CN112559589A - Remote surveying and mapping data processing method and system - Google Patents
Remote surveying and mapping data processing method and system Download PDFInfo
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- CN112559589A CN112559589A CN202011429133.2A CN202011429133A CN112559589A CN 112559589 A CN112559589 A CN 112559589A CN 202011429133 A CN202011429133 A CN 202011429133A CN 112559589 A CN112559589 A CN 112559589A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—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
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- 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
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/26—Visual data mining; Browsing structured data
Abstract
The invention discloses a method and a system for processing remote surveying and mapping data. According to the method, firstly, a telemetering data recording label is obtained from a telemetering data processing request, and then corresponding telemetering data characteristics, a plurality of pieces of to-be-processed telemetering data and telemetering state data of the plurality of pieces of to-be-processed telemetering data are obtained, secondly, telemetering data source characteristics of sample telemetering data carried in the plurality of pieces of to-be-processed telemetering data are obtained from a data information base, and then the telemetering data characteristics, the telemetering state data and the telemetering data source characteristics are input into a data processing model, so that the accuracy of a data processing result can be ensured.
Description
Technical Field
The present disclosure relates to the field of mapping data processing technologies, and in particular, to a method and a system for processing remote mapping data.
Background
At present, when surveying and mapping fields such as building engineering, road construction and terrain, a large amount of telemetric data is generally required to be processed. Generally, there may be multiple data receiving terminals waiting to receive the data processing results for the same telemetry data. However, in the prior art, it is difficult to ensure that the data receiving terminal accurately and timely receives the telemetry data processing result.
Disclosure of Invention
In order to solve the technical problems in the related art, the present disclosure provides a method and a system for processing remote mapping data.
The invention provides a method for processing remote mapping data, which comprises the following steps:
responding to a received telemetry data processing request, and acquiring a telemetry data recording tag carried in the telemetry data processing request;
acquiring telemetry data characteristics, a plurality of to-be-processed telemetry data and telemetry state data of the plurality of to-be-processed telemetry data corresponding to the telemetry data recording tag;
obtaining telemetry data source characteristics of sample telemetry data carried in the plurality of to-be-processed telemetry data from a data information base, wherein the telemetry data source characteristics at least comprise structural characteristics of the sample telemetry data; the data information base is determined based on the data type of each sample telemetering data and initial data information included in a preset initial data information base, and the data type is obtained by performing type prediction on data information except the target type of each sample telemetering data in each initial data information through a trained shaking data type model; the structured features comprise data edge features and telemetry data edge coefficients of the sample telemetry data, the telemetry data edge coefficients being used to characterize feature weights of the data edge features;
inputting the telemetering data characteristics, telemetering state data and telemetering data source characteristics into a trained data processing model to obtain data processing results of the plurality of telemetering data to be processed;
determining at least one target telemetry datum from the plurality of to-be-processed telemetry data based on the data processing result; and sending the data processing result carrying the target telemetering data to a data receiving terminal.
Preferably, the method further comprises: acquiring a telemetry data format, a telemetry data characteristic attribute and a telemetry data amount corresponding to the telemetry data recording label; determining a telemetry data security feature and a telemetry data classification field corresponding to the telemetry data recording tag based on the telemetry data format; acquiring historical telemetry data source characteristics corresponding to the telemetry data classification field; and determining telemetry data characteristics, a plurality of to-be-processed telemetry data and telemetry state data of the plurality of to-be-processed telemetry data corresponding to the telemetry data recording tag based on the telemetry data characteristic attributes, the telemetry data amount, the telemetry data security characteristics and the historical telemetry data source characteristics.
Preferably, obtaining telemetry data source characteristics of sample telemetry data carried in the plurality of telemetry data to be processed comprises: obtaining sample fields of sample telemetering data corresponding to each telemetering data to be processed; acquiring data information corresponding to the sample field from a data information base; telemetry data source characteristics of the sample telemetry data are extracted from the data information.
Preferably, the extracting telemetry data source characteristics of the sample telemetry data from the data information comprises: extracting at least a sample field, a data type and a sample data code of the sample telemetry data from the data information; acquiring data edge characteristics of the sample telemetering data and data distribution information corresponding to the data type; determining telemetry data edge coefficients for the sample telemetry data based on the data edge features and the data distribution information; determining the data edge features and the telemetry data edge coefficients as structured features of the sample telemetry data.
Preferably, the determining at least one target telemetry data from the plurality of to-be-processed telemetry data based on the data processing result comprises: acquiring telemetry data processing duration corresponding to the telemetry data processing request; determining at least one target telemetry datum from the plurality of to-be-processed telemetry data based on the telemetry data processing duration and the data processing result;
the method for acquiring the telemetry data record tag carried in the telemetry data processing request in response to the received telemetry data processing request comprises the following steps: responding to a telemetry data processing request sent by telemetry equipment, and analyzing the telemetry data processing request to obtain a telemetry data recording label;
inputting the telemetry data characteristics, the telemetry state data and the telemetry data source characteristics into a trained data processing model to obtain data processing results of the plurality of to-be-processed telemetry data, wherein the data processing results comprise: integrating the telemetering data characteristics, the telemetering state data and the telemetering data source characteristics into to-be-processed characteristics; inputting the features to be processed into the data processing model to obtain data processing results of the plurality of telemetric data to be processed; the data processing model is a convolutional neural network model, and the data processing result is output by the convolutional neural network model.
Preferably, the method further comprises: and sending the data processing result carrying the target telemetering data to a data receiving terminal as mapping data, and carrying out mapping processing on the mapping data.
The invention also provides a remote mapping data processing system, which comprises a processing server, a remote measuring device and a data receiving terminal which are communicated with each other;
wherein the processing server is configured to:
responding to a received telemetry data processing request, and acquiring a telemetry data recording tag carried in the telemetry data processing request;
acquiring telemetry data characteristics, a plurality of to-be-processed telemetry data and telemetry state data of the plurality of to-be-processed telemetry data corresponding to the telemetry data recording tag;
obtaining telemetry data source characteristics of sample telemetry data carried in the plurality of to-be-processed telemetry data from a data information base, wherein the telemetry data source characteristics at least comprise structural characteristics of the sample telemetry data; the data information base is determined based on the data type of each sample telemetering data and initial data information included in a preset initial data information base, and the data type is obtained by performing type prediction on data information except the target type of each sample telemetering data in each initial data information through a trained shaking data type model; the structured features comprise data edge features and telemetry data edge coefficients of the sample telemetry data, the telemetry data edge coefficients being used to characterize feature weights of the data edge features;
inputting the telemetering data characteristics, telemetering state data and telemetering data source characteristics into a trained data processing model to obtain data processing results of the plurality of telemetering data to be processed;
determining at least one target telemetry datum from the plurality of to-be-processed telemetry data based on the data processing result; and sending the data processing result carrying the target telemetering data to a data receiving terminal.
Preferably, the processing server is further configured to: acquiring a telemetry data format, a telemetry data characteristic attribute and a telemetry data amount corresponding to the telemetry data recording label; determining a telemetry data security feature and a telemetry data classification field corresponding to the telemetry data recording tag based on the telemetry data format; acquiring historical telemetry data source characteristics corresponding to the telemetry data classification field; and determining the corresponding telemetry data characteristics of the telemetry data recording tag based on the telemetry data characteristic attributes, the telemetry data amount, the telemetry data safety characteristics and the historical telemetry data source characteristics.
Preferably, the processing server is configured to: obtaining sample fields of sample telemetering data corresponding to each telemetering data to be processed; acquiring data information corresponding to the sample field from a data information base; telemetry data source characteristics of the sample telemetry data are extracted from the data information.
Preferably, the processing server is configured to: acquiring telemetry data processing duration corresponding to the telemetry data processing request; determining at least one target telemetry datum from the plurality of to-be-processed telemetry data based on the telemetry data processing duration and the data processing result.
The technical scheme provided by the embodiment of the disclosure can have the following beneficial effects.
The present disclosure provides a method and system for processing remote mapping data, first obtaining a remote sensing data recording label from a remote sensing data processing request, and further acquiring corresponding telemetry data characteristics, a plurality of to-be-processed telemetry data and telemetry state data of the plurality of to-be-processed telemetry data, secondly, acquiring the telemetry data source characteristics of the sample telemetry data carried in the plurality of to-be-processed telemetry data from the data information base, then the telemetering data characteristics, the telemetering state data and the telemetering data source characteristics are input into a data processing model, so that the accuracy of the data processing result can be ensured, on the basis, at least one target telemetering data is determined from a plurality of telemetering data to be processed, a data processing result carrying the target telemetering data is further sent to a data receiving terminal, therefore, the accuracy and timeliness of telemetering data receiving of the data receiving terminal can be ensured.
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 communication architecture diagram of a remote mapping data processing system according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for processing remote mapping data according to an embodiment of the present invention.
FIG. 3 is a block diagram of a device for processing remote mapping data according to an embodiment of the present invention.
Fig. 4 is a schematic diagram of a hardware structure of a processing 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.
The invention provides a method and a system for processing remote mapping data, which aim to solve the problem that the prior art is difficult to ensure that a data receiving terminal accurately and timely receives a processing result of telemetering data.
To achieve the above objective, a communication architecture diagram of a remote mapping data processing system as shown in fig. 1 is first provided. The telemapping data processing system 100 may include, among other things, telemetry devices, processing servers, and data receiving terminals. Wherein the telemetry device 200, the processing server 300 and the data receiving terminal 400 communicate. Further, the telemetry device 200 may be an electronic device for testing data, the processing server 300 may be a desktop computer, a notebook computer, or the like, and the receiving terminal 400 may be a mobile phone, an intelligent electronic device, or the like, which is not limited herein.
On the basis of the above, please refer to fig. 2 in combination, a flow chart of a method for processing remote mapping data is provided, which may be applied to the processing server 300 in fig. 1, where the processing server 300 specifically performs the following steps S210 to S250 when implementing the above method.
Step S210, responding to the received telemetry data processing request, and acquiring a telemetry data recording tag carried in the telemetry data processing request.
Step S220, obtaining the corresponding telemetering data characteristics of the telemetering data recording label, a plurality of to-be-processed telemetering data and telemetering state data of the plurality of to-be-processed telemetering data.
Step S230, obtaining telemetry data source characteristics of sample telemetry data carried in the plurality of to-be-processed telemetry data from a data information base, where the telemetry data source characteristics at least include structural characteristics of the sample telemetry data.
In this embodiment, the data information base is determined based on the data type of each sample telemetry data and initial data information included in a preset initial data information base, and the data type is obtained by performing type prediction on data information excluding a target type of each sample telemetry data in each initial data information through a trained shake survey data type model; the structured features include data edge features of the sample telemetry data and telemetry data edge coefficients used to characterize feature weights of the data edge features.
And S240, inputting the telemetric data characteristics, the telemetric state data and the telemetric data source characteristics into a trained data processing model to obtain data processing results of the plurality of telemetric data to be processed.
Step S250, determining at least one target telemetric data from the plurality of telemetric data to be processed based on the data processing result; and sending the data processing result carrying the target telemetering data to a data receiving terminal.
The following beneficial technical effects can be achieved when the method described in the above steps S210 to S250 is executed:
the method comprises the steps of firstly obtaining a telemetering data recording label from a telemetering data processing request, further obtaining corresponding telemetering data characteristics, a plurality of to-be-processed telemetering data and telemetering state data of the plurality of to-be-processed telemetering data, secondly obtaining telemetering data source characteristics of sample telemetering data carried in the plurality of to-be-processed telemetering data from a data information base, and then inputting the telemetering data characteristics, the telemetering state data and the telemetering data source characteristics into a data processing model, so that the accuracy of a data processing result can be ensured.
In an alternative embodiment, the method further comprises: acquiring a telemetry data format, a telemetry data characteristic attribute and a telemetry data amount corresponding to the telemetry data recording label; determining a telemetry data security feature and a telemetry data classification field corresponding to the telemetry data recording tag based on the telemetry data format; acquiring historical telemetry data source characteristics corresponding to the telemetry data classification field; and determining the corresponding telemetry data characteristics of the telemetry data recording tag based on the telemetry data characteristic attributes, the telemetry data amount, the telemetry data safety characteristics and the historical telemetry data source characteristics.
In specific implementation, in order to accurately extract the source characteristics of the telemetry data, the obtaining of the source characteristics of the telemetry data of the sample telemetry data carried in the plurality of to-be-processed telemetry data, which is described in step S230, specifically includes: obtaining sample fields of sample telemetering data corresponding to each telemetering data to be processed; acquiring data information corresponding to the sample field from a data information base; telemetry data source characteristics of the sample telemetry data are extracted from the data information.
Therefore, the source characteristics of the telemetering data can be accurately extracted.
Further, the extracting telemetry data source characteristics of the sample telemetry data from the data information comprises: extracting at least a sample field, a data type and a sample data code of the sample telemetry data from the data information; acquiring data edge characteristics of the sample telemetering data and data distribution information corresponding to the data type; determining telemetry data edge coefficients for the sample telemetry data based on the data edge features and the data distribution information; determining the data edge features and the telemetry data edge coefficients as structured features of the sample telemetry data.
In an implementation, in order to definitely determine at least one target telemetry data from a plurality of to-be-processed telemetry data, the determining at least one target telemetry data from the plurality of to-be-processed telemetry data based on the data processing result in step S250 includes: acquiring telemetry data processing duration corresponding to the telemetry data processing request; determining at least one target telemetry datum from the plurality of to-be-processed telemetry data based on the telemetry data processing duration and the data processing result.
In this manner, at least one target telemetry data can be unambiguously determined from the plurality of telemetry data to be processed based on the telemetry data processing duration and the data processing result.
Further, the step S210 of obtaining a telemetry data record tag carried in the telemetry data processing request in response to the received telemetry data processing request includes: responding to a telemetry data processing request sent by telemetry equipment, analyzing the telemetry data processing request, and obtaining the telemetry data recording tag.
Further, the step S240 of inputting the telemetry data characteristics, the telemetry status data, and the telemetry data source characteristics into the trained data processing model to obtain the data processing results of the plurality of to-be-processed telemetry data includes: integrating the telemetering data characteristics, the telemetering state data and the telemetering data source characteristics into to-be-processed characteristics; inputting the features to be processed into the data processing model to obtain data processing results of the plurality of telemetric data to be processed; the data processing model is a convolutional neural network model, and the data processing result is output by the convolutional neural network model.
Based on the above description, the present invention may further include step S260: and sending the data processing result carrying the target telemetering data to a data receiving terminal as mapping data, and carrying out mapping processing on the mapping data.
Further, the step S260 of sending the data processing result carrying the target telemetry data to a data receiving terminal as mapping data and performing mapping processing on the mapping data may further include the following description.
Step S261, 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.
In this embodiment, the set of mapping data tags includes at least two mapping data tags of the mapping attribute data.
Step S262, 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 S263, sequentially obtaining a feature association degree between each of the key mapping attribute data and the reference mapping attribute data located before the key mapping attribute data.
Step S264, 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.
Step S265, performing mapping processing on the mapping data to be processed according to a preset mapping rule, a mapping template and the second mapping attribute data list.
The following advantageous effects can be achieved when the method described in the above steps S261 to S265 is executed:
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 step S261 describes 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, which 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 S262 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 S263 may further obtain the feature correlation between each key mapping attribute data and the reference mapping attribute data before the key mapping attribute data, which specifically includes the following sub-steps S2631-S2634:
a substep S2631 of obtaining a key attribute category of the key mapping attribute data and a reference attribute category of the reference mapping attribute data;
substep S2632, obtaining an association ratio between the key attribute category and the reference attribute category, wherein the feature association degree includes the association ratio;
substep S2633, obtaining basic associated data in the key mapping attribute data and the reference mapping attribute data;
substep S2634, obtaining a first proportion of the basic associated data in the key mapping property data and a second proportion of the basic associated data in the reference mapping property data; wherein the feature association degree comprises the first and second ratios.
Performing the content described in the foregoing substep S2631-substep S2634, first obtaining the key attribute category of the key mapping attribute data and the reference attribute category of the reference mapping attribute data, then obtaining the association ratio and the basic association data between the key attribute category and the reference attribute category, further obtaining the first ratio of the basic association data in the key mapping attribute data and the second ratio of the basic association data in the reference mapping attribute data, respectively, and 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 acquiring 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 S2631 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 S264, 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 following sub-steps S2641-S2643:
a substep S2641 of acquiring first mapping node data and second mapping node data obtained after mapping the first mapping operation region when the feature association degree meets the integration condition; 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;
sub-step S2642, determining a mapping attribute data sequence of a corresponding mapping node data tag in the first and second mapping node data, and determining a target mapping node data tag which corresponds to the first and second mapping node data and satisfies a predetermined condition based on the mapping attribute data sequence of the corresponding mapping node data tag;
in the sub-step S2643, based on the target mapping node data tag, the mapping attribute data sequence in the first mapping operation region is integrated into the mapping attribute data sequence in the second mapping operation region where the reference mapping attribute data is located, and the homogeneous data in the first mapping node data corresponding to the integrated first mapping attribute data list is adjusted to obtain a second mapping attribute data list.
It is to be appreciated that the determining of the sequence of mapping attribute data for the corresponding mapping node data tag in the first and second mapping node data described in substep S2642 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.
It is to be appreciated that the determining of a target mapping node data tag corresponding between the first and 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 S2642 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 performing the operations described in substeps 2641-2643, it is first ensured that the feature correlation reaches 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 a 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 S265 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.
Based on the same inventive concept, the invention also provides a remote mapping data processing system, which comprises a processing server, a telemetering device and a data receiving terminal, wherein the processing server, the telemetering device and the data receiving terminal are communicated with each other;
wherein the processing server is configured to:
responding to a received telemetry data processing request, and acquiring a telemetry data recording tag carried in the telemetry data processing request;
acquiring telemetry data characteristics, a plurality of to-be-processed telemetry data and telemetry state data of the plurality of to-be-processed telemetry data corresponding to the telemetry data recording tag;
obtaining telemetry data source characteristics of sample telemetry data carried in the plurality of to-be-processed telemetry data from a data information base, wherein the telemetry data source characteristics at least comprise structural characteristics of the sample telemetry data; the data information base is determined based on the data type of each sample telemetering data and initial data information included in a preset initial data information base, and the data type is obtained by performing type prediction on data information except the target type of each sample telemetering data in each initial data information through a trained shaking data type model; the structured features comprise data edge features and telemetry data edge coefficients of the sample telemetry data, the telemetry data edge coefficients being used to characterize feature weights of the data edge features;
inputting the telemetering data characteristics, telemetering state data and telemetering data source characteristics into a trained data processing model to obtain data processing results of the plurality of telemetering data to be processed;
determining at least one target telemetry datum from the plurality of to-be-processed telemetry data based on the data processing result; and sending the data processing result carrying the target telemetering data to a data receiving terminal.
In an alternative embodiment, the processing server is configured to: acquiring a telemetry data format, a telemetry data characteristic attribute and a telemetry data amount corresponding to the telemetry data recording label; determining a telemetry data security feature and a telemetry data classification field corresponding to the telemetry data recording tag based on the telemetry data format; acquiring historical telemetry data source characteristics corresponding to the telemetry data classification field; and determining the corresponding telemetry data characteristics of the telemetry data recording tag based on the telemetry data characteristic attributes, the telemetry data amount, the telemetry data safety characteristics and the historical telemetry data source characteristics.
In an alternative embodiment, the processing server is configured to: obtaining sample fields of sample telemetering data corresponding to each telemetering data to be processed; acquiring data information corresponding to the sample field from a data information base; telemetry data source characteristics of the sample telemetry data are extracted from the data information.
In an alternative embodiment, the processing server is configured to: acquiring telemetry data processing duration corresponding to the telemetry data processing request; determining at least one target telemetry datum from the plurality of to-be-processed telemetry data based on the telemetry data processing duration and the data processing result.
On the basis of the above, please refer to fig. 3, the present invention further provides a block diagram of a remote mapping data processing apparatus 500, which includes the following functional modules.
The tag obtaining module 510 is configured to, in response to the received telemetry data processing request, obtain a telemetry data record tag carried in the telemetry data processing request.
A data characteristic determining module 520, configured to obtain a telemetry data characteristic corresponding to the telemetry data recording tag, a plurality of to-be-processed telemetry data, and telemetry state data of the plurality of to-be-processed telemetry data.
A data source obtaining module 530, configured to obtain, from a database of data information, telemetry data source characteristics of sample telemetry data carried in the plurality of to-be-processed telemetry data, where the telemetry data source characteristics include at least structural characteristics of the sample telemetry data; the data information base is determined based on the data type of each sample telemetering data and initial data information included in a preset initial data information base, and the data type is obtained by performing type prediction on data information except the target type of each sample telemetering data in each initial data information through a trained shaking data type model; the structured features include data edge features of the sample telemetry data and telemetry data edge coefficients used to characterize feature weights of the data edge features.
And a processing result determining module 540, configured to input the telemetry data characteristics, the telemetry state data, and the telemetry data source characteristics to the trained data processing model, so as to obtain data processing results of the plurality of to-be-processed telemetry data.
A processing result sending module 550, configured to determine at least one target telemetry data from the plurality of to-be-processed telemetry data based on the data processing result; and sending the data processing result carrying the target telemetering data to a data receiving terminal.
Further, the system may further include a mapping module 560, configured to send the data processing result carrying the target telemetry data to a data receiving terminal as mapping data, and perform mapping processing on the mapping data.
On the basis of the above, please refer to fig. 4 in combination, which provides a processing server 300, comprising a processor 310, a memory 320 connected to the processor 310, and a bus 330; wherein, the processor 310 and the memory 320 communicate with each other through the bus 330; the processor 310 is used to call the program instructions in the memory 320 to execute the above-mentioned 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 method of remote mapping data processing, comprising:
responding to a received telemetry data processing request, and acquiring a telemetry data recording tag carried in the telemetry data processing request;
acquiring telemetry data characteristics, a plurality of to-be-processed telemetry data and telemetry state data of the plurality of to-be-processed telemetry data corresponding to the telemetry data recording tag;
obtaining telemetry data source characteristics of sample telemetry data carried in the plurality of to-be-processed telemetry data from a data information base, wherein the telemetry data source characteristics at least comprise structural characteristics of the sample telemetry data; the data information base is determined based on the data type of each sample telemetering data and initial data information included in a preset initial data information base, and the data type is obtained by performing type prediction on data information except the target type of each sample telemetering data in each initial data information through a trained shaking data type model; the structured features comprise data edge features and telemetry data edge coefficients of the sample telemetry data, the telemetry data edge coefficients being used to characterize feature weights of the data edge features;
inputting the telemetering data characteristics, telemetering state data and telemetering data source characteristics into a trained data processing model to obtain data processing results of the plurality of telemetering data to be processed;
determining at least one target telemetry datum from the plurality of to-be-processed telemetry data based on the data processing result; and sending the data processing result carrying the target telemetering data to a data receiving terminal.
2. The method of claim 1, further comprising: acquiring a telemetry data format, a telemetry data characteristic attribute and a telemetry data amount corresponding to the telemetry data recording label; determining a telemetry data security feature and a telemetry data classification field corresponding to the telemetry data recording tag based on the telemetry data format; acquiring historical telemetry data source characteristics corresponding to the telemetry data classification field; and determining telemetry data characteristics, a plurality of to-be-processed telemetry data and telemetry state data of the plurality of to-be-processed telemetry data corresponding to the telemetry data recording tag based on the telemetry data characteristic attributes, the telemetry data amount, the telemetry data security characteristics and the historical telemetry data source characteristics.
3. The method of claim 1, wherein obtaining telemetry data source characteristics of sample telemetry data carried in the plurality of telemetry data to be processed comprises: obtaining sample fields of sample telemetering data corresponding to each telemetering data to be processed; acquiring data information corresponding to the sample field from a data information base; telemetry data source characteristics of the sample telemetry data are extracted from the data information.
4. The method of claim 3, wherein extracting telemetry source characteristics of the sample telemetry data from the data information comprises: extracting at least a sample field, a data type and a sample data code of the sample telemetry data from the data information; acquiring data edge characteristics of the sample telemetering data and data distribution information corresponding to the data type; determining telemetry data edge coefficients for the sample telemetry data based on the data edge features and the data distribution information; determining the data edge features and the telemetry data edge coefficients as structured features of the sample telemetry data.
5. The method of claim 1, wherein determining at least one target telemetry datum from the plurality of to-be-processed telemetry data based on the data processing results comprises: acquiring telemetry data processing duration corresponding to the telemetry data processing request; determining at least one target telemetry datum from the plurality of to-be-processed telemetry data based on the telemetry data processing duration and the data processing result;
the method for acquiring the telemetry data record tag carried in the telemetry data processing request in response to the received telemetry data processing request comprises the following steps: responding to a telemetry data processing request sent by telemetry equipment, and analyzing the telemetry data processing request to obtain a telemetry data recording label;
inputting the telemetry data characteristics, the telemetry state data and the telemetry data source characteristics into a trained data processing model to obtain data processing results of the plurality of to-be-processed telemetry data, wherein the data processing results comprise: integrating the telemetering data characteristics, the telemetering state data and the telemetering data source characteristics into to-be-processed characteristics; inputting the features to be processed into the data processing model to obtain data processing results of the plurality of telemetric data to be processed; the data processing model is a convolutional neural network model, and the data processing result is output by the convolutional neural network model.
6. The method of claim 1, further comprising:
and sending the data processing result carrying the target telemetering data to a data receiving terminal as mapping data, and carrying out mapping processing on the mapping data.
7. A telemapping data processing system, the system comprising a processing server, a telemetry device and a data receiving terminal in communication with each other;
wherein the processing server is configured to:
responding to a received telemetry data processing request, and acquiring a telemetry data recording tag carried in the telemetry data processing request;
acquiring telemetry data characteristics, a plurality of to-be-processed telemetry data and telemetry state data of the plurality of to-be-processed telemetry data corresponding to the telemetry data recording tag;
obtaining telemetry data source characteristics of sample telemetry data carried in the plurality of to-be-processed telemetry data from a data information base, wherein the telemetry data source characteristics at least comprise structural characteristics of the sample telemetry data; the data information base is determined based on the data type of each sample telemetering data and initial data information included in a preset initial data information base, and the data type is obtained by performing type prediction on data information except the target type of each sample telemetering data in each initial data information through a trained shaking data type model; the structured features comprise data edge features and telemetry data edge coefficients of the sample telemetry data, the telemetry data edge coefficients being used to characterize feature weights of the data edge features;
inputting the telemetering data characteristics, telemetering state data and telemetering data source characteristics into a trained data processing model to obtain data processing results of the plurality of telemetering data to be processed;
determining at least one target telemetry datum from the plurality of to-be-processed telemetry data based on the data processing result; and sending the data processing result carrying the target telemetering data to a data receiving terminal.
8. The system of claim 7, wherein the processing server is further configured to: acquiring a telemetry data format, a telemetry data characteristic attribute and a telemetry data amount corresponding to the telemetry data recording label; determining a telemetry data security feature and a telemetry data classification field corresponding to the telemetry data recording tag based on the telemetry data format; acquiring historical telemetry data source characteristics corresponding to the telemetry data classification field; and determining the corresponding telemetry data characteristics of the telemetry data recording tag based on the telemetry data characteristic attributes, the telemetry data amount, the telemetry data safety characteristics and the historical telemetry data source characteristics.
9. The system of claim 7, wherein the processing server is configured to: obtaining sample fields of sample telemetering data corresponding to each telemetering data to be processed; acquiring data information corresponding to the sample field from a data information base; telemetry data source characteristics of the sample telemetry data are extracted from the data information.
10. The system of claim 7, wherein the processing server is configured to: acquiring telemetry data processing duration corresponding to the telemetry data processing request; determining at least one target telemetry datum from the plurality of to-be-processed telemetry data based on the telemetry data processing duration and the data processing result.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116089406A (en) * | 2022-11-08 | 2023-05-09 | 速度时空信息科技股份有限公司 | Barrier visualization processing system for ocean mapping |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116089406A (en) * | 2022-11-08 | 2023-05-09 | 速度时空信息科技股份有限公司 | Barrier visualization processing system for ocean mapping |
| CN116089406B (en) * | 2022-11-08 | 2023-08-25 | 速度科技股份有限公司 | Barrier visualization processing system for ocean mapping |
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Application publication date: 20210326 |