CN112559590A - Mapping data resource processing method and device and server - Google Patents
Mapping data resource processing method and device and server Download PDFInfo
- Publication number
- CN112559590A CN112559590A CN202011432465.6A CN202011432465A CN112559590A CN 112559590 A CN112559590 A CN 112559590A CN 202011432465 A CN202011432465 A CN 202011432465A CN 112559590 A CN112559590 A CN 112559590A
- Authority
- CN
- China
- Prior art keywords
- mapping
- data
- attribute data
- attribute
- label
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2465—Query processing support for facilitating data mining operations in structured databases
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C15/00—Surveying instruments or accessories not provided for in groups G01C1/00 - G01C13/00
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/24—Querying
- G06F16/245—Query processing
- G06F16/2458—Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries
- G06F16/2474—Sequence data queries, e.g. querying versioned data
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- General Physics & Mathematics (AREA)
- Software Systems (AREA)
- Probability & Statistics with Applications (AREA)
- Computational Linguistics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Physics (AREA)
- Fuzzy Systems (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The invention discloses a mapping data resource processing method, a mapping data resource processing device and a mapping data resource processing server. The method comprises sequentially extracting the characteristic of each mapping attribute data in the mapping data to obtain corresponding mapping data tag sets, secondly, dividing all the mapping attribute data to obtain a first mapping attribute data list, further obtaining the characteristic association degree between each mapping attribute data and the reference mapping attribute data, after the characteristic association degree is determined, the mapping attribute data sequence corresponding to the key mapping attribute data can be integrated into the mapping attribute data sequence corresponding to the reference mapping attribute data in a targeted manner, obtaining a second mapping attribute data list, which can avoid the disorder integration of mapping attribute data sequence, on the basis, mapping processing is carried out on the mapping data to be processed according to the preset mapping rule, the mapping template and the second mapping attribute data list, and therefore the processing efficiency of mapping data resources can be improved.
Description
Technical Field
The present disclosure relates to the field of mapping data processing technologies, and in particular, to a mapping data resource processing method, apparatus, and server.
Background
Surveying and mapping, which means measuring, collecting and drawing the shape, size, spatial position and attributes of natural geographic elements or surface artificial facilities,
along with the continuous development of mapping technique, survey and drawing demand is more and more, draws the drawing through the data resource that can obtain survey and drawing in survey and drawing, and traditional whole survey and drawing is to the process of drawing: firstly, data resources are collected through the measuring instrument, secondly, the data resources are input into the computer equipment through the staff, if the staff needs to bring the computer equipment back to an office place under the condition of no communication, the drawing can be carried out, and therefore the processing efficiency of the surveying and mapping data resources can be greatly reduced.
Disclosure of Invention
In order to solve the technical problems in the related art, the disclosure provides a mapping data resource processing method, a mapping data resource processing device and a mapping data resource processing server.
The invention provides a mapping data resource processing method in a first aspect, which comprises the following steps:
collecting mapping data to be processed, and sequentially performing feature extraction on each mapping attribute data in the mapping data to obtain a mapping data tag set corresponding to each mapping attribute data; wherein the set of mapping data tags includes at least two mapping data tags of the mapping attribute data;
dividing all mapping attribute data in the mapping data according to the mapping data label set to obtain a first mapping attribute data list; the first mapping attribute data list records mapping attribute data sequences respectively corresponding to a plurality of mapping operation areas contained in the mapping data, and the first mapping attribute data in each mapping attribute data sequence is key mapping attribute data of the mapping operation area;
sequentially acquiring the feature association degree between each key mapping attribute data and reference mapping attribute data positioned in front of the key mapping attribute data;
integrating the mapping attribute data sequence in the first mapping operation area where the key mapping attribute data is located into the mapping attribute data sequence in the second mapping operation area where the reference mapping attribute data is located under the condition that the feature association degree reaches an integration condition, so as to adjust the first mapping attribute data list into a second mapping attribute data list;
and carrying out mapping processing on the mapping data to be processed according to preset mapping rules, mapping templates and the second mapping attribute data list.
In an alternative embodiment, the sequentially performing feature extraction on each mapping attribute data in the mapping data to obtain a mapping data tag set corresponding to each mapping attribute data includes:
sequentially taking each of the mapping attribute data as current mapping attribute data to perform the following feature extraction operations until all of the mapping attribute data in the mapping data are traversed: inputting each attribute value in the current mapping attribute data into a target label coding matrix to obtain a field distribution range of each mapping label coding field of each attribute value in the target label coding matrix, wherein the target label coding matrix comprises at least two mapping label coding fields; determining the mapping data tag set matching the current mapping attribute data according to the field distribution range of the mapping tag encoding field of each attribute value;
the determining the mapping data tag set that matches the current mapping attribute data according to the field distribution range of the mapping tag encoding field for the respective attribute value comprises: and acquiring label description information of field distribution range of the jth mapping label coding field of each attribute value to obtain the jth mapping data label of the current mapping attribute data, wherein j is an integer which is greater than or equal to 1 and less than or equal to M, M is the number of the mapping label coding fields in the target label coding matrix, and M is a positive integer.
In an alternative embodiment, the partitioning all of the mapping attribute data in the mapping data according to the mapping data tag set to obtain a first mapping attribute data list comprises: obtaining label description information of each mapping data label in the mapping data label set, and taking the label description information of the mapping data label as a target mapping data label matched with the mapping attribute data; sequentially comparing the target mapping data labels corresponding to the two adjacent mapping attribute data to obtain a comparison result; dividing all surveying and mapping attribute data according to the comparison result to obtain a first surveying and mapping attribute data list;
the step of sequentially comparing the respective corresponding target mapping data labels of the two adjacent mapping attribute data to obtain a comparison result comprises the following steps: acquiring a tag matching value of a target mapping data tag of the h +1 th mapping attribute data and a target mapping data tag of the h mapping attribute data; wherein h is an integer greater than or equal to 1 and less than or equal to Y-1, and Y is the number of mapping attribute data in the mapping data; comparing the tag matching value with a preset first threshold value to obtain a comparison result;
the dividing all mapping attribute data according to the comparison result to obtain the first mapping attribute data list includes: if the comparison result indicates that the tag matching value is smaller than the preset first threshold value, determining that the h +1 th mapping attribute data and the h th mapping attribute data are data corresponding to the same mapping operation area, and adding the h +1 th mapping attribute data to a mapping attribute data sequence where the h th mapping attribute data is located; and under the condition that the comparison result indicates that the tag matching value is greater than or equal to the preset first threshold value, determining that the h +1 th mapping attribute data and the h mapping attribute data are not data corresponding to the same mapping operation area, and creating a new mapping attribute data sequence for the h +1 th mapping attribute data.
In an alternative embodiment, said sequentially obtaining a feature correlation between each of said key mapping attribute data and reference mapping attribute data preceding said key mapping attribute data comprises:
obtaining a key attribute category of the key mapping attribute data and a reference attribute category of the reference mapping attribute data;
acquiring an association ratio between the key attribute category and the reference attribute category, wherein the feature association degree comprises the association ratio; obtaining basic associated data in the key mapping attribute data and the reference mapping attribute data;
obtaining a first proportion of the base associated data in the key mapping attribute data and a second proportion of the base associated data in the reference mapping attribute data; wherein the feature association degree comprises the first and second ratios.
In an alternative embodiment, the obtaining a key-attribute category of the key mapping-attribute data and a reference-attribute category of the reference mapping-attribute data includes:
screening the key mapping attribute data and the reference mapping attribute data respectively to obtain current key mapping attribute data and current reference mapping attribute data;
inputting the current key mapping attribute data into a category classification thread to obtain the key attribute category, and inputting the current reference mapping attribute data into the category classification thread to obtain the reference attribute category; the category classification thread is an artificial intelligence model for generating attribute categories of the mapping data, which is obtained by performing machine training on a plurality of groups of sample mapping data pairs and corresponding identification information.
In an alternative embodiment, in a case that the feature association reaches an integration condition, integrating the sequence of mapping attribute data in the first mapping operation region where the key mapping attribute data is located into the sequence of mapping attribute data in the second mapping operation region where the reference mapping attribute data is located to adjust the first list of mapping attribute data to the second list of mapping attribute data includes:
under the condition that the feature association degree reaches an integration condition, acquiring first mapping node data and second mapping node data obtained after mapping a first mapping operation area; wherein the first mapping node data is static mapping node data of a first mapping node, and the second mapping node data is dynamic mapping node data comprising a second mapping node;
determining a mapping attribute data sequence of a corresponding mapping node data label in the first mapping node data and the second mapping node data, and determining a target mapping node data label which corresponds to the first mapping node data and the second mapping node data and meets a preset condition based on the mapping attribute data sequence of the corresponding mapping node data label;
integrating a mapping attribute data sequence in a first mapping operation area into a mapping attribute data sequence in a second mapping operation area where the reference mapping attribute data is located based on a target mapping node data label, and adjusting homogeneous data in the first mapping node data corresponding to the first mapping attribute data list after integration processing to obtain a second mapping attribute data list;
wherein determining a sequence of mapping attribute data of corresponding mapping node data tags in the first and second mapping node data comprises: determining the label difference degree of each mapping node data label in the first mapping node data and the label difference degree of each mapping node data label in the second mapping node data; determining data difference weight coefficients of the corresponding mapping node data labels in the first mapping node data and the second mapping node data based on label difference degrees of each mapping node data label in the first mapping node data and label difference degrees of each mapping node data label in the second mapping node data; wherein the sequence of mapping attribute data comprises the data difference weight coefficient;
wherein determining a target mapping node data tag that corresponds between the first mapping node data and the second mapping node data and satisfies a predetermined condition based on a mapping attribute data sequence of the corresponding mapping node data tag comprises: sorting the corresponding mapping node data labels in the first mapping node data and the second mapping node data according to a mapping attribute data sequence from small to large; determining the target mapping node data label from the sorted corresponding mapping node data labels by one of the following methods: sequentially selecting a preset number of the corresponding mapping node data labels as the target mapping node data labels; sequentially selecting the corresponding mapping node data labels in a preset proportion as the target mapping node data labels; determining the corresponding mapping node data label of which the sequence value corresponding to the mapping attribute data sequence is smaller than a preset first threshold value as the target mapping node data label; matching corresponding mapping node data labels included in the corresponding mapping node data labels of which the sequence values corresponding to the mapping attribute data sequences are smaller than a preset second threshold value according to a preset matching number in sequence, and determining the target mapping node data labels based on the matching result; and selecting the target mapping node data label based on the accumulated difference degree proportion change of the corresponding mapping node data label.
In an alternative embodiment, the mapping processing of the mapping data to be processed according to preset mapping rules and mapping templates and the second mapping attribute data list includes:
obtaining mapping index parameters of mapping data to be processed; wherein the mapping index parameters include mapping marker data and index configuration weights; determining a drawing type according to a preset drawing rule and a drawing template; determining whether a weight value corresponding to the minimum index weight in the to-be-processed mapping data needs to be analyzed according to the index configuration weight and the mapping mark data; if the mapping data needs to be analyzed, screening at least part of weight values of the mapping data to be processed to obtain the weight value corresponding to the minimum index weight; determining whether the weighted value corresponding to the minimum index weight needs to be clustered again by using the mapping index parameter; if clustering needs to be carried out again, generating an updated weight value, and carrying out drawing processing on the mapping data to be processed according to the second mapping attribute data list to obtain a target mapping;
wherein the mapping indicia data comprises mapping coordinate data of the mapping data to be processed, mapping distance data of the mapping data to be processed, and mapping area data, the method further comprising: judging whether the category of the mapping marking data, the mapping region data and the index configuration weight are the same; if the category of the mapping marking data, the mapping area data and the index configuration weight are the same, judging whether a data flow chart is set according to mapping coordinate data of the mapping data to be processed when the mapping type is the data flow chart; if the data flow table is set, carrying out drawing processing on the data flow table and the mapping data to be processed; and if the data flow table is not set, carrying out mapping processing on the mapping data to be processed.
The second aspect of the present invention also provides a mapping data resource processing apparatus, including:
the data acquisition module is used for acquiring mapping data to be processed and sequentially performing feature extraction on each mapping attribute data in the mapping data to obtain a mapping data tag set corresponding to each mapping attribute data; wherein the set of mapping data tags includes at least two mapping data tags of the mapping attribute data;
the data dividing module is used for dividing all mapping attribute data in the mapping data according to the mapping data label set to obtain a first mapping attribute data list; the first mapping attribute data list records mapping attribute data sequences respectively corresponding to a plurality of mapping operation areas contained in the mapping data, and the first mapping attribute data in each mapping attribute data sequence is key mapping attribute data of the mapping operation area;
the association degree acquisition module is used for sequentially acquiring the feature association degree between each key mapping attribute data and the reference mapping attribute data positioned in front of the key mapping attribute data;
a data integration module, configured to, when the feature association degree reaches an integration condition, integrate a mapping attribute data sequence in a first mapping operation area where the key mapping attribute data is located into a mapping attribute data sequence in a second mapping operation area where the reference mapping attribute data is located, so as to adjust the first mapping attribute data list to a second mapping attribute data list;
and the drawing processing module is used for carrying out drawing processing on the to-be-processed drawing data according to preset drawing rules, a drawing template and the second drawing attribute data list.
The third aspect of the present invention also provides a server comprising a processor and a memory, which are in communication with each other, wherein the processor is configured to retrieve a computer program from the memory and to implement the method according to any one of the first aspect by running the computer program.
The fourth aspect of the present invention also provides a computer readable storage medium having stored thereon a computer program which, when executed, implements the method of any of the first aspects.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects.
The invention provides a method, a device and a server for processing mapping data resources, which firstly carry out feature extraction on each mapping attribute data in the acquired mapping data to be processed in sequence, further obtain a mapping data label set corresponding to each mapping attribute data one by one, secondly divide all mapping attribute data according to the mapping data label sets to obtain a first mapping attribute data list, further obtain the feature association degree between each mapping attribute data and reference mapping attribute data positioned in front of the key mapping attribute data, and after the feature association degree is determined, can pointedly integrate the mapping attribute data sequence in a first mapping operation area where the key mapping attribute data are positioned into the mapping attribute data sequence in a second mapping operation area where the reference mapping attribute data are positioned so that the first mapping attribute data list is adjusted to be a second mapping attribute data list, can avoid like this carrying out the confusion integration to survey and drawing attribute data sequence, can improve work efficiency simultaneously, on this basis, carry out the drawing processing according to predetermined drawing rule and drawing template and second survey and drawing attribute data list survey and drawing data of treating, through above-mentioned step, can improve the treatment effeciency to survey and drawing data resource.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
Fig. 1 is a flowchart of a mapping data resource processing method according to an embodiment of the present invention.
Fig. 2 is a block diagram of a mapping data resource processing apparatus according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a hardware structure of a server according to an embodiment of 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 embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
Referring to fig. 1, a flow chart of a mapping data resource processing method is provided, which specifically performs the following steps S110 to S150.
Step S110, collecting mapping data to be processed, and sequentially performing feature extraction on each mapping attribute data in the mapping data to obtain a mapping data label set corresponding to each mapping attribute data.
In this embodiment, the set of mapping data tags includes at least two mapping data tags of the mapping attribute data.
Step S120, dividing all mapping attribute data in the mapping data according to the mapping data label set to obtain a first mapping attribute data list; the first mapping attribute data list records mapping attribute data sequences corresponding to a plurality of mapping operation areas contained in the mapping data respectively, and the first mapping attribute data in each mapping attribute data sequence is the key mapping attribute data of the mapping operation area.
Step S130, sequentially obtaining a feature association degree between each of the key mapping attribute data and reference mapping attribute data located before the key mapping attribute data.
Step S140, when the feature association degree reaches an integration condition, integrating the mapping attribute data sequence in the first mapping operation region where the key mapping attribute data is located into the mapping attribute data sequence in the second mapping operation region where the reference mapping attribute data is located, so as to adjust the first mapping attribute data list to a second mapping attribute data list.
And S150, carrying out mapping processing on the mapping data to be processed according to preset mapping rules, mapping templates and the second mapping attribute data list.
The following advantageous effects can be achieved when the method described in the above steps S110 to S150 is performed:
firstly, carrying out feature extraction on each mapping attribute data in the collected mapping data to be processed in sequence, further obtaining a mapping data label set corresponding to each mapping attribute data one by one, secondly, dividing all mapping attribute data according to the mapping data label sets to obtain a first mapping attribute data list, further obtaining the feature association degree between each mapping attribute data and reference mapping attribute data positioned in front of the key mapping attribute data, after determining the feature association degree, integrating the mapping attribute data sequence in a first mapping operation area where the key mapping attribute data is positioned into the mapping attribute data sequence in a second mapping operation area where the reference mapping attribute data is positioned in a targeted manner, so that the first mapping attribute data list is adjusted to be a second mapping attribute data list, and thus, the mapping attribute data sequences can be prevented from being subjected to mismatching and disorganization, meanwhile, the working efficiency can be improved, on the basis, mapping processing is carried out on the mapping data to be processed according to the preset mapping rule, the mapping template and the second mapping attribute data list, and through the steps, the processing efficiency of mapping data resources can be improved.
In specific implementation, in order to obtain mapping data tag sets in a one-to-one correspondence manner and ensure accuracy of the extracted mapping data tag sets, the performing feature extraction on each mapping attribute data in the mapping data in sequence described in step S110 to obtain a mapping data tag set corresponding to each mapping attribute data specifically includes:
sequentially taking each of the mapping attribute data as current mapping attribute data to perform the following feature extraction operations until all of the mapping attribute data in the mapping data are traversed: inputting each attribute value in the current mapping attribute data into a target label coding matrix to obtain a field distribution range of each mapping label coding field of each attribute value in the target label coding matrix, wherein the target label coding matrix comprises at least two mapping label coding fields; determining the mapping data tag set that matches the current mapping attribute data according to a field distribution range of the mapping tag encoding field for the respective attribute value.
By executing the method, the characteristic extraction is sequentially carried out on each mapping attribute data in the mapping data, the mapping data label sets can be obtained in a one-to-one correspondence mode, and meanwhile, the accuracy of the extracted mapping data label sets can be ensured.
Further, the determining the mapping data tag set that matches the current mapping attribute data according to the field distribution range of the mapping tag encoding field for the respective attribute value comprises: and acquiring label description information of field distribution range of the jth mapping label coding field of each attribute value to obtain the jth mapping data label of the current mapping attribute data, wherein j is an integer which is greater than or equal to 1 and less than or equal to M, M is the number of the mapping label coding fields in the target label coding matrix, and M is a positive integer.
In a specific implementation, to avoid inaccurate first mapping attribute data list, the dividing all mapping attribute data in the mapping data according to the mapping data tag set described in step S120 to obtain the first mapping attribute data list includes:
obtaining label description information of each mapping data label in the mapping data label set, and taking the label description information of the mapping data label as a target mapping data label matched with the mapping attribute data;
sequentially comparing the target mapping data labels corresponding to the two adjacent mapping attribute data to obtain a comparison result; and dividing all mapping attribute data according to the comparison result to obtain the first mapping attribute data list.
The method is implemented by firstly acquiring label description information of each mapping data label, wherein the label description information is used as a target mapping data label, and then comparing the target mapping data labels corresponding to two adjacent mapping attribute data in sequence, so that the accuracy of a comparison result can be ensured, and further dividing all mapping attribute data according to the comparison result, so that the inaccuracy of a divided first mapping attribute data list can be avoided.
Further, the sequentially comparing the respective corresponding target mapping data tags of the two adjacent mapping attribute data to obtain a comparison result includes: acquiring a tag matching value of a target mapping data tag of the h +1 th mapping attribute data and a target mapping data tag of the h mapping attribute data; wherein h is an integer greater than or equal to 1 and less than or equal to Y-1, and Y is the number of mapping attribute data in the mapping data; and comparing the tag matching value with a preset first threshold value to obtain a comparison result.
Further, the dividing all mapping attribute data according to the comparison result to obtain the first mapping attribute data list includes: if the comparison result indicates that the tag matching value is smaller than the preset first threshold value, determining that the h +1 th mapping attribute data and the h th mapping attribute data are data corresponding to the same mapping operation area, and adding the h +1 th mapping attribute data to a mapping attribute data sequence where the h th mapping attribute data is located; and under the condition that the comparison result indicates that the tag matching value is greater than or equal to the preset first threshold value, determining that the h +1 th mapping attribute data and the h mapping attribute data are not data corresponding to the same mapping operation area, and creating a new mapping attribute data sequence for the h +1 th mapping attribute data.
In a specific implementation, in order to ensure the accuracy of the correlation of the feature correlation between the key mapping attribute data and the reference mapping attribute data, the step S130 of sequentially obtaining the feature correlation between each key mapping attribute data and the reference mapping attribute data located before the key mapping attribute data may specifically include the following steps S1301 to S1304:
substep S1301, obtaining a key attribute category of the key mapping attribute data and a reference attribute category of the reference mapping attribute data;
sub-step S1302, obtaining an association ratio between the key attribute category and the reference attribute category, wherein the feature association degree includes the association ratio;
a substep S1303, obtaining basic associated data in the key mapping attribute data and the reference mapping attribute data;
sub-step S1304 of obtaining a first proportion of the basic associated data in the key mapping attribute data and a second proportion of the basic associated data in the reference mapping attribute data; wherein the feature association degree comprises the first and second ratios.
And executing the content described in the foregoing substeps 1301-substep S1304, firstly acquiring the key attribute category of the key mapping attribute data and the reference attribute category of the reference mapping attribute data, secondly acquiring the association ratio between the key attribute category and the reference attribute category and the basic association data, and further respectively acquiring a first ratio of the basic association data in the key mapping attribute data and a second ratio of the basic association data in the reference mapping attribute data, wherein the association accuracy of the feature association degree between the key mapping attribute data and the reference mapping attribute data can be ensured through the first ratio and the second ratio.
Further, the obtaining of the key attribute category of the key mapping attribute data and the reference attribute category of the reference mapping attribute data described in the sub-step S1301 may specifically include the following: screening the key mapping attribute data and the reference mapping attribute data respectively to obtain current key mapping attribute data and current reference mapping attribute data; inputting the current key mapping attribute data into a category classification thread to obtain the key attribute category, and inputting the current reference mapping attribute data into the category classification thread to obtain the reference attribute category; the category classification thread is an artificial intelligence model for generating attribute categories of the mapping data, which is obtained by performing machine training on a plurality of groups of sample mapping data pairs and corresponding identification information.
In a specific implementation, in order to avoid an error occurring when the first mapping attribute data list is adjusted to the second mapping attribute data list, in the case that the feature association reaches the integration condition, as described in step S140, the mapping attribute data sequence in the first mapping operation region where the key mapping attribute data is located is integrated into the mapping attribute data sequence in the second mapping operation region where the reference mapping attribute data is located, so as to adjust the first mapping attribute data list to the second mapping attribute data list, which may specifically include the contents described in sub-steps S1401-S1403 below:
in the sub-step S1401, when the feature association degree meets the integration condition, obtaining first mapping node data and second mapping node data obtained after mapping the first mapping operation region; wherein the first mapping node data is static mapping node data of a first mapping node, and the second mapping node data is dynamic mapping node data comprising a second mapping node;
substep S1402, determining a mapping attribute data sequence of a corresponding mapping node data tag in the first mapping node data and the second mapping node data, and determining a target mapping node data tag which corresponds to the first mapping node data and the second mapping node data and satisfies a predetermined condition based on the mapping attribute data sequence of the corresponding mapping node data tag;
and a substep S1403, integrating the mapping attribute data sequence in the first mapping operation region into the mapping attribute data sequence in the second mapping operation region where the reference mapping attribute data is located based on the target mapping node data tag, and adjusting homogeneous data in the first mapping node data corresponding to the integrated first mapping attribute data list to obtain a second mapping attribute data list.
It is to be understood that the determining of the mapping property data sequence of the corresponding mapping node data tag in the first and second mapping node data described in sub-step S1402 includes: determining the label difference degree of each mapping node data label in the first mapping node data and the label difference degree of each mapping node data label in the second mapping node data; determining data difference weight coefficients of the corresponding mapping node data labels in the first mapping node data and the second mapping node data based on label difference degrees of each mapping node data label in the first mapping node data and label difference degrees of each mapping node data label in the second mapping node data; wherein the sequence of mapping attribute data comprises the data difference weight coefficient.
It is to be understood that the determination of a target mapping node data tag corresponding between the first mapping node data and the second mapping node data and satisfying a predetermined condition based on the mapping attribute data sequence of the corresponding mapping node data tag described in sub-step S1402 includes: sorting the corresponding mapping node data labels in the first mapping node data and the second mapping node data according to a mapping attribute data sequence from small to large; determining the target mapping node data label from the sorted corresponding mapping node data labels by one of the following methods: sequentially selecting a preset number of the corresponding mapping node data labels as the target mapping node data labels; sequentially selecting the corresponding mapping node data labels in a preset proportion as the target mapping node data labels; determining the corresponding mapping node data label of which the sequence value corresponding to the mapping attribute data sequence is smaller than a preset first threshold value as the target mapping node data label; matching corresponding mapping node data labels included in the corresponding mapping node data labels of which the sequence values corresponding to the mapping attribute data sequences are smaller than a preset second threshold value according to a preset matching number in sequence, and determining the target mapping node data labels based on the matching result; and selecting the target mapping node data label based on the accumulated difference degree proportion change of the corresponding mapping node data label.
By executing the contents described in sub-steps S1401 to S1403, it is first ensured that the feature association degree meets the integration condition, and the mapping attribute data sequence in the first mapping operation region where the key mapping attribute data is located is integrated into the mapping attribute data sequence in the second mapping operation region where the reference mapping attribute data is located, so that targeted integration can be performed while avoiding errors occurring when the first mapping attribute data list is adjusted to the second mapping attribute data list.
In specific implementation, to avoid unnecessary errors in the mapping process and improve the processing efficiency of mapping data resources, the mapping process performed on the mapping data to be processed according to the preset mapping rule, the mapping template and the second mapping attribute data list in step S150 specifically includes:
obtaining mapping index parameters of mapping data to be processed; wherein the mapping index parameters include mapping marker data and index configuration weights; determining a drawing type according to a preset drawing rule and a drawing template; determining whether a weight value corresponding to the minimum index weight in the to-be-processed mapping data needs to be analyzed according to the index configuration weight and the mapping mark data; if the mapping data needs to be analyzed, screening at least part of weight values of the mapping data to be processed to obtain the weight value corresponding to the minimum index weight; determining whether the weighted value corresponding to the minimum index weight needs to be clustered again by using the mapping index parameter; and if the clustering needs to be carried out again, generating an updated weight value, and carrying out drawing processing on the mapping data to be processed according to the second mapping attribute data list to obtain a target mapping.
It is to be understood that the mapping indicia data includes mapping coordinate data of the mapping data to be processed, mapping distance data of the mapping data to be processed, and mapping area data, the method further including: judging whether the category of the mapping marking data, the mapping region data and the index configuration weight are the same; if the category of the mapping marking data, the mapping area data and the index configuration weight are the same, judging whether a data flow chart is set according to mapping coordinate data of the mapping data to be processed when the mapping type is the data flow chart; if the data flow table is set, carrying out drawing processing on the data flow table and the mapping data to be processed; and if the data flow table is not set, carrying out mapping processing on the mapping data to be processed.
By executing the above content, the mapping data to be processed is judged for a plurality of times, and then the mapping processing is determined to be carried out on the mapping data to be processed, so that unnecessary errors in the mapping processing process can be avoided, and meanwhile, the processing efficiency of mapping data resources can be improved.
On the basis of the above, please refer to fig. 2, the present invention further provides a block diagram of a mapping data resource processing apparatus 200, which specifically includes the following functional modules:
the data acquisition module 210 is configured to acquire mapping data to be processed, and sequentially perform feature extraction on each mapping attribute data in the mapping data to obtain a mapping data tag set corresponding to each mapping attribute data; wherein the set of mapping data tags includes at least two mapping data tags of the mapping attribute data;
a data dividing module 220, configured to divide all mapping attribute data in the mapping data according to the mapping data tag set to obtain a first mapping attribute data list; the first mapping attribute data list records mapping attribute data sequences respectively corresponding to a plurality of mapping operation areas contained in the mapping data, and the first mapping attribute data in each mapping attribute data sequence is key mapping attribute data of the mapping operation area;
an association degree obtaining module 230, configured to sequentially obtain a feature association degree between each of the key mapping attribute data and reference mapping attribute data located before the key mapping attribute data;
a data integration module 240, configured to, if the feature association reaches an integration condition, integrate the mapping attribute data sequence in the first mapping operation region where the key mapping attribute data is located into the mapping attribute data sequence in the second mapping operation region where the reference mapping attribute data is located, so as to adjust the first mapping attribute data list into a second mapping attribute data list;
and the drawing processing module 250 is configured to perform drawing processing on the to-be-processed mapping data according to preset drawing rules, drawing templates and the second mapping attribute data list.
On the basis of the above, please refer to fig. 3 in combination, there is provided a server 110, which includes a processor 111, and a memory 112 and a bus 113 connected to the processor 111; wherein, the processor 111 and the memory 112 complete the communication with each other through the bus 113; the processor 111 is used to call program instructions in the memory 112 to perform the above-described method.
Further, a readable storage medium is provided, on which a program is stored, which when executed by a processor implements the method described above.
It should be understood that, for technical terms that are not noun explanations to the above-mentioned contents, a person skilled in the art can deduce and unambiguously determine the meaning of the present invention according to the above-mentioned disclosure, for example, for some values, coefficients, weights and other terms, a person skilled in the art can deduce and determine according to the logical relationship before and after, the value range of these values can be selected according to the actual situation, for example, 0 to 1, for example, 1 to 10, for example, 50 to 100, but not limited thereto, and a person skilled in the art can unambiguously determine some preset, reference, predetermined, set and target technical features/technical terms according to the above-mentioned disclosure. For some technical characteristic terms which are not explained, the technical solution can be clearly and completely implemented by those skilled in the art by reasonably and unambiguously deriving the technical solution based on the logical relations in the previous and following paragraphs. The foregoing will therefore be clear and complete to those skilled in the art. It should be understood that the process of deriving and analyzing technical terms, which are not explained, by those skilled in the art based on the above disclosure is based on the contents described in the present application, and thus the above contents are not an inventive judgment of the overall scheme.
It will be understood that the invention 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 invention is limited only by the appended claims.
Claims (10)
1. A mapping data resource processing method, comprising:
collecting mapping data to be processed, and sequentially performing feature extraction on each mapping attribute data in the mapping data to obtain a mapping data tag set corresponding to each mapping attribute data; wherein the set of mapping data tags includes at least two mapping data tags of the mapping attribute data;
dividing all mapping attribute data in the mapping data according to the mapping data label set to obtain a first mapping attribute data list; the first mapping attribute data list records mapping attribute data sequences respectively corresponding to a plurality of mapping operation areas contained in the mapping data, and the first mapping attribute data in each mapping attribute data sequence is key mapping attribute data of the mapping operation area;
sequentially acquiring the feature association degree between each key mapping attribute data and reference mapping attribute data positioned in front of the key mapping attribute data;
integrating the mapping attribute data sequence in the first mapping operation area where the key mapping attribute data is located into the mapping attribute data sequence in the second mapping operation area where the reference mapping attribute data is located under the condition that the feature association degree reaches an integration condition, so as to adjust the first mapping attribute data list into a second mapping attribute data list;
and carrying out mapping processing on the mapping data to be processed according to preset mapping rules, mapping templates and the second mapping attribute data list.
2. The method of claim 1, wherein the sequentially performing feature extraction on each mapping attribute data in the mapping data to obtain a mapping data tag set corresponding to each mapping attribute data comprises:
sequentially taking each of the mapping attribute data as current mapping attribute data to perform the following feature extraction operations until all of the mapping attribute data in the mapping data are traversed: inputting each attribute value in the current mapping attribute data into a target label coding matrix to obtain a field distribution range of each mapping label coding field of each attribute value in the target label coding matrix, wherein the target label coding matrix comprises at least two mapping label coding fields; determining the mapping data tag set matching the current mapping attribute data according to the field distribution range of the mapping tag encoding field of each attribute value;
the determining the mapping data tag set that matches the current mapping attribute data according to the field distribution range of the mapping tag encoding field for the respective attribute value comprises: and acquiring label description information of field distribution range of the jth mapping label coding field of each attribute value to obtain the jth mapping data label of the current mapping attribute data, wherein j is an integer which is greater than or equal to 1 and less than or equal to M, M is the number of the mapping label coding fields in the target label coding matrix, and M is a positive integer.
3. The method of claim 1, wherein the partitioning of all of the mapping attribute data in the mapping data according to the mapping data tag set to obtain a first mapping attribute data list comprises: obtaining label description information of each mapping data label in the mapping data label set, and taking the label description information of the mapping data label as a target mapping data label matched with the mapping attribute data; sequentially comparing the target mapping data labels corresponding to the two adjacent mapping attribute data to obtain a comparison result; dividing all surveying and mapping attribute data according to the comparison result to obtain a first surveying and mapping attribute data list;
the step of sequentially comparing the respective corresponding target mapping data labels of the two adjacent mapping attribute data to obtain a comparison result comprises the following steps: acquiring a tag matching value of a target mapping data tag of the h +1 th mapping attribute data and a target mapping data tag of the h mapping attribute data; wherein h is an integer greater than or equal to 1 and less than or equal to Y-1, and Y is the number of mapping attribute data in the mapping data; comparing the tag matching value with a preset first threshold value to obtain a comparison result;
the dividing all mapping attribute data according to the comparison result to obtain the first mapping attribute data list includes: if the comparison result indicates that the tag matching value is smaller than the preset first threshold value, determining that the h +1 th mapping attribute data and the h th mapping attribute data are data corresponding to the same mapping operation area, and adding the h +1 th mapping attribute data to a mapping attribute data sequence where the h th mapping attribute data is located; and under the condition that the comparison result indicates that the tag matching value is greater than or equal to the preset first threshold value, determining that the h +1 th mapping attribute data and the h mapping attribute data are not data corresponding to the same mapping operation area, and creating a new mapping attribute data sequence for the h +1 th mapping attribute data.
4. The method of claim 1, wherein said sequentially obtaining a feature correlation between each of said key mapping property data and a reference mapping property data preceding said key mapping property data comprises:
obtaining a key attribute category of the key mapping attribute data and a reference attribute category of the reference mapping attribute data;
acquiring an association ratio between the key attribute category and the reference attribute category, wherein the feature association degree comprises the association ratio; obtaining basic associated data in the key mapping attribute data and the reference mapping attribute data;
obtaining a first proportion of the base associated data in the key mapping attribute data and a second proportion of the base associated data in the reference mapping attribute data; wherein the feature association degree comprises the first and second ratios.
5. The method of claim 4, wherein the acquiring a key attribute class of the key mapping attribute data and a reference attribute class of the reference mapping attribute data comprises:
screening the key mapping attribute data and the reference mapping attribute data respectively to obtain current key mapping attribute data and current reference mapping attribute data;
inputting the current key mapping attribute data into a category classification thread to obtain the key attribute category, and inputting the current reference mapping attribute data into the category classification thread to obtain the reference attribute category; the category classification thread is an artificial intelligence model for generating attribute categories of the mapping data, which is obtained by performing machine training on a plurality of groups of sample mapping data pairs and corresponding identification information.
6. The method of claim 1, wherein integrating the sequence of mapping attribute data in the first mapping operation region in which the key mapping attribute data is located into the sequence of mapping attribute data in the second mapping operation region in which the reference mapping attribute data is located to adjust the first list of mapping attribute data to a second list of mapping attribute data if the feature association reaches an integration condition comprises:
under the condition that the feature association degree reaches an integration condition, acquiring first mapping node data and second mapping node data obtained after mapping a first mapping operation area; wherein the first mapping node data is static mapping node data of a first mapping node, and the second mapping node data is dynamic mapping node data comprising a second mapping node;
determining a mapping attribute data sequence of a corresponding mapping node data label in the first mapping node data and the second mapping node data, and determining a target mapping node data label which corresponds to the first mapping node data and the second mapping node data and meets a preset condition based on the mapping attribute data sequence of the corresponding mapping node data label;
integrating a mapping attribute data sequence in a first mapping operation area into a mapping attribute data sequence in a second mapping operation area where the reference mapping attribute data is located based on a target mapping node data label, and adjusting homogeneous data in the first mapping node data corresponding to the first mapping attribute data list after integration processing to obtain a second mapping attribute data list;
wherein determining a sequence of mapping attribute data of corresponding mapping node data tags in the first and second mapping node data comprises: determining the label difference degree of each mapping node data label in the first mapping node data and the label difference degree of each mapping node data label in the second mapping node data; determining data difference weight coefficients of the corresponding mapping node data labels in the first mapping node data and the second mapping node data based on label difference degrees of each mapping node data label in the first mapping node data and label difference degrees of each mapping node data label in the second mapping node data; wherein the sequence of mapping attribute data comprises the data difference weight coefficient;
wherein determining a target mapping node data tag that corresponds between the first mapping node data and the second mapping node data and satisfies a predetermined condition based on a mapping attribute data sequence of the corresponding mapping node data tag comprises: sorting the corresponding mapping node data labels in the first mapping node data and the second mapping node data according to a mapping attribute data sequence from small to large; determining the target mapping node data label from the sorted corresponding mapping node data labels by one of the following methods: sequentially selecting a preset number of the corresponding mapping node data labels as the target mapping node data labels; sequentially selecting the corresponding mapping node data labels in a preset proportion as the target mapping node data labels; determining the corresponding mapping node data label of which the sequence value corresponding to the mapping attribute data sequence is smaller than a preset first threshold value as the target mapping node data label; matching corresponding mapping node data labels included in the corresponding mapping node data labels of which the sequence values corresponding to the mapping attribute data sequences are smaller than a preset second threshold value according to a preset matching number in sequence, and determining the target mapping node data labels based on the matching result; and selecting the target mapping node data label based on the accumulated difference degree proportion change of the corresponding mapping node data label.
7. The method according to claim 1, wherein the mapping of the mapping data to be processed according to preset mapping rules and mapping templates and the second mapping attribute data list comprises:
obtaining mapping index parameters of mapping data to be processed; wherein the mapping index parameters include mapping marker data and index configuration weights; determining a drawing type according to a preset drawing rule and a drawing template; determining whether a weight value corresponding to the minimum index weight in the to-be-processed mapping data needs to be analyzed according to the index configuration weight and the mapping mark data; if the mapping data needs to be analyzed, screening at least part of weight values of the mapping data to be processed to obtain the weight value corresponding to the minimum index weight; determining whether the weighted value corresponding to the minimum index weight needs to be clustered again by using the mapping index parameter; if clustering needs to be carried out again, generating an updated weight value, and carrying out drawing processing on the mapping data to be processed according to the second mapping attribute data list to obtain a target mapping;
wherein the mapping indicia data comprises mapping coordinate data of the mapping data to be processed, mapping distance data of the mapping data to be processed, and mapping area data, the method further comprising: judging whether the category of the mapping marking data, the mapping region data and the index configuration weight are the same; if the category of the mapping marking data, the mapping area data and the index configuration weight are the same, judging whether a data flow chart is set according to mapping coordinate data of the mapping data to be processed when the mapping type is the data flow chart; if the data flow table is set, carrying out drawing processing on the data flow table and the mapping data to be processed; and if the data flow table is not set, carrying out mapping processing on the mapping data to be processed.
8. A mapping data resource processing apparatus, the apparatus comprising:
the data acquisition module is used for acquiring mapping data to be processed and sequentially performing feature extraction on each mapping attribute data in the mapping data to obtain a mapping data tag set corresponding to each mapping attribute data; wherein the set of mapping data tags includes at least two mapping data tags of the mapping attribute data;
the data dividing module is used for dividing all mapping attribute data in the mapping data according to the mapping data label set to obtain a first mapping attribute data list; the first mapping attribute data list records mapping attribute data sequences respectively corresponding to a plurality of mapping operation areas contained in the mapping data, and the first mapping attribute data in each mapping attribute data sequence is key mapping attribute data of the mapping operation area;
the association degree acquisition module is used for sequentially acquiring the feature association degree between each key mapping attribute data and the reference mapping attribute data positioned in front of the key mapping attribute data;
a data integration module, configured to, when the feature association degree reaches an integration condition, integrate a mapping attribute data sequence in a first mapping operation area where the key mapping attribute data is located into a mapping attribute data sequence in a second mapping operation area where the reference mapping attribute data is located, so as to adjust the first mapping attribute data list to a second mapping attribute data list;
and the drawing processing module is used for carrying out drawing processing on the to-be-processed drawing data according to preset drawing rules, a drawing template and the second drawing attribute data list.
9. A server, comprising a processor and a memory in communication with each other, the processor being configured to retrieve a computer program from the memory and to implement the method of any one of claims 1-7 by running the computer program.
10. A computer-readable storage medium, on which a computer program is stored which, when executed, implements the method of any of claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011432465.6A CN112559590A (en) | 2020-12-09 | 2020-12-09 | Mapping data resource processing method and device and server |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011432465.6A CN112559590A (en) | 2020-12-09 | 2020-12-09 | Mapping data resource processing method and device and server |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112559590A true CN112559590A (en) | 2021-03-26 |
Family
ID=75060176
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011432465.6A Withdrawn CN112559590A (en) | 2020-12-09 | 2020-12-09 | Mapping data resource processing method and device and server |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112559590A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116821777A (en) * | 2023-02-28 | 2023-09-29 | 广东新禾道信息科技有限公司 | Novel basic mapping data integration method and system |
-
2020
- 2020-12-09 CN CN202011432465.6A patent/CN112559590A/en not_active Withdrawn
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116821777A (en) * | 2023-02-28 | 2023-09-29 | 广东新禾道信息科技有限公司 | Novel basic mapping data integration method and system |
| CN116821777B (en) * | 2023-02-28 | 2024-02-13 | 广东新禾道信息科技有限公司 | Novel basic mapping data integration method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108052984B (en) | Method of counting and device | |
| CN108921166A (en) | Medical bill class text detection recognition method and system based on deep neural network | |
| CN107766418A (en) | A kind of credit estimation method based on Fusion Model, electronic equipment and storage medium | |
| CN106228554B (en) | Fuzzy coarse central coal dust image partition method based on many attribute reductions | |
| Keselman et al. | Many-to-many graph matching via metric embedding | |
| CN106651057A (en) | Mobile terminal user age prediction method based on installation package sequence table | |
| CN103186538A (en) | Image classification method, image classification device, image retrieval method and image retrieval device | |
| CN109284779A (en) | Object detecting method based on the full convolutional network of depth | |
| CN112132014B (en) | Target re-identification method and system based on non-supervised pyramid similarity learning | |
| CN107545038B (en) | Text classification method and equipment | |
| CN109344869A (en) | A kind of disaggregated model optimization method, device and storage equipment, program product | |
| CN107909102A (en) | A kind of sorting technique of histopathology image | |
| CN109784368A (en) | A kind of determination method and apparatus of application program classification | |
| CN106055613A (en) | Cleaning method for data classification and training databases based on mixed norm | |
| CN108564579A (en) | A kind of distress in concrete detection method and detection device based on temporal and spatial correlations | |
| CN106295498A (en) | Remote sensing image target area detection apparatus and method | |
| CN109993187A (en) | A kind of modeling method, robot and the storage device of object category for identification | |
| CN109993042A (en) | A kind of face identification method and its device | |
| Burghardt et al. | Usage of principal component analysis in the process of automated generalisation | |
| CN107451617A (en) | One kind figure transduction semisupervised classification method | |
| CN112559590A (en) | Mapping data resource processing method and device and server | |
| CN112132798B (en) | Method for detecting complex background PCB mark point image based on Mini ARU-Net network | |
| CN110059755A (en) | A kind of seismic properties preferred method of multiple features interpretational criteria fusion | |
| CN111414930B (en) | Deep learning model training method and device, electronic equipment and storage medium | |
| CN107657262A (en) | A kind of computer automatic sorting Accuracy Assessment |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20210326 |
|
| WW01 | Invention patent application withdrawn after publication |