CN111200784B - Method and system for realizing indoor accurate positioning through machine learning - Google Patents
Method and system for realizing indoor accurate positioning through machine learning Download PDFInfo
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- CN111200784B CN111200784B CN202010225666.2A CN202010225666A CN111200784B CN 111200784 B CN111200784 B CN 111200784B CN 202010225666 A CN202010225666 A CN 202010225666A CN 111200784 B CN111200784 B CN 111200784B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S11/00—Systems for determining distance or velocity not using reflection or reradiation
- G01S11/02—Systems for determining distance or velocity not using reflection or reradiation using radio waves
- G01S11/06—Systems for determining distance or velocity not using reflection or reradiation using radio waves using intensity measurements
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/30—Services specially adapted for particular environments, situations or purposes
- H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
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- 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
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Abstract
The invention discloses a method and a system for realizing indoor accurate positioning through machine learning, which comprises the following steps: a wireless signal intensity value of a mobile terminal target is obtained by using a WLAN wireless router; secondly, sending the collected signal intensity value to a specified calculation center server through a router; thirdly, calculating signal intensity values acquired by different routers through an algorithm to obtain position information in the region; the first purpose of the invention is to provide a method for realizing indoor positioning by analyzing the signal intensity between a WLAN wireless network router and an Internet of things terminal, and being capable of identifying the height of a target, thereby realizing three-dimensional positioning of the target; the invention realizes accurate indoor positioning without expensive and complicated external equipment; the calculation of the position is performed at the router side rather than at the device side, so that more efficient algorithms can be employed without considering the computational power of the front-end device or the support power of the battery.
Description
Technical Field
The invention relates to the technical field of intelligent terminals, in particular to a method and a system for realizing indoor accurate positioning through machine learning.
Background
With the development of the ubiquitous internet of things technology, more and more intelligent internet of things terminals are provided. People have a sharp increase in application requirements based on location awareness, and because the existing technologies based on GPS and Beidou positioning are applied to outdoor and open zones, and most of the production and life of people are indoors, the indoor positioning requirements are brought forward. The application requirements of indoor positioning are diversified along with the rapid development of technologies, and the significance of positioning is not limited to positioning of people, including articles, equipment and the like. The indoor positioning capability is rapid and accurate, the operation modes of industries such as retail, manufacturing, logistics, first aid and the like can be greatly changed, and the ideal of interconnection of everything is really realized.
However, the conventional GPS or base station positioning has a problem in that signals of the GPS are completely shielded and cannot be used indoors; and the error of positioning based on the base station often reaches 500 meters or even 1000 meters, and the precision requirement of indoor positioning can not be met completely, so that a method and a system for realizing indoor precise positioning through machine learning are provided for solving the problems provided in the background technology.
Disclosure of Invention
The purpose of the invention can be realized by the following technical scheme: a method and a system for realizing indoor accurate positioning through machine learning comprise the following steps,
firstly, a wireless signal intensity value of a mobile terminal target is obtained by using a WLAN wireless router;
the method comprises the following steps of using a router operating system to carry out customized development on wireless router hardware:
a. modifying the operating system of the wireless router, including cutting a kernel, driving equipment and manufacturing a file system of the operating system of the router, so that a wireless network card driver can be developed by self;
b. on the basis, application development is carried out, and adjustment is carried out aiming at the WLAN wireless network card drive, so that the WLAN wireless network card drive provides a user space interface and can provide the received detection data packet data, namely a wireless signal intensity value;
secondly, sending the collected signal intensity value to a specified calculation center server through a router;
application development of user interface:
a. compiling a data interface for driving the WLAN wireless network card of the access router, and sending a wireless signal strength value to a database interface of a designated server through the router;
b. developing a special application by using a mobile phone to acquire signals;
thirdly, calculating signal intensity values acquired by different routers through an algorithm to obtain position information in the region;
a. separating data collected by different routers, retrieving a positioning target, and acquiring signal strength values of different routers in the same target address;
b. and fitting a relational equation between the distance and the signal intensity value according to a large amount of calculation by using a machine learning method. The distances between different routers and the target positioning point can be obtained through calculation. And then obtaining the position information of the positioning target point according to the position of the fixed router.
An indoor accurate positioning system realized by machine learning comprises the following steps,
firstly, the indoor structure and data of a building are obtained.
And secondly, deploying WLAN wireless network routers at different indoor positions.
And thirdly, calculating the signal intensity distribution data among the routers.
And fourthly, fitting the acquired signal intensity distribution data through a machine learning algorithm to obtain a distance-intensity formula.
And fifthly, when the Internet of things terminal enters a WLAN wireless network coverage area, the wireless router collects a device signal intensity value.
And sixthly, calculating the position information of the target according to the previous algorithm.
Preferably, the testing device comprises a testing frame and a placing plate, the placing plate is arranged on the inner side of the testing frame in a sliding mode, the placing plate is located on the inner side of the testing frame, a wireless network router is arranged on the upper surface of the placing plate, a first sliding block is arranged at one end of the placing plate, the first sliding block is arranged inside the testing frame in a sliding mode, a second sliding block is arranged at the other end of the placing plate, the second sliding block is rotatably clamped inside the placing plate, a baffle is further arranged at the other end of the placing plate, a limiting unit is rotatably arranged on the testing frame, and the baffle can rotate to the upper surface of one end of the limiting unit; during the use, adjust the position of placing the board earlier to adjust to the best test position of wireless network router, specifically will place the board earlier along the vertical slip back of test jig, at the position of adjusting as required, rotate and place the board, make and place the board parallel, and limited bit cell leaves its tip idle, then places the wireless network router and places the board, thereby reaches the best test position.
Preferably, the limiting unit comprises a supporting plate, the supporting plate is vertically arranged on the outer side of the testing jig, a movable plate is arranged on the upper surface of the supporting plate in a sliding mode, a pull rope is arranged on one side of the movable plate, a second rotating plate is arranged at the tail end of the pull rope, a rotating rod is arranged at the end part of the second rotating plate and is rotatably arranged on the testing jig, a movable groove is formed in the testing jig, the rotating rod is rotatably arranged inside the movable groove, and a first rotating plate is arranged on the outer side of the rotating plate; the other side of the movable plate is vertically provided with a spring and a plugboard, one end of the spring is connected with the outer side of the test frame, the plugboard can penetrate through the test frame, a through groove is formed in the test frame and is positioned above the adjacent movable groove, the plugboard can penetrate through the through groove, and both the first rotary plate and the plugboard can be contacted with the baffle plate; under the normality, the second changes the below that the board is located first commentaries on classics board, the picture peg is located the outside of test jig this moment, when the second slider rotates the lower surface of baffle and contacts with first commentaries on classics board, the baffle promotes first commentaries on classics board anticlockwise rotation, second commentaries on classics board anticlockwise rotation this moment, the fly leaf does not receive the influence of second commentaries on classics board this moment, moves back, until the baffle rotates to the below that is located the picture peg, the picture peg keeps off the baffle this moment for the baffle and place the board and keep balance.
Preferably, the inside of test jig has seted up spout, first turn trough and second turn trough respectively, the radian of first turn trough and second turn trough keeps unanimous, and the upper end of first turn trough is linked together with the upper end of test jig, and the tip and the spout of second turn trough are linked together, the inside at the spout is established to first slider sliding card, and the rotatable card of second slider is established in first turn trough or second turn trough, and when the second slider rotated the tank bottom to first turn trough or second turn trough, the board of placing kept the level.
Preferably, the lower extreme of test jig is equipped with the base, and the lower surface of base is equipped with the connecting plate that the symmetry set up, and the lower extreme of connecting plate is equipped with the bottom plate.
Preferably, the one end of placing the board is equipped with the link plate, and the lower surface of link plate is equipped with the lifting rope of symmetric distribution, and the end of lifting rope is equipped with the bearing block for the bearing improves the both ends stability of placing the board.
Preferably, the number of the wireless network routers is at least 3.
Preferably, the number of the support plates is at least 5, and the support plates are arranged on the outer side of the test rack at equal intervals.
The invention has the beneficial effects that:
1. the first purpose of the invention is to provide a method for realizing indoor positioning by analyzing the signal strength between a WLAN wireless network router and an Internet of things terminal, which can achieve the positioning precision of a decimeter level and can identify the height of a target so as to realize three-dimensional positioning of the target;
2. the invention realizes accurate indoor positioning without expensive and complicated external equipment;
3. the calculation of the position is realized at the router side instead of the equipment side, so that a more efficient algorithm can be adopted without considering the calculation capability of front-end equipment or the support capability of a battery;
4. the interface is flexible, and the positioning can be realized only by supporting the Internet of things terminal of the WLAN;
5. and a mathematical model of the distance and the signal intensity is fitted by using a machine learning method, so that the distance is measured and calculated more accurately.
Drawings
The invention will be further described with reference to the accompanying drawings.
FIG. 1 is a flow chart of the present invention for machine learning to generate a fitting model;
FIG. 2 is a schematic flow chart of an indoor precise positioning implementation positioning process of the present invention;
FIG. 3 is a schematic view of a testing apparatus of the present invention;
FIG. 4 is a schematic view of a test rack of the present invention;
FIG. 5 is a schematic view of the subject holding board;
FIG. 6 is a schematic view of the connection of the test rack and the placement board according to the present invention;
FIG. 7 is a schematic cross-sectional view of a test rack of the present invention;
fig. 8 is a schematic view of a spacing unit of the present invention.
In the figure: the wireless network router comprises a wireless network router 1, a test frame 2, a sliding chute 21, a first rotating chute 22, a second rotating chute 23, a 24 penetrating groove, a movable groove 25, a base 3, a connecting plate 31, a bottom plate 32, a placing plate 4, a first sliding block 41, a second sliding block 42, a hanging plate 43, a lifting rope 431, a bearing block 432, a baffle 44, a limiting unit 5, a supporting plate 51, a rotating rod 52, a first rotating plate 53, a second rotating plate 54, a pull rope 55, a movable plate 56, a spring 561 and a plug plate 57.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all 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.
Referring to fig. 1-8, the present invention provides a technical solution: a method and a system for realizing indoor accurate positioning through machine learning comprise the following steps,
firstly, a wireless signal intensity value of a mobile terminal target is obtained by using a WLAN wireless router;
the method comprises the following steps of using a router operating system to carry out customized development on wireless router hardware:
a. modifying the operating system of the wireless router, including cutting a kernel, driving equipment and manufacturing a file system of the operating system of the router, so that a wireless network card driver can be developed by self;
b. on the basis, application development is carried out, and adjustment is carried out aiming at the WLAN wireless network card drive, so that the WLAN wireless network card drive provides a user space interface and can provide the received detection data packet data, namely a wireless signal intensity value;
secondly, sending the collected signal intensity value to a specified calculation center server through a router;
application development of user interface:
a. compiling a data interface for driving the WLAN wireless network card of the access router, and sending a wireless signal strength value to a database interface of a designated server through the router;
b. developing a special application by using a mobile phone to acquire signals;
thirdly, calculating signal intensity values acquired by different routers through an algorithm to obtain position information in the region;
a. separating data collected by different routers, retrieving a positioning target, and acquiring signal strength values of different routers in the same target address;
b. and fitting a relational equation between the distance and the signal intensity value according to a large amount of calculation by using a machine learning method. The distances between different routers and the target positioning point can be obtained through calculation. And then obtaining the position information of the positioning target point according to the position of the fixed router.
An indoor accurate positioning system realized by machine learning comprises the following steps,
firstly, the indoor structure and data of a building are obtained.
And secondly, deploying WLAN wireless network routers at different indoor positions.
And thirdly, calculating the signal intensity distribution data among the routers.
And fourthly, fitting the acquired signal intensity distribution data through a machine learning algorithm to obtain a distance-intensity formula.
And fifthly, when the Internet of things terminal enters a WLAN wireless network coverage area, the wireless router collects a device signal intensity value.
And sixthly, calculating the position information of the target according to the previous algorithm.
The system for indoor accurate positioning through machine learning comprises a testing device, wherein the testing device comprises a testing frame 2 and a placing plate 4, the placing plate 4 is arranged on the inner side of the testing frame 2 in a sliding mode, the placing plate 4 is located on the inner side of the testing frame 2, the wireless network router 1 is arranged on the upper surface of the placing plate 4, a first sliding block 41 is arranged at one end of the placing plate 4, the first sliding block 41 is arranged inside the testing frame 2 in a sliding mode, a second sliding block 42 is arranged at the other end of the placing plate 4, the second sliding block 42 is rotatably clamped inside the placing plate 4, a baffle 44 is further arranged at the other end of the placing plate 4, a limiting unit 5 is rotatably arranged on the testing frame 2, and the baffle 44 can rotate to the upper surface of one end of the limiting; during the use, adjust the position of placing board 4 earlier to adjust to the best test position of wireless network router 1, specifically will place board 4 earlier along the vertical slip back of test jig 2, at the position of adjusting as required, rotate and place board 4, make and place board 4 parallel, and limited bit cell 5 is idle with its tip, then place wireless network router 1 and place board 4, thereby reach best test position.
The limiting unit 5 comprises a supporting plate 51, the supporting plate 51 is vertically arranged on the outer side of the test rack 2, a movable plate 56 is arranged on the upper surface of the supporting plate 51 in a sliding mode, a pull rope 55 is arranged on one side of the movable plate 56, a second rotating plate 54 is arranged at the tail end of the pull rope 55, a rotating rod 52 is arranged at the end part of the second rotating plate 54, the rotating rod 52 is rotatably arranged on the test rack 2, a movable groove 25 is formed in the test rack 2, the rotating rod 52 is rotatably arranged in the movable groove 25, and a first rotating plate 53 is arranged on the outer side of the rotating plate; a spring 561 and an inserting plate 57 are vertically arranged on the other side of the movable plate 56, one end of the spring 561 is connected with the outer side of the testing jig 2, the inserting plate 57 can penetrate through the testing jig 2, a through groove 24 is formed in the testing jig 2, the through groove 24 is positioned above the adjacent movable groove 25, the inserting plate 57 can penetrate through the through groove 24, and both the first rotating plate 53 and the inserting plate 57 can be in contact with the baffle 44; under the normal state, the second rotating plate 54 is located below the first rotating plate 53, the inserting plate 57 is located outside the testing jig 2 at the moment, when the second sliding block 42 rotates until the lower surface of the baffle plate 44 contacts with the first rotating plate 53, the baffle plate 44 pushes the first rotating plate 53 to rotate anticlockwise, the second rotating plate 54 rotates anticlockwise at the moment, the movable plate 56 is not affected by the second rotating plate 54 at the moment, the movable plate moves backwards until the baffle plate 44 rotates to be located below the inserting plate 57, and the inserting plate 57 blocks the baffle plate 44 at the moment, so that the baffle plate 44 and the placing plate 4 are kept in balance.
The inside of test jig 2 has been seted up spout 21, first revolving chute 22 and second revolving chute 23 respectively, the radian of first revolving chute 22 and second revolving chute 23 keeps unanimous, the upper end of first revolving chute 22 is linked together with the upper end of test jig 2, the tip and the spout 21 of second revolving chute 23 are linked together, the inside at spout 21 is established to first slider 41 sliding card, the rotatable card of second slider 42 is established in first revolving chute 22 or second revolving chute 23, when second slider 42 rotates the tank bottom to first revolving chute 22 or second revolving chute 23, standing board 4 keeps the level.
The lower extreme of test jig 2 is equipped with base 3, and the lower surface of base 3 is equipped with the connecting plate 31 that the symmetry set up, and the lower extreme of connecting plate 31 is equipped with bottom plate 32.
One end of the placing plate 4 is provided with a hanging plate 43, the lower surface of the hanging plate 43 is provided with hanging ropes 431 which are symmetrically distributed, and the tail ends of the hanging ropes 431 are provided with bearing blocks 432 for bearing, so that the stability of the two ends of the placing plate 4 is improved.
The number of wireless network routers 1 is at least 3.
The number of the supporting plates 51 is at least 5, and the supporting plates 51 are arranged on the outer side of the test frame 2 at equal intervals.
When the wireless network router is used, the position of the placing plate 4 is adjusted firstly so as to adjust to the optimal testing position of the wireless network router 1, specifically, after the placing plate 4 vertically slides along the testing frame 2, the placing plate 4 is rotated at the position adjusted as required, the placing plate 4 is parallel, the limiting unit 5 leaves the end of the placing plate idle, then the wireless network router 1 is placed on the placing plate 4 so as to achieve the optimal testing position, the second rotating plate 54 is positioned below the first rotating plate 53, the inserting plate 57 is positioned outside the testing frame 2, when the second sliding block 42 rotates until the lower surface of the baffle plate 44 is contacted with the first rotating plate 53, the baffle plate 44 pushes the first rotating plate 53 to rotate anticlockwise, the second rotating plate 54 rotates anticlockwise, the movable plate 56 is not influenced by the second rotating plate 54 at this time, the movable plate moves backwards until the baffle plate 44 rotates to be positioned below the inserting plate 57, at this time, the inserting plate 57 blocks the baffle plate 44, so that the baffle 44 and the placing plate 4 are balanced.
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
Claims (7)
1. A system for indoor accurate positioning through machine learning, comprising the steps of:
firstly, acquiring an indoor structure and data of a building;
secondly, deploying WLAN wireless network routers at different indoor positions;
thirdly, calculating signal intensity distribution data among the routers;
fourthly, fitting the collected signal intensity distribution data through a machine learning algorithm to obtain a distance-intensity formula;
fifthly, after the Internet of things terminal enters a WLAN wireless network coverage area, the wireless router collects a device signal intensity value;
sixthly, calculating the position information of the target according to the previous algorithm;
the system for indoor accurate positioning through machine learning comprises a testing device, the testing device comprises a testing frame (2) and a placing plate (4), the placing plate (4) is arranged on the inner side of the testing frame (2) in a sliding mode, the placing plate (4) is located on the inner side of the testing frame (2), a wireless network router (1) is arranged on the upper surface of the placing plate (4), a first sliding block (41) is arranged at one end of the placing plate (4), the first sliding block (41) is arranged inside the testing frame (2) in a sliding mode, a second sliding block (42) is arranged at the other end of the placing plate (4), the second sliding block (42) is rotatably clamped inside the placing plate (4), and a baffle (44) is further arranged at the other end of the placing plate (4), the testing frame (2) is rotatably provided with a limiting unit (5), and the baffle (44) can rotate to the upper surface of one end of the limiting unit (5);
the limiting unit (5) comprises a supporting plate (51), the supporting plate (51) is vertically arranged on the outer side of the testing frame (2), a movable plate (56) is arranged on the upper surface of the supporting plate (51) in a sliding mode, a pull rope (55) is arranged on one side of the movable plate (56), a second rotating plate (54) is arranged at the tail end of the pull rope (55), a rotating rod (52) is arranged at the end portion of the second rotating plate (54), the rotating rod (52) is rotatably arranged on the testing frame (2), and a first rotating plate (53) is arranged on the outer side of the rotating plate; the other side of the movable plate (56) is vertically provided with a spring (561) and an inserting plate (57), one end of the spring (561) is connected with the outer side of the testing frame (2), the inserting plate (57) can penetrate through the testing frame (2), and both the first rotating plate (53) and the inserting plate (57) can be in contact with the baffle (44).
2. The system for realizing indoor accurate positioning through machine learning according to claim 1, wherein the inner side of the test frame (2) is respectively provided with a sliding groove (21), a first rotating groove (22) and a second rotating groove (23), the upper end of the first rotating groove (22) is communicated with the upper end of the test frame (2), the end part of the second rotating groove (23) is communicated with the sliding groove (21), the first sliding block (41) is slidably clamped inside the sliding groove (21), and the second sliding block (42) is rotatably clamped in the first rotating groove (22) or the second rotating groove (23).
3. The system for realizing indoor accurate positioning through machine learning according to claim 1, wherein the lower end of the test frame (2) is provided with a base (3), the lower surface of the base (3) is provided with symmetrically arranged connecting plates (31), and the lower end of the connecting plates (31) is provided with a bottom plate (32).
4. The system for indoor precise positioning through machine learning according to claim 1 is characterized in that one end of the placing plate (4) is provided with a hanging plate (43), the lower surface of the hanging plate (43) is provided with symmetrically distributed lifting ropes (431), and the tail ends of the lifting ropes (431) are provided with bearing blocks (432).
5. A system for indoor accurate positioning through machine learning according to claim 1, characterized in that the number of wireless network routers (1) is at least 3.
6. The system for indoor precise positioning through machine learning according to claim 1, wherein the number of the support plates (51) is at least 5, and the support plates (51) are arranged at equal intervals outside the test rack (2).
7. A method for indoor accurate positioning through machine learning, comprising a system for indoor accurate positioning through machine learning according to claim 1, wherein the method for indoor accurate positioning comprises the following steps:
firstly, a wireless signal intensity value of a mobile terminal target is obtained by using a WLAN wireless router;
the method comprises the following steps of using a router operating system to carry out customized development on wireless router hardware:
a. modifying the operating system of the wireless router, including cutting a kernel, driving equipment and manufacturing a file system of the operating system of the router, so that a wireless network card driver can be developed by self;
b. on the basis, application development is carried out, and adjustment is carried out aiming at the WLAN wireless network card drive, so that the WLAN wireless network card drive provides a user space interface and can provide the received detection data packet data, namely a wireless signal intensity value;
secondly, sending the collected signal intensity value to a specified calculation center server through a router;
application development of user interface:
a. compiling a data interface for driving the WLAN wireless network card of the access router, and sending a wireless signal strength value to a database interface of a designated server through the router;
b. developing a special application by using a mobile phone to acquire signals;
thirdly, calculating signal intensity values acquired by different routers through an algorithm to obtain position information in the region;
a. separating data collected by different routers, retrieving a positioning target, and acquiring signal strength values of different routers in the same target address;
b. and fitting a relation equation between the distance and the signal intensity value according to a large amount of calculation by using a machine learning method, obtaining the distances between different routers and target positioning points through calculation, and obtaining the position information of the positioning target point according to the position of a fixed router.
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Denomination of invention: A method and system for indoor accurate positioning through machine learning Effective date of registration: 20210802 Granted publication date: 20201027 Pledgee: Agricultural Bank of China Limited by Share Ltd. South Lake branch Pledgor: Jiaxing Jiasai Information Technology Co.,Ltd. Registration number: Y2021330001001 |
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