CN114202952A - Parking lot vehicle positioning method, terminal and storage medium - Google Patents
Parking lot vehicle positioning method, terminal and storage medium Download PDFInfo
- Publication number
- CN114202952A CN114202952A CN202111538001.8A CN202111538001A CN114202952A CN 114202952 A CN114202952 A CN 114202952A CN 202111538001 A CN202111538001 A CN 202111538001A CN 114202952 A CN114202952 A CN 114202952A
- Authority
- CN
- China
- Prior art keywords
- signal
- vehicle
- data
- parking lot
- module
- 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
- 238000000034 method Methods 0.000 title claims abstract description 75
- 238000004422 calculation algorithm Methods 0.000 claims description 34
- 238000012545 processing Methods 0.000 claims description 31
- 230000003287 optical effect Effects 0.000 claims description 21
- 239000011159 matrix material Substances 0.000 claims description 14
- 230000003750 conditioning effect Effects 0.000 claims description 13
- 238000004891 communication Methods 0.000 claims description 10
- 238000005070 sampling Methods 0.000 claims description 7
- 238000010586 diagram Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 9
- 238000004364 calculation method Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 5
- 230000005540 biological transmission Effects 0.000 description 4
- 238000009826 distribution Methods 0.000 description 4
- 239000004065 semiconductor Substances 0.000 description 4
- 239000013598 vector Substances 0.000 description 4
- 238000005457 optimization Methods 0.000 description 3
- 230000008054 signal transmission Effects 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 238000001914 filtration Methods 0.000 description 2
- 230000004807 localization Effects 0.000 description 2
- 230000009471 action Effects 0.000 description 1
- 238000004458 analytical method Methods 0.000 description 1
- 238000013459 approach Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 239000000306 component Substances 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 239000008358 core component Substances 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013480 data collection Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000009365 direct transmission Effects 0.000 description 1
- 238000005315 distribution function Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000005611 electricity Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 239000003822 epoxy resin Substances 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 238000009499 grossing Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 238000002955 isolation Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 229920000647 polyepoxide Polymers 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000003595 spectral effect Effects 0.000 description 1
- 230000002194 synthesizing effect Effects 0.000 description 1
- 238000010998 test method Methods 0.000 description 1
- 238000009827 uniform distribution Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
- H04B10/50—Transmitters
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B10/00—Transmission systems employing electromagnetic waves other than radio-waves, e.g. infrared, visible or ultraviolet light, or employing corpuscular radiation, e.g. quantum communication
- H04B10/60—Receivers
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/12—Messaging; Mailboxes; Announcements
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Signal Processing (AREA)
- Computer Networks & Wireless Communication (AREA)
- Data Mining & Analysis (AREA)
- Electromagnetism (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Biology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Traffic Control Systems (AREA)
Abstract
The application relates to the technical field of intelligent parking lots, and discloses a parking lot vehicle positioning method, a terminal and a storage medium. The parking lot is provided with a light emitting module, the vehicle is provided with a light receiving module, and the method comprises the following steps: acquiring a position database and acquiring positioning data acquired by the light receiving module; the position database is established based on the corresponding relation between the position information of the light emitting module and the positioning data collected by the light receiving module; matching the positioning data with a position database to obtain the position information of the vehicle; and sending the position information of the vehicle to a user terminal. By the aid of the mode, hardware cost of the parking lot can be reduced, efficiency and precision of vehicle positioning of the parking lot are improved, and convenience is brought to users.
Description
Technical Field
The application relates to the technical field of intelligent parking lots, in particular to a parking lot vehicle positioning method, a terminal and a storage medium.
Background
With the improvement of living standard and the development of science and technology, the positioning technology is extended indoors to bring convenience to life of people. Underground parking garage vehicle location belongs to one of indoor location technique, has indoor location techniques such as ultrasonic wave location technique, radio frequency identification location technique, bluetooth location technique, infrared location technique at present, and these techniques arrange the requirement height to the sensor, and the circuit is maintained complicacy, and the hardware is with high costs, and has electromagnetic interference among the signal transmission process, aggravates the very poor underground parking garage signal interference of original signal, and complicated circuit also easily causes the conflagration.
The existing parking lot application adopting the visible light communication technology is more described as system-level multifunctional application, comprises approach identification, driving tracks, departure payment and the like, has no specific positioning algorithm for pertinently describing vehicle positioning and a scheme for reversely searching vehicles, is easily influenced by interference light in an optical channel, and has low positioning efficiency and positioning precision.
Disclosure of Invention
The invention mainly solves the technical problem of providing a parking lot vehicle positioning method, a terminal and a storage medium, which can reduce the hardware cost of a parking lot, improve the efficiency and the precision of parking lot vehicle positioning and bring convenience to users.
In order to solve the technical problem, the application adopts a technical scheme that: the method for positioning the vehicles in the parking lot is provided, wherein a light emitting module is installed in the parking lot, and a light receiving module is installed on the vehicle, and comprises the following steps:
acquiring a position database and acquiring positioning data acquired by a light receiving module; the position database is established based on the corresponding relation between the position information of the light emitting module and the positioning data collected by the light receiving module; matching the positioning data with a position database to obtain the position information of the vehicle; and sending the position information of the vehicle to the user terminal.
Specifically, in the above method, the method of acquiring the location database includes:
dividing the parking lot according to regions, and marking a preset number of reference points; acquiring position data of a reference point; and processing the position data by using a position fingerprint algorithm to obtain a position database.
Specifically, in the above method, the method for processing the location data by using the location fingerprinting algorithm to obtain the location database includes:
determining an initial beacon point in the reference point; predicting the position data of the secondary beacon point by using an IDW algorithm according to the position data of the initial beacon point to obtain a position data matrix; and establishing a position database by utilizing an SVT algorithm according to the position data matrix.
Specifically, in the above method, determining the initial beacon point in the reference point comprises:
and selecting part of reference points as initial beacon points in the reference points by utilizing a Latin hypercube sampling method.
Specifically, in the above method, the method of matching the positioning data with the position database to obtain the position information of the vehicle includes:
determining an area to be positioned by using a STING clustering algorithm according to the positioning data; calculating the conditional probability that the position data of all reference points in the area to be positioned is the same as the positioning data by utilizing kernel probability density estimation; and calculating the position information of the vehicle by using a weighted Bayesian algorithm according to the conditional probability.
Specifically, in the above method, the method for determining the area to be located by using STING clustering algorithm according to the location data includes:
constructing a STING clustering structure according to the position data of the reference point; and inquiring the STING clustering structure according to the correlation degree of the positioning data and the position data of the reference point to determine the area to be positioned.
Specifically, in the above method, the positioning data includes at least one of an optical signal intensity and an optical arrival angle of the optical transmission module.
Specifically, in the above method, the light emitting module includes: a signal modulator for modulating a signal; the LED lamp driving module is connected with the signal modulator and used for driving the LED lamp group according to the signal; and the LED lamp group is connected with the LED lamp driving module and used for converting the signal into an optical signal and emitting the optical signal. The light receiving module includes: a photodiode for receiving an optical signal and converting the optical signal into an electrical signal; the signal conditioning module is connected with the photodiode and is used for conditioning the electric signal into a standard signal; the signal processing unit is connected with the signal conditioning module and used for processing the standard signal to obtain the position information of the vehicle; and the communication module is connected with the signal processing unit and used for sending the position information of the vehicle to the user terminal.
In order to solve the above technical problem, another technical solution adopted by the present application is: a parking lot vehicle location terminal is provided that includes a processor and a memory.
In particular, the memory is configured to store program instructions that the processor is configured to execute to implement the parking lot vehicle localization method described above.
In order to solve the above technical problem, the present application adopts another technical solution: there is provided a storage medium storing program instructions executable to implement the parking lot vehicle positioning method described above.
Different from the prior art, the application provides a parking lot vehicle positioning method, a terminal and a storage medium. The parking lot is provided with a light emitting module and the vehicle is provided with a light receiving module, and the method comprises the following steps: acquiring a position database and acquiring positioning data acquired by a light receiving module; the position database is established based on the corresponding relation between the position information of the light emitting module and the positioning data collected by the light receiving module; matching the positioning data with a position database to obtain the position information of the vehicle; and sending the position information of the vehicle to the user terminal. By the aid of the mode, hardware cost of the parking lot can be reduced, efficiency and precision of vehicle positioning of the parking lot are improved, and convenience is brought to users.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts. Wherein:
FIG. 1 is a schematic flow chart diagram illustrating an embodiment of a parking lot vehicle locating method provided by the present application;
FIG. 2 is a schematic flow chart of S1 in FIG. 1;
FIG. 3 is a schematic flow chart of S13 in FIG. 2;
FIG. 4 is a schematic flow chart of S2 in FIG. 1;
FIG. 5 is a schematic flow chart of S21 in FIG. 4;
FIG. 6 is a schematic structural diagram of a light emitting module according to an embodiment of the parking lot vehicle positioning method provided by the present application;
fig. 7 is a schematic structural diagram of a light-receiving module in an embodiment of a parking lot vehicle positioning method provided by the present application;
FIG. 8 is a schematic structural diagram of an embodiment of a parking lot vehicle locating terminal provided by the present application;
fig. 9 is a schematic structural diagram of an embodiment of a storage medium provided in the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. It is to be understood that the specific embodiments described herein are merely illustrative of the application and are not limiting of the application. It should be further noted that, for the convenience of description, only some of the structures related to the present application are shown in the drawings, not all of the structures. 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 application.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
Referring to fig. 1, fig. 1 is a schematic flow chart of an embodiment of a parking lot vehicle positioning method provided in the present application, in which a light emitting module is installed in a parking lot, and a light receiving module is installed in a vehicle, the method includes:
s1: acquiring a position database and acquiring positioning data acquired by the light receiving module; the position database is established based on the corresponding relation between the position information of the light emitting module and the positioning data collected by the light receiving module.
Specifically, the method is based on the visible light communication technology, a light emitting module is pre-installed in a parking lot, a light receiving module is pre-installed in a vehicle, and information is transmitted by using high-speed bright and dark flashing signals which are emitted by fluorescent lamps or light emitting diodes and cannot be seen by naked eyes. The position database is established based on the corresponding relation of the position information of the light emission module and the positioning data collected by the light receiving module in the off-line data collection stage, and the positioning data can be at least one of the light signal intensity and the light arrival angle of the light emission module.
S2: and matching the positioning data with a position database to obtain the position information of the vehicle.
Specifically, the matching positioning algorithm is utilized to match the positioning data with the position database, and the position information of the vehicle is determined according to the corresponding relation between the positioning data and the position information of the light emitting module. For example, when the position information of a certain position coordinate light emitting module matches with the positioning data, the position coordinate is determined as the position information of the vehicle.
S3: and sending the position information of the vehicle to a user terminal.
Specifically, the position information of the vehicle may be sent to a mobile phone of the user, a car-searching navigation screen of the parking lot, and other terminals, which are not specifically limited herein.
Compared with the prior art, the method provided by the embodiment comprises the following steps: acquiring a position database and acquiring positioning data acquired by a light receiving module; the position database is established based on the corresponding relation between the position information of the light emitting module and the positioning data collected by the light receiving module; matching the positioning data with a position database to obtain the position information of the vehicle; and sending the position information of the vehicle to the user terminal. By the aid of the mode, hardware cost of the parking lot can be reduced, efficiency and precision of vehicle positioning of the parking lot are improved, and convenience is brought to users.
Referring to fig. 2, fig. 2 is a schematic flow chart of S1 in fig. 1, and S1 may further include:
s11: and dividing the parking lot according to regions, and marking a preset number of reference points.
Specifically, the parking lot is divided into appropriate grid areas, a certain number of reference points are marked, and position coordinates of the reference points are recorded. It can be understood that the more and denser the number of reference points, the higher the positioning accuracy, but at the same time, the data amount and the labor time cost are also increased, so that the grid area is divided and the reference points are marked according to the specific situation of the parking lot, for example, the parking lot has a large area and a complex structure, the more the number of reference points are needed, and the parking lot has a small area and a simple structure, the less the number of reference points are needed.
S12: position data of the reference point is acquired.
Specifically, the position data may be at least one of the light signal intensity and the light arrival angle of the light emitting module at a certain reference point, and the collected position data corresponds to the position coordinates of the reference point one by one.
Furthermore, smoothing is carried out on the signal singular value of the acquired position data by a Gaussian filtering method, and the reliability of the position data is improved.
S13: and processing the position data by using a position fingerprint algorithm to obtain the position database.
Specifically, a position database is created by performing prediction processing using a position fingerprint algorithm based on position data of the reference points.
Referring to fig. 3, fig. 3 is a schematic flow chart of S13 in fig. 2, and S13 may further include:
s131: determining an initial beacon point in the reference points.
Specifically, a part of reference points in the reference points are selected as initial beacon points according to a specific method.
Optionally, the method of selecting the initial beacon point is a latin hypercube sampling method. Latin Hypercube Sampling (LHS) is a method for approximate random sampling from multivariate parameter distribution, belongs to a hierarchical sampling technology, and is commonly used for computer experiments or Monte Carlo integration and the like. The realization principle is as follows: suppose there is n in the location area1*n2And the reference points form a coordinate matrix M, each dimensional vector of the matrix M is divided into a plurality of non-overlapping equal-length intervals, and then each interval collects a proper amount of elements to form a final beacon point set. The method can realize the uniform distribution of the beacon points in the positioning area and also ensure that the selected beacon points have certain randomness.
S132: and predicting the position data of the secondary beacon point by using an IDW algorithm according to the position data of the initial beacon point to obtain a position data matrix.
Specifically, the IDW (inverse distance weighting) algorithm is proposed based on the position correlation of reference points, the shorter the distance between two reference points, the higher the correlation between them. The formula of the IDW algorithm is as follows:
wherein, w (d)i) Weighting values with interpolation points for each reference point; diThe calculation formula is as follows:
optionally, in a specific embodiment, the position data is a Received Signal Strength (RSS) value of the reference point. Assuming that a position database needs to contain position data of n reference points, firstly, measuring the position data of m reference points, and then, using an IDW algorithm to perform interpolation to calculate the position data of the remaining n-m reference points, and using the n-m reference points as points to be interpolated. The RSS value calculation formula of a certain point to be interpolated is as follows:
wherein, the lambdaiThe weight value formula of the initial beacon point is
Where p represents a weighted power exponent, this parameter has a relatively large effect on the interpolation accuracy of the algorithm, and in some embodiments is parametrically optimized.
Specifically, after the position data of the point to be interpolated is predicted by using an IDW algorithm on the basis of the initial beacon point, part of the points to be interpolated are selected as secondary beacon points according to a specific method, and the initial beacon point and the secondary beacon points are combined to form a position data matrix.
Optionally, the method of selecting the sub-beacon points is also a latin hypercube sampling method.
S133: and establishing the position database by utilizing an SVT algorithm according to the position data matrix.
Specifically, according to a space propagation model of the signal and simulation analysis of the distribution of the visible light intensity in a space region, it is obtained that position data constructed by reference points in a certain region has high spatial correlation, and therefore the position data is considered to be a low-rank matrix. From this viewpoint, the present embodiment estimates a complete location database from the above location data matrix using SVT (singular value shrinkage) algorithm.
Specifically, let n exist in a certain positioning region1*n2And (3) obtaining RSS values of m initial beacon points by using a matrix filling algorithm according to a mathematical model formula:
minrank(X)
s.t.Xij＝Mij,i,j∈Ω
where X denotes the complete location database and M denotes the location data matrix containing only the initial beacon points.
The above formula can be converted into:
s.t.PΩ(X)＝PΩ(M),i,j∈Ω
by using the formula, the problem is changed from non-convex optimization to convex optimization, and for the convex optimization problem, the SVT algorithm is adopted to solve, and the solving formula is as follows:
where k is the iteration constant, δkDenotes the iteration step size, DτThe soft threshold operation is represented by the following specific calculation formula:
Dτ(X)＝UDτ(Σ)V*
Dτ(X)＝diag({σi-τ}+)
wherein σiRepresenting a matrix XSingular values.
Further, after the RSS values of the reference points of all the areas are predicted according to the above method, the reference points and the corresponding coordinates are combined to form a position database.
Referring to fig. 4, fig. 4 is a schematic flowchart of S2 in fig. 1, and S2 may further include:
s21: and determining an area to be positioned by utilizing a STING clustering algorithm according to the positioning data.
In particular, the STING clustering algorithm (Statistical Information Grid-based method) is a Grid-based multi-resolution clustering technique that divides a spatial region of an input object into rectangular units, and the space can be divided in a hierarchical and recursive manner. Such multi-level rectangular cells correspond to different resolutions and form a hierarchy: each higher level cell is divided into lower level cells. Statistical information about the attributes of each grid cell is pre-computed and stored as statistical parameters that are valid for query processing and other data analysis tasks. The STING clustering algorithm has high clustering speed, grid calculation and query are mutually independent, and the area to be positioned can be quickly determined.
S22: and calculating the conditional probability that the position data of all the reference points in the area to be positioned is the same as the positioning data by utilizing kernel probability density estimation.
In particular, kernel density estimation (kernel density estimation) is a function used in probability theory to estimate unknown density, and belongs to one of the non-parametric test methods. Firstly, the positioning data collected by the light receiving module is obtained, in this embodiment, the obtained RSSI data received by the light receiving module of the vehicle to be positioned has a value of S, and then the posterior probability of all reference points in the area to be positioned is calculated, and the calculation formula is:
wherein, P (l)iS) is the conditional probability of having an RSSI value of S at a known location, P (l)i) Is the a-priori probability in the location space,i.e. the probability that the ith reference point is in the location area. Generally, the reference points are uniformly distributed in the positioning area, so P (l)i) Considered as a constant. For the localization area, p(s) is a constant. Thus, the target location formula is:
argmaxP(li/s)＝argmaxP(s/li)
since it is the magnitude of the posterior probability that is needed, rather than a specific value, P (l) will bei) P(s) is ignored.
Since the signals of the n optical transmission modules can be received at any reference point and the signal strength between different optical transmission modules is independent, no mutual interference occurs. Thus, the total likelihood function of the n light emission modules corresponds to the product of the likelihood functions of each light emission module, which can be represented by a joint probability distribution function, with the formula:
setting the RSSI value distribution corresponding to each reference point as gaussian distribution, obtaining a formula:
therefore, the conditional probability formula with the RSSI value of a certain reference point being S is:
specifically, according to the above method, the conditional probability that the position data of all the reference points in the area to be positioned is the same as the positioning data can be calculated.
S23: and calculating the position information of the vehicle by using a weighted Bayesian algorithm according to the conditional probability.
Specifically, the weighted bayesian algorithm is a concept of introducing a weight value on the basis of the bayesian algorithm. After the posterior probabilities of the reference points are obtained, the reference points are sequenced, the first K maximum posterior probabilities are selected, and the corresponding K position coordinates are obtained. The position information of the vehicle to be positioned is obtained by calculation in a probability weighted summation mode, and the coordinate formula is as follows:
referring to fig. 5, fig. 5 is a schematic flowchart of S21 in fig. 4, and S21 may further include:
s211: and constructing a STING clustering structure according to the position data of the reference points.
Specifically, the granularity of the bottom-layer cells is determined according to the position data of the reference points, then the cells of each layer are divided into the cells of the upper layer, and finally a cell, namely the cells of the top layer, is formed, and each divided cell contains the summary information of the internal reference points. In the present embodiment, centroid fingerprint vectors are used to represent cell information, and it is assumed that the position data of the reference point in the jth cell of the ith layer contains { r }s，rs+1,…,reThe calculation formula is as follows:
wherein E isijRepresenting a centroid fingerprint vector.
S212: and querying the STING cluster structure according to the correlation degree of the positioning data and the position data of the reference point to determine the area to be positioned.
Specifically, the STING cluster structure is queried according to the correlation between the positioning data and the cell centroid fingerprint vector, generally starting from the top layer, selecting several adjacent cells at a time to query to the lower layer until the query reaches the bottom layer. In this embodiment, four adjacent cells are selected for each query, and finally, the four cells on the bottom layer are obtained as the to-be-positioned area, so that the situation of judgment error when positioning data are located at the edges of the cells is effectively avoided, and positioning errors caused by STING clustering are improved.
Referring to fig. 6, fig. 6 is a schematic structural diagram of a light emitting module in an embodiment of the parking lot vehicle positioning method provided by the present application. The light emitting module 600 includes a signal modulator 601, an LED lamp driving module 602, and an LED lamp group 603.
Specifically, the signal modulator 601 is used to modulate a signal; the LED lamp driving module 602 is connected to the signal modulator 601, and is configured to drive the LED lamp group 603 according to the signal; the LED lamp group 603 is connected to the LED lamp driving module 602, and is configured to convert the signal into an optical signal and emit the optical signal.
Specifically, the light emitting module 600 is installed at a suitable position of the parking lot, the number of the LED lamp sets 603 in one area is at least 2, so that the lighting requirement is ensured, the signal transmission is not influenced, and the frequency of the light signals emitted by the LED lamp sets 603 is not perceived by naked eyes.
In particular, the original electrical signal at the transmitting end in a communication system typically has spectral components with very low frequencies, which are generally not suitable for direct transmission in a channel. Therefore, it is usually necessary to convert the original signal into a high frequency signal whose frequency band is suitable for channel transmission, and this process is called modulation, and the signal modulator 601 is used to implement this modulation process in this embodiment.
In particular, an led (light Emitting diode), which is a solid-state semiconductor device capable of converting electrical energy into visible light, can directly convert electricity into light. The heart of the LED is a semiconductor wafer, one end of the wafer is attached to a support, the other end of the wafer is a cathode, and the other end of the wafer is connected with an anode of a power supply, so that the whole wafer is packaged by epoxy resin. LEDs are characteristic sensitive semiconductor devices and have negative temperature characteristics, so they need to be stable and protected during application, resulting in a driving concept. The requirement of the LED device on the driving power is nearly strict, and in this embodiment, the LED lamp driving module 602 performs constant current driving on the LED lamp group 603.
Referring to fig. 7, fig. 7 is a schematic structural diagram of a light receiving module in an embodiment of a parking lot vehicle positioning method provided by the present application. The optical receiving module 700 includes a photodiode 701, a signal conditioning module 702, a signal processing unit 703 and a communication module 704.
Specifically, the photodiode 701 is configured to receive an optical signal and convert the optical signal into an electrical signal; the signal conditioning module 702 is connected to the photodiode 701 and is configured to condition the electrical signal into a standard signal; the signal processing unit 703 is connected to the signal conditioning module 702, and is configured to process the standard signal to obtain the position information of the vehicle; the communication module 704 is connected to the signal processing unit 703 and is configured to send the position information of the vehicle to the user terminal.
Specifically, the light receiving module 700 is installed at a suitable position of the vehicle, the photodiode 701 may be installed below a front windshield in the vehicle to ensure that the light signal can be received, and the signal conditioning module 702, the signal processing unit 703 and the communication module 704 may be installed in a center console area to implement signal processing and send information.
Specifically, the photodiode 701 is a semiconductor device composed of a PN junction, has one-directional conductivity, and is designed and fabricated such that the area of the PN junction is relatively large as much as possible in order to receive incident light. The photodiode works under the action of reverse voltage, and when no light is emitted, reverse current is extremely weak, namely dark current; in the presence of light, the reverse current rapidly increases to tens of microamperes, referred to as photocurrent. The greater the intensity of the light, the greater the reverse current, and the change in light causes a change in photodiode current, which can convert the optical signal into an electrical signal.
In particular, signal conditioning refers to converting various signals detected by the sensing element into standard signals. The signal conditioning in the digital input channel mainly comprises jitter elimination, filtering, protection, level conversion, isolation and the like. In this embodiment, the signal conditioning module 702 is configured to convert the signal detected by the photodiode 701 into a standard signal.
Specifically, signal processing is a generic term for processing various types of standard signals for various intended purposes and requirements. The processing of analog signals is referred to as analog signal processing, and the processing of digital signals is referred to as digital signal processing. The term "signal processing" refers to a process of processing a signal recorded on a certain medium to extract useful information, and is a generic term for processes of extracting, converting, analyzing, and synthesizing the signal. In this embodiment, the signal processing unit 703 processes the standard signal to obtain the positioning data of the vehicle, and matches the positioning data with a preset position database to obtain the position information of the vehicle.
Optionally, the communication module 704 is a wireless communication module, which performs signal transmission by using a wireless technology, and sends the position information of the vehicle to a mobile phone of the user, a car-searching navigation screen of the parking lot, and other terminals.
Referring to fig. 8, fig. 8 is a schematic structural diagram of an embodiment of a parking lot vehicle positioning terminal provided by the present application, where the positioning terminal 800 includes a processor 801 and a memory 802.
In particular, the memory 802 is used to store program instructions that the processor 801 is used to execute to implement the methods provided by any one or any non-conflicting combination of the above embodiments.
Optionally, the processor 801 is a Central Processing Unit (CPU), which is one of the main devices of an electronic computer, and is a core accessory in the computer. Its functions are mainly to interpret computer instructions and to process data in computer software. The CPU is the core component of the computer responsible for reading, decoding and executing instructions. The central processor mainly comprises two parts, namely a controller and an arithmetic unit, and also comprises a cache memory and a bus for realizing data and control of the connection between the cache memory and the arithmetic unit. The central processing unit mainly has the functions of processing instructions, executing operations, controlling time and processing data. In a computer architecture, a CPU is a core hardware unit that performs control and allocation of all hardware resources (such as memory and input/output units) of a computer and performs general operations. The CPU is the computational and control core of the computer. The operation of all software layers in the computer system will eventually be mapped to the operation of the CPU by the instruction set.
Alternatively, the memory 802 may be a Read Only Memory (ROM) or a Random Access Memory (RAM), which is a memory device in a computer system and is mainly used for storing programs and data. All information in the computer, including the input raw data, the computer program, the intermediate run results, and the final run results, is stored in memory. It is based on the position of controller to store and take out information.
Specifically, the positioning terminal 800 may be separately installed in a central control area of a vehicle, or may be integrated in a vehicle-mounted system of the vehicle.
Referring to fig. 9, fig. 9 is a schematic structural diagram of an embodiment of a storage medium provided in the present application, where the storage medium 900 includes program instructions 901, and the program instructions 901 can be executed to implement the method provided by any one or any non-conflicting combination of the above embodiments. The storage medium 900 has a capacity that is sized to meet the requirements of the stored program instructions 901.
Alternatively, the storage medium 900 may be a memory card, an optical disc, a usb-disc, a chip, etc., and is not particularly limited herein.
The above description is only for the purpose of illustrating embodiments of the present application and is not intended to limit the scope of the present application, and all modifications of equivalent structures and equivalent processes, which are made according to the content of the present specification and the accompanying drawings, or which are directly or indirectly applied to other related technical fields, are also included in the scope of the present application.
Claims (10)
1. A parking lot vehicle positioning method is characterized in that a light emitting module is installed in a parking lot, a light receiving module is installed on a vehicle, and the method comprises the following steps:
acquiring a position database and acquiring positioning data acquired by the light receiving module; the position database is established based on the corresponding relation between the position information of the light emitting module and the positioning data collected by the light receiving module;
matching the positioning data with a position database to obtain the position information of the vehicle;
and sending the position information of the vehicle to a user terminal.
2. The method of claim 1, wherein obtaining the location database comprises:
dividing the parking lot according to regions, and marking a preset number of reference points;
acquiring position data of the reference point;
and processing the position data by using a position fingerprint algorithm to obtain the position database.
3. The method of claim 2, wherein the processing the location data using a location fingerprinting algorithm to obtain the location database comprises:
determining an initial beacon point in the reference points;
predicting the position data of the secondary beacon point by using an IDW algorithm according to the position data of the initial beacon point to obtain a position data matrix;
and establishing the position database by utilizing an SVT algorithm according to the position data matrix.
4. The method of claim 3, wherein the determining an initial beacon point of the reference points comprises:
and selecting part of reference points as initial beacon points in the reference points by utilizing a Latin hypercube sampling method.
5. The method of claim 1, wherein matching the positioning data to a location database to obtain location information of the vehicle comprises:
determining an area to be positioned by utilizing a STING clustering algorithm according to the positioning data;
calculating the conditional probability that the position data of all the reference points in the area to be positioned is the same as the positioning data by utilizing kernel probability density estimation;
and calculating the position information of the vehicle by using a weighted Bayesian algorithm according to the conditional probability.
6. The method of claim 5, wherein said determining a region to be located using a STING clustering algorithm based on said location data comprises:
constructing a STING clustering structure according to the position data of the reference points;
and querying the STING cluster structure according to the correlation degree of the positioning data and the position data of the reference point to determine the area to be positioned.
7. The method of claim 1,
the positioning data comprises at least one of the light signal intensity and the light arrival angle of the light emitting module.
8. The method of claim 1,
the light emission module includes:
a signal modulator for modulating a signal;
the LED lamp driving module is connected with the signal modulator and used for driving the LED lamp group according to the signal;
the LED lamp group is connected with the LED lamp driving module and used for converting the signals into optical signals and emitting the optical signals;
the light receiving module includes:
a photodiode for receiving the optical signal and converting the optical signal into an electrical signal;
the signal conditioning module is connected with the photodiode and is used for conditioning the electric signal into a standard signal;
the signal processing unit is connected with the signal conditioning module and used for processing the standard signal to obtain the position information of the vehicle;
and the communication module is connected with the signal processing unit and used for sending the position information of the vehicle to a user terminal.
9. A parking lot vehicle location terminal, characterized in that the terminal comprises a processor and a memory for storing program instructions, the processor being adapted to execute the program instructions to implement the method according to any one of claims 1 to 8.
10. A storage medium, characterized in that program instructions are stored, which can be executed to implement the method according to any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111538001.8A CN114202952A (en) | 2021-12-15 | 2021-12-15 | Parking lot vehicle positioning method, terminal and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111538001.8A CN114202952A (en) | 2021-12-15 | 2021-12-15 | Parking lot vehicle positioning method, terminal and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114202952A true CN114202952A (en) | 2022-03-18 |
Family
ID=80654170
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111538001.8A Pending CN114202952A (en) | 2021-12-15 | 2021-12-15 | Parking lot vehicle positioning method, terminal and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114202952A (en) |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106961724A (en) * | 2017-03-24 | 2017-07-18 | 重庆邮电大学 | A kind of mobile target real-time location method based on visible light communication |
| CN108181609A (en) * | 2017-12-12 | 2018-06-19 | 深圳市易成自动驾驶技术有限公司 | Localization method, device and the computer readable storage medium of vehicle |
| CN109191907A (en) * | 2018-11-12 | 2019-01-11 | 深圳市金证科技股份有限公司 | A kind of indoor reverse car search bootstrap technique and its device based on optical signal |
| CN109727475A (en) * | 2017-10-27 | 2019-05-07 | 中移（杭州）信息技术有限公司 | Vehicle lookup method, device and communication equipment based on parking lot |
| CN111174784A (en) * | 2020-01-03 | 2020-05-19 | 重庆邮电大学 | Visible light and inertial navigation fusion positioning method for indoor parking lot |
| CN113271542A (en) * | 2021-04-08 | 2021-08-17 | 复旦大学 | Indoor mobile terminal positioning method based on Bluetooth and visible light |
-
2021
- 2021-12-15 CN CN202111538001.8A patent/CN114202952A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106961724A (en) * | 2017-03-24 | 2017-07-18 | 重庆邮电大学 | A kind of mobile target real-time location method based on visible light communication |
| CN109727475A (en) * | 2017-10-27 | 2019-05-07 | 中移（杭州）信息技术有限公司 | Vehicle lookup method, device and communication equipment based on parking lot |
| CN108181609A (en) * | 2017-12-12 | 2018-06-19 | 深圳市易成自动驾驶技术有限公司 | Localization method, device and the computer readable storage medium of vehicle |
| CN109191907A (en) * | 2018-11-12 | 2019-01-11 | 深圳市金证科技股份有限公司 | A kind of indoor reverse car search bootstrap technique and its device based on optical signal |
| CN111174784A (en) * | 2020-01-03 | 2020-05-19 | 重庆邮电大学 | Visible light and inertial navigation fusion positioning method for indoor parking lot |
| CN113271542A (en) * | 2021-04-08 | 2021-08-17 | 复旦大学 | Indoor mobile terminal positioning method based on Bluetooth and visible light |
Non-Patent Citations (1)
| Title |
|---|
| 崔泽鹏: "基于可见光的室内定位技术研究与实现", 《中国优秀硕士学位论文全文数据库（电子期刊）工程科技Ⅱ辑》 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Lu et al. | Green energy harvesting strategies on edge-based urban computing in sustainable internet of things | |
| Lin et al. | Many-objective sensor selection in IoT systems | |
| CN104424483A (en) | Face image illumination preprocessing method, face image illumination preprocessing device and terminal | |
| Tran et al. | Reducing the burden of data collection in a fingerprinting-based VLP system using a hybrid of improved co-training semi-supervised regression and adaptive boosting algorithms | |
| Lazaro et al. | Room-level localization system based on LoRa backscatters | |
| Chen et al. | A wifi indoor localization method based on dilated cnn and support vector regression | |
| Sun et al. | Robust mobile geo-location algorithm based on LS-SVM | |
| CN114202952A (en) | Parking lot vehicle positioning method, terminal and storage medium | |
| Tarekegn et al. | Applying long short-term memory (LSTM) mechanisms for fingerprinting outdoor positioning in hybrid networks | |
| Rennane et al. | Design of autonomous multi-sensing passive UHF RFID tag for greenhouse monitoring | |
| CN113438733A (en) | System and method for realizing indoor positioning based on 5G and WIFI and electronic equipment | |
| CN116466335A (en) | Indoor visible light positioning method and system based on clustering and deep neural network | |
| Rosabal et al. | Sustainable RF Wireless Energy Transfer for Massive IoT: Enablers and Challenges | |
| Hu et al. | Could or could not of Grid-Loc: Grid BLE structure for indoor localisation system using machine learning | |
| CN101988962A (en) | Implementation of interactive information auxiliary system based on ontology and location service | |
| CN112036607B (en) | Wind power output fluctuation prediction method and device based on output level and storage medium | |
| Su et al. | Four-dimensional indoor visible light positioning: A deep-learning-based perspective | |
| CN114758364A (en) | Industrial Internet of things scene fusion positioning method and system based on deep learning | |
| Scherer et al. | Widevision: A low-power, multi-protocol wireless vision platform for distributed surveillance | |
| CN105092484A (en) | Liquid identifying device | |
| CN111756460A (en) | Cooperative spectrum sensing method and device based on unsupervised learning in cognitive network | |
| Liu et al. | Hierarchical fingerprinting and feature extraction for indoor localization | |
| CN117008075B (en) | Wireless gesture acquisition method and system based on microwave antenna array | |
| Farkas | Placement optimization of reference sensors for indoor tracking | |
| Liu et al. | Indoor Fingerprinting Positioning System Using Deep Learning with Data Augmentation. |
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: 20220318 |