Efficient Processing System for Map Navigation Data Based on Cloud Storage

Abstract

The present invention relates to the technical field of data processing, and particularly relates to an efficient processing system for map navigation data based on cloud storage. The system includes a processor and a memory. The processor executes the computer program stored in the memory to implement the following steps: analyzing the distribution of position data points on a navigation map, obtaining a low-density set and a high-density set based on the analysis result, compressing the data points in the high-density set using a geometric transformation coding compression algorithm to obtain compressed data, and compressing the data points in the low-density set using an LZ compression algorithm to obtain compressed data. Moreover, the present invention can not only improve the compression effect of data but also improve the data transmission efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly relates to an efficient processing system for map navigation data based on cloud storage. Background Art

[0002] With the rapid development of smartphones, in-vehicle navigation devices, driverless technologies, etc., the application of navigation maps is becoming more and more extensive. Currently, the road conditions are usually viewed and driving routes are planned based on navigation maps. And currently, the position data points of the objects on the road are usually transmitted to the data analysis and processing center or module of the navigation map in real time, and then the road conditions are updated in real time or the driving routes are planned in real time through the data processing and analysis results of the data analysis and processing module. And because generally the data to be transmitted is relatively large, and currently, in order to improve the transmission efficiency and enable the navigation map to plan a better driving or walking route or update the road conditions in a timely manner, usually the data compression algorithm is first used to compress the position data points to be transmitted, and then the compressed data is transmitted.

[0003] However, currently when compressing data, usually only one compression method is used for compression, that is, usually either the geometric transformation coding compression algorithm or the LZ compression algorithm is selected. However, since the tightness between data points is different, and different algorithms have different compression effects on data with different densities, so if any compression algorithm is directly randomly selected to compress the data, this compression method will result in a poor compression effect. For example, in a set of data points to be compressed, if the tightness between some data points is relatively high, then if the LZ compression algorithm is selected for compression at this time, the compression effect on this part of the data points will be relatively poor. If the tightness between some data points is relatively low, then if the geometric transformation coding compression algorithm is selected for compression at this time, the compression effect on this part of the data points will also be relatively poor. The main reason is that the geometric transformation coding compression algorithm can achieve a higher compression effect in high-density regions by making full use of the redundancy and structural features in spatial data, and is particularly suitable for spatial data with regularity and compact distribution. The LZ compression algorithm can effectively compress the data in low-density regions through dictionary replacement technology, identify repeated byte sequences to reduce redundancy, thereby improving the storage efficiency. Therefore, how to improve the compression effect has become an urgent problem to be solved. Summary of the Invention

[0004] In order to solve the above problems, the present invention provides an efficient processing system for map navigation data based on cloud storage, and the specific technical solutions adopted are as follows:

[0005] An embodiment of the present invention provides an efficient processing system for map navigation data based on cloud storage, including a processor and a memory. The processor executes the computer program stored in the memory to implement the following steps:

[0006] Obtain the position data points on the navigation map;

[0007] All the position data points on the navigation map are denoted as the first data points. A clustering algorithm with the clustering parameter as the initial clustering parameter is used to cluster all the first data points. After clustering is completed, the obtained clustering clusters are all denoted as the first clustering clusters; according to the clustering centers of each first clustering cluster, the corresponding core sets of each first clustering cluster are obtained and all denoted as the first core sets; according to all the data points in each first core set and the initial clustering parameter, the compactness characterization value of each first core set is obtained;

[0008] Judge whether the compactness characterization value of each first core set is greater than the first compactness threshold. If so, the corresponding first core set is denoted as a high-density set; otherwise, the corresponding first core set is denoted as a low-density set;

[0009] Obtain the intersection regions between any two first clustering clusters and all denote them as the first intersection regions. Judge whether the total number of the first data points in all the first intersection regions is not greater than the preset quantity threshold. If so, the set composed of all the first data points that do not belong to the first core set is denoted as a low-density set; otherwise, all the first data points that do not belong to the first core set are denoted as the second data points, and the initial clustering parameter is adjusted. According to the adjusted clustering parameter, the second data points are continuously clustered, and the low-density set and the high-density set are continuously obtained according to the clustering result;

[0010] Use the geometric transformation coding compression algorithm to compress the data points in the high-density set to obtain compressed data, and use the LZ compression algorithm to compress the data points in the low-density set to obtain compressed data.

[0011] Beneficial effects: The present invention first obtains the position data points on the navigation map; then all the position data points on the navigation map are denoted as the first data points, and a clustering algorithm with the clustering parameter as the initial clustering parameter is used to cluster all the first data points. After clustering, the obtained clustering clusters are all denoted as the first clustering clusters. According to the clustering centers of each first clustering cluster, the corresponding core sets of each first clustering cluster are obtained and denoted as the first core sets. According to all the data points in each first core set and the initial clustering parameter, the compactness characterization value of each first core set is obtained; then it is judged whether the compactness characterization value of each first core set is greater than the first compactness threshold. If so, the corresponding first core set is denoted as a high-density set; otherwise, the corresponding first core set is denoted as a low-density set; then the intersection area between any two first clustering clusters is obtained and denoted as the first intersection area, and it is judged whether the total number of the first data points in all the first intersection areas is not greater than the preset quantity threshold. If so, the set composed of all the first data points that do not belong to the first core set is denoted as a low-density set; otherwise, all the first data points that do not belong to the first core set are denoted as the second data points, and the initial clustering parameter is adjusted. According to the adjusted clustering parameter, the second data points are continued to be clustered, and the low-density set and the high-density set are continued to be obtained according to the clustering result; finally, the data points in the high-density set are compressed by using the geometric transformation coding compression algorithm to obtain compressed data, and the data points in the low-density set are compressed by using the LZ compression algorithm to obtain compressed data. And by dividing the low-density set and the high-density set, and then using different compression algorithms to compress the low-density set and the high-density set respectively, the present invention can improve the compression effect of the data, thereby also improving the transmission efficiency, that is, the subsequent transmission efficiency when the compressed data is transmitted to the data analysis and processing module in the navigation map system can be improved, and the efficiency and rationality of route planning of the navigation map system can be improved, and more timely traffic condition updates can be ensured. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following-described drawings are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0013] Figure 1 It is a flowchart of a method for efficiently processing map navigation data based on cloud storage according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0014] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art belong to the scope protected by the embodiments of the present invention.

[0015] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs.

[0016] This embodiment provides an efficient processing system for map navigation data based on cloud storage, including a processor and a memory. The processor executes the computer program stored in the memory to implement an efficient processing method for map navigation data based on cloud storage, as Figure 1 shown. The efficient processing method for map navigation data based on cloud storage includes the following steps:

[0017] Step S001, obtain the position data points on the navigation map.

[0018] The main purpose of this embodiment is to improve the compression effect of the position data points to improve the data transmission efficiency, so that the navigation map can plan a more reasonable driving or walking route or can update the road conditions in a timely manner; and for the convenience of analysis, this embodiment takes the compression process of the position data points of all objects on all roads in any area as an example for analysis. For example, this area can refer to any city, and this area is denoted as the monitoring area.

[0019] First, all roads in the monitoring area are denoted as monitoring roads. Then, at the current monitoring moment, obtain the position data of all monitoring objects located on the monitoring roads. The position data of the monitoring objects mainly includes longitude and latitude, and the monitoring objects include, but are not limited to, vehicles and people. However, it is required that the monitoring objects in this embodiment refer to the objects whose position data can be received by the map navigation, that is, the monitoring objects carry position acquisition devices that can be connected to the network or the position information of the monitoring objects can be obtained through remote network monitoring devices. For example, if a vehicle on a certain monitoring road can obtain position information through a GPS device or a vehicle-mounted sensor, then this vehicle belongs to the monitoring object. If an animal on a certain monitoring road not only does not carry a network-connected position acquisition device, but also all remote network monitoring devices cannot locate its position information, then the animal does not belong to the monitoring object.

[0020] Moreover, since the monitored object carries a location acquisition device or a remote networked monitoring device that can obtain the location information of the monitored object, the navigation map can receive the location data of these monitored objects in real time and map the received location data one by one onto the navigation map. Thus, at the current monitoring moment, all the location data points of the monitored objects can be obtained on the navigation map. In this embodiment, the dimension of the location data points is 2D, so the abscissa value of the location data points is longitude and the ordinate value is latitude. As another real-time method, the location data points can also be 3D, composed of longitude, latitude, and altitude respectively.

[0021] Therefore, through the above process, all the location data points on the navigation map at the current moment can be obtained. Usually, the number of location data points on the navigation map at the current monitoring moment is relatively large. In addition, these location data points will be stored based on the cloud storage method. It should be noted that all the subsequent location data points are the location data points at the current monitoring moment, and the map navigation can analyze these location data points to update the road conditions in real time and plan a reasonable route for the map navigation users.

[0022] Step S002: Compress the location data points on the navigation map to obtain compressed data.

[0023] Moreover, due to different times or different regions where the roads are located, there are differences in the locations or quantities of the monitored objects on different roads at different times. Different compression algorithms have different compression effects on different tightness regions. Therefore, in this embodiment, in order to improve the compression effect, the data point tightness of different regions will be analyzed, and the compression algorithm will be adaptively selected based on the analysis results, so as to improve the compression effect. The specific compression process is as follows:

[0024] In this embodiment, all the location data points on the navigation map are first recorded as the first data points, and then the initial clustering parameters are obtained. The clustering algorithm with the initial clustering parameters is used to cluster all the first data points, and the clustering clusters obtained after clustering are all recorded as the first clustering clusters. Then, according to the clustering centers of each first clustering cluster, the core sets corresponding to each first clustering cluster are obtained, and all the core sets corresponding to all the first clustering clusters are recorded as the first core set. Immediately afterwards, according to all the first data points in each first core set and the initial clustering parameters, the tightness characterization values of each first core set are obtained.

[0025] After obtaining the compactness characterization value of each first core set, the first compactness threshold is obtained. Then, it is determined whether the compactness characterization value of each first core set is greater than the first compactness threshold. If so, it is determined that the distribution of data points in the corresponding first core set is high-density. Then, all first core sets with compactness characterization values greater than the first compactness threshold are recorded as high-density sets. Otherwise, it is determined that the distribution of data points in the corresponding first core set is low-density. Then, all first core sets with compactness characterization values not greater than the first compactness threshold are recorded as low-density sets.

[0026] Based on the above analysis, it can be seen that the above process only determines whether the data points in the first core set belong to high-density distribution or low-density distribution, and does not analyze the data points other than the first core set. Therefore, in this embodiment, the intersection area between any two first clustering clusters is obtained next and is recorded as the first intersection area. It is determined whether the total number of first data points in all the first intersection areas is not greater than the preset number threshold. If so, it is determined that the distribution between the data points outside the first core set is relatively discrete. Then, the set composed of all first data points that do not belong to the first core set is recorded as a low-density set. Otherwise, it is determined that the compactness of the data points other than the first core set needs to be analyzed. That is, if the total number of first data points in all the first intersection areas is not less than the preset number threshold, all first data points that do not belong to the first core set are recorded as second data points, and the initial clustering parameters are adjusted. According to the adjusted clustering parameters, all the second data points are clustered continuously, and the low-density set and the high-density set are obtained continuously according to the clustering results.

[0027] The process of adjusting the initial clustering parameters, continuously clustering all the second data points according to the adjusted clustering parameters, and continuously obtaining the low-density set and the high-density set according to the clustering results is as follows:

[0028] First, denote the set composed of all the second data points in the navigation map as the second set, and obtain the compactness characterization value of the second set. Then, adjust the initial clustering parameters according to the compactness characterization value of the second set and the first compactness threshold to obtain the first adjusted clustering parameters. After that, use the clustering algorithm with the first adjusted clustering parameters as the clustering parameters to cluster all the second data points, and denote all the clustering clusters obtained after clustering as the second clustering clusters. After that, obtain the core set corresponding to each second clustering cluster according to the clustering center of each second clustering cluster, and denote them all as the second core sets. Then, obtain the compactness characterization value of each second core set according to all the data points in each second core set and the first adjusted clustering parameters. Immediately afterwards, judge whether the compactness characterization value of each second core set is greater than the second compactness threshold. If so, denote the corresponding second core set as a high-density set; otherwise, denote the corresponding second core set as a low-density set. Then, obtain the intersection region between any two second clustering clusters, and denote them all as the second intersection regions. After that, judge whether the total number of second data points in all the second intersection regions is not greater than the preset number threshold. If so, denote the set composed of all the second data points that do not belong to the second core set as a low-density set; otherwise, denote all the second data points that do not belong to the second core set as the third data points, adjust the first adjusted clustering parameters, continue to cluster all the third data points according to the adjusted clustering parameters, and obtain the low-density set and the high-density set again according to the clustering results, and so on, until all the obtained low-density sets and high-density sets contain all the position data points on the navigation map and then stop.

[0029] In this embodiment, the clustering algorithm used for clustering data points is the DBSCAN clustering algorithm. Moreover, the initial clustering parameters in this embodiment include the initial radius R and the initial minimum number of points M. And in specific applications, the implementer can set the values of R and M according to the actual situation, or can set R and M as empirical values. For example, in this embodiment, R can be set to 2 and M can be set to 25. It should be noted that the initial minimum number of points refers to the minimum number of points in the R neighborhood during clustering. That is, during clustering, if a certain point contains at least M points in the neighborhood with a radius of R, this point is called a core point, and the area where the core point is located is a data-intensive area and can form the core of the clustering. In addition, in this embodiment, the process of clustering data points using the DBSCAN clustering algorithm when the cluster class parameters are known is a well-known technology, so it will not be described in detail in this embodiment.

[0030] In this embodiment, the process of obtaining the cluster center of a cluster is as follows: For any cluster, the abscissa of the cluster center of the cluster is the mean value of the abscissa values of all data points in the cluster, and the ordinate of the cluster center of the cluster is the mean value of the ordinate values of all data points in the cluster.

[0031] In this embodiment, the specific process of obtaining the core set corresponding to each first cluster according to the cluster center of each first cluster is as follows: For any first cluster A1: Among all the first data points except the first cluster A1, obtain the first data point closest to the first cluster A1, and denote it as the out-of-cluster nearest neighbor point of the first cluster A1; then obtain the Euclidean distance between the cluster center of the first cluster A1 and the out-of-cluster nearest neighbor point of the first cluster A1, and denote it as the out-of-cluster nearest neighbor distance of the first cluster A1; then obtain a circle with the cluster center of the first cluster A1 as the center and the out-of-cluster nearest neighbor distance of the first cluster A1 as the radius, and denote the area enclosed by the circle as the core area corresponding to the first cluster A1; then obtain all the first data points that belong to the first cluster A1 and are located in the core area corresponding to the first cluster A1, and denote the set formed by the obtained first data points that belong to the first cluster A1 and are located in the core area corresponding to the first cluster A1 as the core set corresponding to the first cluster A1.

[0032] In this embodiment, the specific process of obtaining the out-of-cluster nearest neighbor point of the first cluster A1 is as follows: Denote all the first data points except the first cluster A1 as the remaining data points, obtain the target out-of-cluster distance corresponding to each remaining data point, and among the target out-of-cluster distances corresponding to all the remaining data points, take the remaining data point corresponding to the minimum target out-of-cluster distance as the out-of-cluster nearest neighbor point of the first cluster A1, and the method for obtaining the target out-of-cluster distance corresponding to the remaining data point is as follows: For any remaining data point, in the first cluster A1, obtain the first data point closest to the remaining data point, and denote it as the nearest neighbor data point of the remaining data point, and denote the Euclidean distance between the nearest neighbor data point of the remaining data point and the remaining data point as the target out-of-cluster distance corresponding to the remaining data point.

[0033] In this embodiment, the specific process of obtaining the compactness characterization value of each first core set according to all the data points in each first core set and the initial clustering parameters is as follows:

[0034] For any first core set B: first, obtain the number of first data points in the R neighborhood of each first data point in the first core set B, and record it as the neighborhood quantity representation value corresponding to the first data point; then record the cluster center of the first cluster cluster to which the first core set B belongs as the reference point of the first core set B; then obtain the Euclidean distance between each first data point in the first core set B and the reference point of the first core set B, and record it as the first distance corresponding to the first data point; then, according to the first distance of each first data point in the first core set B, obtain the feature distance weight value and mapping representation value corresponding to each first data point in the first core set B; then, according to the neighborhood quantity representation value, the corresponding feature distance weight value and mapping representation value corresponding to each first data point in the first core set B, obtain the first core set B. The comprehensive characterization value of each first data point in the first core set B; and for any first data point in the first core set B, the comprehensive characterization value of the first data point refers to the product of the normalized value of the neighborhood quantity characterization value corresponding to the first data point, the feature distance weight value and the mapping characterization value; then the product of the mean of the comprehensive characterization values ​​of all the first data points in the first core set B and the preset closeness weight value is recorded as the closeness characterization value of the first core set B, and in this embodiment, the normalized value of the neighborhood quantity characterization value corresponding to the first data point refers to the ratio of the neighborhood quantity characterization value corresponding to the first data point to the reference quantity characterization value of the first core set B, and the reference quantity characterization value of the first core set B refers to the maximum neighborhood quantity characterization value among the neighborhood quantity characterization values ​​corresponding to all the first data points in the first core set B.

[0035] In this embodiment, according to the first distance of each first data point in the first core set B, the specific process of obtaining the feature distance weight value and the mapping representation value corresponding to each first data point in the first core set B is:

[0036] For any first data point in the first core set B: Obtain the square value of the ratio of the first distance of the first data point to the reference distance of the first core set B, and denote it as the characteristic ratio of the first data point. Denote the reciprocal of the result obtained by adding the characteristic ratio of the first data point to a preset first constant as the characteristic distance weight value corresponding to the first data point. The reference distance of the first core set B refers to the maximum first distance among all the first distances of the first data points in the first core set B. Obtain the mean value of all the first distances of the first data points in the first core set B and denote it as the first mean value. Denote the product of the first mean value and a preset second constant as the target denominator value of the first core set B. Denote the ratio of the square value of the first distance of the first data point to the target denominator value of the first core set B as the discrete representation value of the first data point. Perform a negative correlation mapping on the discrete representation value of the first data point, and denote the mapping result as the mapping representation value corresponding to the first data point. And the discrete representation value of the first data point is obtained based on the Gaussian kernel function. Then the mapping representation value corresponding to the first data point is , and exp() is the exponential function with the constant e as the base, d is the first distance of the first data point, is the first mean value, and c2 is the preset second constant.

[0037] And in this embodiment, the preset first constant, the preset second constant, and the preset compactness weight value need to be set according to the actual situation. For example, in this embodiment, the preset first constant can be set to 1, the preset second constant can be set to 2, and the preset compactness weight value can be set to 1 or 2. As another real-time method, the product of the mean value of the comprehensive representation values of all the first data points in the first core set B and the compactness coefficient corresponding to the first core set B can also be used as the compactness representation value of the first core set B. The role of the preset compactness weight value or the compactness coefficient is to amplify the weight of the high-density region, that is, to make the compactness representation value of the high-density region larger. The process of obtaining the compactness coefficient corresponding to the first core set B is as follows: Denote the set composed of the cluster centers of all the first clusters except the first cluster to which the first core set B belongs as the subset. In the subset, obtain the core set of the first cluster to which the cluster center closest to the reference point of the first core set B belongs, and denote it as the nearest neighbor first core set corresponding to the first core set B. Denote the ratio of the total number of first data points in the first core set B to the total number of first data points in its corresponding nearest neighbor core set as the compactness coefficient corresponding to the first core set B.

[0038] In addition, in this embodiment, the specific expression for obtaining the compactness representation value of the first core set B is:

[0039]

[0040] Among them, C is the compactness characterization value of the first core set B, is the preset compactness weight value, I0 is the number of the first data points in the first core set B, is the neighborhood quantity characterization value corresponding to the i-th first data point in the first core set B, is the maximum neighborhood quantity characterization value among all the neighborhood quantity characterization values corresponding to the first data points in the first core set B, and its main function is to normalize the neighborhood quantity characterization value corresponding to the first data point, is the first distance of the i-th first data point in the first core set B, is the reference distance of the first core set B, is the first mean value, is the square value of the first distance of the i-th first data point in the first core set B, is the characteristic distance weight value corresponding to the i-th first data point, is the mapping characterization value corresponding to the i-th first data point.

[0041] And when is larger, is smaller and is smaller, the value of C is larger, and the larger the value of C, the more closely the data points in the first core set B are distributed; conversely, when is smaller, is larger and is larger, the value of C is smaller, and the smaller the value of C, the less closely the data points in the first core set B are distributed.

[0042] In this embodiment, the specific process of obtaining the intersection region between any two clustering clusters is as follows: For the first clustering cluster K1 and the first clustering cluster K2, the first clustering cluster K1 and the first clustering cluster K2 are not the same first clustering cluster. Obtain the minimum circumscribed circle containing the first clustering cluster K1 and the minimum circumscribed circle containing the first clustering cluster K2, and record the intersection region of the minimum circumscribed circle containing the first clustering cluster K1 and the minimum circumscribed circle containing the first clustering cluster K2 as the intersection region between the first clustering cluster K1 and the first clustering cluster K2, and the method for obtaining the intersection region between any two second clustering clusters is the same as the method for obtaining the intersection region between any two first clustering clusters.

[0043] In addition, in specific applications, it is necessary to set a preset quantity threshold according to the actual situation. For example, in this embodiment, two percent of the total number of position data points on the navigation map can be used as the preset quantity threshold.

[0044] In this embodiment, the process of obtaining the compactness characterization value of the second set is as follows: First, obtain the number of second data points within the R-neighborhood of each second data point in the second set, and denote it as the neighborhood number characterization value corresponding to the corresponding second data point; then, take the average data point of all second data points in the second set as the reference point of the second set, and the abscissa value of the reference point of the second set is the average of the abscissa values of all second data points in the second set, and the ordinate value of the reference point of the second set is the average of the ordinate values of all second data points in the second set; then, obtain the Euclidean distance between each second data point in the second set and the reference point of the second set, and denote it as the distance to be analyzed corresponding to the corresponding second data point; then, according to the distances to be analyzed of each second data point in the second set, obtain the characteristic distance weight value and the mapping characterization value corresponding to each second data point in the second set, and the method of obtaining the characteristic distance weight value and the mapping characterization value corresponding to each second data point in the second set according to the distances to be analyzed of each second data point in the second set is the same as the method described above for obtaining the characteristic distance weight value and the mapping characterization value corresponding to each first data point in the first core set B, so it will not be described in detail here. Immediately afterwards, obtain the comprehensive characterization value of each second data point in the second set, and for any second data point in the second set, the comprehensive characterization value of this second data point is the product of the normalized value of the neighborhood number characterization value corresponding to this second data point, the characteristic distance weight value, and the mapping characterization value; finally, denote the product of the mean value of the comprehensive characterization values of all second data points in the second set and the preset compactness weight value as the compactness characterization value of the second set.

[0045] In this embodiment, according to the compactness characterization value of the second set and the first compactness threshold, the process of adjusting the initial clustering parameters to obtain the first adjusted clustering parameters is as follows:

[0046] First, the normalized value of the absolute value of the difference between the compactness characterization value of the second set and the first compactness threshold is denoted as the first feature difference value. Here, the normalization function norm() is used to normalize the data that needs to be normalized. Then, it is judged whether the first compactness threshold is less than the compactness characterization value of the second set. If so, the result of multiplying the preset first adjustment coefficient by the first feature difference value and then adding the preset first constant is denoted as the first radius adjustment degree, and the reciprocal of the result obtained by multiplying the preset first adjustment coefficient by the nth power of the first feature difference value and then adding the preset first constant is denoted as the first minimum point number adjustment degree. If it is judged that the first compactness threshold is not less than the compactness characterization value of the second set, the result of multiplying the preset second adjustment coefficient by the first feature difference value and then adding the preset first constant is denoted as the first radius adjustment degree, and the reciprocal of the result obtained by multiplying the preset second adjustment coefficient by the nth power of the first feature difference value and then adding the preset first constant is denoted as the first minimum point number adjustment degree. n is the dimension of the position data points, and in this embodiment, n is 2. Immediately afterwards, the product of the first radius adjustment degree and the initial radius R is denoted as the first adjusted radius R1, the result of adding the preset first constant and the preset third adjustment coefficient is denoted as the target adjustment weight, and the result of multiplying the target adjustment weight, the first minimum point number adjustment degree, and the initial minimum point number M is denoted as the first adjusted minimum point number M1. Both the first adjusted radius R1 and the first adjusted minimum point number M1 belong to the first adjusted clustering parameters. The purpose of the above target adjustment weight is mainly to further control the adjustment magnification.

[0047] In addition, when the first compactness threshold is less than the compactness characterization value of the second set, the first radius adjustment degree is , and the first minimum point number adjustment degree is the reciprocal of, where G1 is the first compactness threshold and H is the compactness characterization value of the second set. is the preset first adjustment coefficient. When the first compactness threshold is less than the compactness characterization value of the second set, the first radius adjustment degree is , and the first minimum point number adjustment degree is the reciprocal of. is the preset second adjustment coefficient. And in specific applications, the implementer needs to set the preset first adjustment coefficient, the preset second adjustment coefficient, and the preset third adjustment coefficient according to the actual situation. It is required that the preset first adjustment coefficient is negative and the preset second adjustment coefficient is positive. For example, in this embodiment, the preset first adjustment coefficient can be set to -0.2, the preset second adjustment coefficient can be set to 0.2, and the preset third adjustment coefficient can be set to 0.1.

[0048] And the above adjustment criteria are as follows: when the first density threshold is less than the density characterization value of the second set, it is considered that the density of the data points in the second set is greater than the density of the first core set obtained above. To make the subsequent clustering results more reliable, the value of R should be adjusted smaller and the value of M should be adjusted larger; when the first density threshold is not less than the density characterization value of the second set, it is considered that the density of the data points in the second set is not greater than the density of the first core set obtained above. To make the subsequent clustering results more reliable, the value of R should be adjusted larger and the value of M should be adjusted smaller.

[0049] In this embodiment, the specific process of obtaining the core set corresponding to each second clustering cluster according to the clustering center of each second clustering cluster is as follows: for any second clustering cluster A2, among all the second data points except the second clustering cluster A2, obtain the second data point closest to the second clustering cluster A2, and denote it as the out-of-cluster nearest neighbor point of the second clustering cluster A2. Denote the Euclidean distance between the clustering center of the second clustering cluster A2 and the out-of-cluster nearest neighbor point of the second clustering cluster A2 as the out-of-cluster nearest neighbor distance of the second clustering cluster A2. Denote the area of the circle with the clustering center of the second clustering cluster A2 as the center and the out-of-cluster nearest neighbor distance of the second clustering cluster A2 as the radius as the core area corresponding to the second clustering cluster A2. Denote the set formed by all the second data points that belong to the second clustering cluster A2 and are located in the core area corresponding to the second clustering cluster A2 as the core set corresponding to the second clustering cluster A2; and the method of obtaining the second data point closest to the second clustering cluster A2 among all the second data points except the second clustering cluster A2 is the same as the method of obtaining the first data point closest to the first clustering cluster A1 among all the first data points except the first clustering cluster A1 above, so it will not be described in detail.

[0050] In this embodiment, the specific process of obtaining the compactness characterization value of each second core set according to all data points in each second core set and the first adjusted clustering parameter is as follows: For any second core set V: First, obtain the number of second data points within the R1 neighborhood of each second data point in the second core set V, and denote it as the neighborhood number characterization value corresponding to the corresponding second data point; then, denote the clustering center of the second clustering cluster to which the second core set B belongs as the reference point of the second core set V; after that, denote the Euclidean distance between each second data point in the second core set V and the reference point of the second core set V as the second distance of the corresponding second data point, and obtain the characteristic distance weight value and the mapping characterization value corresponding to each second data point in the second core set V according to the second distances of each second data point in the second core set V; obtain the comprehensive characterization value of each second data point in the second core set V according to the neighborhood number characterization value, the characteristic distance weight value, and the mapping characterization value corresponding to each second data point in the second core set V; finally, denote the product of the mean value of the comprehensive characterization values of each second data in the second core set V and the preset compactness weight value as the compactness characterization value of the second core set V; and the method of obtaining the comprehensive characterization value of each second data point in the second core set V according to the neighborhood number characterization value, the characteristic distance weight value, and the mapping characterization value corresponding to each second data point in the second core set V is the same as the method of obtaining the comprehensive characterization value of each first data point in the first core set B according to the neighborhood number characterization value, the characteristic distance weight value, and the mapping characterization value corresponding to each first data point in the first core set B, so it will not be described in detail here. The method of obtaining the characteristic distance weight value and the mapping characterization value corresponding to each second data point in the second core set V according to the second distances of each second data point in the second core set V is the same as the method of obtaining the characteristic distance weight value and the mapping characterization value corresponding to each first data point in the first core set B according to the first distances of each first data point in the first core set B, so it will not be described in detail either.

[0051] And in this embodiment, the process of obtaining the first compactness threshold and the second compactness threshold is as follows: Denote the mean value of the compactness characterization values of all first core sets as the first compactness threshold, and denote the mean value of the compactness characterization values of all second core sets as the second compactness threshold.

[0052] In this embodiment, the method of adjusting the first adjusted clustering parameter, continuing to cluster the third data points according to the adjusted clustering parameter, and obtaining the low-density set and the high-density set again according to the clustering result is the same as the method of adjusting the initial clustering parameter, continuing to cluster the second data points according to the adjusted clustering parameter, and continuing to obtain the low-density set and the high-density set according to the clustering result, so this embodiment will not be described in detail.

[0053] Therefore, through the above process, this embodiment can obtain multiple low-density sets and multiple high-density sets. After obtaining the low-density sets and high-density sets, the geometric transformation coding compression algorithm is used to compress the data points in the obtained high-density sets, and the data obtained after compression is recorded as compressed data. The LZ compression algorithm is used to compress the data points in the obtained low-density sets, and the data obtained after compression is also recorded as compressed data. The geometric transformation coding compression algorithm utilizes the spatial correlation and regularity of the data, and improves the efficiency of subsequent data transmission through differential coding or morphological compression methods. The LZ compression algorithm is based on the replacement of repeated segments and pattern matching, and effectively processes sparse and scattered data, thereby improving the efficiency of subsequent data transmission. The improvement of the transmission efficiency enables users to obtain more reasonable driving or walking routes when using the navigation map to plan driving or walking routes, and can also update the road conditions more timely to improve the user's usage effect of the navigation map. In addition, it should be noted that through the above compression method, not only can the data transmission efficiency be improved, the cloud storage space be reduced, but also the processing efficiency and access speed of the map navigation data can be greatly improved. It should also be noted that the process of data compression using the geometric transformation coding compression algorithm and the LZ compression algorithm is a well-known technology, so the specific compression process will not be described in detail in this embodiment.

[0054] In summary, in this embodiment, position data points on the navigation map are first obtained; then all the position data points on the navigation map are denoted as the first data points, and a clustering algorithm with the clustering parameter as the initial clustering parameter is used to cluster all the first data points. The obtained clustering clusters after clustering are denoted as the first clustering clusters. According to the clustering centers of each of the first clustering clusters, a core set corresponding to each of the first clustering clusters is obtained and denoted as the first core set. According to all the data points in each of the first core sets and the initial clustering parameter, a tightness characterization value of each of the first core sets is obtained; then it is determined whether the tightness characterization value of each of the first core sets is greater than the first tightness threshold. If so, the corresponding first core set is denoted as a high-density set, otherwise, the corresponding first core set is denoted as a low-density set; then the intersection area between any two of the first clustering clusters is obtained and denoted as the first intersection area, and it is determined whether the total number of the first data points in all the first intersection areas is not greater than a preset quantity threshold. If so, the set composed of all the first data points that do not belong to the first core set is denoted as a low-density set, otherwise, all the first data points that do not belong to the first core set are denoted as the second data points, and the initial clustering parameter is adjusted. According to the adjusted clustering parameter, the second data points are continuously clustered, and low-density sets and high-density sets are continuously obtained according to the clustering results; finally, the data points in the high-density set are compressed by using a geometric transformation coding compression algorithm to obtain compressed data, and the data points in the low-density set are compressed by using an LZ compression algorithm to obtain compressed data. And in this embodiment, by dividing the low-density set and the high-density set, and then using different compression algorithms to compress the low-density set and the high-density set respectively, the compression effect of the data can be improved, thereby the transmission efficiency can also be improved, that is, the transmission efficiency when the compressed data is transmitted to the data analysis and processing module in the navigation map system can be improved subsequently, and the efficiency and rationality of route planning of the navigation map system can also be improved, and more timely road condition updates can be ensured.

[0055] The above-described embodiments are only used to illustrate the technical solutions of the present application, and are not intended to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A cloud storage-based map navigation data efficient processing system, comprising a processor and a memory, characterized in that: The processor executes the computer program stored in the memory to implement the following steps: Get location data points on the navigation map; The location data points on the navigation map are all recorded as first data points, and all the first data points are clustered using a clustering algorithm whose clustering parameters are initial clustering parameters, and the cluster clusters obtained after the clustering are completed are all recorded as first cluster clusters; according to the cluster center of each first cluster cluster, the core set corresponding to each first cluster cluster is obtained, and each is recorded as the first core set; according to all the data points in each first core set and the initial clustering parameter, the compactness representation value of each first core set is obtained; Determine whether the compactness representation value of each of the first core sets is greater than the first compactness threshold, if so, record the corresponding first core set as a high-density set, otherwise, record the corresponding first core set as a low-density set; Obtain the intersection area between any two first clusters, and record them as the first intersection area, determine whether the total number of first data points in all the first intersection areas is not greater than a preset number threshold, if so, record the set composed of all first data points that do not belong to the first core set as a low-density set, otherwise, record all first data points that do not belong to the first core set as second data points, and adjust the initial clustering parameters, continue to cluster the second data points according to the adjusted clustering parameters, and continue to obtain low-density sets and high-density sets according to the clustering results; Compressing the data points in the high-density set using a geometric transformation coding compression algorithm to obtain compressed data, and compressing the data points in the low-density set using an LZ compression algorithm to obtain compressed data; The method of adjusting the initial clustering parameters, continuing to cluster the second data points according to the adjusted clustering parameters, and continuing to obtain a low-density set and a high-density set according to the clustering results includes: Recording a set consisting of all second data points in the navigation map as a second set, obtaining a compactness representation value of the second set, and adjusting the initial clustering parameter according to the compactness representation value of the second set and a first compactness threshold to obtain a first adjusted clustering parameter; All the second data points are clustered using a clustering algorithm whose clustering parameters are the first adjusted clustering parameters, and the cluster clusters obtained after the clustering are all recorded as second cluster clusters; according to the cluster centers of each second cluster cluster, the core sets corresponding to each second cluster cluster are obtained, and are all recorded as second core sets; according to all the data points in each second core set and the first adjusted clustering parameters, the compactness characterization value of each second core set is obtained; the method for obtaining the compactness characterization value of the second core set is the same as the method for obtaining the compactness characterization value of the first core set; Determine whether the compactness representation value of each second core set is greater than the second compactness threshold, if so, record the corresponding second core set as a high-density set, otherwise, record the corresponding second core set as a low-density set; Obtain the intersection area between any two second clusters and record them as the second intersection area, determine whether the total number of second data points in all the second intersection areas is not greater than a preset threshold value, if so, record the set consisting of all second data points that do not belong to the second core set as a low-density set, otherwise, record all second data points that do not belong to the second core set as third data points, and adjust the first adjustment clustering parameters, continue to cluster the third data points according to the adjusted clustering parameters, and obtain low-density sets and high-density sets again according to the clustering results, and so on, until all the low-density sets and high-density sets obtained contain all the location data points on the navigation map and stop.

2. The efficient processing system for map navigation data based on cloud storage according to claim 1, characterized in that: The initial clustering parameters include an initial radius R and an initial minimum number of points M.

3. The efficient processing system for map navigation data based on cloud storage as claimed in claim 2, characterized in that: The method for obtaining the compactness representation value of the first core set includes: For any first core set B: obtain the number of first data points in the R neighborhood of each first data point in the first core set B, and record it as the neighborhood quantity representation value corresponding to the first data point; record the cluster center of the first cluster cluster to which the first core set B belongs as the reference point of the first core set B; record the Euclidean distance between each first data point in the first core set B and the reference point of the first core set B as the first distance of the corresponding first data point; according to the first distance of each first data point in the first core set B, obtain the feature distance weight value and mapping representation value corresponding to each first data point in the first core set B; For any first data point in the first core set B, the product of the normalized value of the neighborhood quantity representation value, the feature distance weight value and the mapping representation value corresponding to the first data point is recorded as the comprehensive representation value of the first data point; The product of the average of the comprehensive representation values ​​of all the first data points in the first core set B and the preset compactness weight value is recorded as the compactness representation value of the first core set B.

4. The cloud storage-based map navigation data efficient processing system according to claim 3, characterized in that: The method of obtaining a feature distance weight value and a mapping representation value corresponding to each first data point in the first core set B according to the first distance of each first data point in the first core set B includes: Among the first distances of all the first data points in the first core set B, the maximum first distance is selected as the reference distance of the first core set B; the square value of the ratio of the first distance of each first data point in the first core set B to the reference distance of the first core set B is recorded as the characteristic ratio of the corresponding first data point; The reciprocal of the result obtained by adding the feature ratio of each first data point in the first core set B to the preset first constant is recorded as the feature distance weight value corresponding to the first data point; Obtain the product of the mean value of the first distances of all the first data points in the first core set B and a preset second constant, and record it as the target denominator value of the first core set B; record the ratio of the square value of the first distance of each first data point in the first core set B to the target denominator value of the first core set B as the discrete representation value corresponding to the first data point, and record the negative correlation mapping value of the discrete representation value of the first data point as the mapping representation value corresponding to the first data point.

5. The cloud storage-based map navigation data efficient processing system according to claim 1, characterized in that: The method for obtaining the core set corresponding to the cluster includes: For any first cluster A1, among all the first data points except the first cluster A1, obtain a first data point closest to the first cluster A1, and record it as the out-cluster nearest neighbor point of the first cluster A1, record the Euclidean distance between the cluster center of the first cluster A1 and the out-cluster nearest neighbor point of the first cluster A1 as the out-cluster nearest neighbor distance of the first cluster A1, record the area of ​​a circle with the cluster center of the first cluster A1 as the center and the out-cluster nearest neighbor distance of the first cluster A1 as the radius as the core area corresponding to the first cluster A1, and record the set formed by all the first data points belonging to the first cluster A1 and located in the core area corresponding to the first cluster A1 as the core set corresponding to the first cluster A1; For any second cluster A2, among all the second data points except the second cluster A2, obtain a second data point closest to the second cluster A2, and record it as the out-cluster nearest neighbor point of the second cluster A2, record the Euclidean distance between the cluster center of the second cluster A2 and the out-cluster nearest neighbor point of the second cluster A2 as the out-cluster nearest neighbor distance of the second cluster A2, record the area of ​​the circle with the cluster center of the second cluster A2 as the center and the out-cluster nearest neighbor distance of the second cluster A2 as the radius as the core area corresponding to the second cluster A2, and record the set formed by all the second data points belonging to the second cluster A2 and located in the core area corresponding to the second cluster A2 as the core set corresponding to the second cluster A2.

6. The efficient processing system for map navigation data based on cloud storage according to claim 1, characterized in that: The first compactness threshold is the average of the compactness representation values ​​of all the first core sets, and the second compactness threshold is the average of the compactness representation values ​​of all the second core sets.

7. The efficient processing system for map navigation data based on cloud storage according to claim 1, characterized in that: The method for obtaining the compactness representation value of the second set includes: Obtaining the number of second data points in the R neighborhood of each second data point in the second set, and recording it as a neighborhood quantity representation value corresponding to the second data point; The average data point of all data points in the second set is used as the reference point of the second set; the Euclidean distance between each second data point in the second set and the reference point of the second set is recorded as the distance to be analyzed of the corresponding second data point; according to the distance to be analyzed of each second data point in the second set, the characteristic distance weight value and mapping characterization value corresponding to each second data point in the second set are obtained; the method for obtaining the characteristic distance weight value and mapping characterization value corresponding to each second data point in the second set according to the distance to be analyzed of each second data point in the second set is the same as the method for obtaining the characteristic distance weight value and mapping characterization value corresponding to each first data point in the first core set B according to the first distance of each first data point in the first core set B; For any second data point in the second set, the product of the normalized value of the neighborhood quantity representation value, the feature distance weight value and the mapping representation value corresponding to the second data point is recorded as the comprehensive representation value of the second data point; The product of the average of the comprehensive representation values ​​of all the second data points in the second set and the preset compactness weight value is recorded as the compactness representation value of the second set.

8. The cloud storage-based map navigation data efficient processing system according to claim 4, characterized in that: The method of adjusting the initial clustering parameters according to the compactness representation value of the second set and the first compactness threshold to obtain a first clustering parameter adjustment includes: Recording a normalized value of the absolute value of the difference between the compactness representation value of the second set and the first compactness threshold as a first feature difference value; If the first closeness threshold is less than the closeness representation value of the second set, the result of multiplying the first characteristic difference value by the preset first adjustment coefficient and adding the preset first constant is recorded as the first radius adjustment degree, and the reciprocal of the result of multiplying the first characteristic difference value by the preset first adjustment coefficient and adding the preset first constant is recorded as the first minimum point adjustment degree; otherwise, the result of multiplying the first characteristic difference value by the preset second adjustment coefficient and adding the preset first constant is recorded as the first radius adjustment degree, and the reciprocal of the result of multiplying the first characteristic difference value by the preset second adjustment coefficient and adding the preset first constant is recorded as the first minimum point adjustment degree, where n is the dimension of the position data point; The product of the first radius adjustment degree and the initial radius R is recorded as the first adjustment radius R1, the result obtained by adding the preset first constant and the preset third adjustment coefficient is recorded as the target adjustment weight, and the result of multiplying the target adjustment weight, the first minimum point adjustment degree and the initial minimum point M is recorded as the first adjustment minimum point M1. The first adjustment radius R1 and the first adjustment minimum point M1 both belong to the first adjustment clustering parameter.

9. The efficient processing system for map navigation data based on cloud storage according to claim 1, characterized in that: The method for obtaining the intersection area includes: For the first cluster K1 and the first cluster K2, the intersection area between the minimum circumscribed circle containing the first cluster K1 and the minimum circumscribed circle containing the first cluster K2 is recorded as the intersection area between the first cluster K1 and the first cluster K2; the method for obtaining the intersection area between any two second clusters is the same as the method for obtaining the intersection area between any two first clusters.

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