CN112800284A - Rapid query method based on environment model database - Google Patents
Rapid query method based on environment model database Download PDFInfo
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/90—Details of database functions independent of the retrieved data types
- G06F16/903—Querying
- G06F16/90335—Query processing
Abstract
The invention relates to a quick query method based on an environment model database, belonging to the technical field of system simulation. The method comprises the following steps: s1, storing the natural environment models in an environment model library, and recording each natural environment model as a dynamic link node of the environment model library; s2, performing node insertion and/or node deletion operations on the dynamic link nodes of the environment model library to establish a red-black tree to obtain an environment model database; wherein the Reddish Black Tree is an index of the environment model database; and S3, based on the established environment model database, performing fuzzy query by adopting a KMP algorithm. The invention solves the problem that the current mode of directly using environmental data and using dynamic environmental model to solve is difficult to adapt to the requirement.
Description
Technical Field
The invention relates to the technical field of system simulation, in particular to a quick query method based on an environment model database.
Background
The dynamic environment model related in the system-level simulation application system is from multiple units, the modeling of the natural environment relates to the cross knowledge of different subjects and different fields, and different development environments and technical means can cause the inconsistency of the model expression form and even the phenomenon of incompatibility. For example, in the design of the integrated natural environment system, the format of the model file established by the user may be various, and the interfaces, function functions and parameter configuration modes of the model may also be completely different, thereby greatly limiting the transplantation and reuse of the model. Therefore, it is necessary to design a standardized interface for the model development of the system, and integrate the interface into the model library management system in a unified manner, which is convenient for integrated management, updating and scheduling, and is also convenient for different simulation systems to use.
When the simulation system uses the comprehensive natural environment to increase the reliability of the simulation system, two modes of directly using the environmental data and using the dynamic environment model to solve are mainly adopted. Because the system simulation is often in a super real-time simulation state, the method of directly using the environmental data is difficult to adapt to the requirements, and thus a dynamic environmental model is required to generate the environmental data.
Disclosure of Invention
In view of the above analysis, the present invention aims to provide a fast query method based on an environment model library to solve some or all of the above problems of the current simulation system.
The purpose of the invention is mainly realized by the following technical scheme:
the invention provides a quick query method based on an environment model database, which comprises the following steps:
s1, storing the natural environment models in an environment model library, and recording each natural environment model as a dynamic link node of the environment model library;
s2, performing node insertion and/or node deletion operations on the dynamic link nodes of the environment model library to establish a red-black tree to obtain an environment model database; wherein the Reddish Black Tree is an index of the environment model database;
and S3, based on the established environment model database, performing fuzzy query by adopting a KMP algorithm.
Further, in the process of constructing the red-black tree, the natural environment model is stored or updated in a storage manner, and the operation of inserting nodes into the dynamic link nodes of the environment model library is performed in step S2, including the following steps:
searching and searching a leaf node position where a new insertion node is located, wherein a child node of the leaf node position has a null node;
inserting a new insertion node into an existing empty node in the child nodes of the leaf node position according to the leaf node position, and setting the color of the new insertion node to be red;
and after adding a new insertion node and setting the new insertion node to be red, correcting the color of the ancestor node of the new insertion node, and performing insertion balance to keep the property of the red-black tree unchanged after the new insertion node is inserted.
Furthermore, the searching and searching of the leaf node position where the new insertion node is located adopts a binary search tree searching mode;
inserting a new insertion node into an existing empty node, and simultaneously adding two empty nodes as child nodes of the new insertion node;
the insertion balancing includes rotating and changing node colors; and after a new node is inserted, in order to keep the property of the red-black tree unchanged, correcting operations of rotating and changing the color of the node are sequentially carried out from the new node upwards until the new node is red due to the insertion operation and the child nodes of the node are black, and then the correction is finished.
Further, when the index information is deleted to update the index of the environment model database, the step S2 of deleting the node is performed on the dynamically linked node of the environment model database, including the following steps:
judging whether the node to be deleted has a child node:
if the node does not exist, the node is directly deleted, and then deletion balance processing is carried out so as to keep the property of the red and black tree unchanged after the node is deleted;
if a child node exists, finding a right subtree B of the node A with the minimum value larger than the node value, adopting B to replace A, copying a key word value in B into the node to be deleted, deleting A, and then carrying out deletion balance processing to keep the property of the red-black tree unchanged after deleting the node;
if two child nodes exist, finding out the node C with the maximum key word value in the left sub-tree of the node value to be deleted, exchanging the key word values of the node C and the node to be deleted, deleting C, and then carrying out deletion balance processing.
Further, in the step S3, based on the environment model database established, a KMP algorithm is adopted to perform fuzzy query, which includes the following steps:
s31, performing word segmentation processing on the keywords input by the user to obtain original keyword strings;
s32, fuzzy comparison is carried out on the original keyword strings and the strings of the environment model database in sequence to obtain fuzzy similarity;
s33, judging whether the fuzzy similarity meets the preset searching requirement; if so, putting the strings related to fuzzy matching into a queue, otherwise, reducing the preset search requirement by one level, turning to execute the step S32, and simultaneously adding 1 to the fuzzy similarity value;
s34, judging whether the fuzzy similarity value reaches the maximum limit value, if so, outputting matching items meeting the preset searching requirement according to the element sequence of the queue, otherwise, prompting that the fuzzy matching is unsuccessful;
s35, the fuzzy inquiry is finished.
Further, in step S31, performing word segmentation processing on the keyword input by the user to obtain an original keyword string, specifically including:
introducing a word segmentation program into the environment model database;
the word segmentation program carries out word segmentation on the keywords input by the user during query according to the entity meaning to obtain an independent atomic entity;
and performing polling combination by taking the independent atomic entities as query conditions to obtain the original keyword string.
Further, in step S32, the fuzzy comparison between the original keyword string and the string in the environment model database in sequence is performed to obtain a fuzzy similarity, which specifically includes:
after the independent atomic entities are used as query conditions to perform polling combination, performing corresponding times of circulating query according to the number of polling groups, and displaying query results which meet the search conditions each time;
and setting a fuzzy similarity value according to the matching precision and the query degree, and the capacity and the configuration of the environment model database, and finding the corresponding fuzzy similarity level according to the query result.
Furthermore, each group of polling combination forms an atomic entity, and each atomic entity is queried once.
Further, the fuzzy similarity level is divided according to the fuzzy similarity value.
Further, the range of the blur similarity value is composed of a maximum limit value and a minimum limit value.
The technical scheme has the beneficial effects that: the invention discloses a quick query method based on an environment model database, which uses a comprehensive natural environment to increase the credibility of a simulation system and mainly adopts two modes of directly using environment data and using a dynamic environment model to solve. Because the system simulation is often in a super real-time simulation state, the method of directly using the environmental data is difficult to adapt to the requirements, and thus a dynamic environmental model is required to generate the environmental data. In order to meet the requirements of a multi-user multi-resolution model, a rapid environment model service mode is adopted to provide more accurate and rapid model service for the multi-user multi-resolution model, so that the required environment model can be rapidly inquired, and the requirements of system simulation are met.
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 claims hereof as well as the appended drawings.
Drawings
The drawings are only for purposes of illustrating particular embodiments and are not to be construed as limiting the invention, wherein like reference numerals are used to designate like parts throughout.
FIG. 1 is a flowchart of a fast query method based on an environment model database according to an embodiment of the present invention;
fig. 2 is a processing flow chart of a fast query method based on an environment model database according to an embodiment of the present invention.
Detailed Description
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate preferred embodiments of the invention and together with the description, serve to explain the principles of the invention and not to limit the scope of the invention.
One embodiment of the present invention, as shown in fig. 1, discloses a fast query method based on an environment model database, which is characterized by comprising the following steps:
s1, storing the natural environment models in an environment model library, and recording each natural environment model as a dynamic link node of the environment model library;
s2, performing node insertion and/or node deletion operations on the dynamic link nodes of the environment model library to establish a red-black tree to obtain an environment model database; wherein the Reddish Black Tree is an index of the environment model database;
specifically, the red and black tree establishment mainly comprises two operations of node insertion and node deletion. Each node of the red and black tree represents a certain piece of index information of an environment model, such as a model category (an atmospheric model, a sea model, a wind field model, a rain model, and the like). The environment model library comprises a plurality of models, namely a plurality of red and black trees for storing model index information. It should be noted that one red-black tree represents all index information of one environment model, and each node on the red-black tree is a certain piece of index information of the environment model, such as a type of the model, a model development author, a model development time, and the like.
And S3, based on the established environment model database, performing fuzzy query by adopting a KMP algorithm.
Compared with the prior art, the fast query algorithm based on the red-black tree improves the query speed of the environment model library system, can quickly query the required environment model, and meets the requirement of system simulation.
In a specific embodiment of the present invention, in the process of constructing a red-black tree, a natural environment model is stored or updated in a database, and the operation of inserting nodes into dynamic link nodes of the environment model library in step S2 is performed, including the following steps:
searching and searching a leaf node position where a new insertion node is located, wherein a child node of the leaf node position has a null node;
inserting a new insertion node into an existing empty node in the child nodes of the leaf node position according to the leaf node position, and setting the color of the new insertion node to be red;
and after adding a new insertion node and setting the new insertion node to be red, correcting the color of the ancestor node of the new insertion node, and performing insertion balance to keep the property of the red-black tree unchanged after the new insertion node is inserted.
Specifically, the red and black tree node insertion steps are as follows:
in the insertion process, a certain leaf node position where an insertion node is located is searched according to a searching mode of a binary search tree; that is, the insertion node is newly added model index information, and the type of the model index information is known, so that a certain leaf node position where the insertion node is located can be searched.
Inserting a new insertion node into any existing empty node in the original tree, setting the color of the insertion node to be red, and simultaneously adding two empty nodes as child nodes of the insertion node;
after adding a new node, determining the color of the ancestor node according to the definition of the red-black tree, and performing insertion balance operation on the tree at the moment, wherein the insertion balance operation comprises rotating or changing the color of the node to keep the property of the red-black tree; that is, after inserting any empty node, in order to maintain the balance of the red and black trees, the rotation and color change operations are performed. The normal query function can be realized as long as the construction characteristics of the red and black trees are met.
For nodes that change color, the tree needs to be revised up, and in the worst case, all paths from leaf nodes to the root node need to be processed.
In a specific embodiment of the present invention, the searching and searching for the leaf node position where the new insertion node is located adopts a binary search tree searching manner;
inserting a new insertion node into an existing empty node, and simultaneously adding two empty nodes as child nodes of the new insertion node;
the insertion balancing includes rotating and changing node colors; and after a new node is inserted, in order to keep the property of the red-black tree unchanged, correcting operations of rotating and changing the color of the node are sequentially carried out from the new node upwards until any node is red due to the insertion operation and the child nodes of the node are black, and then the correction is finished.
In an embodiment of the present invention, when deleting the index information to update the index of the environment model database, the step S2 of executing the operation of deleting the node on the dynamically linked node of the environment model database includes the following steps:
judging whether the node to be deleted has a child node:
if the node does not exist, the node is directly deleted, and then deletion balance processing is carried out so as to keep the property of the red and black tree unchanged after the node is deleted;
if a child node exists, finding a right subtree B of the node A with the minimum value larger than the node value, adopting B to replace A, copying a key word value in B into the node to be deleted, deleting A, and then carrying out deletion balance processing to keep the property of the red-black tree unchanged after deleting the node;
if two child nodes exist, finding out the node C with the maximum key word value in the left sub-tree of the node value to be deleted, exchanging the key word values of the node C and the node to be deleted, deleting C, and then carrying out deletion balance processing.
Specifically, the red and black tree node deleting steps are as follows:
the deleted node has no children (both children are null nodes). Under the condition, the node is directly removed and then enters into balance processing;
there is one child node. In this case, a right subtree B of the node A with the smallest value larger than the node value is found, the node A is replaced by the node B, the keyword value in the node B is copied into the node to be deleted, the node A is deleted, and then balance processing is performed;
there are two child nodes. In this case, the node a with the largest key value in the left sub-tree of the node value to be deleted is found first, the key values of the node a and the node a are exchanged, then the node a is deleted, and then the balancing process is performed.
In a specific embodiment of the present invention, the performing fuzzy query by using the KMP algorithm based on the environment model database established in step S3 includes the following steps:
s31, performing word segmentation processing on the keywords input by the user to obtain original keyword strings;
s32, fuzzy comparison is carried out on the original keyword strings and the strings of the environment model database in sequence to obtain fuzzy similarity;
s33, judging whether the fuzzy similarity meets the preset searching requirement; if so, putting the strings related to fuzzy matching into a queue, otherwise, reducing the preset search requirement by one level, turning to execute the step S32, and simultaneously adding 1 to the fuzzy similarity value;
s34, judging whether the fuzzy similarity value reaches the maximum limit value, if so, outputting matching items meeting the preset searching requirement according to the element sequence of the queue, otherwise, prompting that the fuzzy matching is unsuccessful;
s35, the fuzzy inquiry is finished.
In the specific query method, as shown in fig. 2, a KMP algorithm is adopted, and the specific steps are as follows:
(1) carrying out word segmentation processing on keywords input by a user;
(2) fuzzy comparison is carried out on the original string (key words input by a user) and the string in the database (the field with entity significance in the database);
(3) obtaining fuzzy similarity according to the comparison result of the step 2;
(4) judging the fuzzy similarity, and executing the step 5 if the fuzzy similarity meets the searching requirement; if the fuzzy similarity does not meet the requirement, reducing the first-level steering execution by 2, and adding 1 to the similarity;
(5) the fuzzy matching is associated with a serial-in queue;
(6) if the similarity value reaches the maximum limit value, the matching items are output according to the elements of the queue, and then (7) is executed. Otherwise, prompting that the fuzzy matching is unsuccessful, and turning to (7);
(7) the fuzzy query ends.
In a specific embodiment of the present invention, the obtaining of the original keyword string by performing word segmentation on the keyword input by the user in step S31 specifically includes:
introducing a word segmentation program into the environment model database;
the word segmentation program carries out word segmentation on the keywords input by the user during query according to the entity meaning to obtain an independent atomic entity;
specifically, a fuzzy search principle and a matching basis are defined according to a data structure and a mathematical concept, the search principle is to meet the search requirement of a user, and the matching basis is to perform step-by-step query according to fuzzy similarity. The database word segmentation program is introduced to segment the user query keywords according to the words with entity significance, and query is carried out according to the word segmentation result, for example, when the 'ocean actual measurement data' is queried, the query keywords are compared and matched for 6 times according to 'ocean', 'actual measurement' and 'data', so that the query times of the field are reduced from the original 21 times (6 times for a single Chinese character, 5 times for two Chinese characters in sequence, 4 times for three Chinese characters in sequence, 3 times for four Chinese characters in sequence, 2 times for five Chinese characters in sequence and 1 time for six Chinese characters in sequence) to 6 times, and the query efficiency is obviously improved.
And performing polling combination by taking the independent atomic entities as query conditions to obtain the original keyword string.
For example, when the "ocean measured data" is queried by the algorithm, the system first calls the word segmentation program to decompose the input short sentence "ocean measured data" into "ocean", "measured" and "data" according to the words with entity meaning, and then queries the three words as independent atomic entity query conditions. In the first cycle, the 'sea' is used as an atomic entity for inquiry, and all inquiry results meeting the conditions are displayed. And (4) the program is transferred to a second cycle, and the 'ocean actual measurement' and the 'actual measurement ocean' are respectively used as an atomic entity for further matching query. And after the query is finished, the program is switched to a third circulation, and the program respectively takes the 'ocean actual measurement data', 'ocean data actual measurement' and 'actual measurement ocean data' as an atomic entity to carry out fuzzy matching with higher precision and display the query result of the 'ocean actual measurement data'.
In a specific embodiment of the present invention, in step S32, fuzzy comparison is sequentially performed on the original keyword string and the string in the environment model database to obtain a fuzzy similarity, which specifically includes:
after the independent atomic entities are used as query conditions to perform polling combination, performing corresponding times of circulating query according to the number of polling groups, and displaying query results which meet the search conditions each time;
and setting a fuzzy similarity value according to the matching precision and the query degree, and the capacity and the configuration of the environment model database, and finding the corresponding fuzzy similarity level according to the query result.
It should be noted that the fuzzy similarity may have various levels, and in actual implementation, the value of the fuzzy similarity may be set according to the requirements on the matching precision and the query degree, and the capacity and configuration of the server database, so as to achieve the most reasonable state of the query result.
In an embodiment of the present invention, the polling combinations of each group form an atomic entity, and each atomic entity is queried once.
In an embodiment of the invention, the level of the fuzzy similarity is divided according to the fuzzy similarity value.
In one embodiment of the present invention, the range of the fuzzy similarity value is composed of a maximum limit value and a minimum limit value.
In summary, the invention discloses a fast query method based on an environment model database, which comprises the following steps: s1, storing the natural environment models in an environment model library, and recording each natural environment model as a dynamic link node of the environment model library; s2, performing node insertion and/or node deletion operations on the dynamic link nodes of the environment model library to establish a red-black tree to obtain an environment model database; wherein the Reddish Black Tree is an index of the environment model database; and S3, based on the established environment model database, performing fuzzy query by adopting a KMP algorithm. The invention uses the comprehensive natural environment to increase the credibility of the simulation system, and mainly adopts two modes of directly using the environmental data and using the dynamic environment model to solve. Because the system simulation is often in a super real-time simulation state, the method of directly using the environmental data is difficult to adapt to the requirements, and thus a dynamic environmental model is required to generate the environmental data. In order to meet the requirements of a multi-user multi-resolution model, a rapid environment model service mode is adopted to provide more accurate and rapid model service for the multi-user multi-resolution model, so that the required environment model can be rapidly inquired, and the requirements of system simulation are met.
Those skilled in the art will appreciate that all or part of the processes for implementing the methods in the above embodiments may be implemented by a computer program, which is stored in a computer-readable storage medium, to instruct associated hardware. The computer readable storage medium is a magnetic disk, an optical disk, a read-only memory or a random access memory.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.
Claims (10)
1. A quick query method based on an environment model database is characterized by comprising the following steps:
s1, storing the natural environment models in an environment model library, and recording each natural environment model as a dynamic link node of the environment model library;
s2, performing node insertion and/or node deletion operations on the dynamic link nodes of the environment model library to establish a red-black tree to obtain an environment model database; wherein the Reddish Black Tree is an index of the environment model database;
and S3, based on the established environment model database, performing fuzzy query by adopting a KMP algorithm.
2. The method according to claim 1, wherein during the construction of the red-black tree, the natural environment model is stored or updated in a storage, and the step S2 of inserting nodes into the dynamically linked nodes of the environment model library is performed, including the following steps:
searching and searching a leaf node position where a new insertion node is located, wherein a child node of the leaf node position has a null node;
inserting a new insertion node into an existing empty node in the child nodes of the leaf node position according to the leaf node position, and setting the color of the new insertion node to be red;
and after adding a new insertion node and setting the new insertion node to be red, correcting the color of the ancestor node of the new insertion node, and performing insertion balance to keep the property of the red-black tree unchanged after the new insertion node is inserted.
3. The method of claim 2, further characterized in that the searching and searching for the leaf node position where the new insertion node is located employs a binary search tree search manner;
inserting a new insertion node into an existing empty node, and simultaneously adding two empty nodes as child nodes of the new insertion node;
the insertion balancing includes rotating and changing node colors; and after a new node is inserted, in order to keep the property of the red-black tree unchanged, correcting operations of rotating and changing the color of the node are sequentially carried out from the new node upwards until the new node is red due to the insertion operation and the child nodes of the node are black, and then the correction is finished.
4. The method according to claim 1, wherein when deleting index information to update the index of the environment model database, the step S2 of deleting nodes is performed on dynamically linked nodes of the environment model database, and includes the following steps:
judging whether the node to be deleted has a child node:
if the node does not exist, the node is directly deleted, and then deletion balance processing is carried out so as to keep the property of the red and black tree unchanged after the node is deleted;
if a child node exists, finding a right subtree B of the node A with the minimum value larger than the node value, adopting B to replace A, copying a key word value in B into the node to be deleted, deleting A, and then carrying out deletion balance processing to keep the property of the red-black tree unchanged after deleting the node;
if two child nodes exist, finding out the node C with the maximum key word value in the left sub-tree of the node value to be deleted, exchanging the key word values of the node C and the node to be deleted, deleting C, and then carrying out deletion balance processing.
5. The method according to claim 1, wherein the step S3 of performing fuzzy query by using KMP algorithm based on the environment model database is performed, and comprises the steps of:
s31, performing word segmentation processing on the keywords input by the user to obtain original keyword strings;
s32, fuzzy comparison is carried out on the original keyword strings and the strings of the environment model database in sequence to obtain fuzzy similarity;
s33, judging whether the fuzzy similarity meets the preset searching requirement; if so, putting the strings related to fuzzy matching into a queue, otherwise, reducing the preset search requirement by one level, turning to execute the step S32, and simultaneously adding 1 to the fuzzy similarity value;
s34, judging whether the fuzzy similarity value reaches the maximum limit value, if so, outputting matching items meeting the preset searching requirement according to the element sequence of the queue, otherwise, prompting that the fuzzy matching is unsuccessful;
s35, the fuzzy inquiry is finished.
6. The method according to claim 5, wherein the step S31 of performing word segmentation processing on the keyword input by the user to obtain the original keyword string specifically includes:
introducing a word segmentation program into the environment model database;
the word segmentation program carries out word segmentation on the keywords input by the user during query according to the entity meaning to obtain an independent atomic entity;
and performing polling combination by taking the independent atomic entities as query conditions to obtain the original keyword string.
7. The method according to claim 5, wherein the step S32 of fuzzy-comparing the original keyword string with the string of the environmental model database in sequence to obtain a fuzzy similarity includes:
after the independent atomic entities are used as query conditions to perform polling combination, performing corresponding times of circulating query according to the number of polling groups, and displaying query results which meet the search conditions each time;
and setting a fuzzy similarity value according to the matching precision and the query degree, and the capacity and the configuration of the environment model database, and finding the corresponding fuzzy similarity level according to the query result.
8. The method of claim 7, wherein each group of polling combinations comprises an atomic entity, and wherein each atomic entity is queried separately.
9. The method of claim 7, wherein the level of the fuzzy similarity is divided according to the fuzzy similarity value.
10. The method of claim 7, wherein the range of blur similarity values consists of a limiting maximum and a limiting minimum.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060117000A1 (en) * | 2004-11-30 | 2006-06-01 | International Business Machines Corporation | Method and apparatus for querying program design models |
| CN102033924A (en) * | 2010-12-08 | 2011-04-27 | 浪潮(北京)电子信息产业有限公司 | Data storage method and system |
| CN102610119A (en) * | 2012-03-22 | 2012-07-25 | 广州杰赛科技股份有限公司 | Reverse car locating method and reverse car locating system |
| CN110457533A (en) * | 2019-07-25 | 2019-11-15 | 河南开合软件技术有限公司 | A kind of intelligence data model analysis method |
-
2021
- 2021-02-01 CN CN202110138093.4A patent/CN112800284A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20060117000A1 (en) * | 2004-11-30 | 2006-06-01 | International Business Machines Corporation | Method and apparatus for querying program design models |
| CN102033924A (en) * | 2010-12-08 | 2011-04-27 | 浪潮(北京)电子信息产业有限公司 | Data storage method and system |
| CN102610119A (en) * | 2012-03-22 | 2012-07-25 | 广州杰赛科技股份有限公司 | Reverse car locating method and reverse car locating system |
| CN110457533A (en) * | 2019-07-25 | 2019-11-15 | 河南开合软件技术有限公司 | A kind of intelligence data model analysis method |
Non-Patent Citations (3)
| Title |
|---|
| 刘永 等: "IDSS中面向对象模型库动态链接的研究", 《微电子学与计算机》, no. 08, pages 48 - 50 * |
| 郑志宏 等: "模糊查找算法的设计实现", 《情报科学》, vol. 28, no. 06, pages 915 - 918 * |
| 马博韬 等: "红黑树算法研究综述", 《网络新媒体技术》, vol. 7, no. 04, pages 56 - 62 * |
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