CN-121501522-B - Multi-source data processing method and system for land surface dynamic monitoring

CN121501522BCN 121501522 BCN121501522 BCN 121501522BCN-121501522-B

Abstract

A multi-source data processing method and system for dynamic monitoring of land surface. The method comprises the steps of determining task allocation of all monitoring ends according to working states of all monitoring ends arranged in a space where a land is located, constructing an elastic resource pool according to actual cloud operation states accessed by all the monitoring ends to determine resource allocation of the monitoring ends, constructing a reference database for monitoring the land surface, carrying out data verification on the reference database according to multi-source data generated during the process of executing the monitoring tasks, carrying out feature recognition and screening on the reference database for completing data verification to obtain a plurality of reliable multi-source data, generating a land surface live state representation map, carrying out element change on the land surface live state representation map to obtain a target representation map, and uploading the target representation map to the cloud. By distributing the elastic resources of the monitoring end for executing different tasks, continuous and stable monitoring acquisition of multi-source data is ensured, and the reliability and effectiveness of land surface monitoring are improved.

Inventors

Wan Changxiang
ZHAO LANTIAN

Assignees

南昌零重空间技术有限公司

Dates

Publication Date: 20260508
Application Date: 20260113

Claims (9)

1. A method of multi-source data processing for dynamic monitoring of a surface of a land, comprising: task allocation to all monitoring ends is determined according to the working states of all monitoring ends arranged in a space where the land is located; analyzing a monitoring task instruction received by the monitoring end, and determining resource allocation of the elastic resource pool to the monitoring end; Constructing a reference database for land surface monitoring, and performing data verification on the reference database according to multi-source data generated during the monitoring task execution of the monitoring end; performing feature recognition and screening on the reference database after the data verification is completed to obtain a plurality of trusted multi-source data; Performing time domain and space domain related processing on the plurality of credible multi-source data to obtain a land surface live state representation map, performing element change on the land surface live state representation map according to a query request to obtain a target representation map, and uploading the target representation map to a cloud; The method comprises the steps of analyzing a monitoring task instruction received by a monitoring end, determining resource allocation of an elastic resource pool to the monitoring end, extracting three parameters including a monitoring area space range parameter, a monitoring precision parameter and a monitoring time window parameter from the monitoring task instruction received by the monitoring end, determining a normalized task complexity index of the monitoring end according to a first preset algorithm according to the monitoring area space range parameter, the monitoring precision parameter and the monitoring time window parameter, predicting a dynamic workload perceived by fusion ground surface and illumination of the monitoring end according to a preset fusion algorithm model according to the normalized task complexity index, the monitoring area ground surface characteristics and illumination conditions, and determining resource allocation to the monitoring end according to a preset coupling model according to the dynamic workload of the monitoring end, single-core processing capacity of a cloud server, weather complexity condition and topography fluctuation condition of the monitoring area.
2. A multi-source data processing method for dynamic monitoring of a land surface as claimed in claim 1, wherein: according to the working states of all monitoring ends arranged in the space of the land, determining task allocation to all monitoring ends comprises the following steps: Acquiring respective historical working state records and working condition records of all monitoring terminals arranged in a space of a land, and extracting monitoring data change trends of the monitoring terminals during historical monitoring from the historical working state records, wherein the monitoring data change trends comprise the monitoring data error rate and the monitoring data redundancy of the monitoring terminals during historical monitoring and the related change trends of the generated monitoring data types and data volumes thereof; Comparing the real-time generated monitoring data type, the data quantity and the monitoring data change trend, and estimating the time point of the monitoring end in an unreliable state in a future time interval; and determining the task allocation time sequence of all the monitoring terminals according to the time domain relation among the time points of the monitoring terminals in the un-trusted state.
3. A multi-source data processing method for dynamic monitoring of a land surface as claimed in claim 2, wherein: According to the cloud actual running state of all monitoring terminals, an elastic resource pool is constructed, which comprises the following steps: Acquiring real-time CPU running states of cloud ends accessed by all monitoring ends, and determining remaining CPU resources of the cloud ends in a preset working mode according to the real-time CPU running states; and constructing an elastic resource pool according to the allowance CPU resources and adjusting the actual resource capacity of the elastic resource pool.
4. A multi-source data processing method for dynamic monitoring of a land surface as claimed in claim 1, wherein: constructing a reference database for land surface monitoring, comprising: The method comprises the steps of obtaining a historical monitoring data set of the land surface, preprocessing all historical monitoring data in the historical monitoring data set according to labels of each historical monitoring data in the historical monitoring data set, and constructing the corresponding historical monitoring data to form a reference database after index identification, wherein the preprocessing comprises standardized preprocessing, and the index identification comprises adding a unique index mark to the historical monitoring data.
5. A multi-source data processing method for dynamic monitoring of a land surface as claimed in claim 4, wherein: according to the multi-source data generated during the monitoring task execution of the monitoring end, the data verification of the reference database comprises the following steps: dividing multisource data generated during the monitoring task execution of the monitoring end into a plurality of single-mode data, and carrying out standardized preprocessing on the single-mode data; Comparing the historical monitoring data with the same type attribute as the single-mode data in the reference database with the single-mode data, and determining deviation between the single-mode data and the historical monitoring data; And according to the deviation, checking and correcting the historical monitoring data of the reference database.
6. A multi-source data processing method for dynamic monitoring of a land surface as claimed in claim 1, wherein: Performing feature recognition and screening on the reference database with the data verification completed to obtain a plurality of trusted multi-source data, wherein the method comprises the following steps: Performing feature recognition on the reference database after the data verification is completed to obtain physical quantity features, regional features and time features of all monitoring data in the reference database after the data verification is completed; and screening a plurality of trusted data from the same type of monitoring data according to the spatial fluctuation and the time fluctuation, and integrating the plurality of trusted data screened from the same type of monitoring data corresponding to a plurality of physical quantity attributes into a plurality of trusted multi-source data.
7. A multi-source data processing method for dynamic monitoring of a land surface as claimed in claim 1, wherein: performing time domain and space domain correlation processing on the plurality of trusted multi-source data to obtain a land surface live state representation map, including: determining the time correlation degree and the space correlation degree between any two pieces of the credible multi-source data in the credible multi-source data according to the respective time points and the space points of the credible multi-source data; and according to the time correlation degree and the space correlation degree, carrying out arrangement mapping on the plurality of trusted multisource data to obtain a land surface live state representation map.
8. A multi-source data processing method for dynamic monitoring of a land surface as claimed in claim 1, wherein: according to the query request, performing element change on the land surface live state representation map to obtain a target representation map, and uploading the target representation map to a cloud, wherein the method comprises the following steps of: Determining map elements in the land surface live state representation map, which satisfy preset related conditions with the query target object, according to the query target object; And changing the visual state of the map element according to the distribution of the map element representing the map in the ground surface live state, obtaining a target representation map and uploading the target representation map to a cloud.
9. A multi-source data processing system for dynamic monitoring of a surface of a land, comprising: the resource pool construction module is used for determining task allocation to all monitoring ends according to the working states of all monitoring ends arranged in the space where the land is located; The resource allocation module is used for analyzing the monitoring task instruction received by the monitoring end and determining the resource allocation of the elastic resource pool to the monitoring end; The data verification module is used for constructing a reference database for land surface monitoring and carrying out data verification on the reference database according to multi-source data generated during the monitoring task execution of the monitoring end; The identification screening module is used for carrying out feature identification and screening on the reference database after the data verification is completed to obtain a plurality of credible multi-source data; The data processing module is used for performing time domain and space domain related processing on the plurality of trusted multi-source data to obtain a land surface live state representation map, performing element change on the land surface live state representation map according to a query request to obtain a target representation map and uploading the target representation map to a cloud; The method comprises the steps of analyzing a monitoring task instruction received by a monitoring end, determining resource allocation of an elastic resource pool to the monitoring end, extracting three parameters including a monitoring area space range parameter, a monitoring precision parameter and a monitoring time window parameter from the monitoring task instruction received by the monitoring end, determining a normalized task complexity index of the monitoring end according to a first preset algorithm according to the monitoring area space range parameter, the monitoring precision parameter and the monitoring time window parameter, predicting a dynamic workload perceived by fusion ground surface and illumination of the monitoring end according to a preset fusion algorithm model according to the normalized task complexity index, the monitoring area ground surface characteristics and illumination conditions, and determining resource allocation to the monitoring end according to a preset coupling model according to the dynamic workload of the monitoring end, single-core processing capacity of a cloud server, weather complexity condition and topography fluctuation condition of the monitoring area.

Description

Multi-source data processing method and system for land surface dynamic monitoring Technical Field The invention relates to the field of land monitoring, in particular to a multi-source data processing method and system for land surface dynamic monitoring. Background The land resource has the characteristics of wide space distribution, large local difference, great influence by external environment and the like, and in order to reasonably develop and utilize the land resource and timely and comprehensively master land state transformation in the land construction process, the land, particularly the land surface, needs to be dynamically monitored. Multisource data regarding different states of the ground surface are generated during monitoring of the ground surface, the multisource data are characterized by isomerization, large volumes and the like, and resources required for monitoring and acquiring different types of multisource data are different. In order to ensure the stability and continuity of multi-source data monitoring, resources are uniformly distributed to all monitoring devices, and the multi-source data monitored in real time are directly uploaded, stored and processed to obtain a live characterization result of the land surface. However, the above data monitoring and processing methods do not fully consider the difference between different multi-source data, and even distribution of monitoring resources may lead to the situation of partial multi-source data monitoring missing and error, and the like, and the monitoring data is not verified, so that the reliability and effectiveness of land surface monitoring are reduced, and an accurate land monitoring result cannot be obtained. Disclosure of Invention The existing land surface dynamic monitoring cannot change monitoring resource allocation aiming at multi-source data monitoring live condition, multi-source data cannot be verified and screened, reliability and effectiveness of land surface monitoring are reduced, and accurate land monitoring results cannot be obtained. The present invention proposes a multi-source data processing method and system for dynamic monitoring of a land surface in order to provide a solution to, or at least partially solve, the above-mentioned problems. Some embodiments of the present disclosure provide a multi-source data processing method for dynamic monitoring of a land surface, comprising: task allocation to all monitoring ends is determined according to the working states of all monitoring ends arranged in a space where the land is located; analyzing a monitoring task instruction received by the monitoring end, and determining resource allocation of the elastic resource pool to the monitoring end; Constructing a reference database for land surface monitoring, and performing data verification on the reference database according to multi-source data generated during the monitoring task execution of the monitoring end; performing feature recognition and screening on the reference database after the data verification is completed to obtain a plurality of trusted multi-source data; And according to the query request, carrying out element change on the land surface live state representation map to obtain a target representation map and uploading the target representation map to the cloud. Optionally, determining task allocation to all monitoring ends according to the working states of all monitoring ends arranged in the space where the land is located includes: Acquiring respective historical working state records and working condition records of all monitoring terminals arranged in a space of a land, and extracting monitoring data change trends of the monitoring terminals during historical monitoring from the historical working state records, wherein the monitoring data change trends comprise the monitoring data error rate and the monitoring data redundancy of the monitoring terminals during historical monitoring and the related change trends of the generated monitoring data types and data volumes thereof; Comparing the real-time generated monitoring data type, the data quantity and the monitoring data change trend, and estimating the time point of the monitoring end in an unreliable state in a future time interval; and determining the task allocation time sequence of all the monitoring terminals according to the time domain relation among the time points of the monitoring terminals in the un-trusted state. Optionally, according to the actual cloud operation state accessed by all monitoring ends, an elastic resource pool is constructed, including: Acquiring real-time CPU running states of cloud ends accessed by all monitoring ends, and determining remaining CPU resources of the cloud ends in a preset working mode according to the real-time CPU running states; and constructing an elastic resource pool according to the allowance CPU resources and adjusting the actual resource capacity of the elastic resource pool. Opti