CN112084286B - Spatial data processing method and device, computer equipment and storage medium - Google Patents

Spatial data processing method and device, computer equipment and storage medium Download PDF

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CN112084286B
CN112084286B CN202010959127.1A CN202010959127A CN112084286B CN 112084286 B CN112084286 B CN 112084286B CN 202010959127 A CN202010959127 A CN 202010959127A CN 112084286 B CN112084286 B CN 112084286B
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张楠
张岩
李振军
贾鹏
闫嘉
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Smartsteps Data Technology Co ltd
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Abstract

The invention relates to the technical field of data processing, and provides a spatial data processing method, a spatial data processing device, a computer device and a storage medium, wherein the method comprises the following steps: acquiring a target cluster in a plurality of clusters and a target unknown point in the target cluster; judging whether the target unknown point is in a preset range of the known point in the target cluster; and if the unknown target point is not in the preset range, obtaining the spatial data of the unknown target point according to the preset information point POI in the preset range of each unknown point in the target cluster and the spatial data of the known point in the target cluster. Compared with the prior art, for unknown points of which the spatial data are not in the preset range, the POI is introduced, and the spatial data of the POI and the known points of which the spatial data are known are combined to obtain more accurate spatial data of the unknown points.

Description

Spatial data processing method and device, computer equipment and storage medium
Technical Field
The present invention relates to the field of data processing technologies, and in particular, to a spatial data processing method and apparatus, a computer device, and a storage medium.
Background
Spatial Information (Spatial Information) is Information that reflects the Spatial distribution characteristics of a geographic entity. The spatial information is transferred by means of a spatial information carrier (images and maps). The spatial information can only be combined with attribute information and time information to completely describe the geographic entity. The method is characterized in that the digital earth theory is utilized, and based on key technologies such as remote sensing, GIS, virtual simulation, network, database and multimedia, spatial information is deeply developed and utilized, and urban planning, construction and management are served for construction; serve governments, businesses, the public; an information infrastructure and information system that serve the sustainable development of the population, resource environment, economic society.
In an actual use scene, the data volume of spatial data of spatial information is often large, the spatial data of some position points is difficult to acquire due to the limitation of environment or tools, or the acquired spatial data is abnormal, for such spatial data, an interpolation method is usually adopted to estimate the value of the spatial data, for an unknown point in a preset range of a sampling point, the spatial data of the unknown point obtained by the interpolation method is accurate and close to the actual spatial data, and for spatial data of the unknown point outside the preset range of the sampling point, the spatial data of the unknown point obtained by the interpolation method is inaccurate and has a large difference from the actual spatial data.
Disclosure of Invention
The invention aims to provide a spatial data processing method, a spatial data processing device, a computer device and a storage medium, wherein for unknown points Of which spatial data are unknown and which are not in a preset range, the POI (Point Of Information) is introduced, and the spatial data Of the POI and the known points Of which the spatial data are known are combined to obtain more accurate spatial data Of the unknown points.
In order to achieve the above purpose, the embodiment of the present invention adopts the following technical solutions:
in a first aspect, the present invention provides a spatial data processing method applied to a computer device, where the computer device stores a plurality of clusters in advance, each cluster including a plurality of known points whose spatial data are known and at least one unknown point whose spatial data are unknown, the method including: acquiring a target cluster in a plurality of clusters and a target unknown point in the target cluster; judging whether the target unknown point is in a preset range of the known point in the target cluster; and if the unknown target point is not in the preset range, obtaining the spatial data of the unknown target point according to the preset information point POI in the preset range of each unknown point in the target cluster and the spatial data of the known point in the target cluster.
In a second aspect, the present invention provides a spatial data processing apparatus applied to a computer device, the computer device having a plurality of clusters stored in advance, each cluster including a plurality of known points whose spatial data is known and at least one unknown point whose spatial data is unknown, the apparatus comprising: the acquisition module is used for acquiring a target cluster in the clusters and a target unknown point in the target cluster; the judging module is used for judging whether the target unknown point is in a preset range of the known point in the target cluster; and the processing module is used for obtaining the spatial data of the unknown target point according to the preset information point POI in the preset range of each unknown point in the target cluster and the spatial data of the known point in the target cluster if the unknown target point is not in the preset range.
In a third aspect, the present invention provides a computer apparatus comprising: one or more processors; a memory for storing one or more programs which, when executed by the one or more processors, cause the one or more processors to implement a spatial data processing method as described above.
In a fourth aspect, the present invention provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the spatial data processing method described above.
Compared with the prior art, for unknown points of which the spatial data are not in the preset range, the POI is introduced, and the spatial data of the POI and the known points of which the spatial data are known are combined to obtain more accurate spatial data of the unknown points.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present invention and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained according to the drawings without inventive efforts.
Fig. 1 shows a flowchart of a spatial data processing method according to an embodiment of the present invention.
Fig. 2 is a flowchart illustrating another spatial data processing method according to an embodiment of the present invention.
Fig. 3 is an exemplary diagram illustrating the relative positions of the unknown point and the known point of the target in the preset range according to the embodiment of the present invention.
Fig. 4 is a flowchart illustrating another spatial data processing method according to an embodiment of the present invention.
Fig. 5 is a block diagram illustrating a spatial data processing apparatus according to an embodiment of the present invention.
Fig. 6 shows a block schematic diagram of a computer device provided by an embodiment of the present invention.
Icon: 10-a computer device; 11-a processor; 12-a memory; 13-a bus; 14-a communication interface; 100-spatial data processing means; 110-an obtaining module; 120-a judgment module; 130-processing module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
In the description of the present invention, it should be noted that if the terms "upper", "lower", "inside", "outside", etc. indicate an orientation or a positional relationship based on that shown in the drawings or that the product of the present invention is used as it is, this is only for convenience of description and simplification of the description, and it does not indicate or imply that the device or the element referred to must have a specific orientation, be constructed in a specific orientation, and be operated, and thus should not be construed as limiting the present invention.
Furthermore, the appearances of the terms "first," "second," and the like, if any, are used solely to distinguish one from another and are not to be construed as indicating or implying relative importance.
It should be noted that the features of the embodiments of the present invention may be combined with each other without conflict.
The unknown spatial data of points at certain locations in the geographic space includes at least two cases: (1) the lack of the spatial data may be caused by the limitation of the terrain and environment where the point is located, or the limitation of the measurement tool, so that the spatial data of the point cannot be measured or acquired, and finally the spatial data is lost; (2) the spatial data may be abnormal due to a problem in the measurement tool, an error in the measurement process, or improper storage and maintenance of the measured or acquired spatial data. In the former case, the spatial data of the missing part is usually obtained by technical means and then supplemented to the missing part, and in the latter case, the spatial data of the abnormal part is usually obtained by technical means and corrected according to the obtained spatial data.
The technical means in the above is usually interpolation, which is an important method for discrete function approximation, and values at other points can be calculated by using the value conditions of the function at a limited number of points. Distance weighted interpolation is a commonly used interpolation method, and may also be referred to as the inverse distance multiplication method. The distance inverse power gridding method is a weighted average interpolation method, and accurate or smooth interpolation can be carried out. The power parameter controls how the weight coefficients decrease with increasing distance from a mesh node. For a larger square, the closer data points are given a higher weight share, and for a smaller square, the weights are distributed more evenly to the data points. The weight given to a particular data point when computing a mesh node is proportional to the inverse distance given to that node from the node to the observation point for the specified power.
Because the interpolation method is to realize the interpolation of unknown point values in a distance attenuation mode, only interpolation result values within a certain range from a sample point are in accordance with actual results, grid values of predicted points at other places are almost equivalent in a large area, the interpolation result is inaccurate, and the obtained spatial data is also inaccurate.
In order to solve the above problems, the inventors propose a spatial data processing method, an apparatus, a computer device and a storage medium, which can obtain more accurate spatial data, and will be described in detail below.
Referring to fig. 1, fig. 1 is a flowchart illustrating a spatial data processing method according to an embodiment of the present invention, where the method includes the following steps:
step S100, a target cluster in the plurality of clusters and a target unknown point in the target cluster are obtained.
In this embodiment, all unknown points whose spatial data is unknown and all known points whose spatial data is known are divided into a plurality of clusters according to a preset clustering algorithm, wherein the clustering algorithm may be, but is not limited to, K-means, hierarchical clustering, spectral clustering, etc. The unknown points and the known points can be clustered according to different attribute characteristics of the unknown points and the known points to obtain a plurality of clusters, the attribute characteristics of the unknown points and the known points in the same cluster are also relatively close, for example, the attribute characteristics can be space longitude and latitude, at this time, the spatial positions of the unknown points and the known points in the same cluster are relatively close, and of course, other attribute characteristics, such as terrain characteristics, population density and the like, can also be adopted.
In this embodiment, the spatial data may be surface temperature, wind power of tall buildings, population number, and even land value. The method for processing the spatial data provided by the embodiment of the invention can be used for calculating the spatial data of the unknown point.
In this embodiment, the target unknown point is an unknown point whose spatial data needs to be calculated, and the cluster to which the target unknown point belongs is a target cluster.
It should be noted that, in order to avoid that the abnormal spatial data affects the accuracy of the result, the outliers in the acquired original spatial data are usually cleaned, that is, the abnormal spatial data in the original spatial data are eliminated, the original spatial data is not the abnormal spatial data, but the normal spatial data remains, the normal spatial data is used to calculate the spatial data of the points where the spatial data is missing or the points where the spatial data is abnormal, and finally, the relatively accurate spatial data corresponding to the points is obtained.
Step S110, determine whether the target unknown point is within a preset range of the known point in the target cluster.
In this embodiment, the preset range may be an area range covered by a preset geometric shape with a known point as a center, and the preset geometric shape may be a regular circle, a square, or an irregular preset shape.
In this embodiment, the preset range of the known point of the target unknown point in the target cluster may be a preset range of any one known point of the target unknown point in the target cluster, or may be an overlapping area of the preset ranges of any two known points of the target unknown point in the target cluster.
Step S120, if the target unknown point is not in the preset range, obtaining the spatial data of the target unknown point according to the preset information point POI in the preset range of each unknown point in the target cluster and the spatial data of the known point in the target cluster.
In this embodiment, a POI refers to any non-geographically meaningful point on a map, such as a building, a store, a bus station, a green space, a park, a shop, a bar, a gas station, a hospital, a station, etc.
In this embodiment, the number of POIs in the preset range of each unknown point may be counted to obtain the number of POIs, and the spatial data of the unknown point of the target may be calculated by using the number of POIs and the spatial data of the known point in the target cluster.
According to the spatial data processing method provided by the embodiment of the invention, for unknown points of spatial data which are not in a preset range, POI (point of interest) and spatial data of known points of known spatial data are combined by introducing the POI to obtain more accurate spatial data of the unknown points.
On the basis of fig. 1, an embodiment of the present invention provides a specific implementation manner for determining whether a target unknown point is within a preset range of known points in a target cluster, please refer to fig. 2, fig. 2 shows a flowchart of another spatial data processing method provided by the embodiment of the present invention, and step S110 includes the following sub-steps:
and a substep S110-10, calculating the initial distance between every two known points in the target cluster.
In this embodiment, it is necessary to pair all known points in the target cluster two by two, and calculate the distance between two known points in all the pairs, for example, the target cluster includes A, B, C, D four known points, and pair them two by two can obtain: AB. The AC, AD, BC, BD, CD are provided for six pairs, and the distance between two known points in each of the six pairs, such as the distance between AB, the distance between AC, the distance between AD, etc., is calculated to obtain 6 initial distances, where the distance may be euclidean distance or mahalanobis distance.
And a substep S110-11, taking the average of all the initial distances as the average distance of the target cluster.
And a substep S110-12 of determining a preset range of each known point in the target cluster according to the average distance.
In this embodiment, the determined preset ranges may also be different according to different geometric shapes adopted for determining the preset ranges, for example, the geometric shapes are circles, the preset range of each known point may be an area covered by a circle with the center of the known point and the radius of the circle being the average distance, or the geometric shapes may also be squares, and at this time, the preset range is an area covered by a square with the center of the known point and the distance from each vertex being the average distance.
And a substep S110-13, if the target unknown point is in the preset range of any known point in the target cluster, determining that the target unknown point is in the preset range of the known point in the target cluster.
In the sub-step S110-14, if the target unknown point is not within the preset range of all known points in the target cluster, it is determined that the target unknown point is not within the preset range of the known points in the target cluster.
Referring to fig. 3, fig. 3 is a diagram illustrating an example of relative positions of an unknown point and a known point of a target in a preset range according to an embodiment of the present invention. In the target cluster in fig. 3, 3 black solid points represent known points: known point a, known point B and known point C, the two open points representing unknown points: unknown point 1 and unknown point 2, the area within the dashed circle around each known point representing the preset range of the known point. Since the unknown point 1 is within the preset range of the known point a, it is determined that the unknown point 1 is within the preset range of the known point of the target cluster, and since the unknown point 2 is not within the preset range of any one of the known points A, B, C in the target cluster, it is determined that the unknown point 2 is not within the preset range of the known point of the target cluster.
According to the spatial data processing method provided by the embodiment of the invention, the average distance of the target cluster is obtained through the distance between every two known points in the target cluster, and the preset range of the known points of the target cluster is determined according to the average distance, so that different processing is carried out on the unknown points of the target under two conditions of being in the preset range and not being in the preset range, and finally, more accurate spatial data of the unknown points of the target can be obtained under the two conditions.
On the basis of fig. 1, an embodiment of the present invention provides a specific implementation manner for obtaining spatial data of an unknown point of a target, please refer to fig. 4, where fig. 4 shows a flowchart of another spatial data processing method provided in the embodiment of the present invention, and step S120 includes the following sub-steps:
and a substep S120-10 of calculating an average of the spatial data of all known points in the target cluster.
And a substep S120-11 of obtaining the total number of POI in the preset range of each unknown point in the target cluster.
In this embodiment, the preset range of the unknown point is similar to the preset range of the known point, and is not described herein again.
And a substep S120-12 of obtaining the number of unknown points and the number of known points in the target cluster.
And a substep S120-13 of calculating the spatial data of the target unknown point according to the number of the unknown points in the target cluster, the number of the known points in the target cluster, the average value and each total number.
In this embodiment, as a specific implementation manner, a formula may be adopted
Figure BDA0002679793580000081
Calculating the space data of the unknown point of the target, wherein Z represents the space data of the unknown point of the target, k represents the number of the unknown points in the target cluster, VavgWhich represents the average value of the values,
Figure BDA0002679793580000082
n represents the number of known points in the target cluster, ZjSpatial data representing the jth known point in said target cluster, CPOIiRepresenting the total number of POIs in the preset range of the ith unknown point in the target cluster, CPOIRepresenting the total number of POIs within a preset range of the target unknown point.
According to the spatial data processing method provided by the embodiment of the invention, the spatial data of the unknown target point is calculated according to the number of unknown points in the target cluster, the number and the average value of the known points in the target cluster and the total number of POIs in the preset range of each unknown point, the spatial data of the unknown target point is considered, the spatial data of the known point and the total number of POIs in the preset range of the unknown point are also considered, and finally the spatial data of the unknown target point is more accurate.
In this embodiment, if the target unknown point is within the preset range of the known point in the target cluster, in order to ensure that more accurate spatial data can be obtained under this condition, on the basis of fig. 1, the embodiment of the present invention further provides a specific implementation manner of spatial data processing when the target unknown point is within the preset range of the known point in the target cluster, please refer to fig. 1 again, and the method further includes the following steps:
step S130, if the target unknown point is within the preset range of the known point in the target cluster, obtaining the spatial data of the target unknown point according to the spatial data of each known point in the target cluster and the distance between the target unknown point and each known point in the target cluster.
In this embodiment, as a specific implementation manner, a formula may be adopted:
Figure BDA0002679793580000091
wherein, avg (d)i)∈(0,Davg]Calculating spatial data of the target unknown point, wherein Z0Representing an unknown point of the object, ZiSpatial data representing the ith known point of the target cluster, diRepresenting the distance between the ith known point of the target cluster and the target unknown point; n represents the number of known points of the target cluster, avg (d)i) Denotes all diIs determined by the average value of (a) of (b),
Figure BDA0002679793580000092
Davgthe average distance of the target cluster is represented, the average distance is the average of the distances between every two known points in the target cluster, and the calculation method is the same as the calculation method of the average distance of the target cluster in the sub-steps S110-10 to S110-11.
If avg (d) is satisfiedi)∈(0,Davg]This condition is then defined by the formula
Figure BDA0002679793580000093
Wherein, avg (d)i)∈(0,Davg]And calculating the spatial data of the target unknown point. If avg (d) is not satisfiedi)∈(0,Davg]This condition, then, still uses the formula
Figure BDA0002679793580000094
Spatial data of the target unknown point is calculated.
According to the spatial data processing method provided by the embodiment of the invention, if the target unknown point is within the preset range of the known point in the target cluster, the accurate spatial data can be obtained under the condition according to the spatial data of each known point in the target cluster and the distance between the target unknown point and each known point in the target cluster.
It should be noted that, in this embodiment, a plurality of grids may also be obtained through grid division, and each grid or a preset number of grids is taken as a cluster, and the spatial data of the target unknown point is calculated by using the spatial data processing method.
As a specific application scenario, when the spatial data is a ground surface temperature, in a general case, the ground surface temperature is obtained based on a remote sensing inversion method, but a mountain effect on urban buildings can affect an acquisition result, and weather like fog, haze and the like can also be affected by the mountain effect, so that the ground surface temperature obtained based on the remote sensing inversion method cannot reflect the influence of the mountain effect on the ground surface temperature.
As another specific application scenario, when the spatial data is wind power of a high-rise building, the acquired information is detected through the wind power of a building group, abnormal data in the acquired data can be corrected or missing data can be supplemented by adopting the spatial data processing method, so that accurate wind power data of the high-rise building can be obtained, damage such as blowing off of building affairs or building resonance can be caused due to the fact that the high-rise wind exists in a building dense area, and the damage degree of the density of the building area can be accurately evaluated through analysis of the wind power data of the high-rise building.
In order to perform the corresponding steps in the above embodiments and various possible implementations, an implementation of the spatial data processing apparatus 100 is given below. Referring to fig. 5, fig. 5 is a block diagram illustrating a spatial data processing apparatus 100 according to an embodiment of the invention. It should be noted that the spatial data processing apparatus 100 provided in the present embodiment has the same basic principle and technical effect as those of the above embodiments, and for the sake of brief description, no reference is made to this embodiment.
The spatial data processing apparatus 100 includes an obtaining module 110, a determining module 120, and a processing module 130.
An obtaining module 110, configured to obtain a target cluster in the multiple clusters and a target unknown point in the target cluster.
And the judging module 120 is configured to judge whether the target unknown point is within a preset range of the known point in the target cluster.
As a specific implementation manner, the determining module 120 is specifically configured to: calculating the initial distance between every two known points in the target cluster; taking the average value of all the initial distances as the average distance of the target cluster; determining a preset range of each known point in the target cluster according to the average distance; if the target unknown point is in the preset range of any one known point in the target cluster, judging that the target unknown point is in the preset range of the known point in the target cluster; and if the target unknown point is not in the preset range of all the known points in the target cluster, judging that the target unknown point is not in the preset range of the known points of the target cluster.
The processing module 130 is configured to, if the target unknown point is not within the preset range, obtain spatial data of the target unknown point according to a preset information point POI within the preset range of each unknown point in the target cluster and spatial data of a known point in the target cluster.
As a specific implementation manner, the processing module 130 is specifically configured to: calculating the average value of the spatial data of all known points in the target cluster; acquiring the total number of POI in a preset range of each unknown point in a target cluster; acquiring the number of unknown points and the number of known points in a target cluster; and calculating the spatial data of the unknown target points according to the number of the unknown points in the target cluster, the number of the known points in the target cluster, the average value and each total number.
As an embodiment, the processing module 130, when executing the step of calculating the spatial data of the target unknown point according to the number of unknown points in the target cluster, the number of known points in the target cluster, the average value, and each total number, is specifically configured to: according to the number of unknown points in the target cluster, the number of known points in the target cluster, the average value and each total number, utilizing a formula
Figure BDA0002679793580000111
Computing spatial data of target unknown points(ii) a Wherein Z represents the spatial data of the unknown point of the target, k represents the number of unknown points in the target cluster, VavgThe average value is represented by the average value,
Figure BDA0002679793580000112
n represents the number of known points in the target cluster, ZjSpatial data representing a jth known point in the target cluster,
Figure BDA0002679793580000113
representing the total number of POIs in the preset range of the ith unknown point in the target cluster, CPOIRepresenting the total number of POIs within a preset range of the target unknown point.
As a specific embodiment, the processing module 130 is further configured to: and if the target unknown point is in the preset range of the known point in the target cluster, obtaining the spatial data of the target unknown point according to the spatial data of each known point in the target cluster and the distance between the target unknown point and each known point in the target cluster.
As a specific embodiment, the processing module 130 is specifically configured to, when executing the step of obtaining the spatial data of the unknown target point according to the spatial data of each known point in the target cluster and the distance between the unknown target point and each known point in the target cluster: according to the spatial data of each known point in the target cluster and the distance between the target unknown point and each known point in the target cluster, using a formula:
Figure BDA0002679793580000114
wherein, avg (d)i)∈(0,Davg]Calculating spatial data of the target unknown point, wherein Z0Representing an unknown point of the object, ZiSpatial data representing the ith known point of the target cluster, diRepresenting the distance between the ith known point of the target cluster and the target unknown point; n represents the number of known points of the target cluster, avg (d)i) Denotes all diIs determined by the average value of (a) of (b),
Figure BDA0002679793580000115
Davgrepresenting the average of target clustersDistance, the average distance is the average of the distances between all known points in the target cluster.
Based on the spatial data processing method described above, an embodiment of the present invention further provides a block diagram of a computer device 10 for implementing the spatial data processing method shown in fig. 1, fig. 2, and fig. 4, please refer to fig. 6, and fig. 6 shows a block diagram of the computer device 10 provided in an embodiment of the present invention, where the computer device 10 includes a processor 11, a memory 12, a bus 13, and a communication interface 14. The processor 11 and the memory 12 are connected by a bus 13, and the processor 11 is communicatively connected to an external device via a communication interface 14.
The processor 11 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 11. The Processor 11 may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components.
The memory 12 is used for storing a program, such as the spatial data processing apparatus 100 in fig. 5, the spatial data processing apparatus 100 includes at least one software functional module which can be stored in the memory 12 in a form of software or firmware (firmware), and the processor 11 executes the program after receiving an execution instruction to implement the spatial data processing method described above.
The Memory 12 may include a high-speed Random Access Memory (RAM) and may also include a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. Alternatively, the memory 12 may be a storage device built in the processor 11, or may be a storage device independent of the processor 11.
The bus 13 may be an ISA bus, a PCI bus, an EISA bus, or the like. Fig. 6 is indicated by only one double-headed arrow, but does not indicate only one bus or one type of bus.
The present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a spatial data processing method as described above.
In summary, an embodiment of the present invention provides a spatial data processing method applied to a computer device, where the computer device stores a plurality of clusters in advance, each cluster including a plurality of known points whose spatial data is known and at least one unknown point whose spatial data is unknown, and the method includes: acquiring a target cluster in a plurality of clusters and a target unknown point in the target cluster; judging whether the target unknown point is in a preset range of the known point in the target cluster; and if the unknown target point is not in the preset range, obtaining the spatial data of the unknown target point according to the preset information point POI in the preset range of each unknown point in the target cluster and the spatial data of the known point in the target cluster. Compared with the prior art, for unknown points of which the spatial data are not in the preset range, the POI is introduced, and the spatial data of the POI and the known points of which the spatial data are known are combined to obtain more accurate spatial data of the unknown points.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.

Claims (7)

1. A spatial data processing method applied to a computer apparatus which stores a plurality of clusters in advance, each of the clusters including a plurality of known points whose spatial data is known and at least one unknown point whose spatial data is unknown, the method comprising:
acquiring a target cluster in the plurality of clusters and a target unknown point in the target cluster;
judging whether the target unknown point is in a preset range of the known point in the target cluster;
if the target unknown point is not in the preset range, calculating the average value of the spatial data of all known points in the target cluster;
acquiring the total number of POI in a preset range of each unknown point in the target cluster;
acquiring the number of unknown points and the number of known points in the target cluster;
according to the number of unknown points in the target cluster, the number of known points in the target cluster, the average value and each total number, utilizing a formula
Figure FDA0003030869310000011
Calculating spatial data of the target unknown point;
wherein Z represents the spatial data of the unknown points of the target, k represents the number of unknown points in the target cluster, VavgThe average value is represented by the average value,
Figure FDA0003030869310000012
n represents the number of known points in the target cluster, ZjSpatial data representing a jth known point in the target cluster,
Figure FDA0003030869310000013
representing the total number of POIs in the preset range of the ith unknown point in the target cluster, CPOIRepresenting the total number of POIs within a preset range of the target unknown point.
2. The spatial data processing method of claim 1, wherein the step of determining whether the target unknown point is within a preset range of known points in the target cluster comprises:
calculating the initial distance between every two known points in the target cluster;
taking the average value of all the initial distances as the average distance of the target cluster;
determining the preset range of each known point in the target cluster according to the average distance;
if the target unknown point is in the preset range of any one of the known points in the target cluster, determining that the target unknown point is in the preset range of the known point in the target cluster;
and if the target unknown point is not in the preset range of all the known points in the target cluster, judging that the target unknown point is not in the preset range of the known points of the target cluster.
3. The spatial data processing method of claim 1, wherein the method further comprises:
and if the target unknown point is within the preset range of the known points in the target cluster, obtaining the spatial data of the target unknown point according to the spatial data of each known point in the target cluster and the distance between the target unknown point and each known point in the target cluster.
4. The spatial data processing method of claim 3, wherein the step of obtaining the spatial data of the unknown point according to the spatial data of each known point in the target cluster and the distance between the unknown point of the target and each known point in the target cluster comprises:
according to the spatial data of each known point in the target cluster and the distance between the target unknown point and each known point in the target cluster, utilizing a formula:
Figure FDA0003030869310000021
wherein, avg (d)i)∈(0,Davg]Calculating spatial data of the target unknown point, wherein Z0Representing said target unknown point, ZiSpatial data representing the ith said known point of said target cluster, diRepresenting a distance between an ith known point of the target cluster and the target unknown point; n represents the number of known points of the target cluster, avg (d)i) To representAll of diIs determined by the average value of (a) of (b),
Figure FDA0003030869310000022
Davgrepresents an average distance of the target cluster, the average distance being an average of distances between every two of all known points in the target cluster.
5. A spatial data processing apparatus applied to a computer device which stores a plurality of clusters in advance, each of the clusters including a plurality of known points whose spatial data is known and at least one unknown point whose spatial data is unknown, the apparatus comprising:
an obtaining module, configured to obtain a target cluster in the multiple clusters and a target unknown point in the target cluster;
the judging module is used for judging whether the target unknown point is in a preset range of the known point in the target cluster;
the processing module is used for calculating the average value of the spatial data of all the known points in the target cluster if the target unknown point is not in the preset range; acquiring the total number of POI in a preset range of each unknown point in the target cluster; acquiring the number of unknown points and the number of known points in the target cluster; according to the number of unknown points in the target cluster, the number of known points in the target cluster, the average value and each total number, utilizing a formula
Figure FDA0003030869310000031
Calculating spatial data of the target unknown point; wherein Z represents the spatial data of the unknown points of the target, k represents the number of unknown points in the target cluster, VavgThe average value is represented by the average value,
Figure FDA0003030869310000032
n represents the number of known points in the target cluster, ZjSpatial data representing a jth known point in the target cluster,
Figure FDA0003030869310000033
representing the total number of POIs in the preset range of the ith unknown point in the target cluster, CPOIRepresenting the total number of POIs within a preset range of the target unknown point.
6. A computer device, characterized in that the computer device comprises:
one or more processors;
memory for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the spatial data processing method of any of claims 1-4.
7. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the spatial data processing method according to any one of claims 1 to 4.
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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101090456A (en) * 2006-06-14 2007-12-19 索尼株式会社 Image processing device and method, image pickup device and method
CN108701274A (en) * 2017-05-24 2018-10-23 北京质享科技有限公司 A kind of small scale air quality index prediction technique in city and system
CN108595414A (en) * 2018-03-22 2018-09-28 浙江大学 Heavy metal-polluted soil enterprise pollution source discrimination based on source remittance space variable reasoning
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