WO2022082553A1 - 一种地理围栏数据点密度优化的方法和系统 - Google Patents

一种地理围栏数据点密度优化的方法和系统 Download PDF

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WO2022082553A1
WO2022082553A1 PCT/CN2020/122643 CN2020122643W WO2022082553A1 WO 2022082553 A1 WO2022082553 A1 WO 2022082553A1 CN 2020122643 W CN2020122643 W CN 2020122643W WO 2022082553 A1 WO2022082553 A1 WO 2022082553A1
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data point
data
data points
points
geofence
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PCT/CN2020/122643
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English (en)
French (fr)
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吕代维奇
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四川金瑞麒智能科学技术有限公司
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Priority to PCT/CN2020/122643 priority Critical patent/WO2022082553A1/zh
Priority to CN202080102583.2A priority patent/CN115836285A/zh
Publication of WO2022082553A1 publication Critical patent/WO2022082553A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases

Definitions

  • the present application relates to the field of computer technology, and in particular, to a method and system for optimizing the density of geofence data points.
  • geo-fencing technology has been applied to various fields, such as location judgment of intelligent terminals, judgment of rovers, and so on.
  • the geofence is constructed based on the acquired geofence data point set, and the data point quality of the geofence data point set will also affect the quality of the obtained geofence.
  • various collection methods such as map marking, sequential collection along the road, etc.
  • map marking a lot of road information needs to be considered when collecting sequentially along the road, for example, the length, width, extension direction, number of roadblocks, etc. of the road, a large number of data points will be collected, and there will be a large number of redundant data points in the geofence data point set.
  • a large number of redundant data points will make geo-fence-related algorithms such as judging whether the target (such as rover, terminal device, etc.) is located in the geo-fence area requires high computing power when the geo-fence constructed based on the geo-fence data point set is applied. Operational efficiency is low.
  • the method includes: acquiring a set of geofence data points, the set of geofence data points including a plurality of data points arranged in the order of collection; and repeating the following steps on the set of geofence data points until the geofence data point is reached
  • the set satisfies the first preset condition: the plurality of data points are divided into at least one data point group, and each data point group includes at least 3 data points arranged in sequence;
  • the system includes: an acquisition module: used to acquire a geo-fence data point set, the geo-fence data point set includes a plurality of data points arranged in the order of collection; Repeat the following steps until the set of geofence data points satisfies a first preset condition: dividing the plurality of data points into at least one data point group, each of the data point groups including at least three sequentially arranged data points.
  • the data points determine the degree of correlation between each of the data points except the first data point and the last data point in each of the data point groups and the straight line formed by the first data point and the last data point; Whether the correlation degree satisfies the second preset condition, if yes, the data point corresponding to the correlation degree is a deleteable point, delete all the deleteable points, and update the data points of the geofence data point set Sort the order and the number of data points to complete a round of data point filtering.
  • Another aspect of the present specification provides an apparatus for optimizing the density of geofence data points, including a processor configured to perform a method for optimizing the density of geofence data points.
  • Another aspect of the present specification provides a computer-readable storage medium that stores computer instructions, and after the computer reads the computer instructions in the storage medium, the computer executes a method for optimizing the density of geofence data points.
  • FIG. 1 is a schematic diagram of an application scenario of a system for optimizing the density of geofence data points according to some embodiments of the present specification
  • FIG. 2 is a block diagram of an exemplary geofence data point density optimization system shown in accordance with some embodiments of the present specification
  • FIG. 3 is an exemplary flowchart of a method for optimizing geofence data point density according to some embodiments of the present specification
  • FIG. 4 is an exemplary flowchart of a method for determining M data point groups according to some embodiments of the present specification.
  • system means for distinguishing different components, elements, parts, parts or assemblies at different levels.
  • device means for converting signals into signals.
  • unit means for converting signals into signals.
  • module means for converting signals into signals.
  • FIG. 1 is a schematic diagram of an application scenario of a system for optimizing the density of geofence data points according to some embodiments of the present specification.
  • Geofence data point density system 100 may include processor 110 , network 120 and storage device 130 .
  • the geofence data point density system 100 can be used for data point collection and processing of road-level geofences, geofence construction for location determination of rovers, and geofence construction for location determination of smart terminals.
  • the collection of data points for road-level geofences differs from other ways of constructing geofences by marking data points on a map, such as area-level geofence data points. Map markers are difficult to match with actual road coordinates, widths and other data.
  • the road-level geofence construction based on road information needs to collect data points sequentially along the road to obtain the geofence data point set. Since a lot of road information needs to be considered during collection, for example, the length, width, extension direction, number of roadblocks, etc.
  • the geofence data point density system 100 can reduce road-level geofence data by filtering and removing redundant data points in the road-level geofence data point set by implementing the methods and/or processes disclosed in this specification. number of points to construct a geofence with the optimal density of data points.
  • the processor 110 may obtain data (eg, a set of geofence data points) from a storage device 130 through a network, and the storage device 130 may also upload data (eg, a set of geofence data points) to the processor 110 through a network.
  • the processor 110 and the storage device 130 can also communicate and transmit data with other external devices through the network 120 .
  • the processor 110 may execute the action instructions to implement any of the methods for optimizing the density of geofence data points described in this specification.
  • the information transfer relationship between the above devices is only an example, and the present application is not limited thereto.
  • a storage device 130 may be included in the processor 110 and possibly other system components.
  • the processor 110 may process data and/or information obtained from other devices or system components.
  • the processor may execute program instructions based on such data, information and/or processing results to perform one or more of the functions described herein.
  • processor 110 may include one or more sub-processing devices (eg, single-core processing devices or multi-core multi-core processing devices).
  • the processor 110 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), an application specific instruction processor (ASIP), a graphics processor (GPU), a physical processor (PPU), a digital signal processor ( DSP), field programmable gate array (FPGA), programmable logic circuit (PLD), controller, microcontroller unit, reduced instruction set computer (RISC), microprocessor, etc. or any combination of the above.
  • CPU central processing unit
  • ASIC application specific integrated circuit
  • ASIP application specific instruction processor
  • GPU graphics processor
  • PPU physical processor
  • DSP digital signal processor
  • FPGA field programmable gate array
  • PLD programmable logic circuit
  • controller microcontroller
  • Storage device 130 may be used to store data and/or instructions.
  • the storage device 130 may include one or more storage components, and each storage component may be an independent device or a part of other devices.
  • storage device 130 may include random access memory (RAM), read only memory (ROM), mass storage, removable memory, volatile read-write memory, the like, or any combination thereof.
  • mass storage may include magnetic disks, optical disks, solid state disks, and the like.
  • the storage device 130 may be implemented on a cloud platform.
  • Data refers to the digital representation of information, and can include various types, such as binary data, text data, image data, video data, and so on. Instructions are programs that control a device or device to perform a specific function.
  • Network 120 may connect components of the system and/or connect portions of the system with external resources.
  • the network 120 enables communication between the various components and with other components outside the system, facilitating the exchange of data and/or information.
  • the network 120 may be any one or more of a wired network or a wireless network.
  • the network 120 may include a cable network, a fiber optic network, a telecommunications network, the Internet, a local area network (LAN), a wide area network (WAN), a wireless local area network (WLAN), a metropolitan area network (MAN), a public switched telephone network (PSTN) , Bluetooth network, ZigBee network (ZigBee), near field communication (NFC), intra-device bus, intra-device line, cable connection, etc.
  • LAN local area network
  • WAN wide area network
  • WLAN wireless local area network
  • MAN metropolitan area network
  • PSTN public switched telephone network
  • Bluetooth network ZigBee network
  • ZigBee near field communication
  • intra-device bus intra-device
  • network 150 may include one or more network access points.
  • network 120 may include wired or wireless network access points, such as base stations and/or network switching points 120-1, 120-2, . . . , through which one or more components of system 100 may Connect to network 120 to exchange data and/or information.
  • FIG. 2 is a block diagram of an exemplary geofence data point density optimization system shown in accordance with some embodiments of the present specification.
  • the system 200 for geofence data point density optimization may include an acquisition module 210 , a data point screening module 220 and a data point group determination module 221 .
  • the obtaining module 210 may be configured to obtain a set of geofence data points, the set of geofence data points including a plurality of data points arranged in the order of collection.
  • the data point set and the data point reference may be made to FIG. 3 and its related description, which will not be repeated here.
  • the data point screening module 220 may be configured to repeatedly perform the following steps on the geofence data point set until the geofence data point set satisfies a first preset condition: dividing the plurality of data points into at least one data point group , each said data point group includes at least 3 said data points arranged in sequence; determine each said data point and said first data point except the first data point and last data point in each said data point group and the correlation degree of the straight line formed by the last data point; determine whether the correlation degree satisfies the second preset condition, if yes, then the data point corresponding to the correlation degree is a deleteable point, and all the possible The deletion point is deleted, and the data point arrangement order and the data point quantity of the geofence data point set are updated to complete a round of data point screening.
  • the data point screening module 220 may also be used to determine the correlation coefficient between each data point except the first data point and the last data point and the straight line, and to determine the correlation coefficient of each data point except the first data point and the last data point. The distance of the data point from the line.
  • the degree of correlation satisfying the second preset condition may include: the correlation coefficient is greater than the first threshold, and the distance is less than the second threshold.
  • the first preset condition may include: the number of the data points after the selection of the data points in the current round is the same as the number of the data points after the selection of the data points in the previous round is completed.
  • the scale of the coordinate space of the data points may be determined according to the smallest coordinate value of the data point in the geofence data point set.
  • the data point screening module 220 further includes a data point group determination module 221, and the data point group determination module 221 may be configured to obtain at least three pieces of the data each time according to the arrangement order of the plurality of data points point as one of the data point groups; based on the arrangement order of the data points in the data point group, the data point groups are sequentially arranged to obtain the at least one data point group.
  • the first data point of each data point group is the last data point of the previous data point group.
  • the illustrated system and its modules may be implemented in a variety of ways.
  • the system and its modules may be implemented in hardware, software, or a combination of software and hardware.
  • the hardware part can be realized by using dedicated logic;
  • the software part can be stored in a memory and executed by a suitable instruction execution system, such as a microprocessor or specially designed hardware.
  • a suitable instruction execution system such as a microprocessor or specially designed hardware.
  • the methods and systems described above may be implemented using computer-executable instructions and/or embodied in processor control code, for example on a carrier medium such as a disk, CD or DVD-ROM, such as a read-only memory (firmware) ) or a data carrier such as an optical or electronic signal carrier.
  • the system and its modules of the present application can not only be implemented by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc. , can also be implemented by, for example, software executed by various types of processors, and can also be implemented by a combination of the above-mentioned hardware circuits and software (eg, firmware).
  • the above description of the system 200 for optimizing the density of geofence data points and its modules is only for the convenience of description, and does not limit the description to the scope of the illustrated embodiments. It can be understood that for those skilled in the art, after understanding the principle of the system, various modules may be combined arbitrarily, or a subsystem may be formed to connect with other modules without departing from the principle.
  • the acquisition module 210, the data point screening module 220 and the data point group determination module 221 may share one storage module, and each module may also have its own storage module. Such deformations are all within the protection scope of the present application.
  • FIG. 3 is an exemplary flowchart of a method for geofence data point density optimization according to some embodiments of the present specification.
  • the method 300 for optimizing the density of geofence data points may include:
  • Step 310 obtaining a set of geofence data points.
  • this step 310 may be performed by the obtaining module 210 .
  • a geofence data point set is a set of data points made up of the data points used to construct the geofence.
  • a geofence data point set consists of multiple data points in the order in which they were collected. Specifically, the multiple data points may be data points sequentially collected along a road, or a boundary of a selected area such as a residential area, a park, a lake, and the like.
  • the data points in the geofence data point set may be arranged in a forward order of the collection order, or may be arranged in a reverse order of the collection order.
  • the set of geofence data points may be obtained from a server, terminal, or database.
  • the terminal can be used to collect data points, read the data saved by the terminal to obtain the geofence data point set, or the terminal can send the collected data point information to the server or save it to an external database, and obtain the geo-fence data point information from the server or database.
  • Fence data point set may be used to collect data points, read the data saved by the terminal to obtain the geofence data point set, or the terminal can send the collected data point information to the server or save it to an external database, and obtain the geo-fence data point information from the server or database.
  • further processing may be performed on the geofence data point set, such as data point coordinate transformation, data point statistics, and the like.
  • the coordinate space of the data point refers to the coordinate system space used to represent the coordinates of the data point, for example, the CGCS coordinate system space, the WGS coordinate system space, and the UTM coordinate system space can be used as the coordinate space of the data point.
  • the scale of the coordinate space of the data points may be adjusted. Specifically, the scale of the coordinate space of the data points may be determined according to the minimum coordinate value of the data points in the geofence data point set.
  • the geographic data point set is represented as Data geofence , where the coordinates of the data point a are Point a (X a , Y a ), the scale range of the coordinate space starts from 0, and Min_x and Min_y are the corresponding geofence data points, respectively The smallest X-axis coordinate value and the smallest Y-axis coordinate value in The coordinates are New_Point a (X a -Min_x, Y a -Min_y).
  • the coordinate space of the data points may be transformed.
  • the data point uses the WGS coordinate system space.
  • the coordinate space used by the data point is converted to the UTM coordinate system space.
  • the change of the scale range of the coordinate space of the data points can be understood as the coordinate transformation of the coordinate space of the data points to obtain a coordinate system with the data points (Min_x, Min_y) as the origin, so that the data can be effectively reduced. Coordinate value of point a.
  • the scale of the coordinate space can be adapted to the range of the coordinate value of the data point.
  • the calculated correlation coefficient will not be too large because the coordinate value is too large. If the value is too large, the variation interval of the correlation coefficient equivalent is too small and the difference of the correlation coefficient is too small. After adjustment, it can be more accurately judged whether the data point can be deleted according to whether the correlation coefficient equivalent value meets the conditions. For more details on the calculation of the correlation coefficient, reference may be made to step 324 and its related description, which will not be repeated here.
  • Step 320 Repeat the data point screening step on the geofence data point set until the geofence data point set satisfies the first preset condition.
  • this step 320 may be performed by the data point screening module 220 .
  • Data point filtering refers to filtering the data points in the geofence data point set to delete unnecessary data points, that is, data points that are redundant for building a geofence.
  • Repeating the data point filtering step refers to cyclic filtering of the data points in the geofence data point set, in other words, an iteration of the data point filtering.
  • cyclic screening that is, the iteration of data point screening, it can be ensured that all redundant data points are screened out as much as possible, and the screening accuracy is improved.
  • the first preset condition may mean that the number of data points in the geofence data point set reaches a preset condition, for example, the number reaches a preset value or the number remains stable after multiple screenings, or that the geofence formed by the data points reaches the preset condition. , such as a geofence that completely covers the target area or coincides with the boundary of the target area.
  • the fact that the geofence data point set satisfies the first preset condition can be understood as that redundant data points in the geofence data point set have been deleted, and a geofence data point set with better point density is obtained.
  • the data point screening step may include:
  • Step 322 Divide the N data points into at least one data point group, and each data point group includes at least 3 data points arranged in sequence.
  • the data point group refers to a point set composed of multiple data points, and the data point group includes at least 3 data points, for example, may include 3 or 4 or 5 data points.
  • the number of data points included in each data point group may be the same or different.
  • the order of the data points in the data point group is the order of the data points of the geofence data point set.
  • the data points included in each data point group may or may not overlap with the data points in the preceding and following data point groups.
  • the number of data point groups may be determined based on the number of data points and the number of data points in the data point group.
  • each data point group can include 3 data points
  • the number of data point groups can be is 2
  • the resulting 2 data point groups are ⁇ a, b, c ⁇ and ⁇ d, e, f ⁇ , or ⁇ a, b, c ⁇ and ⁇ c, d, e ⁇
  • each data point group can also include 4 data points
  • the number of data point groups can be 1 or 2
  • one data point group is ⁇ a, b, c, d ⁇ or ⁇ b, c, d, e ⁇ or ⁇ c , d, e, f ⁇ , get 2 data point groups ⁇ a, b, c, d ⁇ and ⁇ c, d, e, f ⁇ respectively.
  • Step 324 Determine the degree of correlation between each of the data points except the first data point and the last data point in each of the data point groups and the straight line formed by the first data point and the last data point.
  • the first data point refers to the first data point in the data point group
  • the last data point refers to the last data point in the data point group.
  • the first data points of the data point groups ⁇ a, b, c ⁇ and the data point groups ⁇ d, e, f ⁇ are the data points a and Data point d
  • the last data point is data point c and data point f respectively.
  • the straight line formed by the first data point and the last data point refers to a straight line formed by connecting the first data point and the last data point in the coordinate system.
  • the straight line can be understood as a linear function determined by the coordinates of the first and last data points.
  • the degree of correlation refers to the closeness of the linear relationship between each data point and the straight line, and can also be understood as the degree of fitting between each data point and the straight line.
  • the degree of correlation between the data points and the line can be perfectly linear (ie, a perfect fit), less linear (ie, less fit), or more linearly related (ie, more fit).
  • the degree of correlation can be determined by calculating the linear correlation value, calculating the value of the degree of fit between the data points and the straight line, and so on.
  • the degree of correlation may be determined by determining a correlation coefficient between each of the data points except the first data point and the last data point and the straight line.
  • the correlation coefficient refers to a numerical representation of a linear relationship, and the larger the correlation coefficient value, the higher the degree of correlation. For example, a correlation coefficient of 0.9 represents a higher degree of correlation than a correlation coefficient of 0.7.
  • the correlation coefficient can be calculated according to residual, total dispersion, etc. Specifically, the following formula can be used to calculate:
  • any effective calculation method can also be adopted to calculate the correlation coefficient between the data point and the straight line.
  • the Pearson correlation coefficient calculation method is not limited to the calculation method shown in the formula (1), and this specification does not limit the calculation method of the correlation coefficient between the above two.
  • the degree of correlation may also be determined by determining the distance between each of the data points except the first data point and the last data point and the straight line.
  • Absolute distance is inversely proportional to the degree of correlation. That is, the larger the absolute distance is, the smaller the corresponding degree of correlation is.
  • the absolute distance D between the data point b and the straight line ac formed by the data points a and c can be expressed as:
  • A, B, C are all calculation parameters
  • x 0 and y 0 are the coordinates of data point b respectively value.
  • Step 326 determine whether the degree of correlation satisfies the second preset condition, if yes, then the data point corresponding to the degree of correlation is a point that can be deleted, delete all the points that can be deleted, and update the geo-fence data
  • the sequence of data points and the number of data points in the point set completes a round of data point filtering.
  • the second preset condition may refer to the degree of correlation between the data points of the geofence data point set and the straight line reaching a preset threshold, or other preset conditions obtained by further processing based on the degree of correlation.
  • the second preset condition may be a preset threshold.
  • the preset threshold may be a preset threshold of the correlation coefficient
  • the correlation coefficient may be compared with the preset threshold
  • the correlation coefficient greater than the preset threshold may be used as a criterion for determining whether the degree of correlation satisfies the second preset condition.
  • the preset threshold of the correlation coefficient is 0.8
  • the calculated correlation coefficient between the data point b and the straight line ac is 0.85
  • the degree of correlation between the data point b and the straight line ac satisfies the predetermined threshold.
  • the preset threshold value may also be the preset threshold value of the distance between each data point and the straight line, the distance can be compared with the preset threshold value, and the distance is smaller than the preset threshold value as the determination of whether the degree of correlation satisfies the second preset condition. standard. Taking the data point group ⁇ a, b, c ⁇ as an example, the preset threshold of the distance is 2 cm, and the calculated distance between the data point b and the straight line ac is 0.7, then the degree of correlation between the data point b and the straight line ac satisfies the preset condition . It should be noted that the size of the preset threshold value can be determined through experimental data, and can also be adjusted according to the actual situation, and this specification does not limit the determination and size range of the preset threshold value.
  • the preset threshold of the correlation coefficient may be referred to as a first threshold
  • the preset threshold of the distance between each data point and the straight line may be referred to as a second threshold
  • the correlation coefficient may be greater than the first threshold
  • the distance Being less than the second threshold is taken as the second preset condition that the correlation degree satisfies.
  • the degree of correlation between the data point b and the straight line formed by the data point a and the data point c satisfies the second preset condition.
  • the correlation coefficient and the distance reference may be made to step 324 and its related description, which will not be repeated here.
  • the geofence data point set can be filtered from the geofence data point set on the basis of a single filtering condition. Do not overscale when deleting points. Deleting data points that are too large can be understood as, after deleting the filtered points that can be deleted, the geofence formed by the geofence data point set has a large boundary change compared with the actual road boundary or area, so that according to the above Geofences created by geofence datasets no longer cover the target area accurately.
  • the data point corresponding to the degree of correlation satisfying the second preset condition is a deleteable point, that is, the data point satisfies any one of the aforementioned degree of correlation and meets the second preset condition, then the data point can be collected from the geofence data point set delete. After the deleteable points are deleted, the order of data points and the number of data points N in the geofence data point set will be updated to complete a round of data point filtering.
  • the obtained 2 data point groups are ⁇ a, b, c ⁇ and ⁇ d, e, f ⁇ , the degree of correlation between data point b and data point e satisfies the second preset condition, and is a point that can be deleted.
  • the geofence data point set after updating the number of data points and the order of data points can be used for the next round of data point filtering.
  • the first preset condition may be: the number of the data points after the selection of the data points in the current round is the same as the number of the data points after the selection of the data points in the previous round. That is, when the preset condition is met, the number of data point sets remains stable and no longer changes. It can be considered that all deleteable points have been filtered out, that is, the point density optimization of the geofence data point set is completed.
  • the starting data point for each round of data point screening may vary.
  • the grouping when at least one data group is obtained by grouping the geofence data point set, the grouping may start from the first data point sequentially arranged in the geofence data point set, that is, the starting data The point is the first data point in the order, or it can be grouped from the second data point in the order or other data points in the geofence data point set, that is, the starting data point is the second data point in the order.
  • the starting data point of one round of data point screening is the first data point arranged in sequence
  • the starting data point of the next round of data point screening may be the second data point or other data points arranged in sequence.
  • the number M of a data point group may be set first, M data point groups are sequentially selected from the data point set, and data point screening is performed on the M data point groups, Then continue to select M data point groups from the data point set to filter the data points until all the data points in the data point set are selected and filtered.
  • the previous round of M data point groups can be screened, and the first data point group of the data point group that has not been continuously screened for deleteable points can be selected.
  • the data points of are the starting data points of the next round of M data point groups.
  • FIG. 4 For more content of the method for determining at least one data point group, reference may be made to FIG. 4 and related descriptions thereof, which will not be repeated here.
  • multiple rounds of data point screening can be performed alternately with the first point as the starting point or the second point as the starting point, ensuring that as many data points as possible can be judged whether they can be deleted, and more accurately find the deleteable data points. point.
  • the data points are grouped, and whether the data points are deleteable points is determined according to the calculated correlation between each data point and the straight line formed by the first data point and the last data point in the data point group, Therefore, redundant data points can be efficiently and accurately screened out and deleted, so as to achieve efficient and accurate point density optimization of the geofence data point set.
  • FIG. 4 is an exemplary flowchart of a method for determining at least one data point group according to some embodiments of the present specification.
  • the method 400 may include:
  • Step 410 according to the arrangement order of the plurality of data points, at least three data points are taken each time as one of the data point groups.
  • this step 410 may be performed by the data point group determination module 221 .
  • the arrangement order of the data points in the data point group is the arrangement order of the data points of the geofence data point set.
  • you can follow the data point order of the geofence data point set you can start from the first data point, or start from the second data point or other data points, and you can take at least 3 data points each time (you can is 3, 4, or 5) as a group of data points.
  • Step 420 Arrange the data point groups in sequence based on the arrangement order of the data points in the data point group to obtain the at least one data point group.
  • this step 420 may be performed by a data point group determination module.
  • the at least one data point group can be obtained by arranging the respective data point groups in sequence according to the arrangement order corresponding to the above data point groups.
  • the first data point as the starting point or the second data point as the starting point can be alternately performed.
  • the data point filtering operation can ensure that as many data points as possible in the geofence data point set are judged whether they are deleteable points, and the data point filtering is more accurate.
  • the first data point of each said data point group is the said data point of the previous said data point group last data point.
  • each time 3 data points are taken as a data point group, starting from the second data point From the point group, the first data point of each data point group is the last data point of the previous data point group, and the data point groups ⁇ a, b, c ⁇ and ⁇ c, d, e ⁇ .
  • the data points in the sequentially arranged data point groups can be connected end to end, which can further ensure that as many data points as possible are determined to be deleteable points, and the data point selection is more accurate.
  • Embodiments of the present specification also provide an apparatus, including a processor configured to perform the aforementioned method for optimizing the density of geofencing data points.
  • the method for optimizing the density of geofence data points may include: acquiring a set of geofence data points, the set of geofence data points including a plurality of data points arranged in the order of collection; and repeating the following steps for the set of geofence data points , until the geofence data point set satisfies the first preset condition: dividing the plurality of data points into at least one data point group, each of the data point groups including at least 3 of the data points arranged in sequence; Determine the degree of correlation between each of the data points except the first data point and the last data point in each of the data point groups and the straight line formed by the first data point and the last data point; determine whether the degree of correlation is If the second preset condition is met, if yes, the data point corresponding to the degree of correlation is a deleteable point, delete all the deleteable points, and update the
  • Embodiments of the present specification further provide a computer-readable storage medium, where the storage medium stores computer instructions, and after the computer reads the computer instructions in the storage medium, the computer executes the aforementioned method for optimizing the density of geofence data points.
  • the method for optimizing the density of geo-fence data points may include: acquiring a set of geo-fence data points, the set of geo-fence data points including a plurality of data points arranged in a collection sequence; and repeating the following steps for the set of geo-fence data points , until the geofence data point set satisfies the first preset condition: dividing the plurality of data points into at least one data point group, each of the data point groups including at least 3 of the data points arranged in sequence; Determine the degree of correlation between each of the data points except the first data point and the last data point in each of the data point groups and the straight line formed by the first data point and the last data point; determine whether the degree of correlation is If the second preset condition is met, if yes,
  • the possible beneficial effects of the embodiments of this specification include, but are not limited to: (1) by grouping data points, and calculating the correlation between each data point in the data point group and the straight line formed by the first data point and the last data point To determine whether a data point is a deleteable point or not, to achieve efficient and accurate point density optimization for the geofence data point set; (2) Select the starting point of the point by changing the data point group, and the starting point of the data point for multiple rounds of data point screening.
  • the correlation coefficient is greater than the first threshold value and the distance is less than the second threshold value to determine that the correlation degree satisfies the second preset condition, so as to further ensure that when selecting the deleteable points from the geofence data point set, the scale will not be too large, ensuring that the constructed Effective coverage of geofencing.
  • the possible beneficial effects may be any one or a combination of the above, or any other possible beneficial effects.
  • aspects of this specification may be illustrated and described in several patentable categories or situations, including any new and useful process, machine, product, or combination of matter, or combinations of them. of any new and useful improvements. Accordingly, various aspects of this specification may be performed entirely in hardware, entirely in software (including firmware, resident software, microcode, etc.), or in a combination of hardware and software.
  • the above hardware or software may be referred to as a "data block”, “module”, “engine”, “unit”, “component” or “system”.
  • aspects of this specification may be embodied as a computer product comprising computer readable program code embodied in one or more computer readable media.
  • a computer storage medium may contain a propagated data signal with the computer program code embodied therein, for example, on baseband or as part of a carrier wave.
  • the propagating signal may take a variety of manifestations, including electromagnetic, optical, etc., or a suitable combination.
  • Computer storage media can be any computer-readable media other than computer-readable storage media that can communicate, propagate, or transmit a program for use by coupling to an instruction execution system, apparatus, or device.
  • Program code on a computer storage medium may be transmitted over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or a combination of any of the foregoing.
  • the computer program coding required for the operation of the various parts of this manual may be written in any one or more programming languages, including object-oriented programming languages such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB.NET, Python etc., conventional procedural programming languages such as C language, Visual Basic, Fortran2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby and Groovy, or other programming languages.
  • the program code may run entirely on the user's computer, or as a stand-alone software package on the user's computer, or partly on the user's computer and partly on a remote computer, or entirely on the remote computer or processing device.
  • the remote computer can be connected to the user's computer through any network, such as a local area network (LAN) or wide area network (WAN), or to an external computer (eg, through the Internet), or in a cloud computing environment, or as a service Use eg software as a service (SaaS).
  • LAN local area network
  • WAN wide area network
  • SaaS software as a service

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Abstract

一种地理围栏数据点密度优化的方法和系统，所述方法包括：获取地理围栏数据点集（310），地理围栏数据点集包括按采集顺序排列的多个数据点；对地理围栏数据点集重复执行以下步骤，直至地理围栏数据点集满足第一预设条件：将多个数据点分为至少一个数据点组（322），每个数据点组包括顺序排列的至少3个所述数据点；确定各个数据点组中除首位数据点和末位数据点外的各个数据点与首位数据点和末位数据点所构成直线的相关程度（324）；判断相关程度是否满足第二预设条件（326），若是，则相关程度对应的数据点为可删除点，将所有可删除点删除，并更新地理围栏数据点集的数据点排列顺序和数据点数量，完成一轮数据点筛选。

Description

一种地理围栏数据点密度优化的方法和系统 技术领域
本申请涉及计算机技术领域，特别涉及一种地理围栏数据点密度优化的方法和系统。
背景技术
随着信息技术的发展，地理围栏技术已经应用到了各个领域，例如智能终端的位置判断、漫游车车辆的判断等等。地理围栏是基于获取的地理围栏数据点集来构建的，地理围栏数据点集的数据点质量也就会影响得到的地理围栏的质量。在获取地理围栏数据点集时，有多种采集方法，例如地图标记、沿道路依次采集等。其中，沿道路依次采集时需要考虑诸多的道路信息，例如，道路的长度、宽度、延伸走向、路障数量等，会采集得到大量的数据点，地理围栏数据点集会存在有大量冗余数据点，大量冗余数据点会使得根据地理围栏数据点集构建的地理围栏在应用时，判断目标(例如漫游车、终端设备等)是否位于地理围栏区域等地理围栏相关算法的算力要求高，而导致运算效率较低。
因此，亟需一种地理围栏数据点密度优化的方法和系统。
发明内容
本说明书一个方面提供一种地理围栏数据点密度优化的方法。所述方法包括：获取地理围栏数据点集，所述地理围栏数据点集包括按采集顺序排列的多个数据点；对所述地理围栏数据点集重复执行以下步骤，直至所述地理围栏数据点集满足第一预设条件：将所述多个数据点分为至少一个数据点组，每个所述数据点组包括顺序排列的至少3个所述数据点；确定各个所述数据点组中除首位数据点和末位数据点外的各个所述数据点与所述首位数据点和所述末位数据点所构成直线的相关程度；判断所述相关程度是否满足第二预设条件，若是，则所述相关程度对应的所述数据点为可删除点，将所有所述可删除点删除，并更新所述地理围栏数据点集的数据点排列顺序和数据点数量，完成一轮数据点筛选。
本说明书另一个方面提供一种地理围栏数据点密度优化的系统。所述系统包括：获取模块：用于获取地理围栏数据点集，所述地理围栏数据点集包括按采集顺序排列的多个数据点；数据点筛选模块：用于对所述地理围栏数据点集重复执行以下步骤，直至所述地理围栏数据点集满足第一预设条件：将所述多个数据点分为至少一个数据点组，每个所述数据点组包括顺序排列的至少3个所述数据点；确定各个所述数据点组中除首位 数据点和末位数据点外的各个所述数据点与所述首位数据点和所述末位数据点所构成直线的相关程度；判断所述相关程度是否满足第二预设条件，若是，则所述相关程度对应的所述数据点为可删除点，将所有所述可删除点删除，并更新所述地理围栏数据点集的数据点排列顺序和数据点数量，完成一轮数据点筛选。
本说明书另一个方面提供一种地理围栏数据点密度优化的装置，包括处理器，所述处理器用于执行地理围栏数据点密度优化的方法。
本说明书另一个方面提供计算机可读存储介质，所述存储介质存储计算机指令，当计算机读取存储介质中的计算机指令后，计算机执行地理围栏数据点密度优化的方法。
附图说明
本说明书将以示例性实施例的方式进一步说明，这些示例性实施例将通过附图进行详细描述。这些实施例并非限制性的，在这些实施例中，相同的编号表示相同的结构，其中：
图1是根据本说明书一些实施例所示的地理围栏数据点密度优化的系统的应用场景示意图；
图2是根据本说明书的一些实施例所示的示例性地理围栏数据点密度优化的系统的模块图；
图3是根据本说明书一些实施例所示的地理围栏数据点密度优化的方法的示例性流程图；
图4是根据本说明书一些实施例所示的M个数据点组的确定方法的示例性流程图。
具体实施方式
为了更清楚地说明本说明书实施例的技术方案，下面将对实施例描述中所需要使用的附图作简单的介绍。显而易见地，下面描述中的附图仅仅是本说明书的一些示例或实施例，对于本领域的普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图将本说明书应用于其它类似情景。除非从语言环境中显而易见或另做说明，图中相同标号代表相同结构或操作。
应当理解，本说明书中所使用的“系统”、“装置”、“单元”和/或“模组”是用于区分不同级别的不同组件、元件、部件、部分或装配的一种方法。然而，如果其他词语可实现相同的目的，则可通过其他表达来替换所述词语。
如本说明书和权利要求书中所示，除非上下文明确提示例外情形，“一”、“一个”、 “一种”和/或“该”等词并非特指单数，也可包括复数。一般说来，术语“包括”与“包含”仅提示包括已明确标识的步骤和元素，而这些步骤和元素不构成一个排它性的罗列，方法或者设备也可能包含其它的步骤或元素。
本说明书中使用了流程图用来说明根据本说明书的实施例的系统所执行的操作。应当理解的是，前面或后面操作不一定按照顺序来精确地执行。相反，可以按照倒序或同时处理各个步骤。同时，也可以将其他操作添加到这些过程中，或从这些过程移除某一步或数步操作。
图1是根据本说明书的一些实施例所示的地理围栏数据点密度优化的系统的应用场景示意图。
地理围栏数据点密度系统100可以包括处理器110、网络120和存储设备130。
地理围栏数据点密度系统100可以用于道路级地理围栏的数据点采集处理、漫游车车辆的位置判断的地理围栏构建、智能终端的位置判断的地理围栏构建等。道路级地理围栏的数据点采集相较于其它在地图上标记数据点来构建地理围栏的方式有所不同，例如区域级地理围栏数据点。地图标记与实际道路的坐标、宽度等数据难以吻合，基于道路信息的道路级地理围栏构建为了保证数据点取点的准确，需要沿着道路依次进行采集获取数据点得到地理围栏数据点集。由于采集时需要考虑诸多的道路信息，例如，道路的长度、宽度、延伸走向、路障数量等，会采集得到大量的数据点，因此，采集的数据点会存在有大量冗余数据点的问题，大量冗余数据点会使得根据地理围栏数据点集构建的地理围栏在应用时，造成判断目标(例如漫游车、终端设备等)是否位于地理围栏区域等地理围栏相关算法的算力和效率较低。在一些实施例中，地理围栏数据点密度系统100可以通过实施本说明书中披露的方法和/或过程，筛选并删除道路级地理围栏数据点集中的冗余数据点，以减少道路级地理围栏数据点数量，从而构建数据点密度最优的地理围栏。
处理器110可以通过网络从存储设备130中获取数据(例如地理围栏数据点集)，存储设备130也可以通过网络上传数据(例如地理围栏数据点集)到处理器110。处理器110和存储设备130也可以通过网络120与其它外部设备进行通讯和数据传输。处理器110可以执行动作指令以实现本说明书中所述的任一种地理围栏数据点密度优化的方法。以上各设备之间的信息传递关系仅作为示例，本申请并不局限于此。
在一些实施例中，处理器110以及其他可能的系统组成部分中可以包括存储设备130。
处理器110可以处理从其他设备或系统组成部分中获得的数据和/或信息。处理器可以基于这些数据、信息和/或处理结果执行程序指令，以执行一个或多个本申请中描述的功能。在一些实施例中，处理器110可以包含一个或多个子处理设备(例如，单核处理设备或多核多芯处理设备)。仅作为示例，处理器110可以包括中央处理器(CPU)、专用集成电路(ASIC)、专用指令处理器(ASIP)、图形处理器(GPU)、物理处理器(PPU)、数字信号处理器(DSP)、现场可编程门阵列(FPGA)、可编辑逻辑电路(PLD)、控制器、微控制器单元、精简指令集电脑(RISC)、微处理器等或以上任意组合。
存储设备130可以用于存储数据和/或指令。存储设备130可以包括一个或多个存储组件，每个存储组件可以是一个独立的设备，也可以是其他设备的一部分。在一些实施例中，存储设备130可包括随机存取存储器(RAM)、只读存储器(ROM)、大容量存储器、可移动存储器、易失性读写存储器等或其任意组合。示例性的，大容量储存器可以包括磁盘、光盘、固态磁盘等。在一些实施例中，所述存储设备130可在云平台上实现。
数据指对信息的数字化表示，可以包括各种类型，比如二进制数据、文本数据、图像数据、视频数据等。指令指可控制设备或器件执行特定功能的程序。
网络120可以连接系统的各组成部分和/或连接系统与外部资源部分。网络120使得各组成部分之间，以及与系统之外其他部分之间可以进行通讯，促进数据和/或信息的交换。在一些实施例中，网络120可以是有线网络或无线网络中的任意一种或多种。例如，网络120可以包括电缆网络、光纤网络、电信网络、互联网、局域网络(LAN)、广域网络(WAN)、无线局域网络(WLAN)、城域网(MAN)、公共交换电话网络(PSTN)、蓝牙网络、紫蜂网络(ZigBee)、近场通信(NFC)、设备内总线、设备内线路、线缆连接等或其任意组合。各部分之间的网络连接可以是采用上述一种方式，也可以是采取多种方式。在一些实施例中，网络可以是点对点的、共享的、中心式的等各种拓扑结构或者多种拓扑结构的组合。在一些实施例中，网络150可以包括一个或以上网络接入点。例如，网络120可以包括有线或无线网络接入点，例如基站和/或网络交换点120-1、120-2、......，通过这些进出点系统100的一个或多个组件可连接到网络120上以交换数据和/或信息。
图2是根据本说明书的一些实施例所示的示例性地理围栏数据点密度优化的系统的模块图。
在一些实施例中，地理围栏数据点密度优化的系统200可以包括获取模块210、数据点筛选模块220和数据点组确定模块221。
获取模块210可以用于获取地理围栏数据点集，所述地理围栏数据点集包括按采集顺序排列的多个数据点。关于数据点集和数据点可以参见图3及其相关描述，此处不再赘述。
数据点筛选模块220可以用于对所述地理围栏数据点集重复执行以下步骤，直至所述地理围栏数据点集满足第一预设条件：将所述多个数据点分为至少一个数据点组，每个所述数据点组包括顺序排列的至少3个所述数据点；确定各个所述数据点组中除首位数据点和末位数据点外的各个所述数据点与所述首位数据点和所述末位数据点所构成直线的相关程度；判断所述相关程度是否满足第二预设条件，若是，则所述相关程度对应的所述数据点为可删除点，将所有所述可删除点删除，并更新所述地理围栏数据点集的数据点排列顺序和数据点数量，完成一轮数据点筛选。关于数据点筛选的步骤可以参见图3及其相关描述，此处不再赘述。在一些实施例中，数据点筛选模块220还可以用于确定除首位数据点和末位数据点外的各个数据点与直线的相关系数，以及确定除首位数据点和末位数据点外的各个数据点与直线的距离。
在一些实施例中，相关程度满足第二预设条件可以包括：相关系数大于第一阈值，以及距离小于第二阈值。在一些实施例中，第一预设条件可以包括：本轮所述数据点筛选完成后的所述数据点数量与前一轮所述数据点筛选完成后的所述数据点数量相同。在一些实施例中，数据点的坐标空间的尺度可以根据地理围栏数据点集中数据点的最小坐标值确定。
在一些实施例中，数据点筛选模块220还包括数据点组确定模块221，数据点组确定模块221可以用于按照所述多个数据点的所述排列顺序每次取至少3个所述数据点作为一个所述数据点组；基于所述数据点组的所述数据点的所述排列顺序，对所述数据点组依次排列，得到所述至少一个数据点组。在一些实施例中，依次得到的至少一个数据点组中，可以从第2个数据点组起，每一个数据点组的首位数据点是前一个数据点组的末位数据点。
应当理解，所示的系统及其模块可以利用各种方式来实现。例如，在一些实施例中，系统及其模块可以通过硬件、软件或者软件和硬件的结合来实现。其中，硬件部分可以利用专用逻辑来实现；软件部分则可以存储在存储器中，由适当的指令执行系统，例如微处理器或者专用设计硬件来执行。本领域技术人员可以理解上述的方法和系统可以使 用计算机可执行指令和/或包含在处理器控制代码中来实现，例如在诸如磁盘、CD或DVD-ROM的载体介质、诸如只读存储器(固件)的可编程的存储器或者诸如光学或电子信号载体的数据载体上提供了这样的代码。本申请的系统及其模块不仅可以有诸如超大规模集成电路或门阵列、诸如逻辑芯片、晶体管等的半导体、或者诸如现场可编程门阵列、可编程逻辑设备等的可编程硬件设备的硬件电路实现，也可以用例如由各种类型的处理器所执行的软件实现，还可以由上述硬件电路和软件的结合(例如，固件)来实现。
需要注意的是，以上对于地理围栏数据点密度优化的系统200及其模块的描述，仅为描述方便，并不能把本说明书限制在所举实施例范围之内。可以理解，对于本领域的技术人员来说，在了解该系统的原理后，可能在不背离这一原理的情况下，对各个模块进行任意组合，或者构成子系统与其他模块连接。例如，获取模块210、数据点筛选模块220和数据点组确定模块221可以共用一个存储模块，各个模块也可以分别具有各自的存储模块。诸如此类的变形，均在本申请的保护范围之内。
图3是根据本说明书的一些实施例所示的地理围栏数据点密度优化的方法的示例性流程图。
如图3所示，该地理围栏数据点密度优化的方法300可以包括：
步骤310，获取地理围栏数据点集。
具体的，该步骤310可以由获取模块210执行。
地理围栏数据点集是指用于构建地理围栏的数据点构成的数据点集。地理围栏数据点集包括按采集顺序排列的多个数据点。具体地，多个数据点可以是沿着道路，或所选区域如小区、公园、湖泊等的边界依次采集得到的数据点。仅作为示例，沿着道路依次采集得到数据点a、b、c、d、e、f，即数据点数量为6，地理围栏数据点集可以用A表示，则地理围栏数据点集A可以表示为，A＝{a，b，c，d，e，f}。
在一些实施例中，地理围栏数据点集中的数据点按采集顺序排列可以是以采集顺序的正序方式依次排列，也可以是以采集顺序的倒序方式依次排列。
在一些实施例中，地理围栏数据点集可以从服务器、终端或数据库中获取。例如，可以使用终端进行采集数据点，读取终端保存的数据获取地理围栏数据点集，又或者终端可以将采集到的数据点信息发送给服务器或保存至外部数据库，从服务器或数据库中获取地理围栏数据点集。
在一些实施例中，获取地理围栏数据点集后，还可以对地理围栏数据点集进行进一 步处理，如数据点坐标转换、数据点统计等。
数据点的坐标空间是指表示数据点的坐标所使用的坐标系空间，例如可以使用CGCS坐标系空间、WGS坐标系空间、UTM坐标系空间作为数据点的坐标空间。
在一些实施例中，可以对数据点的坐标空间的尺度进行调整。具体地，数据点的坐标空间的尺度可以根据地理围栏数据点集中数据点的最小坐标值确定。例如，地理数据点集表示为Data geofence，其中数据点a的坐标为Point a(X a，Y a)，坐标空间的尺度范围是从0开始表示，Min_x和Min_y分别为对应的地理围栏数据点中最小的X轴坐标值和最小的Y轴坐标值，可以将数据点的坐标空间的尺度范围调整为从最小的X轴坐标值和最小的Y轴坐标值开始表示，即数据点a的新坐标为New_Point a(X a-Min_x，Y a-Min_y)。
在一些实施例中，在对数据点的坐标空间的尺度进行调整时，可以对数据点的坐标空间进行转换。例如在坐标空间的尺度调整前，数据点使用的是WGS坐标系空间，尺度调整后，将数据点使用的坐标空间转换为UTM坐标系空间。
在一些实施例中，上述数据点的坐标空间尺度范围变化可以理解为将数据点的坐标空间进行坐标变换，得到一个以数据点(Min_x，Min_y)为原点的坐标系，从而可以有效地缩小数据点a的坐标值。
通过上述坐标空间的调整，可以使得坐标空间尺度适应数据点的坐标值范围，在后续数据点坐标的相关计算例如相关程度判断时，不会因为坐标值过大导致其计算得到的相关系数等值过大，从而相关系数等值的变化区间过小而导致相关系数的差异性过小，调整后可以更准确地根据相关系数等值是否满足条件来判断数据点是否可以删除。关于相关系数计算的更多细节可以参见步骤324及其相关描述，此处不再赘述。
步骤320，对所述地理围栏数据点集重复执行数据点筛选步骤，直至所述地理围栏数据点集满足第一预设条件。
具体的，该步骤320可以由数据点筛选模块220执行。
数据点筛选是指对地理围栏数据点集中的数据点进行筛选，删除不需要的数据点，即对于构建地理围栏来说冗余的数据点。
重复执行数据点筛选步骤是指对地理围栏数据点集中的数据点进行循环筛选，换而言之，也是数据点筛选的迭代。通过循环筛选即数据点筛选的迭代，可以保证尽量筛选出所有冗余的数据点，提高了筛选的准确性。
第一预设条件可以是指地理围栏数据点集的数据点数量达到预设条件，例如数量达到预设值或多次筛选后数量保持稳定，或者是指数据点所构成的地理围栏达到预设条件，例如构成的地理围栏完全覆盖目标区域或与目标区域的边界重合。
地理围栏数据点集满足第一预设条件即可以理解为，地理围栏数据点集中冗余的数据点已被删除，得到了点密度较优的地理围栏数据点集。
所述数据点筛选步骤可以包括：
步骤322，将所述N个数据点分为至少一个数据点组，每个所述数据点组包括顺序排列的至少3个所述数据点。
数据点组是指由多个数据点构成的点集，数据点组中包括至少3个数据点，例如可以包括3个或4个或5个数据点。各个数据点组包括的数据点数量可以相同，也可以不同。
数据点组中数据点的排列顺序为地理围栏数据点集的数据点排列顺序。各个数据点组中包括的数据点可以与前后数据点组中的数据点有重合，也可以不重合。可以根据数据点的数量和数据点组中数据点数量确定数据点组的数量。以地理围栏数据点集A＝{a，b，c，d，e，f}为例，数据点数量为6，每一个数据点组中可以包括3个数据点，则数据点组的数量可以为2，得到的2个数据点组分别为{a，b，c}和{d，e，f}，或者{a，b，c}和{c，d，e}，每一个数据点组中还可以包括4个数据点，则数据点组的数量可以为1或2，得到1个数据点组为{a，b，c，d}或者{b，c，d，e}或者{c，d，e，f}，得到2个数据点组分别为{a，b，c，d}和{c，d，e，f}。所述至少一个个数据点组的确定方法的更多具体内容可以参见图4及其相关说明，此处不再赘述。
步骤324，确定各个所述数据点组中除首位数据点和末位数据点外的各个所述数据点与所述首位数据点和所述末位数据点所构成直线的相关程度。
首位数据点是指所在数据点组中的第一位数据点，末位数据点是指所在数据点组中的最后一位数据点。继续以前述步骤322中的示例，当数据点组的数量为3时，数据点组{a，b，c}和数据点组{d，e，f}的首位数据点分别为数据点a和数据点d，末位数据点分别为数据点c和数据点f。
首位数据点和末位数据点所构成直线是指，首位数据点与末位数据点在坐标系中连接成的直线。可以将该直线理解为由首位数据点和末位数据点的坐标确定的线性函数。例如，该直线可以表示为f(x)＝ax+b，a和b通过根据首位数据点和末位数据点的坐标值联立函数方程求解得到。
相关程度是指各个数据点与所述直线的线性关系的密切程度，也可以理解为各个数据点与所述直线的拟合程度。例如，数据点和直线的相关程度可以是完全线性相关(即完全拟合)、线性相关较小(即拟合程度较低)或线性相关较大(即拟合程度较高)。相关程度可以通过计算线性相关数值、计算数据点与直线的拟合程度数值等来确定。
在一些实施例中，可以通过确定除所述首位数据点和所述末位数据点外的各个所述数据点与所述直线的相关系数来确定相关程度。相关系数是指线性关系的数值表示，相关系数值越大则表示相关程度越高。例如，相关系数为0.9代表的相关程度高于相关系数为0.7代表的相关程度。相关系数可以根据残差、总离差等进行计算，具体地，可以采用如下公式进行计算：
Figure PCTCN2020122643-appb-000001
继续以前述数据点组{a，b，c}为例，其中，R代表数据点b与数据点a、数据点c所构成直线的相关系数，SSR为该直线的回归平方和，SSE为数据点与该直线的残差平方和，SST为数据点与该直线的总离差平方和。
在一些实施例中，还可以采取任何有效的计算方法来计算数据点与所述直线的相关系数。例如，皮尔逊相关系数计算法。因此，并不局限于公式(1)中所示出的计算方法，对于上述两者相关系数的计算方法，本说明书不作限制。
在一些实施例中，还可以通过确定除所述首位数据点和所述末位数据点外的各个所述数据点与所述直线的距离来确定相关程度。绝对距离与相关程度成反比。即，所述绝对距离越大，则对应的相关程度越小。例如，继续以前述数据点组{a，b，c}为例，数据点b与数据点a和c所构成直线ac的绝对距离D可以表示为：
Figure PCTCN2020122643-appb-000002
其中，A、B、C均为计算参数，f(x)＝Ax+By+C为数据点a与数据点c构成直线ac映射的线性函数，x 0和y 0分别为数据点b的坐标值。
步骤326，判断所述相关程度是否满足第二预设条件，若是，则所述相关程度对应的所述数据点为可删除点，将所有所述可删除点删除，并更新所述地理围栏数据点集的数据点排列顺序和数据点数量，完成一轮数据点筛选。
第二预设条件可以是指地理围栏数据点集的数据点与所述直线的相关程度达到预设阈值，或者基于相关程度进行进一步处理得到的其它预设条件。
在一些实施例中，第二预设条件可以是预设阈值。具体地，预设阈值可以是相关系 数的预设阈值，可以将相关系数与预设阈值进行大小比较，将相关系数大于预设阈值，作为确定相关程度是否满足第二预设条件的标准。以数据点组{a，b，c}为例，相关系数的预设阈值为0.8，计算数据点b与所述直线ac的相关系数为0.85，则数据点b与直线ac的相关程度满足预设条件。预设阈值也可以是前述各个数据点与所述直线的距离的预设阈值，可以将距离与预设阈值进行大小比较，将距离小于预设阈值，作为确定相关程度是否满足第二预设条件的标准。以数据点组{a，b，c}为例，距离的预设阈值为2cm，计算数据点b与所述直线ac的距离为0.7，则数据点b与直线ac的相关程度满足预设条件。需要说明的是，预设阈值的大小可以通过实验数据确定，也可以通过实际情况进行调整，本说明书对于预设阈值的确定和大小范围不作限制。
在一些实施例中，相关系数的预设阈值可以称为第一阈值，各个数据点与所述直线的距离的预设阈值可以称为第二阈值，可以将相关系数大于第一阈值，以及距离小于第二阈值作为相关程度满足的第二预设条件。继续以前述数据点组{a，b，c}为例，计算得到数据点b与数据点a、数据点c所构成直线ac的相关系数为0.9，数据点b与数据点a、数据点c所构成直线ac的距离为1cm。令第一阈值为0.8％，第二阈值为2cm，则可以确定数据点b与数据点a、数据点c所构成直线的相关程度满足第二预设条件。相关系数和距离的计算方法可以参见步骤324及其相关描述，此处不再赘述。
通过本实施例，基于同时满足相关系数大于第一阈值和距离小于第二阈值来确定相关程度满足第二预设条件，可以在单一筛选条件的基础上进一步地保证从地理围栏数据点集筛选可删除点时，不会尺度过大。尺度过大的删除数据点可以理解为，经过删除筛选出的可删除点后，地理围栏数据点集构成的地理围栏与实际的道路边界或区域相比产生了较大的边界变化，使得根据上述地理围栏数据点集创建的地理围栏对目标区域的覆盖不再准确。
相关程度满足第二预设条件对应的所述数据点为可删除点，即数据点满足前述任一种相关程度满足第二预设条件的情况，则该数据点可以被从地理围栏数据点集中删除。在对可删除点进行删除后，会更新地理围栏数据点集的数据点排列顺序和数据点数量N，完成一轮数据点筛选。以地理围栏数据点集A＝{a，b，c，d，e，f}为例，N为6，M为2，得到的2个数据点组分别为{a，b，c}和{d，e，f}，数据点b和数据点e对应的相关程度满足第二预设条件，为可删除点，将数据点b、c删除后，地理围栏数据点集更新数据点数量为4，更新排列顺序后为A＝{a，c，d，f}。进行更新数据点数量和数据点排列顺序后的地理围栏数据点集可以用于下一轮的数据点筛选。
在每一轮数据点筛选后，都可以进行判断地理围栏数据点集是否满足第一预设条件，未满足时，会继续进行下一轮的数据点筛选，直到地理围栏数据点集满足第一预设条件。
在一些实施例中，第一预设条件可以为：本轮所述数据点筛选完成后的所述数据点数量与前一轮所述数据点筛选完成后的所述数据点数量相同。即当满足该预设条件时，数据点集的数量保持稳定，不再变化了，可以认为已经筛选出了所有的可删除点，即完成了地理围栏数据点集的点密度优化。
在一些实施例中，多轮数据点筛选时，每一轮数据点筛选的起始数据点可以变化。具体地，一轮数据点筛选的步骤中，在对地理围栏数据点集进行分组得到至少一个数据组时，可以从地理围栏数据点集中顺序排列的第一个数据点开始分组，即起始数据点为顺序排列的第一个数据点，也可以从地理围栏数据点集中顺序排列的第二个数据点或其它数据点开始分组，即起始数据点为顺序排列的第二个数据点。另外，一轮数据点筛选的起始数据点为顺序排列的第一个数据点，下一轮数据点筛选的起始数据点就可以为顺序排列的第二个数据点或其它数据点。在一些实施例中，多轮数据点筛选时，还可以先设定一个数据点组的数量M，先依次从数据点集取M个数据点组，对M个数据点组进行数据点筛选，然后再继续从数据点集中取M个数据点组进行数据点筛选，直至数据点集中的所有数据点都被取点进行了筛选。其中继续从数据点集中取M个数据点组进行数据点筛选时，可以将上一轮M个数据点组筛选时，连续未筛选出可删除点的数据点组的最前面一个数据点组中的数据点作为下一轮M个数据点组的开始数据点。至少一个数据点组的确定方法的更多内容可以参见图4及其相关说明，此处不再赘述。通过本实施例，可以让多轮的数据点筛选以第一点为起点或第二点为起点交替进行，保证对尽可能多的数据点都进行判断是否可以被删除，更加精准地找到可删除点。
通过执行上述数据点筛选步骤，对数据点进行分组，并根据计算出各个数据点与数据点组中首位数据点和末位数据点所构成直线的相关程度来判断数据点是否为可删除点，从而能够高效，准确地筛选出冗余的数据点，进行删除，实现对地理围栏数据点集的高效、准确的点密度优化。
图4是根据本说明书的一些实施例所示的至少一个数据点组的确定方法的示例性流程图。
如图4所示，该方法400可以包括：
步骤410，按照所述多个数据点的所述排列顺序每次取至少3个所述数据点作为一个所述数据点组。
具体的，该步骤410可以由数据点组确定模块221执行。
由前述步骤322的数据点组的相关说明可知，数据点组中数据点的排列顺序为地理围栏数据点集的数据点排列顺序。在取数据点组时，可以按照地理围栏数据点集的数据点排列顺序，可以从第一个数据点开始，或者从第二个数据点或其它数据点开始，每次取至少3个(可以是3个、4个、或5个)作为一个数据点组。
以地理围栏数据点集A＝{a，b，c，d，e，f}为例，可以从数据点a开始，每次取3个数据点作为一个数据点组，也可以从数据点b开始，每次取3个数据点作为一个数据点组。
步骤420，基于所述数据点组的所述数据点的所述排列顺序，对所述数据点组依次排列，得到所述至少一个数据点组。
具体的，该步骤420可以由数据点组确定模块执行。
各个数据点组是按照地理围栏数据点集的数据点排列顺序取数据点获得的，所以根据所取数据点的排列顺序，各个数据点组也会对应有一个排列顺序。例如，以地理围栏数据点集A＝{a，b，c，d，e，f}为例，从数据点a开始，每次取3个数据点作为一个数据点组，得到数据点组{a，b，c}和{d，e，f}，则{a，b，c}为第一个数据点组，{d，e，f}为第二个数据点组。
按照上述数据点组对应的排列顺序，将各个数据点组依次排列，即可得到所述至少一个数据点组。
在对地理围栏数据点集的数据点进行多轮数据点筛选时，基于上述步骤确定的至少一个数据点组，可以实现以第一位数据点为起点或以第二位数据点为起点交替进行数据点筛选操作，从而可以保证对地理围栏数据点集中的尽可能多的数据点都进行判断是否为可删除点，数据点筛选更加准确。
在一些实施例中，获得的至少一个数据点组中，从第2个所述数据点组起，每一个所述数据点组的所述首位数据点是前一个所述数据点组的所述末位数据点。以地理围栏数据点集A＝{a，b，c，d，e，f}为例，从数据点a开始，每次取3个数据点作为一个数据点组，从第2个所述数据点组起，每一个所述数据点组的所述首位数据点是前一个所述数据点组的所述末位数据点，得到数据点组{a，b，c}和{c，d，e}。通过本实施例，可以使得各个顺序排列的数据点组中的数据点首尾相接，可以进一步保证对尽可能多的数据点进行判断是否为可删除点，令数据点筛选更加准确。
本说明书实施例还提供一种装置，包括处理器，所述处理器用于执行前述的地理围 栏数据点密度优化的方法。所述地理围栏数据点密度优化的方法可以包括：获取地理围栏数据点集，所述地理围栏数据点集包括按采集顺序排列的多个数据点；对所述地理围栏数据点集重复执行以下步骤，直至所述地理围栏数据点集满足第一预设条件：将所述多个数据点分为至少一个数据点组，每个所述数据点组包括顺序排列的至少3个所述数据点；确定各个所述数据点组中除首位数据点和末位数据点外的各个所述数据点与所述首位数据点和所述末位数据点所构成直线的相关程度；判断所述相关程度是否满足第二预设条件，若是，则所述相关程度对应的所述数据点为可删除点，将所有所述可删除点删除，并更新所述地理围栏数据点集的数据点排列顺序和数据点数量，完成一轮数据点筛选。
本说明书实施例还提供一种计算机可读存储介质，所述存储介质存储计算机指令，当计算机读取存储介质中的计算机指令后，计算机执行前述的地理围栏数据点密度优化的方法。所述地理围栏数据点密度优化的方法可以包括：获取地理围栏数据点集，所述地理围栏数据点集包括按采集顺序排列的多个数据点；对所述地理围栏数据点集重复执行以下步骤，直至所述地理围栏数据点集满足第一预设条件：将所述多个数据点分为至少一个数据点组，每个所述数据点组包括顺序排列的至少3个所述数据点；确定各个所述数据点组中除首位数据点和末位数据点外的各个所述数据点与所述首位数据点和所述末位数据点所构成直线的相关程度；判断所述相关程度是否满足第二预设条件，若是，则所述相关程度对应的所述数据点为可删除点，将所有所述可删除点删除，并更新所述地理围栏数据点集的数据点排列顺序和数据点数量，完成一轮数据点筛选。
本说明书实施例可能带来的有益效果包括但不限于：(1)通过对数据点进行分组，并根据计算出数据点组中各个数据点与首位数据点和末位数据点所构成直线的相关程度来判断数据点是否为可删除点，实现对地理围栏数据点集的高效、准确的点密度优化；(2)通过变化数据点组选点的起点，多轮的数据点筛选的数据点起点可以交替变化，以及前后数据点组的首位数据点和末位数据点重合，可以尽量保证尽可能多的数据点都被进行判断是否可以被删除，提高数据点筛选的准确性；(3)基于同时满足相关系数大于第一阈值和距离小于第二阈值来确定相关程度满足第二预设条件，实现进一步地保证从地理围栏数据点集筛选可删除点时，不会尺度过大，保证构建的地理围栏的有效覆盖。需要说明的是，不同实施例可能产生的有益效果不同，在不同的实施例里，可能产生的有益效果可以是以上任意一种或几种的组合，也可以是其他任何可能获得的有益效果。
上文已对基本概念做了描述，显然，对于本领域技术人员来说，上述详细披露仅仅作为示例，而并不构成对本说明书的限定。虽然此处并没有明确说明，本领域技术人员可能会对本说明书进行各种修改、改进和修正。该类修改、改进和修正在本说明书中被建议，所以该类修改、改进、修正仍属于本说明书示范实施例的精神和范围。
同时，本说明书使用了特定词语来描述本说明书的实施例。如“一个实施例”、“一实施例”、和/或“一些实施例”意指与本说明书至少一个实施例相关的某一特征、结构或特点。因此，应强调并注意的是，本说明书中在不同位置两次或多次提及的“一实施例”或“一个实施例”或“一个替代性实施例”并不一定是指同一实施例。此外，本说明书的一个或多个实施例中的某些特征、结构或特点可以进行适当的组合。
此外，本领域技术人员可以理解，本说明书的各方面可以通过若干具有可专利性的种类或情况进行说明和描述，包括任何新的和有用的工序、机器、产品或物质的组合，或对他们的任何新的和有用的改进。相应地，本说明书的各个方面可以完全由硬件执行、可以完全由软件(包括固件、常驻软件、微码等)执行、也可以由硬件和软件组合执行。以上硬件或软件均可被称为“数据块”、“模块”、“引擎”、“单元”、“组件”或“系统”。此外，本说明书的各方面可能表现为位于一个或多个计算机可读介质中的计算机产品，该产品包括计算机可读程序编码。
计算机存储介质可能包含一个内含有计算机程序编码的传播数据信号，例如在基带上或作为载波的一部分。该传播信号可能有多种表现形式，包括电磁形式、光形式等，或合适的组合形式。计算机存储介质可以是除计算机可读存储介质之外的任何计算机可读介质，该介质可以通过连接至一个指令执行系统、装置或设备以实现通讯、传播或传输供使用的程序。位于计算机存储介质上的程序编码可以通过任何合适的介质进行传播，包括无线电、电缆、光纤电缆、RF、或类似介质，或任何上述介质的组合。
本说明书各部分操作所需的计算机程序编码可以用任意一种或多种程序语言编写，包括面向对象编程语言如Java、Scala、Smalltalk、Eiffel、JADE、Emerald、C++、C#、VB.NET、Python等，常规程序化编程语言如C语言、Visual Basic、Fortran2003、Perl、COBOL2002、PHP、ABAP，动态编程语言如Python、Ruby和Groovy，或其他编程语言等。该程序编码可以完全在用户计算机上运行、或作为独立的软件包在用户计算机上运行、或部分在用户计算机上运行部分在远程计算机运行、或完全在远程计算机或处理设备上运行。在后种情况下，远程计算机可以通过任何网络形式与用户计算机连接，比如局域网(LAN)或广域网(WAN)，或连接至外部计算机(例如通过因特网)，或在 云计算环境中，或作为服务使用如软件即服务(SaaS)。
此外，除非权利要求中明确说明，本说明书所述处理元素和序列的顺序、数字字母的使用、或其他名称的使用，并非用于限定本说明书流程和方法的顺序。尽管上述披露中通过各种示例讨论了一些目前认为有用的发明实施例，但应当理解的是，该类细节仅起到说明的目的，附加的权利要求并不仅限于披露的实施例，相反，权利要求旨在覆盖所有符合本说明书实施例实质和范围的修正和等价组合。例如，虽然以上所描述的系统组件可以通过硬件设备实现，但是也可以只通过软件的解决方案得以实现，如在现有的处理设备或移动设备上安装所描述的系统。
同理，应当注意的是，为了简化本说明书披露的表述，从而帮助对一个或多个发明实施例的理解，前文对本说明书实施例的描述中，有时会将多种特征归并至一个实施例、附图或对其的描述中。但是，这种披露方法并不意味着本说明书对象所需要的特征比权利要求中提及的特征多。实际上，实施例的特征要少于上述披露的单个实施例的全部特征。
一些实施例中使用了描述成分、属性数量的数字，应当理解的是，此类用于实施例描述的数字，在一些示例中使用了修饰词“大约”、“近似”或“大体上”来修饰。除非另外说明，“大约”、“近似”或“大体上”表明所述数字允许有±20％的变化。相应地，在一些实施例中，说明书和权利要求中使用的数值参数均为近似值，该近似值根据个别实施例所需特点可以发生改变。在一些实施例中，数值参数应考虑规定的有效数位并采用一般位数保留的方法。尽管本说明书一些实施例中用于确认其范围广度的数值域和参数为近似值，在具体实施例中，此类数值的设定在可行范围内尽可能精确。
针对本说明书引用的每个专利、专利申请、专利申请公开物和其他材料，如文章、书籍、说明书、出版物、文档等，特此将其全部内容并入本说明书作为参考。与本说明书内容不一致或产生冲突的申请历史文件除外，对本说明书权利要求最广范围有限制的文件(当前或之后附加于本说明书中的)也除外。需要说明的是，如果本说明书附属材料中的描述、定义、和/或术语的使用与本说明书所述内容有不一致或冲突的地方，以本说明书的描述、定义和/或术语的使用为准。
最后，应当理解的是，本说明书中所述实施例仅用以说明本说明书实施例的原则。其他的变形也可能属于本说明书的范围。因此，作为示例而非限制，本说明书实施例的替代配置可视为与本说明书的教导一致。相应地，本说明书的实施例不仅限于本说明书明确介绍和描述的实施例。

Claims (16)

  1. 一种地理围栏数据点密度优化的方法，其特征在于，包括：
    获取地理围栏数据点集，所述地理围栏数据点集包括按采集顺序排列的多个数据点；
    对所述地理围栏数据点集重复执行以下步骤，直至所述地理围栏数据点集满足第一预设条件：
    将所述多个数据点分为至少一个数据点组，每个所述数据点组包括顺序排列的至少3个所述数据点；
    确定各个所述数据点组中除首位数据点和末位数据点外的各个所述数据点与所述首位数据点和所述末位数据点所构成直线的相关程度；
    判断所述相关程度是否满足第二预设条件，若是，则所述相关程度对应的所述数据点为可删除点，将所有所述可删除点删除，并更新所述地理围栏数据点集的数据点排列顺序和数据点数量，完成一轮数据点筛选。
  2. 如权利要求1所述的方法，所述数据点的坐标空间的尺度根据所述地理围栏数据点集中所述数据点的最小坐标值确定。
  3. 如权利要求1所述的方法，所述至少一个数据点组的确定方法包括：
    按照所述多个数据点的所述排列顺序每次取至少3个所述数据点作为一个所述数据点组；
    基于所述数据点组的所述数据点的所述排列顺序，对所述数据点组依次排列，得到所述至少一个数据点组。
  4. 如权利要求3所述的方法，所述依次得到的所述至少一个数据点组中，从第2个所述数据点组起，每一个所述数据点组的所述首位数据点是前一个所述数据点组的所述末位数据点。
  5. 如权利要求1所述的方法，所述确定所述数据点组中除所述首位数据点和所述末位数据点外的各个所述数据点与所述首位数据点和所述末位数据点所构成直线的相关程度包括：
    确定除所述首位数据点和所述末位数据点外的各个所述数据点与所述直线的相关系数，以及确定除所述首位数据点和所述末位数据点外的各个所述数据点与所述直线的 距离。
  6. 如权利要求5所述的方法，所述相关程度满足第二预设条件包括：
    所述相关系数大于第一阈值，以及所述距离小于第二阈值。
  7. 如权利要求1所述的方法，所述第一预设条件包括：本轮所述数据点筛选完成后的所述数据点数量与前一轮所述数据点筛选完成后的所述数据点数量相同。
  8. 一种地理围栏数据点密度优化的系统，其特征在于，所述系统包括：
    获取模块：用于获取地理围栏数据点集，所述地理围栏数据点集包括按采集顺序排列的多个数据点；
    数据点筛选模块：用于对所述地理围栏数据点集重复执行以下步骤，直至所述地理围栏数据点集满足第一预设条件：
    将所述多个数据点分为至少一个数据点组，每个所述数据点组包括顺序排列的至少3个所述数据点；
    确定各个所述数据点组中除首位数据点和末位数据点外的各个所述数据点与所述首位数据点和所述末位数据点所构成直线的相关程度；
    判断所述相关程度是否满足第二预设条件，若是，则所述相关程度对应的所述数据点为可删除点，将所有所述可删除点删除，并更新所述地理围栏数据点集的数据点排列顺序和数据点数量，完成一轮数据点筛选。
  9. 如权利要求8所述的系统，所述数据点的坐标空间的尺度根据所述地理围栏数据点集中所述数据点的最小坐标值确定。
  10. 如权利要求8所述的系统，所述数据点筛选模块还包括数据点组确定模块，用于：
    按照所述多个数据点的所述排列顺序每次取至少3个所述数据点作为一个所述数据点组；
    基于所述数据点组的所述数据点的所述排列顺序，对所述数据点组依次排列，得到所述至少一个数据点组。
  11. 如权利要求10所述的系统，所述依次得到的所述至少一个数据点组中，从第2个所述数据点组起，每一个所述数据点组的所述首位数据点是前一个所述数据点组的所述末位数据点。
  12. 如权利要求8所述的系统，所述数据点筛选模块还用于：
    确定除所述首位数据点和所述末位数据点外的各个所述数据点与所述直线的相关系数，以及确定除所述首位数据点和所述末位数据点外的各个所述数据点与所述直线的距离。
  13. 如权利要求12所述的系统，所述相关程度满足第二预设条件包括：
    所述相关系数大于第一阈值，以及所述距离小于第二阈值。
  14. 如权利要求8所述的系统，所述第一预设条件包括：本轮所述数据点筛选完成后的所述数据点数量与前一轮所述数据点筛选完成后的所述数据点数量相同。
  15. 一种地理围栏数据点密度优化的装置，包括处理器，所述处理器用于执行如权利要求1～7任一项所述的地理围栏数据点密度优化的方法。
  16. 一种计算机可读存储介质，所述存储介质存储计算机指令，当计算机读取存储介质中的计算机指令后，计算机执行如权利要求1～7任一项所述的地理围栏数据点密度优化的方法。
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