An Intelligent Extension Method for an Electronic Map Data Organization Model

Abstract

The present invention relates to an intelligent expansion method for an electronic map data organization model. The specific steps of the intelligent expansion method are as follows: Traverse all records of a certain layer to obtain the maximum storage length L of a single record max , calculate the maximum number S of records that can be stored in a single file, count the total number N of records, create a file counter, count the total number M of files, initialize it to 0, determine whether it exceeds the last record, calculate the file serial number in which the current record is stored: take the integer part of the record counter divided by S, check whether the file with the current serial number exists, create a new file with the current serial number, and increment the file counter by 1; store the current record in the file with the corresponding serial number; save the total number N of records, the total number M of files, and the maximum number S of records that can be stored in a single file for secondary reading and writing of files. Thus, the intelligent expansion process ends; it is used in the data compilation of ultra-large-scale electronic maps to intelligently expand the generated data files and improve the compilation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of electronic map data organization models, and particularly to an intelligent expansion method for an electronic map data organization model. Background Art

[0002] In the actual use of electronic maps, navigation software cannot directly use the data provided by electronic map suppliers. It is necessary to compile the data provided by map vendors into data that can be used by navigation software. This process is called the data compilation of electronic maps.

[0003] The data volume of the national map provided by map vendors is as high as dozens of gigabytes, and with the development of society, the traffic road network is constantly expanding, and the data volume of the national map will continue to increase. Due to the extremely large data volume of the original map, the total amount of intermediate data generated by the compilation tool has reached 150G. The word length of the existing data structure can no longer support the reading and writing access of massive data, resulting in a series of problems such as file overflow, out-of-bounds reading and writing, and tool crashes. Also, because the data volume of the original map is too large, the reproduction, investigation, modification, and testing of problems are extremely time-consuming. Normally, it takes five days to compile a version of national data. After encountering these problems, it takes about fifteen days to compile a version of national data. If the investigation and modification are not smooth, it takes up to a month.

[0004] The prior art is to compress the data structure and modify the loose data structure into a compact data structure.

[0005] The prior art has solved the compilation problems of the current version or recent versions, but has not completely solved the above problems. In fact, it has pushed the problems to the future. As the data volume of electronic maps continues to increase, such problems will reappear during the compilation of a certain version in the future, and a large amount of time, manpower, material resources, etc. will be spent again. It severely restricts the compilation efficiency and brings great instability to the future data compilation of electronic maps.

[0006] Currently, these problems occur in the compilation tool because the data volume of the road layer is too large. Horizontally expanded, the map has dozens of layers, including a road layer, a node layer, a traffic warning layer, a background layer, a label layer, a retrieval layer, a real-time traffic layer, and a series of other layers. When the data volume of other layers increases to the maximum number, these problems will also occur in the compilation tool. However, the fields of each layer are different, the number of fields is different, and the constructed data structures are also different. It is necessary to investigate the data structure of each layer, and then modify and test. The compilation tool needs to be modified too many times, and the probability of risks occurring at the same time will also increase, reducing the data compilation efficiency. Summary of the Invention

[0007] The problem solved by the present invention is to provide an intelligent expansion method for an electronic map data organization model, which is used in the data compilation of a super-large-scale electronic map to intelligently expand the generated data files and improve the compilation efficiency.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] An intelligent expansion method for an electronic map data organization model, the specific steps of the intelligent expansion method are as follows:

[0010] Step 101: Traverse all records of a layer and obtain the maximum storage length L of a single record max ;

[0011] Step 102: Calculate the maximum number S of records that can be stored in a single file;

[0012] Step 103: Create a record counter to count the total number of records N, which is also used in subsequent steps to calculate the current file number and is initialized to 0;

[0013] Step 104: Create a file counter, count the total number of files M, and initialize it to 0;

[0014] Step 105: Determine whether the record exceeds the last one. If the record ends, execute step 112; otherwise, execute step 106;

[0015] Step 106: Calculate the file number where the current record is stored: divide the record counter by S and round up;

[0016] Step 107: Does the current serial number file exist? If yes, go to step 110; otherwise, go to step 108;

[0017] Step 108: Create a new current sequence number file;

[0018] Step 109: the file counter is incremented by 1;

[0019] Step 110: Store the current record in the file corresponding to the serial number;

[0020] Step 111: add 1 to the record counter, and then execute step 105;

[0021] Step 112: Save the total number of records N, the total number of files M, and the maximum number of records that can be stored in a single file S for secondary reading and writing of files. At this point, the intelligent expansion process ends.

[0022] Preferably, all records are traversed to obtain the storage length of each record. The length set L of all records is defined as: ;

[0023] Among the set L of the lengths of all records, the maximum storage length L max , is defined as: .

[0024] Preferably, the capacity of a single file is defined as C, and the maximum number S of records that a single file can store is defined as: .

[0025] Preferably, in the map data, the total number of data records of a certain layer is defined as N, and the set R of all records of this layer of data is defined as: .

[0026] Preferably, the number of files M required to store all N records is defined as: .

[0027] Preferably, the set F of files for storing all records is defined as: , and the records contained in the i-th file F i are represented by a set as:

[0028] .

[0029] The beneficial effects of the present invention are as follows: In the data compilation of ultra-large-scale electronic maps, when the data volume of a certain layer in the electronic map increases to the maximum number, the data organization model of the present invention can intelligently expand the storage space, completely solve the problem of file overflow, greatly enhance the stability of the electronic map data compilation tool. Through the compilation verification of the national map, the data compilation time of the present invention is shortened by 67%, greatly improving the data compilation efficiency of the electronic map, saving the resources of manpower, material resources, financial resources, etc. consumed on such problems, and greatly reducing the data compilation cost of the electronic map;

[0030] There are dozens of data layers in the map. The present invention abstracts a parent layer, and all layers inherit the parent layer. This data organization model does not require modifying the data structures of all layers, reduces the number of modifications to the compilation tool, greatly improves the compatibility of the electronic map data compilation tool, and improves the compilation efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 is a flowchart of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0032] 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0033] The following gives specific embodiments.

[0034] See Figure 1 , an intelligent expansion method for an electronic map data organization model. The specific steps of the intelligent expansion method are as follows:

[0035] Step 101: Traverse all records in a certain layer to obtain the maximum storage length L of a single record max， Traverse all records to obtain the storage length of each record. The length set L of all records is defined as: ; In the length set L of all records, the maximum storage length L max , is defined as: ;

[0036] Step 102: Calculate the maximum number S of records that can be stored in a single file. The capacity of a single file is defined as C. Then the maximum number S of records that can be stored in a single file is defined as: ;

[0037] Step 103: Create a record counter to count the total number N of records. At the same time, it is also used in subsequent steps to calculate the current file number, initialized to 0. In the map data, the total number of data records in a certain layer is defined as N. The set R of all records in this layer of data is defined as: ;

[0038] Step 104: Create a file counter to count the total number M of files, initialized to 0. The number M of files required to store all N records is defined as:

[0039] ;

[0040] Step 105: Determine whether it exceeds the last record. When it reaches the end of the record, execute Step 112; otherwise, execute Step 106;

[0041] Step 106: Calculate the file number in which the current record is stored: take the integer part of the record counter divided by S;

[0042] Step 107: Determine whether the file with the current number exists. If it exists, execute Step 110; otherwise, execute Step 108;

[0043] Step 108: Create a new file with the current number;

[0044] Step 109: Increment the file counter by 1;

[0045] Step 110: Store the current record into the file corresponding to the serial number;

[0046] Step 111: Increment the record counter by 1, and then execute Step 105;

[0047] Step 112: Save the total number of records N, the total number of files M, and the maximum number of records that a single file can store S for secondary reading and writing of files. Thus, the intelligent expansion process ends.

[0048] As an implementation manner of the present invention, the file set F storing all records is defined as: , where the records included in the i-th file F i are represented by the set as:

[0049] .

[0050] In the data compilation of a very large-scale electronic map, when the data volume of a certain layer in the electronic map increases to the maximum number, the data organization model of the present invention can intelligently expand the storage space, completely solve the file overflow problem, greatly enhance the stability of the electronic map data compilation tool. Through the compilation verification of the national map, the data compilation time of the present invention is shortened by 67%, greatly improving the data compilation efficiency of the electronic map, saving the human, material, and financial resources wasted on such problems, and greatly reducing the data compilation cost of the electronic map;

[0051] There are dozens of data layers in the map. The present invention abstracts a parent layer, and all layers inherit the parent layer. This data organization model does not require modifying the data structures of all layers, reduces the number of modifications to the compilation tool, greatly improves the compatibility of the electronic map data compilation tool, and improves the compilation efficiency.

[0052] A certain layer in the above implementation steps can be a road layer, a node layer, a traffic warning layer, a background layer, a marking layer, a retrieval layer, a real-time traffic condition layer, etc. When implementing in code, use this solution as the parent class of all layers, and each layer inherits this parent class, and implement the different parts of each layer in the subclass.

[0053] The above is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent replacements or changes, and should be covered by the protection scope of the present invention.

Claims

1. An intelligent expansion method for an electronic map data organization model, characterized in that, The electronic map data organization model is as follows: In map data, the total number of records of a certain layer of data is defined as N, and the set R of all records of this layer of data is defined as: ; Traverse all records to obtain the storage length of each record. The length set L of all records is defined as: ; Among the set L of the lengths of all records, the maximum storage length L max , is defined as: ; The capacity of a single file, defined as C, and the maximum number of records S that a single file can store are defined as: ; To store all N records, the number of files M required is defined as: ; The file set F storing all records is defined as: , where the records contained in the i-th file F i are represented as a set: ； The specific steps of the intelligent expansion method are as follows: Step 101: Traverse all records of a certain layer to obtain the maximum storage length L of a single record max ; Step 102: Calculate the maximum number S of records that can be stored in a single file; Step 103: Create a record counter to count the total number of records N, which is also used in subsequent steps to calculate the current file number and is initialized to 0; Step 104: Create a file counter, count the total number of files M, and initialize it to 0; Step 105: Determine whether the record exceeds the last one. When the record ends, execute step 112; Otherwise, execute step 106; Step 106: Calculate the file number where the current record is stored: divide the record counter by S and round up; Step 107: Does the current serial number file exist? If it does, go to step 110; Otherwise, execute step 108; Step 108: Create a new current sequence number file; Step 109: the file counter is incremented by 1; Step 110: Store the current record in the file corresponding to the serial number; Step 111: add 1 to the record counter, and then execute step 105; Step 112: Save the total number of records N, the total number of files M, and the maximum number of records that can be stored in a single file S for secondary reading and writing of files. At this point, the intelligent expansion process ends.

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