CN111190138A - Tool car indoor and outdoor combined positioning method and device based on Internet of things - Google Patents

Tool car indoor and outdoor combined positioning method and device based on Internet of things Download PDF

Info

Publication number
CN111190138A
CN111190138A CN202010088014.9A CN202010088014A CN111190138A CN 111190138 A CN111190138 A CN 111190138A CN 202010088014 A CN202010088014 A CN 202010088014A CN 111190138 A CN111190138 A CN 111190138A
Authority
CN
China
Prior art keywords
internet
things
base station
bluetooth
positioning
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
Application number
CN202010088014.9A
Other languages
Chinese (zh)
Inventor
赵之恒
张梦迪
张登银
赵莎莎
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Nanjing University of Posts and Telecommunications
Original Assignee
Nanjing University of Posts and Telecommunications
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Nanjing University of Posts and Telecommunications filed Critical Nanjing University of Posts and Telecommunications
Priority to CN202010088014.9A priority Critical patent/CN111190138A/en
Publication of CN111190138A publication Critical patent/CN111190138A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/0009—Transmission of position information to remote stations
    • G01S5/0018—Transmission from mobile station to base station
    • G01S5/0036—Transmission from mobile station to base station of measured values, i.e. measurement on mobile and position calculation on base station
    • 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
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/0294—Trajectory determination or predictive filtering, e.g. target tracking or Kalman filtering
    • 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
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/06—Position of source determined by co-ordinating a plurality of position lines defined by path-difference measurements

Landscapes

  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention discloses an indoor and outdoor combined positioning method and device for a tooling vehicle based on the Internet of things.A Bluetooth label of the Internet of things is bound on each tooling vehicle one by one, and the Bluetooth label of the Internet of things broadcasts data including MAC address information in real time and sends the data to a base station of the Internet of things; one or more base stations of the Internet of things receive broadcast data of the Bluetooth tags of the Internet of things, classify, sort and process the collected data signal intensity values and MAC address information, and transmit the data signal intensity values and the MAC address information to a positioning engine system through the mobile internet; and the positioning engine system receives the processed data signals sent by the Internet of things Bluetooth label transmitted by the Internet of things base station, and obtains the accurate position of the tooling vehicle bound by the Internet of things Bluetooth label through a self-learning gene positioning algorithm. The invention realizes a calibration-free and self-learning indoor and outdoor combined positioning scheme aiming at large-scale positioning objects in an industrial park; the energy consumption is saved, the positioning precision is high and the trend is up.

Description

Tool car indoor and outdoor combined positioning method and device based on Internet of things
Technical Field
The invention relates to the field of intelligent manufacturing, in particular to an indoor and outdoor combined positioning method for manufacturing resources.
Background
The establishment of an industrial park aggregates various upstream and downstream companies for better resource sharing and collaboration. The industrial truck is responsible for carrying out main transportation tasks of materials among different departments in the industrial park to ensure the timeliness of production. High mobility can achieve fast and flexible responses, but the recovery of empty truck becomes a serious challenge for industrial parks. The lack of real-time location information for the industrial vehicles causes logistics operations to be confused and becomes a bottleneck that affects overall manufacturing efficiency.
In an industrial park, there are usually a factory building built indoors, and a warehouse and a transfer area built outdoors. GPS is well-established and widely used for outdoor positioning, however, GPS is inaccurate indoors due to non-line-of-sight transmission and signal attenuation, so existing indoor and outdoor positioning is usually realized by combining GPS and some indoor positioning technologies. The existing indoor and outdoor positioning methods have some breakthroughs, but the main problems are that firstly, most researches use a mobile phone APP as a basic tracking object, tracking or navigation is carried out by people carrying mobile phones, positioning is carried out outdoors through a mobile phone with a GPS device, however, in the industrial park scene, the tracking object is thousands of tool cars, the power consumption of the GPS is high, each tool car is provided with one mobile phone, and charging every day is not feasible, so that the real-time positioning of large-scale tool cars is a difficult problem to solve.
Secondly, the indoor and outdoor positioning method using signals as fingerprint features generally needs a large amount of complicated signal feature collection and calibration work, a fingerprint feature database is established, the industrial park positioning environment range is large, and due to staging engineering, the area of the later stage can be continuously expanded, so that the preparation work of the early stage of positioning becomes extremely time-consuming and labor-consuming. And as the database is overdue due to the change of time and environment, the maintenance of the database becomes difficult, and the positioning accuracy gradually decreases. Therefore, an indoor and outdoor combined positioning method capable of self-learning and cross-free for the environment is urgently needed.
Disclosure of Invention
In order to solve the practical problems in intelligent manufacturing, the invention provides an indoor and outdoor combined positioning method and device of a tooling vehicle based on the Internet of things.
An Internet of things-based tool car indoor and outdoor combined positioning method is characterized in that Internet of things Bluetooth tags are bound on each tool car one to one, and the Internet of things Bluetooth tags broadcast data including MAC address information in real time and send the data to an Internet of things base station;
one or more base stations of the Internet of things receive broadcast data of the Bluetooth tags of the Internet of things, classify, sort and process the collected data signal intensity values and MAC address information, and transmit the data signal intensity values and the MAC address information to a positioning engine system through the mobile internet;
and the positioning engine system receives the processed data signals sent by the Internet of things Bluetooth label transmitted by the Internet of things base station, and obtains the accurate position of the tooling vehicle bound by the Internet of things Bluetooth label through a self-learning gene positioning algorithm.
Further, the self-learning gene localization algorithm comprises the following steps:
1) a preparation stage: establishing a Cartesian coordinate system for an area needing to be positioned, determining coordinate information of an Internet of things base station, and dividing a reference position unit;
2) a collection stage: calculating the physical distance between each reference position unit and each Internet of things base station, wherein 1 physical distance is regarded as a gene, a vector set of the physical distances from one reference position unit to all Internet of things base stations is regarded as a DNA fragment, the DNA fragments of all reference position units form an innate chromosome, and the innate chromosome also comprises protein information, and in the positioning process, the protein information is the current timestamp;
3) and (3) a pre-estimation stage: the method comprises the steps that an Internet of things base station collects Internet of things label signals from a tooling vehicle at an unknown position, the actual distance from the unknown position to a fixedly deployed Internet of things base station is calculated through a signal propagation model, Euclidean distance matching is carried out on the Internet of things base station and a known reference position unit vector through a K-neighbor algorithm, and the smallest Internet of things label signal is an unknown position estimation result;
4) a learning stage: updating the vector set of the estimation result by a moving average method, changing the distance from the reference position unit to the base station of the Internet of things by changing the distance from the reference position unit to all the base stations of the Internet of things, and forming an acquired chromosome with an update time stamp or constructing a new acquired chromosome with the update time stamp.
Further, in the collection phase, the ith reference position cell LCiWith jth thing networking base station GjPhysical distance PD of(i,j)Comprises the following steps:
Figure BDA0002382727440000021
wherein i belongs to M, j belongs to N, M, N is a natural number,
Figure BDA0002382727440000022
is LCiThe value of the abscissa of (a) is,
Figure BDA0002382727440000023
is LCiThe ordinate values of (a) and (b),
Figure BDA0002382727440000024
is GjThe value of the abscissa of (a) is,
Figure BDA0002382727440000025
is GjThe ordinate value of (a).
Can mix LCiThe congenital DNA fragment of (a) is defined as CCi ═ { PD ═ PD(i,1),PD(i,2),......,PD(i,j)Since physical distance (gene) is objectively present, the innate DNA fragment of each reference position unit is automatically generated and does not require any signal collection work.
Further, in the estimation stage, each internet of things base station scans the RSSI of all internet of things bluetooth tags, and performs filtering and smoothing processing through a kalman filter, wherein the RSSI vector of the RSSI at the unknown position ulc is defined as the RSSIulc＝{R(ulc,1),R(ulc,2),......,R(ulc,j)In the formula, R(ulc,j)From unknown position ulc to the jth internet of things base stationThe received signal strength of.
Further, the distance from the unknown position to the fixedly deployed base station of the internet of things is calculated through the following signal propagation model:
P＝P0-10n log10(MDut/MD0)+Xσ(2)
in the formula, P is the received signal intensity value of the distance between the base station of the Internet of things and the Bluetooth tag of the Internet of things at the unknown position, MDutThe actual distance between the base station of the Internet of things and the unknown position is obtained; p0Is at the distance MD between the base station of the Internet of things and the Bluetooth label of the Internet of things0Average power received in meters, MD0Setting a value for the initial quotient; the variable n is a propagation constant related to the environment; xσIs a zero mean gaussian random variable.
Further, calculating Euclidean distances from the reference position units to the unknown position units by adopting a K-neighbor algorithm, and estimating a fingerprint vector according to each reference position unit, wherein the expression is as follows:
Figure BDA0002382727440000031
in the formula, FD(ulc,i)As unknown location units EFulcWith reference position unit position CFiThe euclidean distance of the fingerprint vector of (a); MD(ulc,j)Is the measured distance from unknown position ulc to the jth internet of things base station; PD (photo diode)(i,j)The physical distance between the ith reference position unit and the jth Internet of things base station is defined;
the known location element of the smallest euclidean distance is assigned to the unknown location estimate.
Further, in the learning stage, the formula for updating data by using the moving average method is as follows:
Figure BDA0002382727440000032
in the formula, AG(i,j,k)From the ith reference location unit to the jth IOT base station for the kth measurementA novel gene; MD(i,j,k)Is the kth measurement from the ith reference location unit to the jth internet of things base station; PD (photo diode)(i,j)The physical distance between the ith reference location unit and the jth internet of things base station is shown.
An indoor and outdoor combined positioning device of a tooling vehicle based on the Internet of things comprises an Internet of things Bluetooth tag, an Internet of things base station and a positioning engine system;
the plurality of Internet of things Bluetooth tags are bound with the tool cars one to one, and data including MAC address information are broadcasted in real time and sent to an Internet of things base station;
one or more Internet of things base stations are deployed in indoor or/and outdoor environments, collect broadcast data of the Internet of things Bluetooth labels, classify and process received data signal strength values and MAC address information, and transmit the data signal strength values and the MAC address information to a positioning engine system through the mobile Internet;
and the positioning engine system receives the processed data signals sent by the Internet of things Bluetooth label transmitted by the Internet of things base station, and obtains the accurate position of the tooling vehicle bound by the Internet of things Bluetooth label through a self-learning gene positioning algorithm.
Further, the internet of things Bluetooth tag comprises a Bluetooth 4.2 transceiver chip;
the Bluetooth 4.2 transceiver chip broadcasts data including MAC address information in real time in an iBeacon format.
Further, the base station of the internet of things comprises a Bluetooth 4.2 transceiver chip, a storage unit, a processor and a network transmission unit;
the Bluetooth 4.2 transceiver chip is used for receiving broadcast data of the Bluetooth label of the Internet of things, classifying and sorting the received data signal strength value and the MAC address information, and storing the data signal strength value and the MAC address information into a storage unit;
the processor adopts a Kalman filtering algorithm to carry out smoothing processing on the signal intensity value; and transmitted to the positioning engine system through the network transmission unit.
The invention achieves the following beneficial effects:
the invention realizes a calibration-free and self-learning indoor and outdoor combined positioning scheme aiming at large-scale positioning objects in an industrial park by adopting a self-learning gene positioning algorithm through an Internet of things hardware device; the energy consumption is saved, the positioning precision is high and the trend is up. Provides a feasible scheme for industrial enterprises to realize the aim of '2025' manufactured in China, and provides technical support for cost reduction, efficiency improvement and full-transparent management of the enterprises.
The invention can also be used for indoor and outdoor combined positioning of other manufacturing resources such as industrial park personnel.
The above and other objects, advantages and features of the present invention will become more apparent to those skilled in the art from the following detailed description of specific embodiments thereof, as illustrated in the accompanying drawings.
Drawings
Figure 1 locates a data transport stream;
FIG. 2 is a flow chart of self-learning gene mapping.
Detailed Description
Fig. 1 is a schematic diagram of a positioning data transport stream of this scheme. The device comprises an Internet of things Bluetooth tag, an Internet of things base station and a positioning engine system, wherein,
the Bluetooth label of the Internet of things is composed of a power supply, a microprocessor and a Bluetooth 4.2 transceiver chip. Through the real-time data broadcast of bluetooth 4.2 transceiver chip with iBeacon format, its broadcast data mainly includes MAC address information.
In the invention, a plurality of Bluetooth tags of the Internet of things broadcast data simultaneously.
The base station of the internet of things is composed of a Bluetooth 4.2 transceiver chip, a storage unit, a processor and a network transmission unit. When receiving broadcast data from the Bluetooth tag of the Internet of things, classifying and sorting MAC address information and received signal strength values (RSSI) of the broadcast data, storing the broadcast data in a storage unit, wherein a processor runs a Kalman filtering algorithm, smoothens the RSSI and finally transmits the RSSI to a positioning engine system through the mobile internet.
The positioning engine system collects the processed Bluetooth label information and signals transmitted by the base station of the Internet of things, performs a self-learning gene positioning algorithm, obtains an accurate position and provides the accurate position for a user.
Because of the particularities of the industrial park environment, all the acquisition of positioning data must be done in a wireless manner. After a large amount of thing networking bluetooth labels are bound one to one with the frock car, with bluetooth iBeacon data broadcast to thing networking basic station. The base stations of the internet of things are deployed in indoor or outdoor environments of an industrial park, one or more base stations of the internet of things collect data sent by Bluetooth tags, current signal strength (RSSI) is obtained, and Kalman filtering smoothing processing is carried out on the RSSI. And pushing the data to a positioning engine system for position analysis in a mobile internet mode. The positioning engine system comprises a self-learning gene positioning method.
Wherein bluetooth 4.2 is low-power consumption thing networking chip, changes the problem of GPS high energy consumption.
FIG. 2 is a flow chart of a self-learning gene mapping algorithm.
The invention introduces the positioning process and principle in the self-learning gene positioning algorithm by using a biological metaphor. The congenital genes are first produced after birth in an animal. A large number of genes form DNA fragments. The multiple DNA fragments are then combined into DNA. Finally, chromosomes are established using DNA and proteins. In the process of positioning, the physical location of the chromosome is the natural chromosome, and with the change of the environment (such as obstacles, humidity, battery level) and other factors, the characteristics of the signal intensity can change, so that the mutation of the gene causes the change of the chromosome.
The self-learning gene location is divided into 4 stages, namely a preparation stage, a collection stage, an estimation stage and a learning stage.
In the preparation stage, aiming at the area needing to be positioned, no matter the area is an indoor area or an outdoor area, as long as the positioning requirement exists, a Cartesian coordinate system can be established, the base station of the Internet of things is deployed, and the coordinate information of the base station of the Internet of things is established. And dividing the reference position unit according to the positioning precision requirement. By analogy with a petri dish, a petri dish resembles a cartesian coordinate system or a localized area.
In the collection stage, different from the traditional work of collecting signals at each reference position unit in indoor positioning, the method of calculating the distance between the center point of each reference position unit and each base station of the internet of things is simply used for calculating the physical distance between the center point of each reference position unit and each base station of the internet of things by the known two-point coordinates, and 1 physical distance is regarded as one gene. Then, a vector set of physical distances from a reference location unit to all internet of things base stations is regarded as a DNA fragment, and the DNA fragments of all reference location units form an innate chromosome, which also includes protein information, and in the positioning process, the protein information is the current timestamp. Since all signal acquisitions are not performed during the collection phase, the resulting chromosomes are innate chromosomes.
In the mathematical model of the Collection phase, the LCiDefined as the ith reference position unit (i belongs to M), M is a natural number, and GjDefined as the jth internet of things base station (j belongs to N), N is a natural number, LCiAnd GjPD of the physical distance between(i,j)The following can be defined:
Figure BDA0002382727440000061
in the formula (I), the compound is shown in the specification,
Figure BDA0002382727440000062
is LCiThe value of the abscissa of (a) is,
Figure BDA0002382727440000063
is LCiThe ordinate values of (a) and (b),
Figure BDA0002382727440000064
is GjThe value of the abscissa of (a) is,
Figure BDA0002382727440000065
is GjThe ordinate value of (a). Can mix LCiThe congenital DNA fragment of (a) is defined as CCi ═ { PD ═ PD(i,1),PD(i,2),......,PD(i,j)Since physical distance (gene) is objectively present, the innate DNA fragment of each reference position unit is automatically generated and does not require any signal collection work.
In the estimation stage, the internet of things base station collects internet of things label signals from unknown positions, converts the internet of things label signals into actual distances through a signal propagation model, and performs Euclidean distance matching with known reference position unit vectors through a K-neighbor algorithm, wherein the smallest one is the most probable position unit.
In the estimation stage, each internet of things base station firstly scans the received signal strength values RSSI of all Bluetooth labels, and firstly carries out filtering and smoothing processing in the distributed internet of things base stations through a Kalman filter, wherein the RSSI vector at an unknown position ulc can be defined as the RSSIulc＝{R(ulc,1),R(ulc,2),......,R(ulc,j)In the formula, R(ulc,j)Is the received signal strength from an unknown location to the internet of things base station j. The distance from an unknown location to a fixedly deployed base station of the internet of things can be calculated by a radio propagation model as follows:
P＝P0-10n log10(MDut/MD0)+Xσ(2)
in the formula, P is expressed in decibel dBm and is the received signal strength of the distance between the base station of the Internet of things and the Bluetooth tag at the unknown position. MDutThe actual distance between the base station of the Internet of things and the Bluetooth tag at the unknown position is determined; p0Is at the distance MD between the base station of the Internet of things and the Bluetooth label0Average power received in meters, MD0Taking the value as an initial quotient value of 1 meter; the variable n is a propagation constant related to the environment; xσIs a zero mean gaussian random variable. These parameters depend to a large extent on the environment and the operating frequency. Therefore, these parameters are first estimated by testing between known location units before calculating the distance. Thus, the unknown site unit has a predicted DNA segment, which can be expressed as EFulc＝{MD(ulc,1),MD(ulc,2),......,MD(ulc,j)In the formula, MD(ulc,j)Is the measured distance from an unknown location to the internet of things base station j. Once the measured genes (individual distances) for the unknown location units are calculated, the reference location units can be classified into unknown location units by the K-nearest neighbor algorithm. K-neighbor algorithm calculates Euclidean distance from reference position unit to unknown position unit, and calculates Euclidean distance according to each reference position unitThe meta-estimated fingerprint vector is expressed as follows:
Figure BDA0002382727440000071
in the formula, FD(ulc,i)As unknown location units EFulcWith reference position unit position CFiThe minimum euclidean distance known location element is assigned to the unknown location estimate.
In the learning stage, the vector set is updated according to the position estimation result by a moving average method, and the DNA fragments (the distances from the reference position unit to all the base stations of the Internet of things) are changed by changing the genes (the distances from the reference position unit to the base stations of the Internet of things) and then the acquired chromosomes are formed or new acquired chromosomes (carrying new timestamps) are constructed. The purpose of learning is to keep the chromosome structure (signal feature database) constantly updated, thereby constantly improving the positioning accuracy and usability.
In the mathematical model of the learning phase, each genetic variation (learning) will affect the chromosome structure and have an updated time stamp. The scheme adopts a moving average method to update data. The formula is as follows:
Figure BDA0002382727440000072
in the formula, define AG(i,j,k)New genes from the ith reference location unit to the jth internet of things base station at timestamp k (accurately calculated as the number of measurements); MD(i,j,k)Defined as the kth measurement from the ith reference location unit to the jth internet of things base station.
The main advantages of the self-learning gene localization method are summarized as follows: (1) the signal collection phase of conventional fingerprinting methods is very time-consuming and labor-intensive. One needs to record a signal at each reference position. In the method provided by the invention, the collection work can be reduced to the physical distance between the reference position unit and the base station of the Internet of things. Time and labor can be saved at this stage. (2) The traditional indoor tracking only focuses on position estimation, and the positioning precision becomes a fixed numerical value. However, in the present invention, after the localization result is estimated, the gene will be continuously mutated according to the real measured value to keep the whole chromosome continuously updated so as to ensure its timeliness. The positioning result is no longer a fixed value but an ascending process. (3) The signal is affected by many factors such as obstructions, multipath effects and environmental changes. The training fingerprint with the signal indicator in the traditional fingerprint identification method is limited to the model of equipment, the environment, the battery power and even the activity of human beings. After a period of use, the tracking effectiveness of the tracking system may become obsolete and become inapplicable. In the self-learning gene mapping method, the change of the physical world can be reflected in the acquired chromosome structure map synchronously. Thus, the effectiveness of location tracking is ensured.

Claims (10)

1. An indoor and outdoor combined positioning method for tool cars based on the Internet of things is characterized in that Bluetooth tags of the Internet of things are bound on each tool car one to one, and the Bluetooth tags of the Internet of things broadcast data including MAC address information in real time and send the data to a base station of the Internet of things;
one or more base stations of the Internet of things receive broadcast data of the Bluetooth tags of the Internet of things, classify, sort and process the collected data signal intensity values and MAC address information, and transmit the data signal intensity values and the MAC address information to a positioning engine system through the mobile internet;
and the positioning engine system receives the processed data signals sent by the Internet of things Bluetooth label transmitted by the Internet of things base station, and obtains the accurate position of the tooling vehicle bound by the Internet of things Bluetooth label through a self-learning gene positioning algorithm.
2. The indoor and outdoor combined positioning method for the tooling vehicle based on the Internet of things as claimed in claim 1, wherein the self-learning gene positioning algorithm comprises the following steps:
1) a preparation stage: establishing a Cartesian coordinate system for an area needing to be positioned, determining coordinate information of an Internet of things base station, and dividing a reference position unit;
2) a collection stage: calculating the physical distance between each reference position unit and each Internet of things base station, wherein 1 physical distance is regarded as a gene, a vector set of the physical distances from one reference position unit to all Internet of things base stations is regarded as a DNA fragment, the DNA fragments of all reference position units form an innate chromosome, and the innate chromosome also comprises protein information, and in the positioning process, the protein information is the current timestamp;
3) and (3) a pre-estimation stage: the method comprises the steps that an Internet of things base station collects Internet of things label signals from a tooling vehicle at an unknown position, the actual distance from the unknown position to a fixedly deployed Internet of things base station is calculated through a signal propagation model, Euclidean distance matching is carried out on the Internet of things base station and a known reference position unit vector through a K-neighbor algorithm, and the smallest Internet of things label signal is an unknown position estimation result;
4) a learning stage: updating the vector set of the estimation result by a moving average method, changing the distance from the reference position unit to the base station of the Internet of things by changing the distance from the reference position unit to all the base stations of the Internet of things, and forming an acquired chromosome with an update time stamp or constructing a new acquired chromosome with the update time stamp.
3. The method for integrated indoor and outdoor positioning of tooling vehicle based on internet of things as claimed in claim 2, wherein in the collection stage, the ith reference position unit LCiWith jth thing networking base station GjPhysical distance PD of(i,j)Comprises the following steps:
Figure FDA0002382727430000011
wherein i belongs to N, j belongs to N,
Figure FDA0002382727430000021
is LCiThe value of the abscissa of (a) is,
Figure FDA0002382727430000022
is LCiThe ordinate values of (a) and (b),
Figure FDA0002382727430000023
is GjThe value of the abscissa of (a) is,
Figure FDA0002382727430000024
is GjThe ordinate value of (a).
Can mix LCiThe congenital DNA fragment of (a) is defined as CCi ═ { PD ═ PD(i,1),PD(i,2),......,PD(i,j)Since physical distance (gene) is objectively present, the innate DNA fragment of each reference position unit is automatically generated and does not require any signal collection work.
4. The Internet of things-based tool car indoor and outdoor combined positioning method of claim 2, wherein in the estimation stage, each Internet of things base station scans the RSSI of all Internet of things Bluetooth labels, and performs filtering and smoothing processing through a Kalman filter, wherein the RSSI vector of the signal strength value is defined as RSSI at an unknown position ulculc＝{R(ulc,1),R(ulc,2),......,R(ulc,j)In the formula, R(ulc,j)Is the received signal strength from unknown location ulc to the jth internet of things base station.
5. The Internet of things-based tooling vehicle indoor and outdoor combined positioning method is characterized in that the distance from an unknown position to a fixedly deployed Internet of things base station is calculated through a signal propagation model as follows:
P＝P0-10nlog10(MDut/MD0)+Xσ(2)
in the formula, P is the received signal intensity value of the distance between the base station of the Internet of things and the Bluetooth tag of the Internet of things at the unknown position, MDutThe actual distance between the base station of the Internet of things and the unknown position is obtained; p0Is at the distance MD between the base station of the Internet of things and the Bluetooth label of the Internet of things0Average power received in meters, MD0Setting a value for the initial quotient; the variable n is a propagation constant related to the environment; xσIs zeroMean gaussian random variable.
6. The Internet of things-based tooling vehicle indoor and outdoor combined positioning method is characterized in that a K-neighbor algorithm is adopted to calculate the Euclidean distance from a reference position unit to an unknown position unit, and a fingerprint vector is estimated according to each reference position unit, and is represented as follows:
Figure FDA0002382727430000025
in the formula, FD(ulc,i) As unknown location units EFulcWith reference position unit position CFiThe euclidean distance of the fingerprint vector of (a); MD(ulc,j)Is the measured distance from unknown position ulc to the jth internet of things base station; PD (photo diode)(i,j)The physical distance between the ith reference position unit and the jth Internet of things base station is defined;
the known location element of the smallest euclidean distance is assigned to the unknown location estimate.
7. The internet-of-things-based tool car indoor and outdoor combined positioning method is characterized in that in the learning stage, a formula for updating data by adopting a moving average method is as follows:
Figure FDA0002382727430000031
in the formula, AG(i,j,k)A new gene from the ith reference location unit to the jth internet of things base station for the kth measurement; MD(i,j,k)Is the kth measurement from the ith reference location unit to the jth internet of things base station; PD (photo diode)(i,j)The physical distance between the ith reference location unit and the jth internet of things base station is shown.
8. An indoor and outdoor combined positioning device of a tooling vehicle based on the Internet of things is characterized by comprising an Internet of things Bluetooth tag, an Internet of things base station and a positioning engine system;
the plurality of Internet of things Bluetooth tags are bound with the tool cars one to one, and data including MAC address information are broadcasted in real time and sent to an Internet of things base station;
one or more Internet of things base stations are deployed in indoor or/and outdoor environments, collect broadcast data of the Internet of things Bluetooth labels, classify and process received data signal strength values and MAC address information, and transmit the data signal strength values and the MAC address information to a positioning engine system through the mobile Internet;
and the positioning engine system receives the processed data signals sent by the Internet of things Bluetooth label transmitted by the Internet of things base station, and obtains the accurate position of the tooling vehicle bound by the Internet of things Bluetooth label through a self-learning gene positioning algorithm.
9. The internet-of-things-based tool car indoor and outdoor combined positioning device is characterized in that the internet-of-things Bluetooth tag comprises a Bluetooth 4.2 transceiver chip;
the Bluetooth 4.2 transceiver chip broadcasts data including MAC address information in real time in an iBeacon format.
10. The tool car indoor and outdoor combined positioning device based on the internet of things of claim 8, wherein the base station of the internet of things comprises a Bluetooth 4.2 transceiver chip, a storage unit, a processor and a network transmission unit;
the Bluetooth 4.2 transceiver chip is used for receiving broadcast data of the Bluetooth label of the Internet of things, classifying and sorting the received data signal strength value and the MAC address information, and storing the data signal strength value and the MAC address information into a storage unit;
the processor adopts a Kalman filtering algorithm to carry out smoothing processing on the signal intensity value; and transmitted to the positioning engine system through the network transmission unit.
CN202010088014.9A 2020-02-12 2020-02-12 Tool car indoor and outdoor combined positioning method and device based on Internet of things Pending CN111190138A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010088014.9A CN111190138A (en) 2020-02-12 2020-02-12 Tool car indoor and outdoor combined positioning method and device based on Internet of things

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010088014.9A CN111190138A (en) 2020-02-12 2020-02-12 Tool car indoor and outdoor combined positioning method and device based on Internet of things

Publications (1)

Publication Number Publication Date
CN111190138A true CN111190138A (en) 2020-05-22

Family

ID=70708347

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010088014.9A Pending CN111190138A (en) 2020-02-12 2020-02-12 Tool car indoor and outdoor combined positioning method and device based on Internet of things

Country Status (1)

Country Link
CN (1) CN111190138A (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113534046A (en) * 2021-07-12 2021-10-22 珠海极海半导体有限公司 Indoor positioning method and system based on Bluetooth communication and BLE chip
CN115113255A (en) * 2022-08-30 2022-09-27 广州辰创科技发展有限公司 Vehicle positioning and tracking device and method and vehicle

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1566365A (en) * 2003-06-24 2005-01-19 北京大学 Microbe gene prediction method based on polynary entropy distance method
US20060253226A1 (en) * 2005-04-12 2006-11-09 Ehud Mendelson System and method of detecting and navigating to empty parking spaces
CN103152409A (en) * 2013-02-26 2013-06-12 韩成浩 System for accurately positioning and evacuating people trapped in high-rise buildings based on Internet of Things technology
CN110475203A (en) * 2018-05-09 2019-11-19 北京智慧图科技有限责任公司 A kind of localization method returned based on internet label and mobile phone

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1566365A (en) * 2003-06-24 2005-01-19 北京大学 Microbe gene prediction method based on polynary entropy distance method
US20060253226A1 (en) * 2005-04-12 2006-11-09 Ehud Mendelson System and method of detecting and navigating to empty parking spaces
CN103152409A (en) * 2013-02-26 2013-06-12 韩成浩 System for accurately positioning and evacuating people trapped in high-rise buildings based on Internet of Things technology
CN110475203A (en) * 2018-05-09 2019-11-19 北京智慧图科技有限责任公司 A kind of localization method returned based on internet label and mobile phone

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
ZHIHENG ZHAO等: ""IoT edge computing-enabled collaborative tracking system for manufacturing resources in industrial park"", 《ADVANCED ENGINEERING INFORMATICS》 *
ZHIHENG ZHAO等: ""Logistics sustainability practices: an IoT-enabled smart indoor parking system for industrial hazardous chemical vehicles"", 《INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH》 *

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113534046A (en) * 2021-07-12 2021-10-22 珠海极海半导体有限公司 Indoor positioning method and system based on Bluetooth communication and BLE chip
CN115113255A (en) * 2022-08-30 2022-09-27 广州辰创科技发展有限公司 Vehicle positioning and tracking device and method and vehicle

Similar Documents

Publication Publication Date Title
CN108696932B (en) Outdoor fingerprint positioning method using CSI multipath and machine learning
CN109275095B (en) Bluetooth-based indoor positioning system, positioning equipment and positioning method
CN102204372B (en) Indoor localization system for locating electronic mobile device within indoor environment
CN102098780B (en) Positioning method and device
CN101860959B (en) Locating method of wireless sensor network based on RSSI (Received Signal Strength Indicator)
CN103905992B (en) Indoor positioning method based on wireless sensor networks of fingerprint data
CN106125045B (en) A kind of ADAPTIVE MIXED indoor orientation method based on Wi-Fi
CN101620270B (en) Wireless location method based on cluster-fusion
CN104066172B (en) Method for positioning AP in wireless local area network
CN102291817B (en) Group positioning method based on location measurement sample in mobile communication network
CN112533163B (en) Indoor positioning method based on NB-IoT (NB-IoT) improved fusion ultra-wideband and Bluetooth
CN102932911A (en) Positioning method and positioning system of location fingerprints
CN106507475B (en) Room area WiFi localization method and system based on EKNN
Tomažič et al. Confidence-interval-fuzzy-model-based indoor localization
CN104735781B (en) A kind of indoor locating system and its localization method
CN103338516A (en) Two-step positioning method of wireless sensor network based on total least squares
CN102014489A (en) Environment adaptive RSSI local positioning system and method
KR20170091811A (en) An indoor positioning method using the weighting the RSSI of Bluetooth beacon and pedestrian pattern
CN104038901A (en) Indoor positioning method for reducing fingerprint data acquisition workload
CN103369571A (en) Method for correction and coverage self-optimization of propagation model based on multi-network combined measurement
CN111190138A (en) Tool car indoor and outdoor combined positioning method and device based on Internet of things
CN107509165A (en) A kind of method for being calculated based on big data, determining AP positions
CN115914355A (en) Method suitable for indoor and outdoor joint positioning connection of Internet of things
CN102967848A (en) Positioning method based on distance relationship library and received signal intensity
CN109141420B (en) Indoor positioning method based on crowdsourcing data

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
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20200522