Vehicle-mounted navigation map dynamic fragmentation issuing method capable of reducing local memory

Abstract

The invention relates to the technical field of navigation map data processing, in particular to a vehicle-mounted navigation map dynamic fragment issuing method capable of reducing local memory, which comprises the following steps of: segmenting map data of a vehicle-mounted navigation position to obtain each pre-stored map block, and obtaining a use frequency vector of a historical map block; clustering the road information similarity of each pre-stored map block and each historical map block to obtain a road consistency factor, determining the storable degree of each pre-stored map block, and extracting each effective map block; obtaining the contact closeness of each historical map block by using the difference of frequency vectors, determining the cache validity of each historical map block in combination with the similarity between the road information of each historical map block and the road information of each effective map block, and performing cache updating on the historical map blocks to obtain a cache update result. And the vehicle-mounted navigation map is dynamically fragmented and issued. According to the invention, the occupation of the local memory of the client of the dynamic navigation system can be reduced, and the transmission burden of the vehicle-mounted server is reduced.

Description

Technical Field

[0001] The present application relates to the technical field of navigation map data processing, and in particular to a method for dynamically segmenting and sending an in-vehicle navigation map for reducing local memory. Background Art

[0002] The in-vehicle navigation system is divided into a static navigation system and a dynamic navigation system according to the way it uses information. The static navigation system cannot timely reflect the current traffic status of the road and the topological structure of the road network, making it impossible to accurately plan the correct vehicle route. The dynamic navigation system is based on wireless communication and has a cache system for caching vector maps, road network structures and other map data. By storing, updating and issuing high-precision map data, it realizes information sharing between the vehicle and the server to provide more intelligent driving services.

[0003] In the process of storing, updating and issuing high-precision map data, the existing technology uses a method of segmenting high-precision vehicle navigation maps to form map blocks, and stores the map blocks in a cache system. However, due to the limited storage capacity of the cache system, in order to reduce the memory of the map blocks and ensure that new map blocks can be stored, the original map blocks need to be replaced. The existing cache system uses a FIFO (First In First Out) cache replacement algorithm, and this replacement method ignores the frequency of use and the most recent use time of the original map blocks, resulting in the high-frequency use of map blocks being easily replaced, making the stability of caching the map blocks poor, affecting the caching effect of the vehicle cache system. Summary of the invention

[0004] In order to solve the above technical problems, the present application provides a method for dynamically segmenting and sending in-vehicle navigation maps to reduce local memory, so as to solve the existing problems.

[0005] A method for dynamically slicing and sending an in-vehicle navigation map to reduce local memory in the present application adopts the following technical solution: An embodiment of the present application provides a method for dynamically slicing and sending a vehicle navigation map to reduce local memory, comprising the following steps: The map data of the vehicle navigation position at the current moment is divided into various pre-stored map blocks, and the usage counts of all historical map blocks before the current moment are obtained to construct the usage frequency vector of each historical map block; Obtain the similarity between the road information of each pre-stored map block and each historical map block and perform clustering. According to the degree of deviation between the elements in each cluster and the center point of the cluster, obtain the road consistency factor of each cluster corresponding to each pre-stored map block at the current moment. Combined with the average level of the elements in each cluster, determine the storability of each pre-stored map block at the current moment, perform threshold segmentation on the storability of all pre-stored map blocks, and extract each valid map block. Through the difference between the usage frequency vectors of each historical map block and the average level of the usage frequency vectors of each historical map block, the connection closeness of each historical map block at the current moment is obtained, and the cache validity of each historical map block at the current moment is determined by combining the similarity of road information between each historical map block and each valid map block; The cache of the historical map blocks is updated through the cache validity of the historical map blocks, and then the dynamic fragments of the vehicle navigation map are issued.

[0006] Preferably, the method for constructing the usage frequency vector is: arranging the number of times each historical map block is used every day from the time of storage to the current moment in chronological order to form the usage frequency vector of each historical map block.

[0007] Preferably, the obtaining of the similarity between the road information of each pre-stored map block and each historical map block and performing clustering division includes: For each pre-stored map block, the road name, road intersection information and traffic sign information in the pre-stored map block are used as the input of the bag-of-words model to extract the road information vector of the pre-stored map block at the current moment, calculate the similarity between the road information vector of the pre-stored map block and the road information vector of each historical map block, and cluster the similarities to obtain the cluster clusters corresponding to the pre-stored map block.

[0008] Preferably, the elements in each cluster corresponding to the pre-stored map block are the similarities between the pre-stored map block and each historical map block regarding the road information vector, and the similarity is the Jaccard similarity.

[0009] Preferably, the method for calculating the road consistency factor of each cluster corresponding to each pre-stored map block at the current moment is: ; In the formula, is the road consistency factor of the jth cluster corresponding to the uth pre-stored map block at the current moment, is an exponential function with a natural constant as base, is the number of elements in the jth cluster corresponding to the uth pre-stored map block at the current moment, is the sth element in the jth cluster corresponding to the uth pre-stored map block at the current moment, It is the center point of the jth cluster corresponding to the uth pre-stored map block at the current moment.

[0010] Preferably, the calculation method of the storability of each pre-stored map block at the current moment is: ; In the formula, is the storability of the u-th pre-stored map block at the current moment, is the number of clusters corresponding to the u-th pre-stored map block at the current moment, is the exponential normalization function, It is the mean value of the elements in the jth cluster corresponding to the uth pre-stored map block at the current moment.

[0011] Preferably, the valid map blocks are map blocks whose storability is higher than a segmentation threshold.

[0012] Preferably, the calculation method of the connection density of each historical map block at the current moment is: ; In the formula, is the connection density of the vth historical map block at the current moment, is the mean of the usage frequency vector of the vth historical map block at the current moment, is the number of historical map blocks in the vehicle cache system at the current moment, is the distance between the usage frequency vectors of the vth historical map block and the ith historical map block at the current moment, and exp() is an exponential function with a natural constant as the base.

[0013] Preferably, the calculation method of the cache validity of each historical map block at the current moment is: ; In the formula, is the cache validity of the vth historical map block at the current moment, is the number of valid map blocks at the current moment, It is the Jaccard similarity between the road information vector of the vth historical map block at the current moment and the road information vector of the hth valid map block.

[0014] Preferably, the cache updating of the historical map blocks includes: for the current moment, arranging all the historical map blocks from small to large according to cache validity, and replacing and updating the first H historical map blocks after the arrangement by all valid map blocks.

[0015] This application has at least the following beneficial effects: This application measures the road consistency characteristics represented by each cluster cluster through cluster analysis of the Jaccard similarity of the road information vector between the pre-stored map blocks and the historical map blocks, and uses the exponential normalization result of the road consistency factor to perform weighted summation on the element means in each cluster cluster, so as to more accurately extract the storability characteristics of the pre-stored map blocks, thereby obtaining the effective map blocks that need to be stored, which is used to avoid the problem of cache jitter caused by frequent storage of redundant map blocks in the subsequent process, and improve the stability of caching the map blocks; Furthermore, by analyzing the closeness of the connections between different historical map blocks, and combining the similarity characteristics between the historical map blocks and the road information in the effective map blocks, the cache effectiveness of each historical map block is measured to search for historical map blocks with poor cache effectiveness and strong substitutability, thereby reducing the risk of high-frequency map blocks being easily replaced, improving the stability of the on-board cache system in caching map blocks, and avoiding adverse effects on the cache effect of the on-board cache system; The present application constructs a replacement update vector of historical map blocks through the cache validity of each historical map block, and realizes the storage of valid map blocks in the vehicle cache system through the replacement update vector, thereby avoiding frequent storage of redundant map blocks, and can reduce the client local memory usage of the dynamic navigation system, thereby alleviating the transmission burden of the vehicle server. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present application or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0017] Figure 1 A flowchart of the steps of a method for dynamically slicing and sending a vehicle navigation map to reduce local memory provided by this application; Figure 2 Schematic diagram of the map blocks provided for this application. DETAILED DESCRIPTION

[0018] In order to further explain the technical means and effects adopted by the present application to achieve the predetermined invention purpose, the following is a detailed description of the specific implementation method, structure, features and effects of a method for dynamically slicing and sending a vehicle navigation map to reduce local memory proposed by the present application in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0019] Unless otherwise defined, terms such as "comprises", "comprising" or any other variants thereof are intended to cover non-exclusive inclusion, so that a circuit structure, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such articles or devices. In the absence of further restrictions, an element defined by the statement "comprising a ..." does not exclude the presence of other identical elements in the article or device comprising the element. In addition, the term "and\or" used herein includes any and all combinations of one or more related listed items. All technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which the present application belongs.

[0020] The following is a detailed description of a method for dynamically segmenting and sending an in-vehicle navigation map to reduce local memory provided by the present application in conjunction with the accompanying drawings.

[0021] An embodiment of the present application provides a method for dynamically slicing and sending a vehicle navigation map to reduce local memory. For details, please refer to Figure 1 .

[0022] The purpose of this embodiment is to analyze the correlation characteristics between the map blocks and the historical map blocks during vehicle driving, and consider the usage frequency characteristics of the historical map blocks, perform substitutability analysis on each historical map block, and improve the cache update method in the vehicle cache system based on the results of the substitutability analysis, reduce the risk of high-frequency used map blocks being easily replaced, improve the stability of the vehicle cache system in caching map blocks, and avoid adverse effects on the cache effect in the vehicle cache system. Specifically, the following steps are included: Step 1: Divide the map data of the vehicle navigation position at the current moment into pre-stored map blocks, obtain the usage counts of all historical map blocks before the current moment, and construct the usage frequency vector of each historical map block.

[0023] When a vehicle is driving on a city road, the client of the dynamic navigation system obtains the vehicle navigation map within the range around the vehicle navigation location at the current moment through the vehicle GPS positioning. The surrounding range refers to a geographical area: take the longitude of the vehicle navigation location at the current moment and dimensions , in longitude and dimensions The geographical area enclosed by is the change in longitude, is the latitude change. In this embodiment, the longitude change and dimensional change The values ​​are 10 and 10 respectively. The implementer can adaptively select the longitude change and latitude change according to the actual planned route of the vehicle navigation.

[0024] Furthermore, the vehicle navigation map corresponding to the current moment is segmented, and p dividing longitude lines and q dividing dimension lines are evenly selected within the longitude range and latitude range of the vehicle navigation map respectively, and the vehicle navigation map is evenly divided into various map blocks through all the dividing longitude lines and dividing dimension lines, which are recorded as various pre-stored map blocks in the vehicle navigation map corresponding to the current moment, wherein the number of dividing longitude lines and dividing dimension lines is 50 respectively, and the implementer can make adaptive selection according to the size of the vehicle navigation map.

[0025] Specifically, in this embodiment, the map block diagram is as follows Figure 2 As shown, Figure 2 middle They represent the first dividing longitude line, the p-2th dividing longitude line, the p-1th dividing longitude line, and the pth dividing longitude line, respectively. They respectively represent the 1st dividing latitude line, the q-2th dividing latitude line, the q-1th dividing latitude line, and the qth dividing latitude line. The small squares formed by the dividing longitude lines and the dividing latitude lines represent each map block.

[0026] At the same time, all historical map blocks in the vehicle-mounted cache system at the current moment are obtained through the client of the dynamic navigation system, and the number of times each historical map block is used every day from the time it is stored to the current moment is obtained. The number of times each historical map block is used every day from the time it is stored to the current moment is arranged in chronological order to form a vector, which is recorded as the usage frequency vector of each historical map block in the vehicle-mounted cache system at the current moment.

[0027] Step 2: Obtain the similarity between the road information of each pre-stored map block and each historical map block and perform clustering. According to the degree of deviation between the elements in each cluster and the center point of the cluster, obtain the road consistency factor of each cluster corresponding to each pre-stored map block at the current moment. Combined with the average level of the elements in each cluster, determine the storability of each pre-stored map block at the current moment, perform threshold segmentation on the storability of all pre-stored map blocks, and extract each valid map block.

[0028] In the process of storing, updating and issuing high-precision map data, it is necessary to dynamically update the map blocks stored in the storage system. However, the existing storage update method uses the FIFO (First In First Out) cache replacement algorithm, which easily causes the frequently used map blocks to be replaced, resulting in the frequent replacement of cache items in the cache system, which easily causes cache jitter in the vehicle cache system. Therefore, in order to avoid the risk of cache jitter in the vehicle cache system, it is necessary to perform a substitutability analysis on the historical map blocks in the vehicle cache system.

[0029] Through each pre-stored map block in the vehicle navigation map corresponding to the current moment, in this embodiment, the road name, road intersection information and traffic sign information in each pre-stored map block are collected. It should be noted that in the actual application scenario, the implementer can select the specific road information by himself, and there is no special restriction on this in this embodiment. The road name, road intersection information and traffic sign information in the pre-stored map block are used as the input of the bag-of-words model, and the output of the bag-of-words model is recorded as the road information vector of each pre-stored map block at the current moment. The bag-of-words model is a well-known technology, and the specific process is not repeated. Similarly, the road information, road intersection and traffic sign information in each historical map block at the current moment are collected, and the bag-of-words model is used to obtain the road information vector of each historical map block at the current moment.

[0030] Generally, not all pre-stored map blocks will be stored in the vehicle storage system. Instead, only map blocks with large changes in road information will be updated and stored in the storage system. Therefore, it is necessary to perform storability analysis on all pre-stored map blocks at the current moment.

[0031] Through the above analysis, the similarity between the road information vector of the u-th pre-stored map block and the road information vectors of all historical map blocks is calculated, wherein the measurement method of the similarity is not limited, and the implementer can select Jaccard similarity, Tanimoto coefficient or Sørensen-Dice coefficient. In this embodiment, Jaccard similarity is selected to measure the similarity between road information vectors, and the data set composed of Jaccard similarities corresponding to the u-th pre-stored map block is input into the clustering algorithm, wherein the clustering algorithm is not limited, and the implementer can select CURE hierarchical clustering algorithm (Clustering Using REpresentatives), DPC density peak clustering algorithm (density peak clustering) or DBSCAN clustering algorithm (Density-Based Spatial Clustering of Applications with Noise), in this embodiment, the DPC density peak clustering algorithm is selected to cluster the data set composed of Jaccard similarity. The cutoff distance in the algorithm is selected so that the average number of data with a distance around each data less than the cutoff distance accounts for 2% of all the data in the data set. The output of the DPC density peak clustering algorithm is recorded as each clustering cluster corresponding to the u-th pre-stored map block, wherein the DPC density peak clustering algorithm is a well-known technology and the specific process will not be repeated.

[0032] By performing cluster analysis on the similarity between the road information vectors of each pre-stored map block and the road information vectors of all historical map blocks, clusters with different similarity levels can be mined. If the consistency of the elements within a cluster is higher, it indicates that the similarity of road information between multiple historical map blocks and pre-stored map blocks at the current moment is close to the same, which more accurately reflects the overall similarity characteristics between the pre-stored map blocks and all historical map blocks in the cache system at the current moment, thereby more accurately performing storability analysis on each pre-stored map block.

[0033] Furthermore, according to the clustering results of the Jaccard similarity set and the degree of deviation between the elements in each cluster and the center point of the cluster, the storability of each pre-stored map block at the current moment is calculated: ; In the formula, is the road consistency factor of the jth cluster corresponding to the uth pre-stored map block at the current moment, is an exponential function with a natural constant as base, is the number of elements in the jth cluster corresponding to the uth pre-stored map block at the current moment, is the sth element in the jth cluster corresponding to the uth pre-stored map block at the current moment, It is the center point of the jth cluster corresponding to the uth pre-stored map block at the current moment.

[0034] If the degree to which all elements in a cluster deviate from the cluster center element is smaller, and the elements in the cluster are the similarity between the road information vectors of the pre-stored map blocks and multiple historical map blocks, the higher the consistency of the Jaccard similarity between the road information in the cluster, the more it can reflect the similarity consistency characteristics between the pre-stored map blocks and the multiple historical map blocks in the cache system at the current moment, which is used to analyze the storability of each pre-stored map block more accurately in the future. Therefore, in this embodiment, according to the road consistency factor of each cluster corresponding to each pre-stored map block at the current moment, combined with the average level of elements in each cluster, the storability of each pre-stored map block at the current moment is calculated. In this embodiment, the calculation formula is: ; In the formula, is the storability of the u-th pre-stored map block at the current moment, is the number of clusters corresponding to the u-th pre-stored map block at the current moment, is the exponential normalization function, It is the mean value of the elements in the jth cluster corresponding to the uth pre-stored map block at the current moment.

[0035] Since the road consistency factor can reflect the consistency degree of Jaccard similarity between road information in the cluster, the exponential normalization result of the road consistency factor is used as the weight for storability analysis of each cluster, and the weighted sum of the element means in each cluster is performed to more accurately analyze the overall similarity characteristics between the pre-stored map blocks and all historical map blocks in the cache system at the current moment, so as to more accurately measure the storability of each pre-stored map block, which is used to avoid frequent storage of redundant map blocks in the future, causing cache jitter problems in the on-board cache system, and improve the stability of caching map blocks.

[0036] Furthermore, the storability of all pre-stored map blocks at the current moment is used as the input of the maximum inter-class variance algorithm, and the segmentation threshold is obtained using the maximum inter-class variance algorithm, wherein the maximum inter-class variance algorithm is a well-known technology, and the specific process is not repeated. Then, the map blocks corresponding to the storability higher than the segmentation threshold are used as the valid map blocks at the current moment, and the remaining map blocks are recorded as redundant map blocks. Only valid map blocks are selected to be stored in the vehicle storage system to avoid the phenomenon that the cache items in the vehicle cache system are frequently replaced by redundant map blocks.

[0037] Step 3: Obtain the connection closeness of each historical map block at the current moment through the difference between the usage frequency vectors of each historical map block and the average level of the usage frequency vector of each historical map block. Combined with the similarity of road information between each historical map block and each valid map block, determine the cache validity of each historical map block at the current moment.

[0038] The substitutability of each historical map block in the vehicle cache system is analyzed through all valid map blocks at the current moment. If the similarity between the usage frequency vectors of each historical map block in the vehicle cache system at the current moment is stronger, and the usage frequency between each historical map block is at a higher level, it can be explained that the connection between the use of historical map blocks is closer, and the corresponding map blocks should be retained. For example, in a vehicle navigation application, if multiple historical map blocks with similar distances are used at the same time, it means that these historical map blocks belong to the map blocks on the user's frequently traveled routes. Then, the closer the connection between these map blocks, the less likely these historical map blocks should be eliminated.

[0039] Through the above analysis, the connection density of each historical map block at the current moment is calculated: ; In the formula, is the connection density of the vth historical map block at the current moment, is the mean of the usage frequency vector of the vth historical map block at the current moment, is the number of historical map blocks in the vehicle cache system at the current moment, It is the distance between the usage frequency vectors of the vth historical map block and the ith historical map block at the current moment. This distance is used to measure the difference between the usage frequency vectors. You can choose to use DTW distance or Euclidean distance for calculation. In this embodiment, DTW distance is used to calculate the difference between the usage frequency vectors.

[0040] It can be understood that the degree of connection reflects the degree of connection between each historical map block and other historical map blocks at the current moment. The closer the connection between historical map blocks used in vehicle navigation applications, the less likely these historical map blocks should be eliminated, but the more corresponding historical map blocks should be retained.

[0041] At the same time, when caching and updating the historical map blocks in the vehicle cache system, the historical map blocks with less similarity to the road information in the valid map blocks should be updated, otherwise the invalid update of the historical map blocks will increase the risk of cache jitter in the vehicle cache system. At the same time, the higher the degree of connection between the historical map blocks at the current moment, the more it can reflect the importance of the historical map blocks. Conversely, the lower the degree of connection between the historical map blocks at the current moment, the lower the importance of the historical map blocks, and the more it should be cached and updated.

[0042] Through the above analysis, the cache validity of each historical map block at the current moment is calculated: ; In the formula, is the cache validity of the vth historical map block at the current moment, is the number of valid map blocks at the current moment, It is the Jaccard similarity between the road information vector of the vth historical map block at the current moment and the road information vector of the hth valid map block.

[0043] The cache effectiveness reflects the effectiveness characteristics of each historical map block in the vehicle cache system at the current moment. If the cache effectiveness characteristics of a historical map block at the current moment are smaller, it means that the historical map block is more replaceable. Cache updating of historical map blocks with poor cache effectiveness in the vehicle cache system can reduce the risk of frequently used map blocks being easily replaced, improve the stability of the vehicle cache system in caching map blocks, and avoid adverse effects on the cache effect in the vehicle cache system.

[0044] Step 4: Update the cache of the historical map blocks based on the cache validity of the historical map blocks, and then issue the dynamic slices of the vehicle navigation map.

[0045] In this embodiment, for ease of understanding and expression, a vector consisting of all historical map blocks at the current moment in order of cache validity from small to large is recorded as the replacement update vector of all historical map blocks at the current moment. Each element in the replacement update vector represents each historical map block. The closer the historical map block is to the vector head position in the replacement update vector, the stronger the substitutability of the historical map block is.

[0046] Furthermore, each valid map block at the current moment is stored in the on-board cache system, the number H of valid map blocks at the current moment is counted, the cache index of the first H historical map blocks in the replacement update vector is obtained through the on-board cache system, and the first H historical map blocks in the replacement update vector are deleted in the on-board cache system through the cache index, and the H valid map blocks at the current moment are cached on the on-board cache system, thereby realizing the cache update of the first H historical map blocks in the replacement update vector corresponding to the current moment.

[0047] In vehicle navigation applications, the map search engine retrieves the updated map blocks cached in the vehicle cache system according to the route planned by the user, and retrieves the map blocks that the route planned by the user passes through. The dynamic navigation system obtains the complete high-precision map required for vehicle driving by splicing the map blocks that the route planned by the user passes through, and dynamically sends the complete high-precision map required for vehicle driving to the vehicle navigation application of the driver according to the real-time position of the vehicle, thereby realizing the task of dynamically sending the on-board navigation map segments, reducing the client's local memory usage, and alleviating the transmission burden of the on-board transmission server.

[0048] It is understood that references to "one embodiment" or "some embodiments" etc. described in the specification of the present application mean that one or more embodiments of the present application include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, if "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. appear in different places in this specification, they do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0049] It should be noted that the sequence of the above-mentioned embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the process depicted in the accompanying drawings does not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multi-tasking and parallel processing are also possible or may be advantageous. At the same time, the size of the sequence number of each step in the embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments in this specification.

[0050] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the 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 method for dynamically slicing and sending a vehicle navigation map to reduce local memory, characterized in that: The following steps are involved: The map data of the vehicle navigation position at the current moment is divided into various pre-stored map blocks, and the usage counts of all historical map blocks before the current moment are obtained to construct the usage frequency vector of each historical map block; Obtain the similarity between the road information of each pre-stored map block and each historical map block and perform clustering. According to the degree of deviation between the elements in each cluster and the center point of the cluster, obtain the road consistency factor of each cluster corresponding to each pre-stored map block at the current moment. Combined with the average level of the elements in each cluster, determine the storability of each pre-stored map block at the current moment, perform threshold segmentation on the storability of all pre-stored map blocks, and extract each valid map block. Through the difference between the usage frequency vectors of each historical map block and the average level of the usage frequency vectors of each historical map block, the connection closeness of each historical map block at the current moment is obtained, and the cache validity of each historical map block at the current moment is determined by combining the similarity of road information between each historical map block and each valid map block; The cache of the historical map blocks is updated through the cache validity of the historical map blocks, and then the dynamic fragments of the vehicle navigation map are issued.

2. A method for dynamically slicing and sending a vehicle navigation map to reduce local memory as claimed in claim 1, characterized in that: The method for constructing the usage frequency vector is as follows: the number of times each historical map block is used every day from the time it is stored to the current time is arranged in chronological order to form the usage frequency vector of each historical map block.

3. The method for dynamically slicing and sending a vehicle navigation map to reduce local memory as claimed in claim 1, characterized in that: The obtaining of the similarity between the road information of each pre-stored map block and each historical map block and performing clustering division includes: For each pre-stored map block, the road name, road intersection information and traffic sign information in the pre-stored map block are used as the input of the bag-of-words model to extract the road information vector of the pre-stored map block at the current moment, calculate the similarity between the road information vector of the pre-stored map block and the road information vector of each historical map block, and cluster the similarities to obtain the cluster clusters corresponding to the pre-stored map block.

4. A method for dynamically slicing and sending a vehicle navigation map to reduce local memory as claimed in claim 3, characterized in that: The elements in each cluster corresponding to the pre-stored map block are the similarities between the pre-stored map block and each historical map block regarding the road information vector, and the similarity is the Jaccard similarity.

5. The method for dynamically slicing and sending a vehicle navigation map to reduce local memory as claimed in claim 1, characterized in that: The calculation method of the road consistency factor of each cluster corresponding to each pre-stored map block at the current moment is: ; In the formula, is the road consistency factor of the jth cluster corresponding to the uth pre-stored map block at the current moment, is an exponential function with a natural constant as base, is the number of elements in the jth cluster corresponding to the uth pre-stored map block at the current moment, is the sth element in the jth cluster corresponding to the uth pre-stored map block at the current moment, It is the center point of the jth cluster corresponding to the uth pre-stored map block at the current moment.

6. A method for dynamically slicing and sending a vehicle navigation map to reduce local memory as claimed in claim 5, characterized in that: The calculation method of the storability of each pre-stored map block at the current moment is: ; In the formula, is the storability of the u-th pre-stored map block at the current moment, is the number of clusters corresponding to the u-th pre-stored map block at the current moment, is the exponential normalization function, It is the mean value of the elements in the jth cluster corresponding to the uth pre-stored map block at the current moment.

7. The method for dynamically slicing and sending a vehicle navigation map to reduce local memory as claimed in claim 1, characterized in that: The valid map blocks are map blocks whose storability is higher than the segmentation threshold.

8. The method for dynamically slicing and sending a vehicle navigation map to reduce local memory as claimed in claim 1, characterized in that: The calculation method of the connection density of each historical map block at the current moment is: ; In the formula, is the connection density of the vth historical map block at the current moment, is the mean of the usage frequency vector of the vth historical map block at the current moment, is the number of historical map blocks in the vehicle cache system at the current moment, is the distance between the usage frequency vectors of the vth historical map block and the ith historical map block at the current moment, and exp() is an exponential function with a natural constant as the base.

9. The method for dynamically slicing and sending a vehicle navigation map to reduce local memory as claimed in claim 3, characterized in that: The calculation method of the cache validity of each historical map block at the current moment is: ; In the formula, is the cache validity of the vth historical map block at the current moment, is the number of valid map blocks at the current moment, It is the Jaccard similarity between the road information vector of the vth historical map block at the current moment and the road information vector of the hth valid map block.

10. The method for dynamically slicing and sending a vehicle navigation map to reduce local memory as claimed in claim 9, characterized in that: The cache updating of the historical map blocks includes: for the current moment, arranging all the historical map blocks from small to large according to the cache validity, and replacing and updating the first H historical map blocks after the arrangement by all the valid map blocks.

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