CN109670114B - Drawing rule recommendation method and device - Google Patents
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Abstract
The embodiment of the application provides a charting rule recommendation method and device, which are used for generating a keyword set by extracting character string information in unknown type data uploaded by a user terminal and searching a plurality of keywords from the extracted character string information. And then constructing a corresponding dictionary index table according to the keyword set, generating a corresponding feature vector according to the dictionary index table, and calculating the distance between the feature vector and each preset feature vector in a preset feature vector set. And then, determining a target characteristic vector closest to the characteristic vector in the characteristic vector set according to the calculation result, acquiring a drawing rule matched with the target characteristic vector from a preset drawing rule set, and recommending the drawing rule to the user terminal. Therefore, the classification of unknown type data can be accurately identified, the corresponding drawing rule is recommended, the problem that the display symbols are disordered and irregular when the electronic map is configured is solved, and the workload of configuring the electronic map is reduced.
Description
Technical Field
The application relates to the technical field of computers, in particular to a drawing rule recommendation method and device.
Background
At present, with the advancement of informatization, the types of spatial data related to geographic positions in an organization are gradually increased, and for convenience of management, data related to geographic positions are required to be presented in a centralized and unified manner by using an electronic map. Generally, a standard symbol is used to present the same kind of data inside an organization, so as to facilitate work exchange and information communication. For example, each large organization has a set of geographic identifier specifications, and various information systems need to follow the specifications in the organization when configuring the electronic map style. However, with the increase of spatial data categories and the increase of the demand of users for electronic map services with various topics, the system is required to quickly make electronic map services which meet the organization specifications and specify several categories within a specified time in a specified geographic area, which often brings heavy mapping workload.
In the process of drawing an electronic map, a drafter usually does not know the geographic identifier standard in an organization, and an information system does not have a set of strict checking mechanism to check whether the geographic identifier meets the standard, so that the electronic map which is manufactured is various and does not meet the standard, and the accurate expression of map information is influenced.
Disclosure of Invention
In order to overcome the above-mentioned deficiencies in the prior art, the present application aims to provide a drawing rule recommendation method and apparatus to solve or improve the above-mentioned problems.
In order to achieve the above purpose, the embodiments of the present application employ the following technical solutions:
in a first aspect, an embodiment of the present application provides a drawing rule recommendation method, which is applied to a server, and the method includes:
acquiring unknown type data uploaded by a user terminal;
extracting character string information in the unknown type data, and searching a plurality of keywords from the extracted character string information to generate a keyword set;
constructing a corresponding dictionary index table according to the keyword set, and generating a corresponding feature vector according to the dictionary index table, wherein the feature vector comprises a plurality of sequence number positions, each sequence number position represents a word, the value of each sequence number position represents the frequency information of the word in the current unknown data, and the length of the feature vector is the same as that of the dictionary index table;
calculating the distance between the feature vector and each preset feature vector in a preset feature vector set, and determining a target feature vector closest to the feature vector in the feature vector set according to the calculation result;
and obtaining the drawing rule matched with the target characteristic vector from a preset drawing rule set, and recommending the drawing rule to the user terminal.
In one possible embodiment, the method further comprises:
the characteristic vector set and the drawing rule set are configured in advance, and a mapping relation between each preset characteristic vector in the characteristic vector set and each drawing rule in the drawing rule set is configured, wherein each drawing rule in the drawing rule set at least comprises one or more combinations of symbol information, scale information, labeled font size information and labeled color information related to drawing.
In a possible implementation, the step of pre-configuring the set of feature vectors includes:
acquiring a training data set, wherein the training data set comprises a plurality of elements, and each element identifies a geographical noun character string of a type of address data;
searching a plurality of keywords from the training data set to generate a keyword set, and generating a corresponding index code for each keyword in the keyword set to construct a dictionary index table corresponding to the keyword set;
traversing each element in the training data set, generating a corresponding feature vector according to a keyword array of the element and the dictionary index table, wherein each sequence number position of the feature vector represents a word, the value of each sequence number position represents the frequency information of the word in the current element, and the length of the feature vector is consistent with that of the dictionary index table;
and configuring a feature vector set according to the generated feature vector corresponding to each element.
In one possible embodiment, the step of acquiring the training data set includes:
acquiring data files from various types;
character string information including a geographical noun is extracted from each type of data file, and a training data set is constructed from the extracted character string information each including a geographical noun.
In one possible embodiment, the step of searching for a plurality of keywords from the training data set includes:
traversing each element in the training data set, and filtering out irrelevant symbols in each element;
a plurality of keywords are extracted from each element from which the extraneous symbols are filtered.
In a possible embodiment, the calculation formula for calculating the distance between the feature vector and each preset feature vector in the preset feature vector set includes:
wherein d is the distance, A is the feature vector, B is the preset feature vector in the feature vector set, and n represents the number of words in the dictionary index table.
In a second aspect, an embodiment of the present application further provides a drawing rule recommendation device, which is applied to a server, and the device includes:
the acquisition module is used for acquiring unknown type data uploaded by the user terminal;
the extraction module is used for extracting the character string information in the unknown type data and searching a plurality of keywords from the extracted character string information to generate a keyword set;
the construction module is used for constructing a corresponding dictionary index table according to the keyword set and generating a corresponding feature vector according to the dictionary index table, wherein the feature vector comprises a plurality of sequence number positions, each sequence number position represents a word, the value of each sequence number position represents the frequency information of the word in the current unknown data, and the length of the feature vector is the same as that of the dictionary index table;
the calculation module is used for calculating the distance between the feature vector and each preset feature vector in a preset feature vector set and determining a target feature vector which is closest to the feature vector in the feature vector set according to a calculation result;
and the recommending module is used for acquiring the drawing rule matched with the target characteristic vector from a preset drawing rule set and recommending the drawing rule to the user terminal.
In a third aspect, an embodiment of the present application further provides a readable storage medium, on which a computer program is stored, where the computer program, when executed, implements the mapping rule recommendation method described above.
Compared with the prior art, the method has the following beneficial effects:
the embodiment of the application provides a charting rule recommendation method and device, which are used for generating a keyword set by extracting character string information in unknown type data uploaded by a user terminal and searching a plurality of keywords from the extracted character string information. And then constructing a corresponding dictionary index table according to the keyword set, generating a corresponding feature vector according to the dictionary index table, and calculating the distance between the feature vector and each preset feature vector in a preset feature vector set. And then, determining a target characteristic vector closest to the characteristic vector in the characteristic vector set according to the calculation result, acquiring a drawing rule matched with the target characteristic vector from a preset drawing rule set, and recommending the drawing rule to the user terminal. Therefore, the classification of unknown type data can be accurately identified, the corresponding drawing rule is recommended, the problem that the display symbols are disordered and irregular when the electronic map is configured is solved, and the workload of configuring the electronic map is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and it will be apparent to those skilled in the art that other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic flowchart of a drawing rule recommendation method according to an embodiment of the present application;
FIG. 2 is a functional block diagram of a charting rule recommendation apparatus according to an embodiment of the present application;
FIG. 3 is a block diagram of another embodiment of a drawing rule recommendation apparatus;
fig. 4 is a block diagram schematically illustrating a structure of a server for implementing the drawing rule recommendation method according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. The components of the embodiments of the present application, generally described and illustrated in the figures herein, can be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
Please refer to fig. 1, which is a flowchart illustrating a drawing rule recommendation method according to an embodiment of the present application, it should be noted that the drawing rule recommendation method according to the embodiment of the present application is not limited by the specific sequence shown in fig. 1 and described below. The method comprises the following specific processes:
step S210, obtaining unknown type data uploaded by the user terminal.
In this embodiment, the unknown type data may be understood as identification data on an electronic map sent by a draftsman through a user terminal.
Step S220, extracting the character string information in the unknown type data, and searching a plurality of keywords from the extracted character string information to generate a keyword set.
In this embodiment, in the process of extracting the character string information in the unknown type data, some special symbols such as symbols, numbers, letters, spaces, and the like may be filtered out, so as to avoid interference caused by the special symbols.
After filtering the special symbols, a plurality of keywords may be looked up from the extracted character string information to generate a keyword set.
Step S230, constructing a corresponding dictionary index table according to the keyword set, and generating a corresponding feature vector according to the dictionary index table.
In this embodiment, a corresponding index code may be generated for each keyword in the keyword set to construct a dictionary index table corresponding to the keyword set, and then a corresponding feature vector may be generated according to the dictionary index table, where the feature vector includes a plurality of sequence number positions, each sequence number position represents a word, a value of each sequence number position represents frequency information of the word in current unknown data, and a length of the feature vector is the same as a length of the dictionary index table.
Step S240, calculating a distance between the feature vector and each preset feature vector in a feature vector set configured in advance, and determining a target feature vector closest to the feature vector in the feature vector set according to a calculation result.
In a possible implementation manner, the drawing rule recommendation method provided in this embodiment further includes a step of training a feature vector set and the drawing rule set in advance, and before further describing step S240, a description is first given below of a training process of the feature vector set.
Firstly, a training data set train is obtained, wherein the training data set train comprises a plurality of element items, and each element item identifies a geographical noun character string of a type of address data.
Then, a plurality of keywords are searched from the training data set train to generate a keyword set words, and a corresponding index code ID is generated for each keyword word in the keyword set words to construct a dictionary index table word _ index _ map corresponding to the keyword set words.
Then, traversing each element item in the training data set train, and generating a corresponding feature vector according to the keyword array of the element item and the dictionary index table word _ index _ map, wherein each sequence number position of the feature vector represents a word, the value of each sequence number position represents the frequency information of the word in the current element, and the length of the feature vector is consistent with the length of the dictionary index table.
And finally, configuring feature vector sets Vectors according to the generated feature vector corresponding to each element.
In addition, in this embodiment, a drawing rule set R needs to be configured in advance according to standard specifications, where each drawing rule in the drawing rule set R at least includes one or more combinations of symbol information, scale information, labeled font size information, and labeled color information related to drawing. Thus, after the feature vector set Vectors are generated, the mapping relationship between each preset feature vector in the feature vector set Vectors and each drawing rule in the drawing rule set R can be configured.
On the basis, a distance d between the feature vector and each preset feature vector in a preset feature vector set may be calculated, and a specific calculation formula may include:
wherein d is the distance, A is the feature vector, B is the preset feature vector in the feature vector set, and n represents the number of words in the dictionary index table.
Then, a target feature vector closest to the feature vector in the feature vector set may be determined according to the calculation result.
And step S250, obtaining the drawing rule matched with the target characteristic vector from a preset drawing rule set, and recommending the drawing rule to the user terminal.
In this embodiment, after the target feature vector closest to the feature vector in the feature vector set is obtained, according to the foregoing description, because the mapping relationship between each preset feature vector in the feature vector set and each drawing rule in the drawing rule set R is pre-configured, the drawing rule matched with the target feature vector may be obtained from the pre-configured drawing rule set, and the drawing rule is recommended to the user terminal.
Therefore, the drawing rule recommendation method provided by the embodiment can accurately identify the category of unknown type data and recommend the corresponding drawing rule, solves the problem that the display symbols are disordered and irregular when the electronic map is configured, and reduces the workload of configuring the electronic map.
Further, referring to fig. 3, an embodiment of the present application further provides a drawing rule recommending apparatus 200, where the drawing rule recommending apparatus 200 may include:
the obtaining module 210 is configured to obtain unknown type data uploaded by the user terminal.
The extracting module 220 is configured to extract the character string information in the unknown type data, and search a plurality of keywords from the extracted character string information to generate a keyword set.
A constructing module 230, configured to construct a corresponding dictionary index table according to the keyword set, and generate a corresponding feature vector according to the dictionary index table, where the feature vector includes a plurality of sequence number positions, each sequence number position represents a word, a value of each sequence number position represents frequency information of the word in the current unknown data, and a length of the feature vector is the same as a length of the dictionary index table.
And a calculating module 240, configured to calculate a distance between the feature vector and each preset feature vector in a feature vector set configured in advance, and determine, according to a calculation result, a target feature vector closest to the feature vector in the feature vector set.
And the recommending module 250 is configured to obtain the drawing rule matched with the target feature vector from a preset drawing rule set, and recommend the drawing rule to the user terminal.
In a possible implementation manner, referring further to fig. 3, the drawing rule recommending apparatus 200 may further include:
a configuration module 209, configured to pre-configure the feature vector set and the drawing rule set, and configure a mapping relationship between each preset feature vector in the feature vector set and each drawing rule in the drawing rule set, where each drawing rule in the drawing rule set at least includes one or more combinations of symbol information, scale information, labeled font size information, and labeled color information related to drawing.
In a possible implementation, the configuration module 209 may specifically pre-configure the feature vector set by:
acquiring a training data set, wherein the training data set comprises a plurality of elements, and each element identifies a geographical noun character string of a type of address data;
searching a plurality of keywords from the training data set to generate a keyword set, and generating a corresponding index code for each keyword in the keyword set to construct a dictionary index table corresponding to the keyword set;
traversing each element in the training data set, generating a corresponding feature vector according to a keyword array of the element and the dictionary index table, wherein each sequence number position of the feature vector represents a word, the value of each sequence number position represents the frequency information of the word in the current element, and the length of the feature vector is consistent with that of the dictionary index table;
and configuring a feature vector set according to the generated feature vector corresponding to each element.
In a possible implementation manner, the configuration module 209 may specifically obtain the training data set by:
acquiring data files from various types;
character string information including a geographical noun is extracted from each type of data file, and a training data set is constructed from the extracted character string information each including a geographical noun.
It can be understood that, for the specific operation method of each functional module in this embodiment, reference may be made to the detailed description of the corresponding step in the foregoing method embodiment, and no repeated description is provided herein.
Further, please refer to fig. 4, which is a block diagram illustrating a structure of a server 100 for the drawing rule recommendation method according to an embodiment of the present application. In this embodiment, the server 100 may be implemented by a bus 110 as a general bus architecture. The bus 110 may include any number of interconnecting buses and bridges depending on the specific application of the server 100 and the overall design constraints. Bus 110 connects various circuits together, including processor 120, storage medium 130, and bus interface 140. Alternatively, the server 100 may connect a network adapter 150 or the like via the bus 110 using the bus interface 140. The network adapter 150 may be used to implement signal processing functions of a physical layer in the server 100 and implement transmission and reception of radio frequency signals through an antenna. The user interface 160 may connect external devices such as: a keyboard, a display, a mouse or a joystick, etc. The bus 110 may also connect various other circuits such as timing sources, peripherals, voltage regulators, or power management circuits, which are well known in the art, and therefore, will not be described in detail.
Alternatively, the server 100 may be configured as a general purpose processing system, such as what is commonly referred to as a chip, including: one or more microprocessors providing processing functions, and an external memory providing at least a portion of storage medium 130, all connected together with other support circuits through an external bus architecture.
Alternatively, the server 100 may be implemented using: an ASIC (application specific integrated circuit) having a processor 120, a bus interface 140, a user interface 160; and at least a portion of the storage medium 130 integrated in a single chip, or the server 100 may be implemented using: one or more FPGAs (field programmable gate arrays), PLDs (programmable logic devices), controllers, state machines, gate logic, discrete hardware components, any other suitable circuitry, or any combination of circuitry capable of performing the various functions described throughout this application.
Among other things, processor 120 is responsible for managing bus 110 and general processing (including the execution of software stored on storage medium 130). Processor 120 may be implemented using one or more general-purpose processors and/or special-purpose processors. Examples of processor 120 include microprocessors, microcontrollers, DSP processors, and other circuits capable of executing software. Software should be construed broadly to mean instructions, data, or any combination thereof, whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.
The processor 120 may execute the above-mentioned embodiments, specifically, the storage medium 130 may store the mapping rule recommending apparatus 200 therein, and the processor 120 may be configured to execute the mapping rule recommending apparatus 200.
Further, an embodiment of the present application also provides a non-volatile computer storage medium, where the computer storage medium stores computer-executable instructions, and the computer-executable instructions may execute the mapping rule recommendation method in any method embodiment described above.
To sum up, the embodiment of the present application provides a drawing rule recommendation method and apparatus, and the specific implementation principle is as follows: acquiring a data report to be monitored from each data source; determining each report form early warning area in the data report form to be monitored according to the preset early warning rule of each data source; and detecting each report form early warning area, and triggering an early warning action corresponding to an early warning condition when any report form early warning area is detected to meet the early warning condition corresponding to the report form early warning area in a preset early warning rule. Therefore, the data reports under the same data source can be monitored in a unified mode, an early warning scheme does not need to be configured for each data report independently, individual early warning is conducted on each report early warning area in each data report in a targeted mode, flexibility and usability of early warning rules are improved, and complex and diverse early warning requirements are met.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The apparatus and method embodiments described above are illustrative only, as the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
Alternatively, all or part of the implementation may be in software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as an electronic device, server, data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (6)
1. A drawing rule recommendation method applied to a server comprises the following steps:
pre-configuring a characteristic vector set;
acquiring unknown type data uploaded by a user terminal;
extracting character string information in the unknown type data, and searching a plurality of keywords from the extracted character string information to generate a keyword set;
constructing a corresponding dictionary index table according to the keyword set, and generating a corresponding feature vector according to the dictionary index table, wherein the feature vector comprises a plurality of sequence number positions, each sequence number position represents a word, the value of each sequence number position represents the frequency information of the word in the current unknown data, and the length of the feature vector is the same as that of the dictionary index table;
calculating the distance between the feature vector and each preset feature vector in a preset feature vector set, and determining a target feature vector closest to the feature vector in the feature vector set according to the calculation result;
obtaining a drawing rule matched with the target feature vector from a preset drawing rule set, and recommending the drawing rule to the user terminal;
wherein the step of pre-configuring the set of feature vectors comprises:
acquiring a training data set, wherein the training data set comprises a plurality of elements, and each element identifies a geographical noun character string of a type of address data;
searching a plurality of keywords from the training data set to generate a keyword set, and generating a corresponding index code for each keyword in the keyword set to construct a dictionary index table corresponding to the keyword set;
traversing each element in the training data set, generating a corresponding feature vector according to the keyword array of each element in the training data set and the dictionary index table, wherein each sequence number position of the generated feature vector represents a word, the value of each sequence number position represents the frequency information of the word in the current element, and the length of the generated feature vector is consistent with the length of the dictionary index table;
configuring a feature vector set according to the generated feature vector corresponding to each element;
wherein the method further comprises:
the method comprises the steps of configuring a drawing rule set in advance, and configuring a mapping relation between each preset feature vector in the feature vector set and each drawing rule in the drawing rule set, wherein each drawing rule in the drawing rule set at least comprises one or more combinations of symbol information, scale information, labeled font size information and labeled color information related to drawing.
2. The charting rule recommendation method of claim 1, wherein the step of obtaining a training data set comprises:
acquiring data files from various types;
character string information including a geographical noun is extracted from each type of data file, and a training data set is constructed from the extracted character string information each including a geographical noun.
3. The charting rule recommendation method of claim 1, wherein said step of finding a plurality of keywords from said training data set comprises:
traversing each element in the training data set, and filtering out irrelevant symbols in each element;
a plurality of keywords are extracted from each element from which the extraneous symbols are filtered.
4. The drawing rule recommendation method according to claim 1, wherein the calculation formula for calculating the distance between the feature vector and each preset feature vector in the feature vector set configured in advance comprises:
wherein d is the distance, A is the feature vector, B is the preset feature vector in the feature vector set, and n represents the number of words in the dictionary index table.
5. A drawing rule recommendation apparatus applied to a server, the apparatus comprising:
the configuration module is used for configuring a characteristic vector set in advance;
the acquisition module is used for acquiring unknown type data uploaded by the user terminal;
the extraction module is used for extracting the character string information in the unknown type data and searching a plurality of keywords from the extracted character string information to generate a keyword set;
the construction module is used for constructing a corresponding dictionary index table according to the keyword set and generating a corresponding feature vector according to the dictionary index table, wherein the feature vector comprises a plurality of sequence number positions, each sequence number position represents a word, the value of each sequence number position represents the frequency information of the word in the current unknown data, and the length of the feature vector is the same as that of the dictionary index table;
the calculation module is used for calculating the distance between the feature vector and each preset feature vector in a preset feature vector set and determining a target feature vector which is closest to the feature vector in the feature vector set according to a calculation result;
the recommendation module is used for acquiring the drawing rule matched with the target feature vector from a preset drawing rule set and recommending the drawing rule to the user terminal;
the configuration module is specifically configured to pre-configure the feature vector set by the following means:
acquiring a training data set, wherein the training data set comprises a plurality of elements, and each element identifies a geographical noun character string of a type of address data;
searching a plurality of keywords from the training data set to generate a keyword set, and generating a corresponding index code for each keyword in the keyword set to construct a dictionary index table corresponding to the keyword set;
traversing each element in the training data set, generating a corresponding feature vector according to the keyword array of each element in the training data set and the dictionary index table, wherein each sequence number position of the generated feature vector represents a word, the value of each sequence number position represents the frequency information of the word in the current element, and the length of the generated feature vector is consistent with the length of the dictionary index table;
configuring a feature vector set according to the generated feature vector corresponding to each element;
the configuration module is further configured to pre-configure the drawing rule set, and configure a mapping relationship between each preset feature vector in the feature vector set and each drawing rule in the drawing rule set, where each drawing rule in the drawing rule set at least includes one or more combinations of symbol information, scale information, labeled font size information, and labeled color information related to drawing.
6. The charting rule recommendation device of claim 5, wherein the configuration module obtains the training data set by:
acquiring data files from various types;
character string information including a geographical noun is extracted from each type of data file, and a training data set is constructed from the extracted character string information each including a geographical noun.
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| CN103207899A (en) * | 2013-03-19 | 2013-07-17 | 新浪网技术(中国)有限公司 | Method and system for recommending text files |
| CN103838886A (en) * | 2014-03-31 | 2014-06-04 | 辽宁四维科技发展有限公司 | Text content classification method based on representative word knowledge base |
| CN106782034A (en) * | 2017-04-07 | 2017-05-31 | 北京慧图科技股份有限公司 | A kind of flood control, the map publishing of drought resisting data localization, rendering intent and system |
| CN107992542A (en) * | 2017-11-27 | 2018-05-04 | 中山大学 | A kind of similar article based on topic model recommends method |
| CN108922373A (en) * | 2018-07-02 | 2018-11-30 | 深圳中兴网信科技有限公司 | A kind of generation method of map, system and computer installation |
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