CN-122020518-A - Multi-source space data and index integration method, device and equipment

CN122020518ACN 122020518 ACN122020518 ACN 122020518ACN-122020518-A

Abstract

The invention discloses a method, a device and equipment for integrating multisource spatial data and indexes. The method comprises the following steps of dynamic semantic mapping of multi-source heterogeneous data, integrated semantic consistency monitoring and regulation and control, and dynamic demand adaptation efficiency evaluation and resource regulation and control. The method and the device have the advantages that space and index data are associated through dynamic semantic mapping, whether semantic regulation is carried out is judged based on integrated semantic consistency, and further, the dynamic demand adaptation efficiency is evaluated, whether resource regulation is carried out is judged according to the effective efficiency, whether the data integration flow is re-executed is decided according to the effective efficiency, closed-loop negative feedback is formed, high-quality and high-timeliness fusion space information is stably output, and the problem that in the prior art, the integration accuracy of multi-source space data and indexes is low due to the fusion and disconnection of indexes and space data and the problem of semantic gap and the contradiction between dynamic updating and computational power resources is solved.

Inventors

MAO MINGRUI
Xie Luhua
MA DING
LI JINGMEI
LIAO SHUGUANG
GUAN XUEMENG
SHI JINLEI
WANG TENG

Assignees

北京城市象限科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260106

Claims (10)

1. The multi-source space data and index integration method is characterized by comprising the following steps of: S100, acquiring original data from at least two independent data sources, wherein the original data comprises space data and index data space data, analyzing the acquired original data, and dynamically associating and mapping the analyzed space data with the index data based on a preset semantic mapping rule set; S200, acquiring integrated semantic data in a dynamic association and mapping process, wherein the integrated semantic data is used for quantifying the matching degree of multi-source space data and business indexes in integrated semantics to obtain multi-source space data-index integrated semantic consistency, judging whether to execute index integrated semantic consistency self-adaptive regulation based on the multi-source space data-index integrated semantic consistency so as to self-adaptively balance calculation cost and regulation precision and avoid regulation loss, if so, acquiring demand adaptation data after regulation, if not, directly acquiring the demand adaptation data, and quantifying the effective response capability of double demands on high-precision space modeling and real-time multi-source space data flow dynamic integration to obtain dynamic demand adaptation efficiency; S300, judging whether to execute demand adaptation effective self-adaptive regulation based on dynamic demand adaptation efficiency so as to achieve optimal matching between processing resources and dynamic demands, avoiding resource idling and response oscillation caused by excessive regulation, if yes, re-executing step S100 after regulation, if not, directly executing step S100, and continuously and stably outputting high-quality and high-timeliness fusion space information through self-adaptive negative feedback regulation.
2. The method for integrating multi-source spatial data and indicators according to claim 1, wherein the specific steps of obtaining multi-source spatial data-indicator integration semantic consistency are as follows: The integrated semantic data comprises index-data mappable rate, time consuming for space-time reference alignment and parallelization acceleration ratio of alignment tasks; compensating the ratio of the index-data mappable rate to the mappable rate reference value by using the mappable rate correction factor to obtain a mappable rate influence component; Compensating the ratio of the alignment time reference value to the space-time reference alignment time by using the alignment time correction factor to obtain an alignment time influence component; Compensating the ratio of the acceleration ratio correction factor alignment task parallelization acceleration ratio to the acceleration ratio reference value to obtain an acceleration ratio influence component; Coupling the mappable rate influence component, the alignment time-consuming influence component and the acceleration ratio influence component to obtain multi-source space data-index integrated semantic consistency; The specific steps for judging whether to execute the index integrated semantic consistency self-adaptive regulation are as follows: If the multi-source space data-index integrated semantic consistency is greater than or equal to the integrated semantic consistency critical value, the index integrated semantic consistency self-adaptive regulation and control is not executed, and if not, the self-adaptive semantic granularity tuning mechanism and the reference drift-conflict resolution delay coupling self-adaptive threshold regulating mechanism are executed.
3. The method for integrating multi-source spatial data and indicators according to claim 2, wherein the specific steps of executing the adaptive semantic granularity tuning mechanism are as follows: the method comprises the steps of receiving source data, and mapping the source data into target data based on a preset initial mapping rule set, wherein the initial mapping rule set comprises at least one mapping rule with specific granularity; Monitoring and counting semantic ambiguity redundancy of each rule in the initial mapping rule set in the mapping process, and generating granularity adjustment instructions for the initial mapping rule set according to semantic ambiguity redundancy and multi-source space data-index integrated semantic consistency, wherein the granularity adjustment instructions specifically comprise: If the semantic ambiguity redundancy is smaller than or equal to the redundancy reference lower limit, generating a granularity refinement instruction, wherein the granularity refinement instruction is used for driving to execute the following operations of dividing a current mapping rule into sub-rules; if the semantic ambiguity redundancy is greater than or equal to the redundancy reference upper limit, generating a granularity coarsening instruction, wherein the granularity coarsening instruction is used for driving the following operations of merging mapping rules with consistent output results into a more general rule; If the semantic ambiguity redundancy is in a redundancy reference interval, generating a granularity maintaining instruction, and keeping the granularity of the current mapping rule set unchanged, wherein the redundancy reference interval represents an open interval formed by a redundancy reference lower limit and a redundancy reference upper limit; And executing the granularity adjustment instruction, and correspondingly adding, deleting, detaching and operating the initial mapping rule set to form an optimized new mapping rule set for a subsequent data mapping task.
4. The method for integrating multi-source spatial data and indicators according to claim 2, wherein the specific steps of the reference drift-conflict resolution delay coupling adaptive thresholding mechanism are as follows: The method comprises the steps of obtaining a multi-source space-time data stream, aligning and fusing the multi-source space-time data based on a preset space-time reference, monitoring the space-time reference drift amount of the multi-source space-time data in the aligning and fusing process, and triggering a space-time reference drift correction process when the space-time reference drift amount exceeds a preset correction time delay threshold value, wherein the method specifically comprises the following steps: Based on the event of each conflict resolution rule, calculating the time from the time when the event is detected to the time when the conflict resolution rule is completely resolved, and defining the time as conflict resolution rule adaptation time delay; Establishing a dynamic mapping relation of the time-space reference drift correction time delay threshold, the conflict resolution rule adaptation time delay and the multisource spatial data-index integrated semantic consistency, and generating a threshold adjustment instruction according to the dynamic mapping relation, wherein the dynamic mapping relation is configured to be a negative correlation relation between the conflict resolution rule adaptation time delay and the time-space reference drift correction time delay threshold.
5. The method for integrating multi-source spatial data and indicators as set forth in claim 4, wherein said threshold adjustment instruction generation logic is specifically configured to: if the conflict resolution rule adaptation time delay is higher than the adaptation time delay critical upper limit, generating a threshold value down-regulating instruction for reducing the correction time delay threshold value; If the conflict resolution rule adaptation time delay is lower than the adaptation time delay critical lower limit, generating a threshold up-regulation instruction for improving the correction time delay threshold; If the conflict resolution rule adaptation time delay is in an adaptation time delay critical interval, generating a threshold maintaining instruction, and keeping the corrected time delay threshold unchanged, wherein the adaptation time delay critical interval represents a closed interval formed by an adaptation time delay critical lower limit and an adaptation time delay critical upper limit; and executing the threshold adjustment instruction to dynamically update the corrected time delay threshold, wherein the updated time-space reference drift correction time delay threshold is applied to subsequent time-space reference drift monitoring and time-space reference drift correction.
6. The method for integrating multi-source spatial data and indexes according to claim 1, wherein the demand adaptation data comprises an integrated semantic consistency effective value, heterogeneous resource cooperative scheduling time delay and incremental data volume duty ratio; Compensating the ratio of the integrated semantic consistency effective value to the consistency effective reference value by the consistency effective correction factor to obtain a consistency effective influence component; Compensating the ratio of the scheduling delay correction factor to the scheduling delay reference value and the cooperative scheduling delay of the heterogeneous resource to obtain a scheduling delay influence component; compensating the ratio of the volume duty ratio of the incremental data to the volume duty ratio reference value by the volume duty ratio correction factor to obtain a volume duty ratio influence component; coupling the consistent effective influence component, the scheduling delay influence component and the volume duty ratio influence component to obtain dynamic demand adaptation efficiency; the specific steps for judging whether to execute the demand adaptation effective self-adaptive regulation are as follows: If the dynamic demand adaptation efficiency is greater than or equal to the demand adaptation effective critical value, the demand adaptation effective self-adaptive regulation is not executed, and if not, a conflict arbitration time-consuming single-threshold back pressure control mechanism and a rule generation period-driven incremental calculation acceleration ratio unidirectional regulation mechanism are executed.
7. The method for integrating multi-source spatial data and indicators as set forth in claim 6, wherein the specific steps of performing the collision arbitration time consuming single threshold backpressure control mechanism are as follows: the method comprises the steps of receiving task requests from a plurality of task sources, injecting the task requests into a dynamic task queue, scheduling tasks from the dynamic task queue, and distributing the tasks to at least one rule execution engine for processing, wherein the rule execution engine comprises a plurality of business rules with potential execution condition overlapping, and executing rule conflict detection and arbitration logic in the task processing process; in the process of processing tasks by the rule execution engine, events triggering rule conflict detection and arbitration are monitored and recorded in real time, and time consumption is acquired for single rule conflict arbitration aiming at each event of rule conflict detection and arbitration; Establishing a negative feedback regulation relation among the dynamic demand adaptation efficiency, rule conflict detection and arbitration time consumption and the average arrival rate of a dynamic task queue, and generating an arrival rate regulation instruction according to the relation, wherein the negative feedback regulation relation is configured in such a way that the rule conflict detection and arbitration time consumption is inversely related to the average arrival rate of the dynamic task queue, and the arrival rate regulation instruction generation logic specifically comprises: If the rule conflict detection and arbitration time is less than or equal to the arbitration time consumption critical value, generating an arrival rate maintaining instruction, and maintaining the average arrival rate of the target dynamic task queue unchanged; And if the rule conflict detection and arbitration time is greater than the arbitration time threshold, generating an arrival rate down-regulating instruction, wherein the arrival rate down-regulating instruction is used for reducing the target average arrival rate of the dynamic task queue.
8. The method for integrating multi-source spatial data and indexes as set forth in claim 6, wherein the specific steps of the rule generating period driven incremental calculation acceleration ratio unidirectional regulation mechanism are as follows: The method comprises the steps of monitoring rule generation behaviors in real time, recording continuous rule generation time points, calculating intervals of adjacent time points, defining a rule generation period, establishing a forward dynamic mapping relation among the rule generation period, dynamic demand adaptation efficiency and increment fusion and updating calculation acceleration ratio, and generating an acceleration ratio adjustment instruction, wherein the forward dynamic mapping relation is configured in such a way that the rule learning and generation period and the increment fusion and updating calculation acceleration ratio are in a positive correlation relation; If the rule learning and generating period is greater than or equal to the generating period critical value, generating a speed-up command, wherein the speed-up command is used for improving the increment fusion and updating the calculation speed-up ratio so as to obtain higher calculation performance improvement; and if the rule learning and generating period is smaller than the generating period critical value, generating an acceleration ratio down-regulating instruction, wherein the acceleration ratio down-regulating instruction is used for reducing the increment fusion and updating the calculated acceleration ratio.
9. The multi-source spatial data and index integrating device, applying the multi-source spatial data and index integrating method according to any one of claims 1-8, is characterized by comprising a multi-source heterogeneous data dynamic semantic mapping module, an integrated semantic consistency monitoring and regulating module and a dynamic demand adaptation efficiency evaluation and resource regulating module: The multi-source heterogeneous data dynamic semantic mapping module is used for acquiring original data from at least two independent data sources, wherein the original data comprises space data and index data space data, analyzing the acquired original data, and dynamically associating and mapping the analyzed space data with the index data based on a preset semantic mapping rule set; The integrated semantic consistency monitoring and regulating module is used for acquiring integrated semantic data in a dynamic association and mapping process, quantifying the matching degree of multi-source space data and business indexes in integrated semantics to obtain multi-source space data-index integrated semantic consistency, judging whether to execute index integrated semantic consistency self-adaptive regulation or not based on the multi-source space data-index integrated semantic consistency so as to adaptively balance calculation expenditure and regulation precision and avoid regulation loss, if so, acquiring demand adaptation data after regulation, if not, directly acquiring the demand adaptation data, and quantifying the effective response capability of double demands on high-precision space modeling and real-time multi-source space data flow integration to obtain dynamic demand adaptation efficiency; The dynamic demand adaptation efficiency evaluation and resource regulation module is used for judging whether to execute demand adaptation effective self-adaptive regulation based on dynamic demand adaptation efficiency so as to achieve optimal proportioning between processing resources and dynamic demands and avoid resource idling and response oscillation caused by excessive regulation, if yes, step S100 is re-executed after regulation, if not, step S100 is directly executed, and high-quality and high-timeliness fusion space information can be continuously and stably output through self-adaptive negative feedback regulation.
10. A computing device, comprising: one or more processors; Storage means for storing one or more programs which when executed by the one or more processors cause the one or more processors to implement the method of any of claims 1 to 8.

Description

Multi-source space data and index integration method, device and equipment Technical Field The present invention relates to the field of data integration technologies, and in particular, to a method, an apparatus, and a device for integrating multisource spatial data and indexes. Background Format conversion is performed by means of tools such as FME (Feature Manipulation Engine, element manipulation engine), all data are unified to the same coordinate system in ArcGIS or QGIS, optical images are processed by radiometric calibration and atmospheric correction, and geometric deviation is eliminated by means of geometric fine correction and image registration technology. For scale differences, resampling (e.g., bicubic convolution) and spatio-temporal interpolation (e.g., kriging) are employed to unify to a reference grid. And then entering a data fusion and feature extraction stage, and lifting space details by adopting pixel-level fusion (such as Pan-sharp), or generating height features by combining Light Detection (Light Detection AND RANGING) and ranging) point clouds by using object-oriented image analysis. For time sequence analysis, a spatio-temporal adaptive reflectivity fusion model is applied to generate a high spatio-temporal resolution sequence. And extracting initial indexes from the fused data through a deep learning model, and calling a professional model. For example, integrating multi-layer data of land utilization, terrain, etc., calculating carbon reserves through InVEST models, or calculating public service reachability using network analysis in combination with road networks and POIs (Point Of Interest, points of interest). And (3) carrying out Min-Max standardization on the index to eliminate dimension, then determining weight by using a hierarchical analysis method or an entropy weight method, and finally generating a comprehensive index map by weighting linear combination or principal component analysis. The accuracy is verified by using high-resolution images or field samples through confusion matrix and Kappa coefficient, and the result is dynamically visualized through WebGIS (such as GeoServer for releasing WMS service) or interactive instrument board (such as Tableau) and embedded into a space decision support system. However, in the process of implementing the technical scheme of the embodiment of the application, the application discovers that the above technology has at least the following technical problems: In the prior art, the semantic modeling capability is insufficient, the existing scheme is mostly in data format fusion and lacks a semantic model for uniformly expressing space features and business logic, business and technical requirements are split in a business process, data matchability is not fully considered in index design, and deep understanding of business connotation is lacked in technical integration, so that the requirements are misplaced; High-precision modeling (such as centimeter-level DSM construction) and complex simulation (such as geological disaster process) have the increasing demand on computational power, conflict with the dynamic integration demand of real-time data streams (such as sensors and unmanned aerial vehicles), and real-time decision making is difficult to support. The method mainly comprises the steps of stiff computing power resource allocation, incapability of elastically responding to dynamic load of 'mass data+complex model' in a static scheduling mode, low efficiency of a fusion algorithm, high computational complexity and lack of a lightweight scheme for adaptive edge computing when a traditional pixel-level and feature-level algorithm processes high-dimensional data, unsound data updating mechanism, missing of a full-flow automatic link from acquisition to updating, slow updating caused by manual work, vicious circle which causes computing power idling and unsatisfied updating requirements, and low accuracy of multi-source space data and index integration caused by fusion and disjoint of indexes and space data and contradiction between dynamic updating and computing power resources. Disclosure of Invention In order to solve the technical problems of low accuracy of multi-source spatial data and index integration caused by fusion and disjoint of indexes and spatial data and semantic gap problems and contradiction between dynamic updating and computing power resources in the prior art, the embodiment of the invention provides a multi-source spatial data and index integration method, device and equipment. The technical scheme is as follows: On the one hand, the multi-source spatial data and index integration method comprises the steps of S100, acquiring original data from at least two independent data sources, analyzing the acquired original data, wherein the original data comprises spatial data and index data spatial data, dynamically associating and mapping the analyzed spatial data with the index data based on a preset semantic