when the mobile terminal moves to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and the direction when the mobile terminal is stationary, and the mobile terminal can be used for recognizing the gesture of the mobile terminal (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; of course, the mobile terminal may also be configured with other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, an infrared sensor, and the like, which are not described herein.
It will be appreciated by those skilled in the art that the terminal structure shown in fig. 1 is not limiting of the terminal and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a real-time traffic accurate positioning program based on communication big data may be included in a memory 1005 as a kind of computer storage medium.
In the terminal shown in fig. 1, the network interface 1004 is mainly used for connecting to a background server and performing data communication with the background server; the user interface 1003 is mainly used for connecting a client (user side) and performing data communication with the client; and the processor 1001 may be configured to call a real-time traffic accurate positioning program based on communication big data stored in the memory 1005, and perform the following operations:
acquiring signaling data of a target base station cell to which a target area belongs, wherein the signaling data comprises user identification information, first time and a first base station cell identification of the target base station cell;
and determining target offline people flow distribution corresponding to the target base station cell according to the first time and the preset offline people flow distribution model matched with the first base station cell identification.
Optionally, the processor 1001 may call the real-time traffic accurate positioning program based on the communication big data stored in the memory 1005, and further perform the following operations:
acquiring signaling data of a target base station cell to which a target area belongs, wherein the signaling data comprises user identification information, first time and a first base station cell identification of the target base station cell;
according to the first time and the first base station cell identification, matching a preset offline people flow distribution model, and determining target offline people flow distribution corresponding to the target base station cell;
and determining the real-time traffic of the target area according to the user identification information and the target offline traffic distribution.
Optionally, the processor 1001 may invoke the real-time traffic accurate positioning program based on the communication big data stored in the memory 1005, and before the operation of acquiring the signaling data of the target base station cell to which the target area belongs, further perform the following operations:
acquiring minimization of drive test data within a preset duration;
determining offline user position data according to the minimization of drive tests data;
and establishing a preset offline people flow distribution model according to the offline user position data.
Optionally, the processor 1001 may call the real-time traffic accurate positioning program based on the communication big data stored in the memory 1005, and further perform the following operations:
performing rasterization processing on the offline user position data to obtain rasterized data;
carrying out classified statistical treatment on the rasterized data to obtain a single-class model sample set;
and carrying out normalization processing on the single model sample set to obtain a preset offline people flow distribution model.
Optionally, the processor 1001 may call the real-time traffic accurate positioning program based on the communication big data stored in the memory 1005, and further perform the following operations:
dividing the off-line user position data into each preset round grid based on a preset round grid dividing rule to obtain grid data 。
Optionally, the rasterized data includes a second time; the processor 1001 may call the real-time traffic accurate positioning program based on the communication big data stored in the memory 1005, and further perform the following operations:
based on the second time, the rasterized data is divided into at least one single-class model sample set by two time dimensions, a date and a time slice.
Optionally, the processor 1001 may call the real-time traffic accurate positioning program based on the communication big data stored in the memory 1005, and further perform the following operations:
determining the first sample number corresponding to each preset rounding grid in each single-type model sample set and the second sample number in each single-type model sample set;
based on a preset normalization formula, normalizing the first sample number according to the second sample number and a preset round grid number limit value to obtain the corresponding normalized sample number of each preset round grid;
and according to the normalized sample number, establishing a sample data sequence corresponding to each preset round grid to obtain a preset offline people flow distribution model.
Optionally, the processor 1001 may call the real-time traffic accurate positioning program based on the communication big data stored in the memory 1005, and further perform the following operations:
determining the number of the user identification information as a first number, and randomly generating a random number of the first number based on the total offline traffic in the target offline traffic distribution, wherein the random number is greater than or equal to zero, and the random number is smaller than the total offline traffic;
Generating real-time people flow distribution according to the random numbers and the target offline people flow distribution;
and inquiring the real-time traffic of the target area based on the real-time traffic distribution.
Referring to fig. 2, a first embodiment of the present invention provides a method for precisely positioning real-time traffic based on communication big data, where the method for precisely positioning real-time traffic based on communication big data includes:
step S10, obtaining signaling data of a target base station cell to which a target area belongs, wherein the signaling data comprises user identification information, first time and a first base station cell identification of the target base station cell;
the execution subject of the method of the embodiment can be a real-time traffic accurate positioning device based on communication big data, or can be a real-time traffic accurate positioning terminal device or a server based on communication big data.
In this embodiment, it should be noted that, the signaling data refers to XDR (X Data Record) signaling data, and the signaling data reported by the ue may be obtained as long as the ue is in a mobile communication network connection state. The signaling data may include user identification information, a first time, a first base station cell identification of the target base station cell, and other field information, where the user identification information may be, but is not limited to, a mobile phone number, and the first base station cell identification may be, but is not limited to, a global cell identification code (Cell Global Identifier, CGI), that is, a cell (or a base station, etc.) code where the first user number is located, and the first time may refer to a latest signaling service occurrence time on the user terminal.
One base station cell usually comprises a plurality of areas, and only the total number of the corresponding base station cell can be determined by acquiring signaling data, but the specific area where the people are located cannot be determined, so that the current real-time people flow in each area can be estimated by combining an offline people flow distribution model.
After determining a target area to be precisely positioned based on the real-time traffic of communication big data, the method may further lack a target base station cell to which the target area belongs, further collect and analyze signaling data of a current whole network user of the target base station cell in real time, and extract information such as user identification information, first time, first base station cell identification of the target base station cell, and the like.
Optionally, referring to fig. 3, the step of acquiring signaling data of the target base station cell to which the target area belongs further includes:
step A10, obtaining minimization of drive tests data in a preset duration;
in this embodiment, it should be noted that, the minimization of drive test data (Minimization of drive tests) may be referred to as MDT data for short, and mainly refers to acquiring relevant parameters required for network optimization through a measurement report reported by a user terminal, so as to reduce drive test overhead and shorten an optimization period, thereby reducing network optimization and maintenance costs of a mobile communication carrier, where the minimization of drive test data may collect measurement information (such as a narrow road, a forest, a private place, etc.) of an entire area that cannot be performed by conventional drive test. The preset duration may be set according to practical situations, which is not limited in this embodiment.
For example, a range to be detected may be determined according to actual requirements, where the range to be detected may include one or more base station cells, so as to collect the minimization of drive test data of all users in the range to be detected within a preset duration.
Step A20, determining offline user position data according to the minimization of drive tests data;
for example, offline user location data may be extracted from the minimization of drive tests data.
In one implementation, due to the problem of partial MDT data caused by the high-speed movement of the terminal and the delay of data uploading, after the MDT data of all users in each base station cell are respectively acquired, the MDT data with the problem can be removed by using a 3-sigma criterion or other algorithms, so as to obtain more accurate offline user position data.
And step A30, establishing a preset offline people flow distribution model according to the offline user position data.
The data modeling of the key scene area can be performed by associating the user traffic usage habit in soft mining with the user dimension traffic data in hard mining according to the offline user position data, or by fully utilizing the offline user position data and combining time characteristics, distinguishing base station cells, date classification and time slicing, performing fine distinction on an offline model, and performing traffic distribution model modeling by taking the base station cells, the date classification and the time slicing as dimensions.
In this embodiment, the present invention makes full use of the offline user location data to establish the preset offline people flow distribution model. Because the accuracy of the MDT data is high, after the MDT data is acquired, problematic MDT data is screened to obtain the offline user position data, the accuracy of the offline user position data is further improved, and the accuracy of the preset offline people flow distribution model can be effectively ensured.
Step S20, determining target offline people flow distribution corresponding to the target base station cell according to the first time and the preset offline people flow distribution model matched with the first base station cell identification;
in this embodiment, it should be noted that the offline traffic distribution model is a traffic distribution model established by performing traffic statistics on a preset statistical time and a preset location area based on historical data. The distribution of the people flow generally has certain regularity, and the regularity of the people flow distribution is greatly influenced by two factors of time and place, for example, the number of people in weekends and holiday scenic spots can be obviously higher than the number of people in workday scenic spots, the number of people in subway stations in the early and late peak periods can be obviously higher than the number of people in subway stations in non-early and late peak periods, and the like, and the offline people flow distribution model is used for finding the regularity of the people flow distribution. Therefore, according to the first time and the first base station cell identifier, a target offline people flow distribution corresponding to the target base station cell can be determined, and the target offline people flow distribution can represent people flow distribution rules of each area in the target base station cell.
The target offline people flow distribution matched with the first time and the first base station cell identifier is determined, namely the target offline people flow distribution corresponding to the target base station cell.
And step S30, determining the real-time people flow of the target area according to the user identification information and the target offline people flow distribution.
The current total number of people in the target base station cell can be determined according to the total number of the user identification information which can be acquired by the target base station cell, and then the target offline people flow distribution is collected, and the real-time people flow distribution of the current total number of people in the target base station cell in each area of the target base station cell can be estimated and determined, so that the real-time people flow of the target area can be determined.
Optionally, the step of determining the real-time traffic of the target area according to the user identification information and the target offline traffic distribution includes:
step S31, determining the number of the user identification information as a first number, and randomly generating a random number of the first number based on the total number of the offline people flow in the target offline people flow distribution, wherein the random number is greater than or equal to zero, and the random number is smaller than the total number of the offline people flow;
Step S32, generating real-time people flow distribution according to the random numbers and the target offline people flow distribution;
in this embodiment, it should be noted that the preset offline traffic distribution model may be a sample data sequence, for example, a base station cell identifier q1, a working day, 7:00-8:00, a round grid identifier 11, and the sample data sequence 11 is {0,1, 2-19, 20}, which indicates that the base station cell identifier q1 includes 4 areas, and the number of people in the time period of 7:00-8:00 of the working day in the 4 areas is 1 person, 18 person, and 1 person, respectively.
Illustratively, determining the number of the user identification information as a first number, taking the total number of offline people traffic in the target offline people traffic distribution as an upper limit value, randomly generating a random number smaller than the upper limit value of the first number, wherein the random number is larger than or equal to zero, and the random number is smaller than the total number of offline people traffic; and further, according to the numerical value of each random number and the numerical value range corresponding to each region in the offline people flow distribution model, determining the region corresponding to each random number, further counting the number of random numbers in each region, and generating the real-time people flow distribution corresponding to the target base station cell according to the number of random numbers in each region.
For example, if the sample data sequence 11 of the base station cell identifier q1 is {0,1, 2-19, 20} and the total number of people traffic is 21, then random numbers may be randomly generated in 0-20, possibly 0,1, 12, 20, etc., if the first number is 25, 25 random numbers are randomly generated, and according to the values of the 25 random numbers and the value range corresponding to each region in the sample data sequence, the regions corresponding to the 25 random numbers are determined, for example, 1, 21, 20, and 21 random numbers in the range of 2-19 are generated, so that according to the number of random numbers corresponding to each of the 4 regions, the distribution of random numbers of the 4 regions can be determined, that is, the real-time people traffic distribution corresponding to the base station cell identifier q1 is {0, 1-2, 3-24, 25}.
And step S33, inquiring the real-time traffic of the target area based on the real-time traffic distribution.
Illustratively, the real-time traffic distribution is searched based on the information such as the position or the area identification of the target area, so that the real-time traffic of the target area can be determined.
For example, the real-time traffic distribution corresponding to the base station cell identifier q1 is {0, 1-2, 3-24, 25}, the real-time traffic distribution corresponding to the base station cell identifier q1 is arranged according to the sequence of the areas A1, A2, A3, A4, and the target area is A3, and the real-time traffic of A3 is 21.
In this embodiment, the number of the user identification information may be expressed as the total number of people who acquire the area, the real-time people flow is detected according to the real-time total number and a preset people flow distribution model established by adopting historical data, and random data is randomly extracted from the target data sequence, so that uniform distribution of users can be ensured, a real-time positioning result is infinitely close to a user distribution trend of an offline actual scene, and accuracy of detecting the people flow is improved.
The method for precisely positioning the traffic volume in real time based on the big communication data provided by the embodiment of the invention can acquire the signaling data reported by the user terminal as long as the user terminal is in a mobile communication network connection state by acquiring the signaling data of the target base station cell to which the target area belongs, wherein the signaling data comprises the user identification information, the first time and the first base station cell identification of the target base station cell, so that the current total number of users in the target base station cell can be determined; further, according to the first time and the first base station cell identification, a preset offline people flow distribution model is matched, and the target offline people flow distribution corresponding to the target base station cell is determined, so that the offline people flow distribution conditions of all areas including the target area in the target base station cell are determined; furthermore, by determining the real-time traffic of the target area according to the user identification information and the target offline traffic distribution, the target offline traffic distribution can be matched with the current total number of users, so that the real-time traffic of the target area can be determined. Therefore, compared with the mode of acquiring the position of the user when the user terminal initiates the real-time traffic accurate positioning request based on the communication big data in the mobile phone application software or in other modes, the mode of acquiring the signaling data only needs to acquire the signaling data as long as the user terminal in the target base station cell is in the mobile communication network connection state, the current total number of the target base station cell can be determined without depending on the user to execute specific operation, so that the detected current total number of the target base station cell is closer to the actual situation, the accuracy is higher, the real-time traffic accurate positioning error based on the communication big data can be reduced, and the accuracy of real-time traffic accurate positioning based on the communication big data is improved. The method overcomes the technical defect that in the related art, the position of a user can be obtained only when a user terminal initiates a real-time traffic accurate positioning request based on communication big data in mobile phone application software or in other modes, and because the data of each mobile phone application software service provider is not in open sharing, the number of users of which the communication service provider can obtain position information is greatly reduced, thereby the accuracy of performing the real-time traffic accurate positioning based on the communication big data in application scenes such as traffic accurate monitoring, intelligent traveling, public safety and the like is lower, and the accuracy of performing the real-time traffic accurate positioning based on the communication big data in application scenes such as traffic accurate monitoring, intelligent traveling, public safety and the like is improved.
Further, referring to fig. 4, in another embodiment of the present application, the same or similar contents as those of the above embodiment may be referred to the above description, and will not be repeated. On the basis, the step of establishing a preset offline people flow distribution model according to the offline user position data comprises the following steps:
step B10, rasterizing the off-line user position data to obtain rasterized data;
for example, the input range to be detected may be divided into at least one grid according to a preset shape and size, the divided grid may be a rectangular grid, a triangular grid, or the like, and the longitude and latitude in the offline user position data are matched with the grid, so that the rasterized data may be obtained.
In an embodiment, in order to reduce errors, rounding processing may be performed on each grid to obtain at least one rounded grid, and longitude and latitude in the offline user position data may be matched with each preset rounded grid to generate rounded grid position data corresponding to each preset rounded grid, so as to obtain the rasterized data.
Optionally, the step of rasterizing the offline user position data to obtain rasterized data includes:
And dividing the offline user position data into each preset round grid based on a preset round grid dividing rule to obtain grid data.
In this embodiment, it should be noted that the offline user location data includes a second time, a second base station cell identifier, an offline user longitude, and an offline user latitude. The preset rounding grid dividing rule refers to dividing the longitude and latitude range in a certain area into at least one grid according to the designated grid size, and rounding the longitude and latitude in each grid area into uniform rounding grid longitude and latitude for reducing errors and facilitating description. The grid size of the rasterization process may be set according to practical situations, and this implementation is not limited thereto. In one embodiment, the grid size is set to 50 meters by 50 meters.
For example, the offline user position data may be divided into each grid area according to the offline user longitude and the offline user latitude, and the offline user longitude and the offline user latitude in each grid area are rounded into a uniform rounded grid longitude and rounded grid latitude, so as to obtain the rasterized data. The rounded grid longitude and the rounded grid latitude corresponding to each grid may be set according to practical situations, which is not limited in this embodiment. In one embodiment, the latitude and longitude corresponding to the top left corner vertex of each grid may be set. For example, offline user location data 1 includes: second time 1, second base station cell identity 1, offline user longitude 1 (116 ° 24'50 "E), offline user latitude 1 (37 ° 52' 48" N); the preset grid mark 1 is (116 DEG 0'0' -117 DEG 30 '50' E,37 DEG 0'0' N-38 DEG 30 '50' N). It can be seen that the offline user position data 1 is within the preset grid identifier 1, so that the offline user longitude 1 can be rounded to 116 ° 0'0 "E, i.e. to round the grid longitude 1, and the offline user latitude 1 can be rounded to 38 ° 30' 50" N, i.e. to round the grid latitude 1, so that the rasterized data 1 can be obtained including: a second time 1, a second base station cell identity 1, an offline user longitude 1, an offline user latitude 1, a round grid longitude 1, and a round grid latitude 1.
Step B20, carrying out classified statistical treatment on the rasterized data to obtain a single-class model sample set;
the rasterized data may be classified according to characteristics of time, base station cells, and the like, so as to generate a single model sample set of each base station cell in each time period, and then the number of samples in the single model sample set, the number of grids, and the number of samples in each grid may be respectively summarized and counted, so as to form the single model sample set.
Optionally, the rasterized data includes a second time; the step of carrying out classified statistical processing on the rasterized data to obtain a single-class model sample set comprises the following steps:
dividing the rasterized data into at least one single-class model sample set according to two time dimensions of date and time slicing based on the second time;
in this embodiment, it should be noted that the date dimension may be divided into a weekday, a weekend holiday, a legal holiday, and the like. Wherein, the working day is from monday to friday on a normal duty, the weekend rest day is Saturday and sunday, and the legal holidays are a primordial denier section, a spring section, a Qing Ming section, a labor section, a midautumn section, a national celebration section and the like. The time slice dimension refers to setting a time slice according to 24 hours of the whole day, and a certain time interval is set as a time slice, and in one implementation manner, each hour may be set as a time slice, for example: 7:00-8:00, 8:00-9:00, etc.
For example, the rasterized data may be divided into a single-class model sample set of time slices on weekdays, time slices on weekends, and time slices on legal holidays.
And step B30, carrying out normalization processing on the single model sample set to obtain a preset offline people flow distribution model.
The normalization processing method includes that, in the embodiment, a normalization processing is performed on a single model sample set according to different actual requirements to obtain a preset offline people flow distribution model, a specific mode of the normalization processing can be determined according to actual conditions, and the embodiment is not limited to the specific mode, for example, if abnormal values and more noise exist in data, a standard normalization formula can be used to avoid influences of the abnormal values and the extreme values. For another example, if a range of output data is required, the data may be normalized to a specified range by calculating weights.
Optionally, the step of normalizing the single model sample set to obtain a preset offline people flow distribution model includes:
step B31, determining the first sample number corresponding to each preset rounding grid in each single-type model sample set and the second sample number in each single-type model sample set;
In this embodiment, it should be noted that the first sample number refers to the total number of samples in each preset round grid, and the second sample number refers to the total number of all samples in the single-class model statistical sample.
Illustratively, a total number of samples within each region of each preset rounded grid in each of the single-class model sample sets and a total number of all samples in each of the single-class model sample sets are determined.
Step B32, based on a preset normalization formula, carrying out normalization processing on the first sample numbers according to the second sample numbers and preset rounding grid number limiting values to obtain normalized sample numbers corresponding to the preset rounding grids;
for example, the first sample number, the second sample number and the preset grid number limit may be substituted into the preset normalization formula to obtain the corresponding normalized sample number of each preset rounding grid. The preset normalization formula may be set as:
and B33, establishing respective corresponding sample data sequences of each preset round grid according to the normalized sample number and preset conditions to obtain a preset offline people flow distribution model.
In this embodiment, it should be noted that the preset condition means that the sample data sequence is a subsequence of a preset data sequence, and each normalized sample number is a data number in each sample data sequence.
The sample data sequence corresponding to each preset round grid is established according to the preset conditions and the normalized sample number, so that a preset offline people flow distribution model is obtained. For example, if the preset data sequence is (0, 1, 2-58, 59), the normalized sample number 1 corresponding to the rounding grid identifier 1 is 20, the normalized sample number 2 corresponding to the rounding grid identifier 2 is 40, the sample data sequence 1 corresponding to the rounding grid 1 is (0, 1, 2-18, 19), the sample data sequence 2 corresponding to the rounding grid 2 is (20, 21, 22-58, 59), and the preset offline people flow distribution model is two arrays of the sample data sequence 1 and the sample data sequence 2.
In the embodiment, through rasterization processing, unified and standardized processing can be performed on the offline user position, and the availability and accuracy of the offline user position data are ensured, so that the accuracy of the offline people flow distribution model is ensured.
Further, the embodiment of the invention also provides a real-time traffic accurate positioning device based on communication big data, referring to fig. 5, fig. 5 is a schematic structural diagram of the real-time traffic accurate positioning device based on communication big data, where the real-time traffic accurate positioning device based on communication big data includes:
an information obtaining module 10, configured to obtain signaling data of a target base station cell to which a target area belongs, where the signaling data includes user identification information, a first time, and a first base station cell identifier of the target base station cell;
the target offline people flow distribution determining module 20 is configured to determine a target offline people flow distribution corresponding to the target base station cell according to the first time and the first base station cell identifier matching a preset offline people flow distribution model;
and the real-time people flow determining module 30 is configured to determine the real-time people flow of the target area according to the user identification information and the target offline people flow distribution.
Optionally, before the operation of obtaining the signaling data of the target base station cell to which the target area belongs, the real-time traffic accurate positioning device based on the communication big data further includes an offline traffic distribution model building module, where the offline traffic distribution model building module is configured to:
Acquiring minimization of drive test data within a preset duration;
determining offline user position data according to the minimization of drive tests data;
and establishing a preset offline people flow distribution model according to the offline user position data.
Optionally, the offline people flow distribution model building module is further configured to:
performing rasterization processing on the offline user position data to obtain rasterized data;
carrying out classified statistical treatment on the rasterized data to obtain a single-class model sample set;
and carrying out normalization processing on the single model sample set to obtain a preset offline people flow distribution model.
Optionally, the offline people flow distribution model building module is further configured to:
and dividing the offline user position data into each preset round grid based on a preset round grid dividing rule to obtain grid data.
Optionally, the offline people flow distribution model building module is further configured to:
based on the second time, the rasterized data is divided into at least one single-class model sample set by two time dimensions, a date and a time slice.
Optionally, the offline people flow distribution model building module is further configured to:
Determining the first sample number corresponding to each preset rounding grid in each single-type model sample set and the second sample number in each single-type model sample set;
based on a preset normalization formula, normalizing the first sample number according to the second sample number and a preset round grid number limit value to obtain the corresponding normalized sample number of each preset round grid;
and according to the normalized sample number, establishing a sample data sequence corresponding to each preset round grid to obtain a preset offline people flow distribution model.
Optionally, the real-time traffic determination module 30 is further configured to:
determining the number of the user identification information as a first number, and randomly generating a random number of the first number based on the total offline traffic in the target offline traffic distribution, wherein the random number is greater than or equal to zero, and the random number is smaller than the total offline traffic;
generating real-time people flow distribution according to the random numbers and the target offline people flow distribution;
and inquiring the real-time traffic of the target area based on the real-time traffic distribution.
The real-time traffic accurate positioning device based on the communication big data provided by the invention solves the technical problem of lower accuracy of real-time traffic accurate positioning based on the communication big data in the related technology by adopting the method for accurately positioning the real-time traffic based on the communication big data in the embodiment. Compared with the prior art, the real-time traffic accurate positioning device based on the communication big data has the same beneficial effects as the real-time traffic accurate positioning method based on the communication big data provided by the embodiment, and other technical features in the real-time traffic accurate positioning device based on the communication big data are the same as the features disclosed by the method of the embodiment, and are not repeated herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (10)
1. The real-time traffic accurate positioning method based on the communication big data is characterized by comprising the following steps of:
acquiring signaling data of a target base station cell to which a target area belongs, wherein the signaling data comprises user identification information, first time and a first base station cell identification of the target base station cell;
according to the first time and the first base station cell identification, matching a preset offline people flow distribution model, and determining target offline people flow distribution corresponding to the target base station cell;
and determining the real-time traffic of the target area according to the user identification information and the target offline traffic distribution.
2. The method for precisely positioning traffic in real time based on big data according to claim 1, wherein the step of obtaining signaling data of the target base station cell to which the target area belongs further comprises:
acquiring minimization of drive test data within a preset duration;
determining offline user position data according to the minimization of drive tests data;
and establishing a preset offline people flow distribution model according to the offline user position data.
3. The method for precisely positioning traffic in real time based on big data according to claim 2, wherein the step of establishing a preset offline traffic distribution model according to the offline user position data comprises:
performing rasterization processing on the offline user position data to obtain rasterized data;
carrying out classified statistical treatment on the rasterized data to obtain a single-class model sample set;
and carrying out normalization processing on the single model sample set to obtain a preset offline people flow distribution model.
4. The method for precisely positioning the traffic volume in real time based on the big data of communication according to claim 3, wherein the step of rasterizing the off-line user position data to obtain rasterized data comprises the steps of:
and dividing the offline user position data into each preset round grid based on a preset round grid dividing rule to obtain grid data.
5. The method for accurate real-time traffic localization based on big data of claim 3, wherein the rasterized data comprises a second time; the step of carrying out classified statistical processing on the rasterized data to obtain a single-class model sample set comprises the following steps:
Based on the second time, the rasterized data is divided into at least one single-class model sample set by two time dimensions, a date and a time slice.
6. The method for precisely positioning the traffic volume in real time based on the big communication data according to claim 3, wherein the step of normalizing the single model sample set to obtain a preset offline traffic volume distribution model comprises the steps of:
determining the first sample number corresponding to each preset rounding grid in each single-type model sample set and the second sample number in each single-type model sample set;
based on a preset normalization formula, normalizing the first sample number according to the second sample number and a preset round grid number limit value to obtain the corresponding normalized sample number of each preset round grid;
and according to the normalized sample number, establishing a sample data sequence corresponding to each preset round grid to obtain a preset offline people flow distribution model.
7. The method for precisely locating real-time traffic based on big data according to claim 1, wherein the step of determining the real-time traffic of the target area according to the user identification information and the target offline traffic distribution comprises:
Determining the number of the user identification information as a first number, and randomly generating a random number of the first number based on the total offline traffic in the target offline traffic distribution, wherein the random number is greater than or equal to zero, and the random number is smaller than the total offline traffic;
generating real-time people flow distribution according to the random numbers and the target offline people flow distribution;
and inquiring the real-time traffic of the target area based on the real-time traffic distribution.
8. Real-time traffic accurate positioner based on communication big data, its characterized in that, real-time traffic accurate positioner based on communication big data includes:
the information acquisition module is used for acquiring signaling data of a target base station cell to which a target area belongs, wherein the signaling data comprises user identification information, first time and a first base station cell identification of the target base station cell;
the target offline people flow distribution determining module is used for determining target offline people flow distribution corresponding to the target base station cell according to the first time and the preset offline people flow distribution model matched with the first base station cell identification;
and the real-time people flow determining module is used for determining the real-time people flow of the target area according to the user identification information and the target offline people flow distribution.
9. Real-time traffic accurate positioning device based on communication big data, characterized in that it comprises: a memory, a processor and a communication big data based real-time traffic accurate positioning program stored on the memory and executable on the processor, the communication big data based real-time traffic accurate positioning program being configured to implement the steps of the communication big data based real-time traffic accurate positioning method according to any of claims 1 to 7.
10. A computer-readable storage medium, wherein the computer-readable storage medium has stored thereon a real-time traffic-volume accurate positioning program based on communication big data, which when executed by a processor, implements the steps of the method for real-time traffic-volume accurate positioning based on communication big data as claimed in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311290017.0A CN117221824A (en) | 2023-09-28 | 2023-09-28 | Real-time traffic accurate positioning method based on communication big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311290017.0A CN117221824A (en) | 2023-09-28 | 2023-09-28 | Real-time traffic accurate positioning method based on communication big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117221824A true CN117221824A (en) | 2023-12-12 |
Family
ID=89035297
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311290017.0A Pending CN117221824A (en) | 2023-09-28 | 2023-09-28 | Real-time traffic accurate positioning method based on communication big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117221824A (en) |
-
2023
- 2023-09-28 CN CN202311290017.0A patent/CN117221824A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR101982159B1 (en) | A method of measuring floating population using information on floating population divided by category | |
| US9888361B2 (en) | System and method for determining characteristics of a plurality of people at an event based on mobile phone tracking and mobile data transmission | |
| EP2712488B1 (en) | Prediction of indoor level and location using a three stage process | |
| US11647074B2 (en) | Methods and systems for multi-access edge compute node selection based on a geolocation-indexed performance dataset | |
| US20170222901A1 (en) | Network selection using current and historical measurements | |
| CN108009688B (en) | Aggregation event prediction method, device and equipment | |
| US10567922B2 (en) | Wireless network service assessment | |
| US8830909B1 (en) | Methods and systems to determine user relationships, events and spaces using wireless fingerprints | |
| CN114173356B (en) | Network quality detection method, device, equipment and storage medium | |
| JP4032125B1 (en) | Mobile object position estimation system and mobile object position estimation method | |
| CN103068035A (en) | Wireless network location method, device and system | |
| Higuchi et al. | Mobile devices as an infrastructure: A survey of opportunistic sensing technology | |
| CN110727752B (en) | Position fingerprint database processing method, device and computer readable storage medium | |
| CN109688535A (en) | Determine method, apparatus, electronic equipment and the medium in hotspot geographical location | |
| JP5986028B2 (en) | Apparatus, program, and method for estimating short-time stay location of user having portable terminal | |
| CN113365306A (en) | Network analysis method and device, storage medium and computer system | |
| CN108574927B (en) | Mobile terminal positioning method and device | |
| Hung et al. | Model-driven traffic data acquisition in vehicular sensor networks | |
| CN111314177B (en) | Work and rest time period identification method based on wireless signals and related device | |
| CN117221824A (en) | Real-time traffic accurate positioning method based on communication big data | |
| CN116227929A (en) | Communication data analysis method, device, equipment and storage medium | |
| CN108540926B (en) | Wireless signal fingerprint construction method and device | |
| CN112601177B (en) | Method, system, server and storage medium for guiding people flow in public area | |
| Cho et al. | PlaceWalker: An energy-efficient place logging method that considers kinematics of normal human walking | |
| US9736704B1 (en) | Providing an overlay network using multiple underlying networks |
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 |