CN110824421A - Position information processing method and device, storage medium and electronic equipment - Google Patents
Position information processing method and device, storage medium and electronic equipment Download PDFInfo
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- 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
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Abstract
The invention discloses a position information processing method, a position information processing device, a storage medium and electronic equipment, wherein the method comprises the following steps: acquiring a preset number of position points determined by the target positioning equipment based on the received electronic pulse; for any position point, determining a neighborhood subsample set of the position point according to a preset density parameter, wherein the neighborhood subsample set comprises at least one position point in the density parameter range of the position point; and determining the classification information of the position points in each neighborhood subsample set according to the neighborhood subsample set. The neighborhood subsample set of each position point is determined through the density parameters, and the classification information of each position point is determined based on the neighborhood subsample set, so that the position points are classified unsupervised, the behavior information determined by the target positioning equipment can be accurately classified and recognized based on the classification information, and the position information can be conveniently managed and screened.
Description
Technical Field
The embodiment of the invention relates to the technical field of positioning, in particular to a position information processing method and device, a storage medium and electronic equipment.
Background
At present, with the increasing popularization of intelligent equipment, people put forward higher and higher demands on positioning. In outdoor open fields, positioning schemes based on global positioning satellite systems are mature. However, in order to obtain a high quality location service, the location terminal must satisfy no blocking of high-rise buildings within a 30 degree elevation angle. This condition is increasingly difficult to satisfy for modern large cities. In addition, in the indoor environment, since the satellite signal is blocked, the positioning service cannot be provided.
Nowadays, positioning technologies based on electromagnetic pulses are proposed, such as Ultra Wide Band (Ultra Wide Band) positioning technologies, which can provide centimeter-level positioning services. However, the positioning technology based on electromagnetic pulses can have good accuracy in a line-of-sight range, but in real life, a channel between a base station and a positioning device is not necessarily guaranteed to be free of obstacles, so that it cannot be determined whether the positioning data determined by the positioning technology based on electromagnetic pulses is determined in a line-of-sight scene.
Disclosure of Invention
The invention provides a position information processing method, a position information processing device, a storage medium and electronic equipment, which are used for realizing the classification of positioning results.
In a first aspect, an embodiment of the present invention provides a method for processing location information, including:
acquiring a preset number of position points determined by the target positioning equipment based on the received electronic pulse;
for any position point, determining a neighborhood subsample set of the position point according to a preset density parameter, wherein the neighborhood subsample set comprises at least one position point in the density parameter range of the position point;
and determining the classification information of the position points in each neighborhood subsample set according to the neighborhood subsample set.
In a second aspect, an embodiment of the present invention further provides a location information processing apparatus, including:
the position point determining module is used for acquiring a preset number of position points determined by the target positioning equipment based on the received electronic pulse;
the neighborhood subsample set determining module is used for determining a neighborhood subsample set of any position point according to a preset density parameter, wherein the neighborhood subsample set comprises at least one position point in the density parameter range of the position point;
and the first classification information determining module is used for determining the classification information of the position points in each neighborhood subsample set according to the neighborhood subsample set.
In a third aspect, an embodiment of the present invention further provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor executes the computer program to implement a location information processing method according to any embodiment of the present invention.
In a fourth aspect, embodiments of the present invention further provide a storage medium containing computer-executable instructions, where the computer-executable instructions, when executed by a computer processor, implement a location information processing method according to any of the embodiments of the present invention.
According to the technical scheme provided by the embodiment of the invention, the neighborhood subsample set of each position point is determined through the density parameter, and the classification information of each position point is determined based on the neighborhood subsample set, so that the position points are classified unsupervised, the behavior information determined by the target positioning equipment can be accurately classified and identified based on the classification information, and the position information is conveniently managed and screened.
Drawings
Fig. 1 is a schematic flowchart of a location information processing method according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating a neighborhood subsample set according to an embodiment of the present invention;
fig. 3 is a schematic structural diagram of a position information processing apparatus according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
Example one
Fig. 1 is a schematic flowchart of a position information processing method according to an embodiment of the present invention, where the embodiment is applicable to a case of classifying position information determined based on electromagnetic pulses, and the method may be executed by a position information processing apparatus according to an embodiment of the present invention, where the apparatus may be implemented by software and/or hardware, and the method specifically includes the following steps:
s110, acquiring a preset number of position points determined by the target positioning equipment based on the received electronic pulse.
S120, for any position point, determining a neighborhood subsample set of the position point according to a preset density parameter, wherein the neighborhood subsample set comprises at least one position point in the density parameter range of the position point.
S130, according to the neighborhood subsample set, determining the classification information of the position points in each neighborhood subsample set.
In this embodiment, the target positioning device determines position information, i.e., a position point, based on a positioning technique of electromagnetic pulses. Optionally, the target positioning device determines the position information according to the Time of Arrival (ToA) of the electromagnetic pulse, and specifically, the target positioning device determines the flight Time t of the electromagnetic pulse in the air, which arrives at the target positioning device, and is sent from a plurality of known coordinate base stations1，t2…tnBased on the flight time multiplied by the speed of light, the distance between the target positioning device and each base station is obtained by adopting the vacuum speed of light for the general speed of light. The target positioning device is located at the intersection of circles whose radii are the distances mentioned above, with the base station being a circle. Obtaining coordinates of the intersection point, i.e. the position point of the target positioning equipment, by solving an over-determined equation set. Optionally, the target positioning device may further determine the position information according to a Time Difference of Arrival (TDoA) of the electromagnetic pulse, and specifically, the target positioning device determines a Difference t between a Time of flight in the air of the electromagnetic pulse arriving at the target positioning device and sent by the reference base station12，t13…t1n(assuming that the reference base station is base station number 1), the distance difference between the target positioning device and each base station is obtained based on the time-of-flight difference multiplied by the speed of light. The target positioning equipment is positioned on the intersection point of the hyperbolas formed by the distance differences, and the overdetermined equation set is solved to obtain the coordinates of the intersection point, namely the position point of the target positioning equipment.
In this embodiment, because the target positioning device determines the position information based on the positioning technology of the electromagnetic pulse, the electromagnetic pulse may affect the fingerprint characteristics of the electromagnetic pulse when encountering no obstacle (i.e., in a line-of-sight scene), an obstacle (i.e., in a non-line-of-sight scene), and different obstacles in the flight process, which further causes a difference in position points.
In this embodiment, the target positioning device collects a preset number of location points within a preset time period, where the preset number may be determined according to a classification precision requirement, and the higher the classification precision requirement is, the larger the preset number may be, and correspondingly, the lower the classification precision requirement is, the smaller the preset number may be. After the location points are collected, the collected location points may be added to the set of fingerprint feature points M, for example, (a, B, C, D, E, F), it should be noted that the set of fingerprint feature points M is only one example. Determining a neighborhood subsample set of the position point based on the preset density parameter, optionally, determining the neighborhood subsample set of the position point according to the preset density parameter for any position point, including: determining a neighborhood subsample set of the current position point according to the density parameter; and iteratively determining a neighborhood subsample set of other position points except the current position point in the neighborhood subsample set. Referring to fig. 2, fig. 2 is a schematic diagram of a neighborhood subsample set according to an embodiment of the present invention. In fig. 2, a neighborhood subsample set of each location point is determined based on a density parameter, for example, the density parameter τ may be 0.1, corresponding to any location point, a domain range is determined by taking the location point as a center and the density parameter as a radius, and a location point included in the domain range is determined as a location point included in the neighborhood subsample set of the location point, for example, in fig. 2, a location point a and a location point B are included in the neighborhood range of the location point a, and then a neighborhood subsample set N e (a) of the location point a includes the location points a and B, wherein, for a newly added location point B in the neighborhood subsample set of the location point a, the neighborhood subsample set N e (B) of the location point B includes the location points A, B and C, and so on, for a newly added location point C in the neighborhood subsample set of the location point a and the neighborhood subsample set of the location point B, and determining that the neighborhood subsample set N epsilon (C) of the position point C comprises the position points B and C until no new position point exists, and stopping iteration to determine the neighborhood subsample set.
Optionally, a minimum point set MinPts is preset, and when the number of position points in the neighborhood subsample set is less than MinPts, the neighborhood subsample set is discarded, for example, MinPts may be 1. The interference of the neighborhood subsample set which does not meet the minimum point set on the position point classification is reduced by setting the minimum point set MinPts for screening the neighborhood subsample set.
Optionally, determining classification information of the position points in each neighborhood subsample set according to the neighborhood subsample set, includes: and determining the position points in the neighborhood subsample sets with the intersection to belong to the same type. For example, in fig. 2, N ∈ (a) ═ a, B, N ∈ (B) ═ a, B, C, N ∈ (C) ═ B, C, N ∈ (D) ═ D, F, N ∈ (E) ═ E, F, N ∈ (F) ═ D, E, F, and N ∈ (F), and thus it can be understood that there is an intersection between N ∈ (a) and N ∈ (B), and N ∈ (C), that is, a location point B, and a location point C included in N ∈ (a), N ∈ (B), and N ∈ (C), and that there is an intersection between N ∈ (D), N ∈ (E), and N ∈ (F), and that there can be a location point D, a location point E, and a location point F included in N ∈ (F), and that can be determined as one type. And removing the position points for determining the classification information from the fingerprint feature point set M until the fingerprint feature point set M is empty, and determining that the classification is finished.
Optionally, determining classification information of the position points in each neighborhood subsample set according to the neighborhood subsample set, includes: and iteratively determining that the current position point and other position points in the neighborhood subsample set of the current position point belong to the same type. Creating a class by using the current position point, determining other position points in the neighborhood sub-sample set of the current position point as belonging to the class, determining the neighborhood sub-sample set of the other position points in an iterative manner, determining the position in the new neighborhood sub-sample set determined in the iterative manner as belonging to the class, and the like. Illustratively, a first class is created based on the position point A, and the position point B is determined to belong to the first class according to N e (A), further, the position point C can be determined to belong to the first class for N e (B), and the position point determination of the first class is completed for N e (C) without new position points; and creating a second class for the position point D, determining that the position point F belongs to the second class according to N belongs to (D), further determining that the position point E belongs to the second class according to N belongs to (F), and completing the position point determination of the second class for the condition that the new position point is not included in the N belongs to (E). And removing the position points for determining the classification information from the fingerprint feature point set M until the fingerprint feature point set M is empty, and determining that the classification is finished.
According to the technical scheme of the embodiment, the neighborhood sub-sample set of each position point is determined through the density parameter, the classification information of each position point is determined based on the neighborhood sub-sample set, the position points are classified unsupervised, the behavior information determined by the target positioning equipment can be accurately classified and identified based on the classification information, and the position information is conveniently managed and screened.
Optionally, the position point in step S110 includes a first position point determined in a line-of-sight scene, and correspondingly, the classification information includes the line-of-sight scene; optionally, the location point in step S110 may further include a second location point determined in a non-line-of-sight scene, and accordingly, the classification information includes the non-line-of-sight scene. Optionally, when it is determined that the position point determined by the target positioning device belongs to a non-line-of-sight scene, or when it is determined that the position point determined by the target positioning device does not belong to a line-of-sight scene, it is determined that the position point is inaccurate, and the position point is deleted. By dividing the position point into two categories of a line-of-sight scene and a non-line-of-sight scene, whether the position point is accurately positioned or not is conveniently determined through the classification information of the position point.
In some embodiments, a first number of first location points are collected by a target positioning device in a line-of-sight scene, classification information of the line-of-sight scene is determined based on the first location points, a second number of second location points are collected by the target positioning device in a non-line-of-sight scene, non-classification information of the line-of-sight scene is determined based on the second location points, classification of location points subsequently collected by the target positioning device is facilitated by setting classification information of the line-of-sight scene and the non-classification information, and a collection scene of the location points is determined.
In some embodiments, after determining classification information for a positioning point acquired by the target positioning device in a preset time period, for example, the classification information includes a first class and a second class, acquiring a first position point in a line-of-sight scene, determining a class to which the first position point belongs, for example, the first position point belongs to the first class, determining that the first class is the line-of-sight scene, acquiring a second position point in the line-of-sight scene, determining a class to which the second position point belongs, for example, the second position point belongs to the second class, and determining that the second class is a non-line-of-sight scene. The classes created based on the above embodiments are verified by the location points with tags, which facilitates the definition of the respective classes.
On the basis of the above embodiment, the method further includes: and acquiring a new position point, determining the distance between the position point and each type of centroid, and determining the classification information of the new position point according to the distance. For example, for any class, the class centroid of the class is determined based on the coordinates of the location points included in the class, and for example, the class centroid may be the mean value of the coordinates of the location points included in the class. Illustratively, referring to FIG. 2, the first class includes location points A, B and C, the class centroid is P1, the second class includes location points D, E and F, and the class centroid is P2.
And when a new position point exists, determining the distance between the new position point and each type of centroid respectively, wherein the Euclidean distance between the new position point and each type of centroid can be determined. Optionally, when the classification information of the position points is at least two, the classification information corresponding to the minimum distance is determined as the classification information of the new position point, for example, in fig. 2, the distances between the new position point and the centroid class P1 and P2 are respectively determined, and when the distance from the centroid class P1 is smaller than the distance from the centroid class P2, the new position point is determined to belong to the first class. Optionally, when the classification information of the position point is one, when the distance between the new position point and the centroid is less than or equal to a second preset distance, it is determined that the position point belongs to the classification information.
It should be noted that, when a new location point joins a class, the class centroid of the class is updated based on the new location point and the original location point.
Optionally, when the classification information of the position points is at least two, and the minimum distance in the distances between the new position point and each class centroid is greater than a first preset distance, determining the position point as an outlier; optionally, when the classification information of the position point is one, and the distance between the new position point and the centroid is greater than a second preset distance, the position point is determined to be an outlier. In this embodiment, a new class may be created according to the outlier, or the outlier may be marked, which is convenient for counting the outlier, and the reason for the occurrence of the outlier may be determined according to the analysis of the outlier. For example, when the classification information includes only the line-of-sight scene, the cluster location point is determined as a location point in the non-line-of-sight scene. It should be noted that the first preset distance and the second preset distance are greater than the density parameter.
In some embodiments, the target positioning device is disposed in a parking space for determining a location point of the parking space, and accordingly, the classification information at least includes an unparked state. The method comprises the steps of determining a preset number of position points, determining a neighborhood sub-sample set of each position point based on a density parameter corresponding to the positioning precision of the parking space, and further determining classification information of each position point, namely the non-parking state. The target positioning device determines a new position point in real time or based on a preset interval, determines whether the new position point belongs to an unparked state, determines that the parking space is unparked if the new position point belongs to the unparked state, and determines that a vehicle exists in the parking space if the new position point does not belong to the unparked state, namely, an outlier position end, and the vehicle shields the target positioning device. Through an electronic equipment and each target positioning equipment communication connection, receive the categorised information of the current position point that target positioning equipment sent to make statistics of the parking state to individual parking stall, when the vehicle got into the parking area, through visiting electronic equipment, can confirm the parking rate in this parking area, and the parking stall of not parkking, the car owner of being convenient for confirms the parking stall fast, has reduced the time of looking for the parking stall, the car owner's of being convenient for parks.
Example two
Fig. 3 is a schematic structural diagram of a location information processing apparatus according to a second embodiment of the present invention, where the apparatus includes:
a location point determining module 210, configured to obtain a preset number of location points determined by the target positioning device based on the received electronic pulse;
a neighborhood subsample set determining module 220, configured to determine, for any location point, a neighborhood subsample set of the location point according to a preset density parameter, where the neighborhood subsample set includes at least one location point within a density parameter range of the location point;
a first classification information determining module 230, configured to determine, according to the neighborhood subsample sets, classification information of position points in each of the neighborhood subsample sets.
Optionally, the neighborhood subsample set determining module 220 includes:
the first neighborhood subsample set determining unit is used for determining a neighborhood subsample set of the current position point according to the density parameter;
a second neighborhood subsample set determining unit, configured to iteratively determine a neighborhood subsample set of other location points except the current location point in the neighborhood subsample set.
Optionally, the first classification information determining module 230 is configured to:
and determining the position points in the neighborhood subsample sets with the intersection to belong to the same type.
Optionally, the first classification information determining module 230 is configured to:
and iteratively determining that the current position point and other position points in the neighborhood subsample set of the current position point belong to the same type.
On the basis of the above embodiment, the apparatus further includes:
the distance determining module is used for acquiring a new position point and determining the distance between the position point and each type of centroid, wherein the centroid is determined according to the classification information and each position point;
and the second classification information determining module is used for determining the classification information of the new position point according to the distance.
Optionally, the second classification information determining module is configured to:
when the classification information of the position points is at least two, determining the classification information corresponding to the minimum distance as the classification information of the new position point;
when the minimum distance is larger than a first preset distance, determining the position point as an outlier position point;
optionally, the second classification information determining module is configured to:
when the classification information of the position point is one, determining that the position point belongs to the classification information when the distance is less than or equal to a second preset distance;
and when the distance is greater than the second preset distance, determining the position point as an outlier position point.
Optionally, the location point includes a first determined location point in a line-of-sight scene, and the classification information includes the line-of-sight scene; and/or the position point comprises a determined second position point in a non-line-of-sight scene, and the classification information comprises the non-line-of-sight scene.
Optionally, the target positioning device is disposed in a parking space, and the classification information at least includes an unparked state.
The position information processing device provided by the embodiment of the invention can execute the position information processing method provided by any embodiment of the invention, and has the corresponding functional module and the beneficial effect of executing the position information processing method.
EXAMPLE III
Fig. 4 is a schematic structural diagram of an electronic device according to a third embodiment of the present invention. FIG. 4 illustrates a block diagram of an exemplary electronic device 12 suitable for use in implementing embodiments of the present invention. The electronic device 12 shown in fig. 4 is only an example and should not bring any limitation to the function and the scope of use of the embodiment of the present invention.
As shown in FIG. 4, electronic device 12 is embodied in the form of a general purpose computing device. The components of electronic device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, and a bus 18 that couples various system components including the system memory 28 and the processing unit 16.
The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM)30 and/or cache memory 32. The electronic device 12 may further include other removable/non-removable, volatile/nonvolatile computer system storage media. By way of example only, storage system 34 may be used to read from and write to non-removable, nonvolatile magnetic media (not shown in FIG. 4, and commonly referred to as a "hard drive"). Although not shown in FIG. 4, a magnetic disk drive for reading from and writing to a removable, nonvolatile magnetic disk (e.g., a "floppy disk") and an optical disk drive for reading from or writing to a removable, nonvolatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 by one or more data media interfaces. Memory 28 may include at least one program product having a set of program modules that are configured to carry out the functions of embodiments of the invention.
A program/utility 44 having a set of program modules 46 may be stored, for example, in memory 28, such program modules 46 including, but not limited to, an operating system, one or more application programs, other program modules, and program data, each of which examples or some combination thereof may comprise an implementation of a network environment. Program modules 46 generally carry out the functions and/or methodologies of the described embodiments of the invention.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, to implement a location information processing method provided by an embodiment of the present invention, the method including:
acquiring a preset number of position points determined by the target positioning equipment based on the received electronic pulse;
for any position point, determining a neighborhood subsample set of the position point according to a preset density parameter, wherein the neighborhood subsample set comprises at least one position point in the density parameter range of the position point;
and determining the classification information of the position points in each neighborhood subsample set according to the neighborhood subsample set.
The processing unit 16 executes various functional applications and data processing by executing programs stored in the system memory 28, for example, to implement a location information processing method provided by an embodiment of the present invention.
Of course, those skilled in the art can understand that the processor can also implement the technical solution of the location information processing method provided in any embodiment of the present invention.
Example four
The fourth embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method for processing location information, the method including:
acquiring a preset number of position points determined by the target positioning equipment based on the received electronic pulse;
for any position point, determining a neighborhood subsample set of the position point according to a preset density parameter, wherein the neighborhood subsample set comprises at least one position point in the density parameter range of the position point;
and determining the classification information of the position points in each neighborhood subsample set according to the neighborhood subsample set.
Of course, the computer program stored on the computer-readable storage medium provided by the embodiment of the present invention is not limited to the above method operations, and may also perform related operations in a location information processing method provided by any embodiment of the present invention.
Computer storage media for embodiments of the invention may employ any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device.
A computer readable signal medium may include a video clip, feature encoding of a second video, feature encoding of respective video clips, etc., having computer readable program code embodied therein. Such forms of the broadcast video clip, feature encoding of the second video, feature encoding of each video clip, and the like. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, or the like, as well as conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It should be noted that, in the embodiment of the video processing apparatus, the modules included in the embodiment are only divided according to functional logic, but are not limited to the above division as long as the corresponding functions can be implemented; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (10)
1. A position information processing method, characterized by comprising:
acquiring a preset number of position points determined by the target positioning equipment based on the received electronic pulse;
for any position point, determining a neighborhood subsample set of the position point according to a preset density parameter, wherein the neighborhood subsample set comprises at least one position point in the density parameter range of the position point;
and determining the classification information of the position points in each neighborhood subsample set according to the neighborhood subsample set.
2. The method of claim 1, wherein for any location point, determining a neighborhood subsample set of the location point according to a preset density parameter comprises:
determining a neighborhood subsample set of the current position point according to the density parameter;
iteratively determining a neighborhood subsample set of other position points except the current position point in the neighborhood subsample set.
3. The method of claim 2, wherein determining classification information of the location points in each of the neighborhood subsample sets according to the neighborhood subsample sets comprises:
determining the position points in the neighborhood subsample set with the intersection to belong to the same type;
or,
and iteratively determining that the current position point and other position points in the neighborhood subsample set of the current position point belong to the same type.
4. The method of claim 1, further comprising:
acquiring a new position point, and determining the distance between the position point and each type of centroid, wherein the centroid is determined according to the classification information including each position point;
and determining the classification information of the new position point according to the distance.
5. The method of claim 4, wherein determining classification information for the new location point based on the distance comprises:
when the classification information of the position points is at least two, determining the classification information corresponding to the minimum distance as the classification information of the new position point;
when the minimum distance is larger than a first preset distance, determining the position point as an outlier position point;
or,
when the classification information of the position point is one, determining that the position point belongs to the classification information when the distance is less than or equal to a second preset distance;
and when the distance is greater than the second preset distance, determining the position point as an outlier position point.
6. The method according to any of claims 1-5, wherein the location point comprises a first determined location point in a line-of-sight scene, and the classification information comprises the line-of-sight scene; and/or the position point comprises a determined second position point in a non-line-of-sight scene, and the classification information comprises the non-line-of-sight scene.
7. The method according to any one of claims 1 to 5, wherein the target positioning device is disposed in a parking space, and the classification information includes at least an out-of-park state.
8. A positional information processing apparatus characterized by comprising:
the position point determining module is used for acquiring a preset number of position points determined by the target positioning equipment based on the received electronic pulse;
the neighborhood subsample set determining module is used for determining a neighborhood subsample set of any position point according to a preset density parameter, wherein the neighborhood subsample set comprises at least one position point in the density parameter range of the position point;
and the first classification information determining module is used for determining the classification information of the position points in each neighborhood subsample set according to the neighborhood subsample set.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements a method of processing location information according to any one of claims 1 to 7 when executing the computer program.
10. A storage medium containing computer-executable instructions, which when executed by a computer processor implement a location information processing method according to any one of claims 1 to 7.
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