CN113326343B - Road network data storage method and system based on multi-level grids and file indexes - Google Patents
Road network data storage method and system based on multi-level grids and file indexes Download PDFInfo
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Abstract
The invention discloses a road network data storage method and a road network data storage system based on multilevel grids and file indexes, wherein the method comprises the following steps: the method comprises the steps of calling road network and node data from a mother library, preprocessing the called road network data and the node data to obtain a preprocessing result, carrying out geographic grade meshing and section grade meshing on a target road network based on the preprocessing result to obtain a meshing result and a section dividing result, carrying out cost value calculation on the section dividing result by using a preset cost model, storing the calculated cost value of each place and the target road network data of the section in a grid corresponding to the meshing result, and constructing index files of road network data of different levels. Aiming at the requirement of mass data processing, a grid storage mode is introduced, the requirement on the computing performance of the server is effectively reduced, the file reading speed can be increased to the maximum extent by establishing indexes for specified data, and the algorithm efficiency is effectively improved.
Description
Technical Field
The invention relates to the technical field of data storage, in particular to a road network data storage method and system based on a multi-level grid and file index.
Background
In recent years, with the rapid development of science and technology and the popularization of social informatization, the traditional method of manually collecting information is gradually replaced by electronic equipment. Meanwhile, with the diversification of data acquisition equipment, the rapid development of a 5G network and the more and more intensive research of people on the field of spatial geographic information, large-scale geographic spatial data are generated, the data volume is exponentially increased, and meanwhile, the high calculation complexity of the spatial data brings huge challenges to a series of processing processes such as data storage and query.
In the application of a geographic information system facing large-scale road network data, the road network is complex and various, and the information amount is large. In the design of a navigation system, path searching for a large-scale geographic network often has high computational complexity, the dynamic property of the network cannot be supported, the query time is increased, the user experience is influenced, network analysis under massive road network data draws more and more attention along with the development of a big data technology, a traditional algorithm often has high computational complexity and memory occupation when solving the problem of network analysis under the condition of massive network data, and the following problems exist when the traditional algorithm is used for storing and calling road network data: in the path planning process, the requirement on the reading efficiency of road sections and nodes is high, the existing vector or database file cannot meet the algorithm reading efficiency, meanwhile, the road network data storage capacity is large, the topological relation among the road network data storage capacity and the road network data storage capacity needs to be accurately expressed, and the requirement on the performance of a computer in the whole processing process is high.
Disclosure of Invention
Aiming at the problems shown above, the invention provides a road network data storage method and a road network data storage system based on multi-level grids and file indexes, which are used for solving the problems that the requirement on the reading efficiency of road sections and nodes is high in the process of path planning, the existing vector or database file cannot meet the algorithm reading efficiency, the road network data storage capacity is large, the topological relation among the road network data storage capacity and the overall processing requirement on the performance of a computer are high.
A road network data storage method based on multilevel grids and file indexes comprises the following steps:
the method comprises the steps of calling road network and node data from a mother library, preprocessing the called road network data and node data, and obtaining a preprocessing result;
based on the preprocessing result, performing geographic grade meshing and section grade meshing on the target road network to obtain a meshing result and a section dividing result;
calculating the cost value of the section division result by using a preset cost model, and storing the calculated cost value of each site and the target road network data of the section in a grid corresponding to the grid division result;
and constructing index files of road network data of different levels.
Preferably, the retrieving road network and node data from the mother library, and preprocessing the retrieved road network data and node data to obtain a preprocessing result includes:
determining a plurality of corresponding road section information according to the called road network data;
determining n boundary points between the plurality of nodes and the plurality of road sections according to the called node data, and constructing a target road section between two target nodes connected by each boundary point;
connecting the two target nodes with the target road section, and obtaining preprocessed road network data after the connection is finished;
and taking the preprocessed road network data as the preprocessing result.
Preferably, the performing, based on the preprocessing result, geographical grade meshing and segment grade meshing on the target road network to obtain a meshing result and a segment dividing result includes:
acquiring attribute information of each road section information in the road network data according to the preprocessing result;
determining the spatial information and the data magnitude of each road section information according to the attribute information of each road section information;
dividing the target road network into three equal-level section intervals and geographic grid intervals according to the spatial information and the data magnitude of each section information;
and determining the three levels of the section intervals and the geographic grid intervals as the grid division result and the section division result.
Preferably, performing cost value calculation on the segment division result by using a preset cost model, and storing the calculated cost value of each location and the target road network data of the segment in a grid corresponding to the grid division result, including:
calculating the time cost value and the distance cost value of each section information in the section division result by using the preset cost model;
associating each segment information with its corresponding time cost value and distance cost value;
and after the association is finished, storing each section information and the corresponding target road network data into the grid block corresponding to the grid division result according to the defined structure.
Preferably, the constructing the index file of road network data of different hierarchies includes:
determining a plurality of nodes corresponding to each target road network data in each level;
acquiring attribute information of each node in a plurality of nodes corresponding to each target road network data;
generating an index instruction of each node according to the attribute information of each node, importing the index instruction of each node into the target road network data corresponding to the node and associating the index instruction with the target road network data;
and counting the index instruction sets of each target road network data, and uniformly storing the index instruction sets of the target road network data of the same level.
Preferably, the method further comprises:
performing first operation on a storage medium for storing the called road network data to acquire a first operation response fed back;
determining the feedback response time length of the storage medium according to the first operation response;
if the feedback response time length is less than a preset time length, acquiring an available storage space of the storage medium;
and evaluating the performance index of the available storage interval, if the performance index is less than or equal to a preset threshold value, releasing the used storage space of the storage medium, and deleting the backup data and the non-use data in the used storage space.
Preferably, the method further comprises:
counting the current data read-write times in each grid block in the multi-level grid;
calculating the safety factor of each grid block according to the current data read-write times in each grid block and the preset monthly maximum read-write times of each grid block;
acquiring the success probability of each data read-write in each grid block, and determining the fault abnormal probability of each grid block according to the success probability;
calculating the safety performance index of each grid block according to the safety coefficient and the fault abnormal probability of each grid block:
wherein k isiDenoted as the ith grid areaSafety performance index of block, ci1Expressed as the current number of data reads and writes in the ith grid block, ci2Expressed as the preset monthly mean maximum read-write times, beta, in the ith grid blockiExpressed as the safety factor, p, of the ith grid blockiProbability of failure anomaly, q, expressed as the ith trellis blockiFrequency of use of data, F, expressed as the ith grid blockiThe data read-write efficiency of the ith grid block is expressed, e is a natural constant and takes a value of 2.72iRepresenting the importance of storing road network data in the ith grid block;
counting the number of target grid blocks with the safety performance index smaller than a preset index, acquiring the number of each target grid block, and generating an abnormal report of each target grid block;
and sending the target grid blocks with the target number, the corresponding numbers and the abnormal reports to a server for maintenance and repair of workers.
Preferably, the road network data storage method based on multi-level grids and file indexes, which evaluates the performance index of the available storage interval, includes:
dividing the available storage interval into N sub-storage intervals according to a preset division rule;
comparing the sub-storage interval with a preset sub-storage interval, judging whether the sub-storage interval is complete, and if so, setting the storage space defect ratio corresponding to the complete sub-storage area to be 1;
if the storage space is incomplete, acquiring a corresponding storage space incomplete ratio based on the incomplete degree of the incomplete self-storage area;
acquiring the current read-write times and the read-write frequency index of each sub-storage interval;
acquiring a corresponding first performance index in each sub-storage interval;
calculating a performance index weight value corresponding to each sub-storage interval based on the space incomplete ratio of the incomplete sub-storage intervals, the current read-write times and the read-write frequency index of each sub-storage interval and a first performance index corresponding to each sub-storage interval:
in the formula, τiThe performance index weight value corresponding to the ith sub-storage interval obtained by dividing the available storage interval, N is the total number of the sub-storage intervals obtained by dividing the available storage interval, and alphaiA storage space defect ratio, gamma, corresponding to the ith sub-storage interval obtained by dividing the available storage intervaliA first performance index, n, corresponding to the ith sub-storage interval obtained by dividing the available storage intervaliDividing the available storage interval to obtain the current read-write times, n, corresponding to the ith sub-storage interval0For a preset total number of times θ of read and write operations in said sub-storage sectioniDividing the available storage interval to obtain a corresponding read-write frequency index of the ith sub-storage interval, wherein exp () is an exponential function with a natural constant e as a base, the value of e is 2.72, and max () is a maximum value;
calculating the performance index of the available storage interval based on the performance index weight value corresponding to each sub storage interval and the first performance index corresponding to each sub storage interval:
where σ is a performance index of the available memory interval, and min () is a minimum value.
A road network data storage system based on multilevel grids and file indexing, the system comprising:
the system comprises a calling module, a preprocessing module and a data processing module, wherein the calling module is used for calling road network and node data in a mother library, preprocessing the called road network data and node data and acquiring a preprocessing result;
the division module is used for carrying out geographic grade meshing and section grading on the target road network based on the preprocessing result to obtain a meshing result and a section division result;
the calculation module is used for calculating the cost value of the division result of the section by using a preset cost model and storing the calculated cost value of each site and the target road network data of the section in a grid corresponding to the grid division result;
and the building module is used for building index files of road network data of different levels.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
FIG. 1 is a flowchart illustrating a road network data storage method based on multi-level grids and file indexes according to the present invention;
FIG. 2 is another flowchart of the road network data storage method based on multi-level grids and file indexes according to the present invention;
FIG. 3 is a flowchart of another operation of the road network data storage method based on multi-level grids and file indexes according to the present invention;
fig. 4 is a schematic structural diagram of a road network data storage system based on a multi-level grid and file indexes provided in the present invention.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the exemplary embodiments below are not intended to represent all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present disclosure, as detailed in the appended claims.
In recent years, with the rapid development of science and technology and the popularization of social informatization, the traditional method of manually collecting information is gradually replaced by electronic equipment. Meanwhile, with the diversification of data acquisition equipment, the rapid development of a 5G network and the more and more intensive research of people on the field of spatial geographic information, large-scale geographic spatial data are generated, the data volume is exponentially increased, and meanwhile, the high calculation complexity of the spatial data brings huge challenges to a series of processing processes such as data storage and query.
In the application of a geographic information system facing large-scale road network data, the road network is complex and various, and the information amount is large. In the design of a navigation system, path searching for a large-scale geographic network often has high computational complexity, the dynamic property of the network cannot be supported, the query time is increased, the user experience is influenced, network analysis under massive road network data draws more and more attention along with the development of a big data technology, a traditional algorithm often has high computational complexity and memory occupation when solving the problem of network analysis under the condition of massive network data, and the following problems exist when the traditional algorithm is used for storing and calling road network data: in the path planning process, the requirement on the reading efficiency of road sections and nodes is high, the existing vector or database file cannot meet the algorithm reading efficiency, meanwhile, the road network data storage capacity is large, the topological relation among the road network data storage capacity and the road network data storage capacity needs to be accurately expressed, and the requirement on the performance of a computer in the whole processing process is high. In order to solve the above problem, the present embodiment discloses a road network data storage method based on a multi-level grid and a file index.
A road network data storage method based on multi-level grids and file indexes, as shown in fig. 1, includes the following steps:
s101, calling road network and node data from a mother library, and preprocessing the called road network data and node data to obtain a preprocessing result;
step S102, based on the preprocessing result, carrying out geographic grade meshing and section grade meshing on the target road network to obtain a meshing result and a section dividing result;
step S103, performing cost value calculation on the section division result by using a preset cost model, and storing the calculated cost value of each position and the target road network data of the section in a grid corresponding to the grid division result;
and step S104, constructing index files of road network data of different levels.
The working principle of the technical scheme is as follows: the method comprises the steps of calling road network and node data from a mother library, preprocessing the called road network data and the node data to obtain a preprocessing result, carrying out geographic grade meshing and section grade meshing on a target road network based on the preprocessing result to obtain a meshing result and a section dividing result, carrying out cost value calculation on the section dividing result by using a preset cost model, storing the calculated cost value of each place and the target road network data of the section in a grid corresponding to the meshing result, and constructing index files of road network data of different levels.
The beneficial effects of the above technical scheme are: aiming at the requirement of mass data processing, a grid storage mode, block processing and step processing are introduced, so that the requirement on the computing performance of the server is effectively reduced, and the cost of the server is saved. Meanwhile, the file reading speed can be increased to the maximum extent by establishing indexes for the designated data, the algorithm efficiency is effectively improved, the cost values among different node road sections are preset according to different cost models, the route calculation process cost calculation time is saved, the overlong calculation time of a server can be effectively avoided, and the algorithm efficiency is further improved. The method solves the problems that in the prior art, the requirement on the reading efficiency of road sections and nodes is high in the path planning process, the existing vector or database file cannot meet the algorithm reading efficiency, the road network data storage capacity is large, the topological relation among the road networks needs to be accurately expressed, and the requirement on the performance of a computer in the whole processing is high.
In an embodiment, the retrieving road network and node data from the mother library, and preprocessing the retrieved road network data and node data to obtain a preprocessing result includes:
determining a plurality of corresponding road section information according to the called road network data;
determining n boundary points between the plurality of nodes and the plurality of road sections according to the called node data, and constructing a target road section between two target nodes connected by each boundary point;
connecting the two target nodes with the target road section, and obtaining preprocessed road network data after the connection is finished;
and taking the preprocessed road network data as the preprocessing result.
The beneficial effects of the above technical scheme are: by constructing the target road section between the two targets connected by the boundary point, more perfect data can be provided for the subsequent storage of the road network data, the subsequent calling of the corresponding road network data is faster and more efficient, a server is not needed to generate a path in real time, and the working efficiency is improved.
In an embodiment, as shown in fig. 2, the performing, based on the preprocessing result, geographical grade meshing and segment grade meshing on the target road network to obtain a meshing result and a segment dividing result includes:
step S201, acquiring attribute information of each road section information in road network data according to the preprocessing result;
step S202, determining spatial information and data magnitude of each road section information according to the attribute information of each road section information;
step S203, dividing the target road network into three equal-level section intervals and geographic grid intervals according to the spatial information and the data magnitude of each section information;
and step S204, confirming the section intervals and the geographic grid intervals of the three levels as the grid division result and the section division result.
The beneficial effects of the above technical scheme are: the method has the advantages that the target road network is subjected to grade division of grids and road sections, so that the block processing and the step processing can be further realized, the requirement on the calculation performance of the server is effectively reduced, the cost is saved to a certain extent, meanwhile, the required road network data can be quickly called aiming at the planned path of the user in a well-known and orderly manner, and the working efficiency is further improved.
In one embodiment, performing cost value calculation on the segment division result by using a preset cost model, and storing the calculated cost value of each location and the target road network data of the segment in a grid corresponding to the grid division result, includes:
calculating the time cost value and the distance cost value of each section information in the section division result by using the preset cost model;
associating each segment information with its corresponding time cost value and distance cost value;
and after the association is finished, storing each section information and the corresponding target road network data into the grid block corresponding to the grid division result according to the defined structure.
The beneficial effects of the above technical scheme are: according to the preset cost model, the cost values among different node road sections are preset, so that the cost calculation time of a route calculation process can be saved, the algorithm efficiency is further improved, the self-calculation time of a server is saved, the operation load of the server is reduced, and the working efficiency is further improved.
In one embodiment, the constructing the index file of road network data at different levels includes:
determining a plurality of nodes corresponding to each target road network data in each level;
acquiring attribute information of each node in a plurality of nodes corresponding to each target road network data;
generating an index instruction of each node according to the attribute information of each node, importing the index instruction of each node into the target road network data corresponding to the node and associating the index instruction with the target road network data;
and counting the index instruction sets of each target road network data, and uniformly storing the index instruction sets of the target road network data of the same level.
The beneficial effects of the above technical scheme are: the index instructions of each target road network data are generated according to the attribute information, so that the uniqueness of the index instructions of each target road network data can be ensured, the condition that a plurality of target road network data are indexed by the same index instruction is avoided, the stability is improved, further, the index range of each level can be independent by uniformly storing the index instruction sets of the target road network data of the same level, the user can quickly call the stored road network data, and the experience of the user is improved.
In one embodiment, as shown in fig. 3, the method further comprises:
step S301, performing a first operation on a storage medium for storing the called road network data, and acquiring a first operation response of feedback;
step S302, determining the feedback response time length of the storage medium according to the first operation response;
step S303, if the feedback response time length is less than a preset time length, acquiring an available storage space of the storage medium;
step S304, evaluating the performance index of the available storage interval, if the performance index is less than or equal to a preset threshold value, releasing the used storage space of the storage medium, and deleting the backup data and the non-use data in the used storage space.
The beneficial effects of the above technical scheme are: the storage space of the storage medium is optimized, so that the reading integrity and efficiency of the stored target road network data can be guaranteed, the problem that the data transmission efficiency is slow due to the fact that the storage space of the storage medium is full is avoided, the calling time of the user data to the road network data is prolonged, the experience of a user is further improved, and meanwhile, the work of periodically cleaning the storage space of the storage medium is also achieved.
In one embodiment, the method further comprises:
counting the current data read-write times in each grid block in the multi-level grid;
calculating the safety factor of each grid block according to the current data read-write times in each grid block and the preset monthly maximum read-write times of each grid block;
acquiring the success probability of each data read-write in each grid block, and determining the fault abnormal probability of each grid block according to the success probability;
calculating the safety performance index of each grid block according to the safety coefficient and the fault abnormal probability of each grid block:
wherein k isiExpressed as the safety performance index of the ith grid block, ci1Expressed as the current number of data reads and writes in the ith grid block, ci2Expressed as the preset monthly mean maximum read-write times, beta, in the ith grid blockiExpressed as the safety factor, p, of the ith grid blockiProbability of failure anomaly, q, expressed as the ith trellis blockiFrequency of use of data, F, expressed as the ith grid blockiThe data read-write efficiency of the ith grid block is expressed, e is a natural constant and takes a value of 2.72iRepresenting the importance of storing road network data in the ith grid block;
counting the number of target grid blocks with the safety performance index smaller than a preset index, acquiring the number of each target grid block, and generating an abnormal report of each target grid block;
and sending the target grid blocks with the target number, the corresponding numbers and the abnormal reports to a server for maintenance and repair of workers.
The beneficial effects of the above technical scheme are: the performance of each grid block can be periodically evaluated according to the data calling condition of each grid block by calculating the safety performance index of each target grid block, and then workers can be informed to maintain and repair when the performance is poor, so that the calling efficiency of subsequent users on road network data is improved, and the experience of the users is further improved.
In one embodiment, the method for storing road network data based on multi-level grids and file indexes, which evaluates the performance index of the available storage interval, includes:
dividing the available storage interval into N sub-storage intervals according to a preset division rule;
comparing the sub-storage interval with a preset sub-storage interval, judging whether the sub-storage interval is complete, and if so, setting the storage space defect ratio corresponding to the complete sub-storage area to be 1;
if the storage space is incomplete, acquiring a corresponding storage space incomplete ratio based on the incomplete degree of the incomplete self-storage area;
acquiring the current read-write times and the read-write frequency index of each sub-storage interval;
acquiring a corresponding first performance index in each sub-storage interval;
calculating a performance index weight value corresponding to each sub-storage interval based on the space incomplete ratio of the incomplete sub-storage intervals, the current read-write times and the read-write frequency index of each sub-storage interval and a first performance index corresponding to each sub-storage interval:
in the formula, τiThe performance index weight value corresponding to the ith sub-storage interval obtained by dividing the available storage interval, N is the total number of the sub-storage intervals obtained by dividing the available storage interval, and alphaiA storage space defect ratio, gamma, corresponding to the ith sub-storage interval obtained by dividing the available storage intervaliA first performance index, n, corresponding to the ith sub-storage interval obtained by dividing the available storage intervaliDividing the available storage interval to obtain the current read-write times, n, corresponding to the ith sub-storage interval0For a preset total number of times θ of read and write operations in said sub-storage sectioniThe index of the read-write frequency corresponding to the ith sub-storage interval obtained by dividing the available storage interval is shown in exp (), andan exponential function with a natural constant e as a base, wherein the value of e is 2.72, and max () is a maximum value;
calculating the performance index of the available storage interval based on the performance index weight value corresponding to each sub storage interval and the first performance index corresponding to each sub storage interval:
where σ is a performance index of the available memory interval, and min () is a minimum value.
The beneficial effects of the above technical scheme are: the available storage interval is divided according to a preset division rule to obtain sub-storage intervals, and corresponding performance index weighted values are determined based on the calculation of relevant storage data of the sub-storage intervals contained in the available storage interval, so that the performance index of the available storage interval can be calculated more accurately.
In one embodiment, the method comprises the following steps:
the road network is a large network and has intersection turning limitation, so that a proper storage structure needs to be searched for according to the characteristics of the road network and the requirements of route optimization in navigation, the storage capacity is as small as possible, meanwhile, the road network is convenient to operate by a path planning algorithm, and the requirements of intersection turning limitation and the like can be expressed correctly. Aiming at the requirements for the road network storage data, a linked list type storage structure based on technologies such as multilevel space grids and multilevel file indexes is designed. The steps for mainly realizing the operation processing are as follows:
1. according to the spatial information and the data magnitude of the road network data, the road network data are divided into three levels of regular grids, so that the problems of spatial positioning, a spatial retrieval mechanism, data volume in the spatial grids and the like are solved.
2. Aiming at boundary points of a road section interrupted by boundary areas in the process of dividing the space grid, the connection relation between nodes and the road section is established in advance in the data processing process.
3. The method constructs cost models in different route calculation modes, calculates the cost values of different node road sections respectively, and stores the cost values into corresponding positions. The cost values calculated in advance can be directly read in the path planning process, and the algorithm efficiency is greatly improved.
4. And in the data processing process, data of 8 grids close to the current three-level grid are read respectively for processing, so that the data volume of the current processing is reduced, and the requirement on the memory of the server is reduced.
5. Index files are respectively built for the first, second and third levels of grids, and the data of the corresponding blocks are sequentially positioned according to the levels when the algorithm reads the data.
The beneficial effects of the above technical scheme are: 1. aiming at the requirement of processing mass data, a grid computing mode, block processing and step processing are introduced, so that the requirement on the computing performance of the server is effectively reduced, and the cost of the server is saved. 2. Indexes are established for the specified data, the file reading speed is increased to the maximum extent, and the algorithm efficiency is effectively improved. 3. According to different experience cost models, the cost values among different node road sections are preset, the cost calculation time of the route calculation process is saved, and the algorithm efficiency is effectively improved.
The embodiment also discloses a road network data storage system based on multi-level grids and file indexes, as shown in fig. 4, the system includes:
the calling module 401 is configured to call road network and node data in the mother library, and pre-process the called road network data and node data to obtain a pre-processing result;
a dividing module 402, configured to perform geographic grade meshing and segment grade dividing on the target road network based on the preprocessing result, and obtain a meshing result and a segment dividing result;
a calculating module 403, configured to perform cost value calculation on the segment division result by using a preset cost model, and store the calculated cost value of each segment and the target road network data of the segment in a grid corresponding to the grid division result;
and a building module 404, configured to build index files of road network data at different levels.
The working principle and the advantageous effects of the above technical solution have been explained in the method claims, and are not described herein again.
It will be understood by those skilled in the art that the first and second terms of the present invention refer to different stages of application.
Other embodiments of the disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the disclosure disclosed herein. This application is intended to cover any variations, uses, or adaptations of the disclosure following, in general, the principles of the disclosure and including such departures from the present disclosure as come within known or customary practice within the art to which the disclosure pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the disclosure being indicated by the following claims.
It will be understood that the present disclosure is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.
Claims (7)
1. A road network data storage method based on multilevel grids and file indexes is characterized by comprising the following steps:
the method comprises the steps of calling road network and node data from a mother library, preprocessing the called road network data and node data, and obtaining a preprocessing result;
based on the preprocessing result, performing geographic grade meshing and section grade meshing on the target road network to obtain a meshing result and a section dividing result;
calculating the cost value of the section division result by using a preset cost model, and storing the calculated cost value of each site and the target road network data of the section in a grid corresponding to the grid division result;
constructing index files of road network data of different levels;
calculating the cost value of the section division result by using a preset cost model, and storing the calculated cost value of each site and the target road network data of the section in a grid corresponding to the grid division result, wherein the method comprises the following steps:
calculating the time cost value and the distance cost value of each section information in the section division result by using the preset cost model;
associating each segment information with its corresponding time cost value and distance cost value;
after the association is finished, storing each section information and the corresponding target road network data into the grid block corresponding to the grid division result according to a defined structure;
the method further comprises the following steps:
counting the current data read-write times in each grid block in the multi-level grid;
calculating the safety factor of each grid block according to the current data read-write times in each grid block and the preset monthly maximum read-write times of each grid block;
acquiring the success probability of each data read-write in each grid block, and determining the fault abnormal probability of each grid block according to the success probability;
calculating the safety performance index of each grid block according to the safety coefficient and the fault abnormal probability of each grid block:
wherein k isiExpressed as the safety performance index of the ith grid block, ci1Expressed as the current number of data reads and writes in the ith grid block, ci2Expressed as the preset monthly mean maximum read-write times, beta, in the ith grid blockiExpressed as the safety factor, p, of the ith grid blockiProbability of failure anomaly, q, expressed as the ith trellis blockiFrequency of use of data, F, expressed as the ith grid blockiThe data read-write efficiency of the ith grid block is expressed, e is a natural constant and takes a value of 2.72iRepresenting the importance of storing road network data in the ith grid block;
counting the number of target grid blocks with the safety performance index smaller than a preset index, acquiring the number of each target grid block, and generating an abnormal report of each target grid block;
and sending the target grid blocks with the target number, the corresponding numbers and the abnormal reports to a server for maintenance and repair of workers.
2. The road network data storage method based on the multilevel grids and the file index as claimed in claim 1, wherein the step of retrieving road network and node data from the master library, preprocessing the retrieved road network data and node data, and obtaining a preprocessing result comprises:
determining a plurality of corresponding road section information according to the called road network data;
determining n boundary points between the plurality of nodes and the plurality of road sections according to the called node data, and constructing a target road section between two target nodes connected by each boundary point;
connecting the two target nodes with the target road section, and obtaining preprocessed road network data after the connection is finished;
and taking the preprocessed road network data as the preprocessing result.
3. The road network data storage method based on multilevel gridding and file index as claimed in claim 1, wherein said pre-processing result based on geographic grade gridding and segment grade gridding of target road network to obtain gridding result and segment grade result comprises:
acquiring attribute information of each road section information in the road network data according to the preprocessing result;
determining the spatial information and the data magnitude of each road section information according to the attribute information of each road section information;
dividing the target road network into three equal-level section intervals and geographic grid intervals according to the spatial information and the data magnitude of each section information;
and determining the three levels of the section intervals and the geographic grid intervals as the grid division result and the section division result.
4. The road network data storage method based on multilevel grids and file indexes according to claim 1, wherein the constructing of the index files of the road network data of different levels comprises:
determining a plurality of nodes corresponding to each target road network data in each level;
acquiring attribute information of each node in a plurality of nodes corresponding to each target road network data;
generating an index instruction of each node according to the attribute information of each node, importing the index instruction of each node into the target road network data corresponding to the node and associating the index instruction with the target road network data;
and counting the index instruction sets of each target road network data, and uniformly storing the index instruction sets of the target road network data of the same level.
5. The method for storing road network data based on multilevel grids and file indexes according to claim 1, further comprising:
performing first operation on a storage medium for storing the called road network data to acquire a first operation response fed back;
determining the feedback response time length of the storage medium according to the first operation response;
if the feedback response time length is less than a preset time length, acquiring an available storage space of the storage medium;
and evaluating the performance index of the available storage interval, if the performance index is less than or equal to a preset threshold value, releasing the used storage space of the storage medium, and deleting the backup data and the non-use data in the used storage space.
6. The multi-level mesh and file index based road network data storage method according to claim 5, wherein evaluating the performance index of the available storage interval comprises:
dividing the available storage interval into N sub-storage intervals according to a preset division rule;
comparing the sub-storage interval with a preset sub-storage interval, judging whether the sub-storage interval is complete, and if so, setting the storage space defect ratio corresponding to the complete sub-storage area to be 1;
if the storage space is incomplete, acquiring a corresponding storage space incomplete ratio based on the incomplete degree of the incomplete self-storage area;
acquiring the current read-write times and the read-write frequency index of each sub-storage interval;
acquiring a corresponding first performance index in each sub-storage interval;
calculating a performance index weight value corresponding to each sub-storage interval based on the space incomplete ratio of the incomplete sub-storage intervals, the current read-write times and the read-write frequency index of each sub-storage interval and a first performance index corresponding to each sub-storage interval:
in the formula, τiThe performance index weight value corresponding to the ith sub-storage interval obtained by dividing the available storage interval, N is the total number of the sub-storage intervals obtained by dividing the available storage interval, and alphaiA storage space defect ratio, gamma, corresponding to the ith sub-storage interval obtained by dividing the available storage intervaliA first performance index, n, corresponding to the ith sub-storage interval obtained by dividing the available storage intervaliDividing the available storage interval to obtain the current read-write times, n, corresponding to the ith sub-storage interval0For a preset total number of times θ of read and write operations in said sub-storage sectioniDividing the available storage interval to obtain a corresponding read-write frequency index of the ith sub-storage interval, wherein exp () is an exponential function with a natural constant e as a base, the value of e is 2.72, and max () is a maximum value;
calculating the performance index of the available storage interval based on the performance index weight value corresponding to each sub storage interval and the corresponding first performance index in each sub storage interval:
where σ is a performance index of the available memory interval, and min () is a minimum value.
7. A road network data storage system based on multilevel grids and file indexes is characterized by comprising:
the system comprises a calling module, a preprocessing module and a data processing module, wherein the calling module is used for calling road network and node data in a mother library, preprocessing the called road network data and node data and acquiring a preprocessing result;
the division module is used for carrying out geographic grade meshing and section grading on the target road network based on the preprocessing result to obtain a meshing result and a section division result;
the calculation module is used for calculating the cost value of the division result of the section by using a preset cost model and storing the calculated cost value of each site and the target road network data of the section in a grid corresponding to the grid division result;
the construction module is used for constructing index files of road network data of different levels;
the calculation module comprises:
calculating the time cost value and the distance cost value of each section information in the section division result by using the preset cost model;
associating each segment information with its corresponding time cost value and distance cost value;
after the association is finished, storing each section information and the corresponding target road network data into the grid block corresponding to the grid division result according to a defined structure;
the calculation module further comprises:
counting the current data read-write times in each grid block in the multi-level grid;
calculating the safety factor of each grid block according to the current data read-write times in each grid block and the preset monthly maximum read-write times of each grid block;
acquiring the success probability of each data read-write in each grid block, and determining the fault abnormal probability of each grid block according to the success probability;
calculating the safety performance index of each grid block according to the safety coefficient and the fault abnormal probability of each grid block:
wherein k isiExpressed as the safety performance index of the ith grid block, ci1Expressed as the current number of data reads and writes in the ith grid block, ci2Expressed as the preset monthly mean maximum read-write times, beta, in the ith grid blockiExpressed as the safety factor, p, of the ith grid blockiProbability of failure anomaly, q, expressed as the ith trellis blockiFrequency of use of data, F, expressed as the ith grid blockiThe data read-write efficiency of the ith grid block is expressed, e is a natural constant and takes a value of 2.72iRepresenting the importance of storing road network data in the ith grid block;
counting the number of target grid blocks with the safety performance index smaller than a preset index, acquiring the number of each target grid block, and generating an abnormal report of each target grid block;
and sending the target grid blocks with the target number, the corresponding numbers and the abnormal reports to a server for maintenance and repair of workers.
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