Multi-source spatial data and index integration method, device and equipment

Abstract

The invention discloses a multi-source spatial data and index integration method, device and equipment. The method comprises the following steps of multi-source heterogeneous data dynamic semantic mapping, integrated semantic consistency monitoring and regulation and control, and dynamic demand adaptation efficiency evaluation and resource regulation and control. According to the method, space and index data are associated through dynamic semantic mapping; judging whether to perform semantic regulation or not based on the integrated semantic consistency, and further evaluating the dynamic demand adaptation efficiency; whether resource regulation is carried out is judged according to the effective rate, and whether a data integration process is executed again is decided according to the effective rate, so that closed-loop negative feedback is formed, and high-quality and high-timeliness fusion space information is stably output; the problem that in the prior art, the integration accuracy of the multi-source spatial data and the indexes is low due to the problems of fusion disjunction and semantic gaps of the indexes and the spatial data and the contradiction between dynamic updating and computing power resources is solved.

Description

Technical Field

[0001] This invention relates to the field of data integration technology, and in particular to methods, apparatus and equipment for integrating multi-source spatial data and indicators. Background Technology

[0002] Format conversion is performed using tools such as FME (Feature Manipulation Engine). All data is then unified to the same coordinate system in ArcGIS or QGIS. Optical images are processed through radiometric calibration and atmospheric correction, and geometric correction and image registration techniques are used to eliminate geometric biases. For scale differences, resampling (e.g., bicubic convolution) and spatiotemporal interpolation (e.g., Kriging) are employed to unify the data to the reference grid.

[0003] The data then proceeds to the data fusion and feature extraction stage. Pixel-level fusion (such as pan-sharpening) is used to enhance spatial details, or object-oriented image analysis combined with LiDAR (Light Detection and Ranging) point clouds is used to generate height features. For time-series analysis, a spatiotemporal adaptive reflectivity fusion model is applied to generate high spatiotemporal resolution sequences. After fusion, the data is used to extract initial indicators through deep learning models and then specialized models are invoked. For example, integrating multi-layered data such as land use and topography, carbon storage is calculated using the InVEST model, or network analysis is used to calculate public service accessibility by combining road networks and POIs (Points of Interest).

[0004] The indicators are standardized using Min-Max to eliminate dimensions, and then weights are determined using the analytic hierarchy process (AHP) or entropy weighting method. Finally, a comprehensive index map is generated through weighted linear combination or principal component analysis. Accuracy is verified using high-resolution imagery or field samples via confusion matrices and Kappa coefficients. The results are dynamically visualized through WebGIS (such as GeoServer publishing WMS services) or interactive dashboards (such as Tableau) and embedded into a spatial decision support system.

[0005] However, in the process of implementing the inventive technical solution in the embodiments of this application, it was found that the above-mentioned technology has at least the following technical problems: In existing technologies, semantic modeling capabilities are insufficient. Existing solutions mostly focus on data format integration and lack a unified semantic model that expresses spatial features and business logic. In terms of business processes, there is a disconnect between business and technical requirements. The design of indicators does not fully consider data compatibility, while the technical integration lacks a deep understanding of the business connotation, resulting in misaligned requirements. In terms of management, due to the imperfect metadata system, the lineage of indicators and data traceability cannot be connected, and semantic ambiguity is difficult to resolve. The surge in demand for computing power from high-precision modeling (such as centimeter-level DSM construction) and complex simulations (such as geological disaster processes) conflicts with the dynamic integration requirements of real-time data streams (such as sensors and drones), making it difficult to support real-time decision-making. This is mainly due to the rigid allocation of computing resources, with static scheduling modes unable to flexibly respond to the dynamic load of "massive data + complex models"; the inefficiency of fusion algorithms, with traditional pixel-level and feature-level algorithms having high computational complexity when processing high-dimensional data and lacking lightweight solutions adapted to edge computing; and the imperfect data update mechanism, with the lack of a fully automated link from collection to update, relying on manual processes, resulting in update delays and creating a vicious cycle of idle computing power and unmet update needs. There is also the problem of low accuracy in the integration of multi-source spatial data and indicators due to the disconnect between indicators and spatial data fusion and semantic gaps, as well as the contradiction between dynamic updates and computing resources. Summary of the Invention

[0006] To address the technical problems of low accuracy in integrating multi-source spatial data and indicators due to the disconnect between indicators and spatial data, the semantic gap, and the conflict between dynamic updates and computing resources, this invention provides a method, apparatus, and device for integrating multi-source spatial data and indicators. The technical solution is as follows: On the one hand, a method for integrating multi-source spatial data and indicators is provided. This method includes: S100, acquiring raw data from at least two independent data sources, including spatial data and indicator spatial data; parsing the acquired raw data; and dynamically associating and mapping the parsed spatial data with indicator data based on a preset semantic mapping rule set; S200, acquiring integrated semantic data during the dynamic association and mapping process to quantify the degree of matching between multi-source spatial data and business indicators in integrated semantics, obtaining the multi-source spatial data-indicator integrated semantic consistency; and determining whether to perform adaptive adjustment of indicator integrated semantic consistency based on the multi-source spatial data-indicator integrated semantic consistency to adaptively balance computational overhead and... To improve the accuracy of regulation and avoid regulation losses, if the regulation is accurate, demand adaptation data is obtained after regulation; otherwise, demand adaptation data is obtained directly. This data is used to quantify the effective response capability to the dual demands of high-precision spatial modeling and real-time multi-source spatial data stream dynamic integration, thus obtaining the efficiency of dynamic demand adaptation. In step S300, based on the efficiency of dynamic demand adaptation, it is determined whether to perform effective adaptive regulation of demand adaptation to achieve the optimal ratio between processing resources and dynamic demands, avoiding resource idleness and response oscillation caused by over-regulation. If the regulation is accurate, step S100 is re-executed after regulation; otherwise, step S100 is executed directly. Through adaptive negative feedback adjustment, high-quality and timely fused spatial information can be continuously and stably output.

[0007] On the other hand, a multi-source spatial data and indicator integration device is provided. This device is applied to the multi-source spatial data and indicator integration method and includes: a multi-source heterogeneous data dynamic semantic mapping module, an integration semantic consistency monitoring and control module, and a dynamic demand adaptation efficiency evaluation and resource control module. Specifically, the multi-source heterogeneous data dynamic semantic mapping module is used to acquire raw data from at least two independent data sources, including spatial data and indicator spatial data. The acquired raw data is parsed, and based on a preset semantic mapping rule set, the parsed spatial data and indicator data are dynamically associated and mapped. The integration semantic consistency monitoring and control module is used to acquire integrated semantic data during the dynamic association and mapping process. This data is used to quantify the degree of matching between multi-source spatial data and business indicators in the integration semantics, obtaining the multi-source spatial data-indicator integration semantic consistency. The indicator integration semantic consistency judgment determines whether to perform indicator integration semantic consistency adaptive control to adaptively balance computational overhead and control accuracy, avoiding control losses. If yes, demand adaptation data is obtained after control; otherwise, demand adaptation data is directly obtained to quantify the effective response capability to the dual requirements of high-precision spatial modeling and real-time multi-source spatial data stream dynamic integration, obtaining the efficiency of dynamic demand adaptation. The dynamic demand adaptation efficiency evaluation and resource control module is used to determine whether to perform effective adaptive control based on the efficiency of dynamic demand adaptation, so as to achieve the optimal ratio between processing resources and dynamic demands, avoiding resource idleness and response oscillation caused by over-control. If yes, step S100 is re-executed after control; otherwise, step S100 is directly executed. It can continuously and stably output high-quality and timely fused spatial information through adaptive negative feedback adjustment.

[0008] On the other hand, a computing device includes: one or more processors; and a storage device for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to implement the method.

[0009] One or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: 1. By acquiring raw data from at least two independent data sources, including spatial data and indicator spatial data, the acquired raw data is parsed. Based on a preset semantic mapping rule set, the parsed spatial data and indicator data are dynamically associated and mapped to obtain integrated semantic data during the dynamic association and mapping process. This data is used to quantify the degree of matching between multi-source spatial data and business indicators in integrated semantics, thus obtaining the integrated semantic consistency of multi-source spatial data and indicators. Based on the integrated semantic consistency of multi-source spatial data and indicators, it is determined whether to perform adaptive adjustment of indicator integrated semantic consistency to adaptively balance computational overhead and adjustment accuracy, avoiding adjustment losses. Based on the efficiency of dynamic demand adaptation, it is determined whether to perform effective adaptive adjustment of demand adaptation to achieve the optimal ratio between processing resources and dynamic demands, avoiding resource idleness and response oscillation caused by over-adjustment, thereby improving the accuracy of multi-source spatial data and indicator integration.

[0010] 2. By implementing an adaptive semantic granularity tuning mechanism, the granularity of mapping rules can be dynamically refined or coarsened based on real-time monitoring of semantic ambiguity and redundancy. When the data semantics are complex and the ambiguity is high, more refined rules are automatically adopted to improve mapping accuracy. When the semantics are clear and the rules are redundant, rules are automatically merged and simplified to improve processing efficiency. This achieves an adaptive optimal balance between mapping accuracy and system performance under different data scenarios. Through a benchmark drift-conflict resolution delay coupling adaptive threshold adjustment mechanism, the delay threshold for triggering spatiotemporal benchmark correction can be dynamically adjusted based on the real-time time consumption of the conflict resolution process. When the resolution delay increases, the threshold is automatically lowered to intervene in correction in advance and suppress conflict generation from the source. When the delay decreases, the threshold is automatically raised to reduce unnecessary correction overhead. This achieves an optimal dynamic balance between system processing load and correction frequency while maintaining data consistency, thereby improving the accuracy of multi-source spatial data and indicator integration.

[0011] 3. By implementing a single-threshold backpressure control mechanism for conflict arbitration time, the system can automatically reduce the task arrival rate of the dynamic task queue when the rule conflict arbitration time exceeds a preset threshold. This reduces pressure on the rule execution engine, prevents arbitration congestion, and ensures that the system can maintain stable processing latency and reliability under high conflict loads, avoiding overall performance collapse due to task overload. Through a one-way adjustment mechanism for incremental computation speedup driven by the rule generation cycle, the incremental computation speedup can be dynamically adjusted according to the stable rule generation cycle: when the rule generation cycle is long and stable, the speedup is automatically increased to maximize computational performance; when the rule generation cycle is short or unstable, the speedup is automatically decreased to prioritize computational correctness. This ensures data reliability when business logic changes frequently and fully leverages the system's performance potential when business logic is stable, thereby improving the accuracy of multi-source spatial data and indicator integration. Attached Figure Description

[0012] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 A flowchart illustrating the multi-source spatial data and index integration method provided in this application embodiment; Figure 2 Flowchart of the adaptive semantic granularity tuning mechanism of the multi-source spatial data and index integration method provided in the embodiments of this application; Figure 3 This is a schematic diagram of the structure of the multi-source spatial data and index integration device provided in the embodiments of this application. Detailed Implementation

[0014] The following provides explanations for some of the terms used in this application. It should be noted that these explanations are for the convenience of those skilled in the art and do not constitute a limitation on the scope of protection claimed in this application.

[0015] The embodiments of this application involve at least one, including one or more; where "multiple" means two or more. Furthermore, it should be understood that in the description of this specification, terms such as "first," "second," and "third" are used only for descriptive purposes and should not be construed as indicating relative importance or order. For example, "first device" and "second device" do not represent the degree of importance of the two or their order, but are merely for descriptive distinction. In the embodiments of this application, "and / or" merely describes an association relationship, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0016] The directional terms mentioned in the embodiments of this application, such as "up", "down", "left", "right", "inner", and "outer", are only for reference to the directions in the accompanying drawings. Therefore, the directional terms used are for better and clearer explanation and understanding of the embodiments of this application, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application.

[0017] References to "one embodiment," "in some examples," or "some embodiments" as described in the embodiments of this application mean that one or more embodiments of this specification include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in some examples," "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0018] In this embodiment of the invention, sometimes a subscript such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0019] like Figure 1 The diagram shown is a flowchart of a multi-source spatial data and index integration method provided in an embodiment of this application. This method is applied in a multi-source spatial data and index integration device and includes the following steps: S100: Obtain raw data from at least two independent data sources, including spatial data and indicator spatial data. Analyze the obtained raw data and dynamically associate and map the parsed spatial data with the indicator data based on a preset semantic mapping rule set.

[0020] It should be understood that spatial data includes data containing information on geometric location and spatial relationships, including at least one of vector data, raster data, and spatiotemporal trajectory data; as well as indicator data and attribute, statistical, or business metric data associated with spatial data.

[0021] S200: Acquire integrated semantic data during the dynamic association and mapping process to quantify the degree of matching between multi-source spatial data and business indicators in integrated semantics, obtain the semantic consistency of multi-source spatial data-indicator integration, and determine whether to perform adaptive adjustment of indicator integration semantic consistency based on the semantic consistency of multi-source spatial data-indicator integration to adaptively balance computational overhead and adjustment accuracy and avoid adjustment loss. If yes, obtain demand adaptation data after adjustment; otherwise, directly obtain demand adaptation data to quantify the effective response capability to the dual requirements of high-precision spatial modeling and real-time dynamic integration of multi-source spatial data streams, and obtain the efficiency of dynamic demand adaptation.

[0022] It should be understood that the specific steps to obtain semantic consistency of multi-source spatial data-indicator integration are as follows: The integrated semantic data includes the indicator-data mappability rate, spatiotemporal benchmark alignment time, and alignment task parallelization speedup ratio. Specifically, the indicator-data mappability rate refers to the ratio of the number of sample pairs meeting the effective mapping conditions to the total number of sample pairs; the spatiotemporal benchmark alignment time refers to the total processing time required for multi-source spatial data and business indicators to achieve spatial benchmark unification and temporal benchmark synchronization; the difference between the end time of the final subtask and the start time of the first subtask, recorded through the task scheduling and monitoring module, is also recorded as the spatiotemporal benchmark alignment time; and the alignment task parallelization speedup ratio refers to the ratio of the total serial processing time to the total parallel processing time.

[0023] The mapping rate correction factor compensates for the ratio of the index-data mapping rate to the mapping rate reference value, yielding the mapping rate impact component; the alignment time correction factor compensates for the ratio of the alignment time reference value to the spatiotemporal benchmark alignment time, yielding the alignment time impact component; the speedup correction factor compensates for the ratio of the alignment task parallelization speedup ratio to the speedup reference value, yielding the speedup impact component; coupling the mapping rate impact component, the alignment time impact component, and the speedup impact component yields the semantic consistency of multi-source spatial data-index integration. The specific constraint expression for the semantic consistency of multi-source spatial data-index integration is as follows: ; ; In the formula, W represents the semantic consistency of multi-source spatial data-indicator integration; a1 represents the mappability correction factor obtained from the indicator integration database; a2 represents the alignment time correction factor obtained from the indicator integration database; a3 represents the speedup correction factor obtained from the indicator integration database; D0 represents the mappability reference value obtained from the indicator integration database; Y0 represents the alignment time reference value obtained from the indicator integration database; E0 represents the speedup reference value obtained from the indicator integration database; D represents the indicator-data mappability rate; Y represents the spatiotemporal benchmark alignment time; and E represents the parallelization speedup of the alignment task.

[0024] It should be explained that the higher the index-data mapping rate, the less need there is to handle additional tasks such as "spatial-temporal range mismatch caused by semantic ambiguity" and "data resampling caused by insufficient accuracy," thus shortening the time for spatiotemporal benchmark alignment. The higher the parallelization speedup ratio of the alignment task, the more reasonable the parallelization decomposition, the higher the utilization rate of computing resources, the more significant the parallel optimization effect on time-consuming subtasks, and the shorter the time for spatiotemporal benchmark alignment. The higher the index-data mapping rate, the more invalid tasks such as conflict resolution are eliminated in the alignment process, providing a "high-quality, low-conflict" task set for parallel processing, avoiding the waste of computing resources on meaningless conflict resolution, and thus the higher the parallelization speedup ratio of the alignment task. Meanwhile, there is a positive correlation between the indicator-data mapping rate and the semantic consistency of multi-source spatial data-indicator integration. The higher the indicator-data mapping rate, the higher the calculation scope, accuracy requirements, and spatiotemporal range of the business indicators are, and the higher the feature parameters, acquisition capabilities, and semantic definitions of the multi-source spatial data are, resulting in higher semantic consistency of multi-source spatial data-indicator integration. There is a negative correlation between the time taken for spatiotemporal benchmark alignment and the semantic consistency of multi-source spatial data-indicator integration. The longer the time taken for spatiotemporal benchmark alignment, the more serious the spatiotemporal benchmark conflict is, resulting in lower semantic consistency of multi-source spatial data-indicator integration. There is a positive correlation between the parallelization acceleration ratio of the alignment task and the semantic consistency of multi-source spatial data-indicator integration. The higher the parallelization acceleration ratio of the alignment task, the faster the alignment task can be completed, without the need for "over-preprocessing" of the data (such as multiple interpolations and resampling), thus preserving the original spatial features of the data and ensuring the accuracy of semantic association, resulting in higher semantic consistency of multi-source spatial data-indicator integration.

[0025] Furthermore, the specific steps for determining whether to perform adaptive control of indicator integration semantic consistency are as follows: if the semantic consistency of multi-source spatial data-indicator integration is greater than or equal to the threshold value of integration semantic consistency, then adaptive control of indicator integration semantic consistency is not performed; otherwise, the adaptive semantic granularity tuning mechanism and the benchmark drift-conflict resolution delay coupling adaptive threshold adjustment mechanism are performed.

[0026] It needs to be explained that, such as Figure 2The diagram shows the flowchart of the adaptive semantic granularity tuning mechanism of the multi-source spatial data and index integration method provided in this application embodiment. The specific logic is as follows: First, rule adjustment begins by receiving source rules and performing initial operations. Then, the semantic definition redundancy is monitored and calculated. By judging its interval (less than or equal to the lower limit of the redundancy benchmark, greater than or equal to the upper limit of the redundancy benchmark, or within the benchmark interval), rule refinement / coarsening / maintenance instructions are generated respectively. After the corresponding instruction is triggered, rule refinement (splitting a single rule into multiple sub-rules), coarsening (adding semantic verification context filtering to the rule, or merging rules with consistent results), and maintenance (performing addition / deletion / modification / merging operations on the rule) are executed to form an optimized new rule set, which is finally applied to subsequent data transformation tasks.

[0027] As further explained in detail, the specific steps for implementing the adaptive semantic granularity tuning mechanism are as follows: Receive source data and map the source data to target data based on a preset initial mapping rule set; wherein the initial mapping rule set contains at least one mapping rule with a specific granularity.

[0028] Monitor and statistically analyze the semantic ambiguity redundancy of each rule in the initial mapping rule set during the mapping process. Based on the semantic ambiguity redundancy and the semantic consistency of multi-source spatial data-indicator integration, generate granular adjustment instructions for the initial mapping rule set, specifically: If the semantic ambiguity redundancy is less than or equal to the redundancy baseline lower limit, a granularity refinement instruction is generated. The granularity refinement instruction is used to drive the following operations: split a current mapping rule into multiple sub-rules with stricter constraints and more specific semantic contexts; add additional semantic verification conditions or context filters to existing rules.

[0029] If the semantic ambiguity redundancy is greater than or equal to the redundancy baseline upper limit, a granularity coarsening instruction is generated. The granularity coarsening instruction is used to drive the following operation: merge mapping rules with consistent output results into a more general rule.

[0030] If the semantic ambiguity redundancy is within the redundancy benchmark interval, a granularity maintenance instruction is generated to keep the granularity of the current mapping rule set unchanged. The redundancy benchmark interval represents the open interval formed by the lower limit and upper limit of the redundancy benchmark.

[0031] Execute granularity adjustment instructions to perform corresponding add, delete, split, and merge operations on the initial mapping rule set to form an optimized new mapping rule set for subsequent data mapping tasks.

[0032] It needs to be explained that the system receives source data and transforms it into target data using a preset initial mapping rule set. During this process, the system simultaneously monitors and quantifies the semantic ambiguity redundancy exhibited by each rule in the mapping. This indicator comprehensively reflects the degree of semantic overlap and the frequency of ambiguity conflicts between rules. Subsequently, the system performs joint analysis of semantic ambiguity redundancy and the semantic consistency of multi-source spatial data-indicators to generate granular adjustment instructions: When the semantic ambiguity redundancy is below or equal to the lower limit of the redundancy benchmark, it indicates that the existing rules are insufficient in distinguishing complex semantics, and the system generates a granular refinement instruction. This instruction drives the rule engine to perform refinement operations, such as splitting a broad rule into multiple sub-rules with stricter constraints and more specific semantic contexts, or adding semantic checks and context filters to existing rules. This step directly improves the rules' sensitivity to subtle semantic differences, thereby enhancing mapping accuracy, especially suitable for processing highly heterogeneous and ambiguous multi-source spatial data. When the semantic ambiguity redundancy is above or equal to the upper limit of the redundancy benchmark, it indicates that there are a large number of entries with redundant functions or excessive specialization in the rule set, and the system generates a granular coarsening instruction. This instruction drives the rule engine to merge rules with consistent outputs, forming a more general and concise rule expression. This step significantly reduces the number of rules and matching overhead, improves mapping efficiency, and avoids decision conflicts and increased maintenance costs caused by rule redundancy. When the semantic ambiguity redundancy is within the baseline range, the system generates granular maintenance instructions to keep the existing rule set stable, ensuring that the system is in a balance between semantic processing accuracy and execution efficiency, avoiding unnecessary adjustment oscillations. Finally, the system executes the above adjustment instructions, dynamically optimizing the initial mapping rule set through add, delete, split, and merge operations to form a new rule set adapted to the current data characteristics, which is immediately applied to subsequent data mapping tasks.

[0033] In this embodiment, through this closed-loop process, the system achieves adaptive optimization of the mapping rule granularity: it can ensure mapping accuracy by refining rules when semantic complexity is high, and improve processing efficiency by coarsening rules when rule redundancy is high, thereby continuously maintaining a high level of semantic consistency of multi-source spatial data-indicator integration.

[0034] As further explained in detail, the specific steps of the reference drift-conflict resolution time-delay coupling adaptive thresholding mechanism are as follows: Acquire multi-source spatiotemporal data streams, and align and fuse the multi-source spatiotemporal data based on a preset spatiotemporal reference. During the alignment and fusion process, monitor the spatiotemporal reference drift between the multi-source spatiotemporal data. When the spatiotemporal reference drift exceeds a preset correction delay threshold, trigger the spatiotemporal reference drift correction process, specifically: Real-time monitoring and recording of events that trigger conflict resolution rules due to inconsistencies in spatiotemporal references, data contradictions, or logical conflicts; based on each conflict resolution rule event, the time elapsed from its detection to the completion of conflict resolution by the conflict resolution rule is calculated and defined as the conflict resolution rule adaptation delay.

[0035] A dynamic mapping relationship is established between the spatiotemporal reference drift correction delay threshold, the conflict resolution rule adaptation delay, and the semantic consistency of multi-source spatial data-indicator integration. Threshold adjustment instructions are generated based on the dynamic mapping relationship. The dynamic mapping relationship is configured such that the conflict resolution rule adaptation delay is negatively correlated with the spatiotemporal reference drift correction delay threshold.

[0036] The specific logic for generating the threshold adjustment instruction is as follows: If the adaptation delay of the conflict resolution rule is higher than the critical upper limit of the adaptation delay, a threshold reduction instruction is generated to reduce the correction delay threshold.

[0037] If the adaptation delay of the conflict resolution rule is lower than the critical lower limit of the adaptation delay, a threshold adjustment instruction is generated to increase the correction delay threshold.

[0038] If the adaptation delay of the conflict resolution rule is within the critical interval of the adaptation delay, a threshold maintenance instruction is generated to keep the correction delay threshold unchanged. The critical interval of the adaptation delay represents the closed interval formed by the lower critical limit of the adaptation delay and the upper critical limit of the adaptation delay.

[0039] The threshold adjustment command is executed to dynamically update the correction delay threshold; the updated spatiotemporal reference drift correction delay threshold is applied to subsequent spatiotemporal reference drift monitoring and correction.

[0040] It should be noted that, firstly, the system acquires multi-source spatiotemporal data streams and performs data alignment and fusion based on a preset spatiotemporal benchmark. During this process, the system continuously monitors the spatiotemporal benchmark drift between different data sources and sets a key correction latency threshold as the delay threshold for triggering benchmark correction. This design avoids frequent corrections due to small, transient drifts, thereby reducing unnecessary system overhead. Simultaneously, during alignment and fusion, the system monitors and records conflict resolution events triggered in real time due to spatiotemporal benchmark inconsistencies, data contradictions, or logical conflicts. For each event, the system accurately calculates the time elapsed from its detection to successful processing by the corresponding conflict resolution rule, i.e., the conflict resolution rule adaptation latency. This latency directly reflects the system's real-time efficiency and load pressure in handling data contradictions arising from benchmark drift. Next, the system establishes a negatively correlated dynamic mapping relationship between the conflict resolution rule adaptation latency and the current spatiotemporal benchmark drift correction latency threshold, and performs a comprehensive judgment based on the semantic consistency of multi-source spatial data-indicator integration to generate threshold adjustment instructions. The specific logic is as follows: When the conflict resolution adaptation delay exceeds the set critical upper limit, it indicates that the system is overburdened and inefficient in handling data conflicts caused by baseline drift, and the system generates a threshold reduction instruction. This operation aims to proactively reduce the latency tolerance for triggering baseline correction, allowing the system to intervene in baseline correction earlier and more frequently, thereby reducing the generation of subsequent complex data conflicts from the source, effectively preventing the conflict resolution engine from overloading, and ensuring the smoothness of the overall processing flow. When the conflict resolution adaptation delay is below the set critical lower limit, it indicates that the system has ample capacity to handle conflicts and is highly efficient, and the system generates a threshold increase instruction. This operation allows the system to appropriately relax the requirements for immediate response to baseline drift, reducing the triggering frequency of baseline correction, thereby avoiding system oscillations, increased resource consumption, and potential interference with data continuity caused by excessive correction. When the conflict resolution adaptation delay is within the critical range, the system generates a threshold maintenance instruction. This indicates that the current correction threshold is well matched with the system's conflict handling capacity, and maintaining the status quo is conducive to maintaining the optimal balance between correction timeliness and operational stability. Finally, the system executes the above threshold adjustment instructions to dynamically update the spatiotemporal baseline drift correction delay threshold. The updated threshold takes effect immediately and is used for subsequent drift monitoring and correction trigger logic.

[0041] In this embodiment, through this closed-loop feedback mechanism, the system achieves adaptive optimization of the baseline correction strategy: when conflict handling becomes a bottleneck, the threshold is lowered to switch to the "source governance" mode to improve overall consistency; when the system is lightly loaded, the threshold is raised to switch to the "economic operation and maintenance" mode to improve efficiency and stability, thereby intelligently maintaining the high quality and timeliness of multi-source spatiotemporal data fusion in a dynamically changing data environment.

[0042] It needs to be explained that the required adaptation data includes: the effective value of integrated semantic consistency, the latency of heterogeneous resource collaborative scheduling, and the proportion of incremental data volume. The effective value of integrated semantic consistency refers to the value of the newly acquired multi-source spatial data-indicator integrated semantic consistency if adaptive control of indicator integrated semantic consistency is performed; otherwise, the current multi-source spatial data-indicator integrated semantic consistency is recorded as the effective value. The latency of heterogeneous resource collaborative scheduling refers to the total latency generated by different types of computing power resources, such as CPUs, GPUs, edge computing nodes, and cloud servers, during the collaborative scheduling process of integrating multi-source spatial data and indicators. This latency is obtained through resource monitoring agents deployed on each heterogeneous computing power node and the task log module of the scheduling system. The proportion of incremental data volume refers to the ratio of the storage volume of newly added / changed data to the total spatial data storage volume under the multi-source spatial data incremental update mode. The ratio of incremental data volume to the total data volume is recorded as the proportion of incremental data volume.

[0043] The consistency and validity correction factor compensates for the ratio of the integrated semantic consistency and validity value to the consistency and validity reference value, yielding the consistency and validity impact component; the scheduling delay correction factor compensates for the ratio of the scheduling delay reference value to the heterogeneous resource collaborative scheduling delay, yielding the scheduling delay impact component; the volume proportion correction factor compensates for the ratio of the incremental data volume proportion to the volume proportion reference value, yielding the volume proportion impact component; coupling the consistency and validity impact component, the scheduling delay impact component, and the volume proportion impact component yields the dynamic demand adaptation efficiency. The specific constraint expression for the dynamic demand adaptation efficiency is as follows: ; ; In the formula, Q represents the efficiency of dynamic demand adaptation; c1 represents the consistent and valid correction factor obtained from the indicator integration database; c2 represents the scheduling latency correction factor obtained from the indicator integration database; c3 represents the volume proportion correction factor obtained from the indicator integration database; R0 represents the consistent and valid reference value obtained from the indicator integration database; H0 represents the scheduling latency reference value obtained from the indicator integration database; K0 represents the volume proportion reference value obtained from the indicator integration database; R represents the consistent and valid value of integration semantics; H represents the heterogeneous resource collaborative scheduling latency; and K represents the incremental data volume proportion.

[0044] It should be understood that a higher proportion of incremental data volume indicates a significant change in the spatial scenario. Incremental data contains core semantic features that support the updating of business metrics. The fusion and updating based on incremental data can accurately match the dynamic semantic requirements of business metrics, resulting in a higher effective value for integrated semantic consistency. A higher proportion of incremental data volume means that heterogeneous resources (such as edge nodes and cloud nodes) only need to allocate a small amount of computing power to complete the verification and fusion of incremental data, resulting in a shorter delay in the collaborative scheduling of heterogeneous resources. A longer delay in the collaborative scheduling of heterogeneous resources will lead to semantic misalignment due to data update delays, resulting in a smaller effective value for integrated semantic consistency. Meanwhile, there is a positive correlation between the effective value of integrated semantic consistency and the efficiency of dynamic demand adaptation. The larger the effective value of integrated semantic consistency, the more "dynamically adaptable" the semantic association between multi-source spatial data and business indicators is, and the higher the efficiency of dynamic demand adaptation. There is a negative correlation between the latency of heterogeneous resource collaborative scheduling and the efficiency of dynamic demand adaptation. The longer the latency of heterogeneous resource collaborative scheduling, the slower the resource scheduling response, and the longer the incremental data transmission and computation queuing time, the slower the data update speed can keep up with the speed of demand changes, and the lower the efficiency of dynamic demand adaptation. There is a positive correlation between the proportion of incremental data volume and the efficiency of dynamic demand adaptation. The larger the proportion of incremental data volume, the more moderate the changes in the spatial scenario, the more the incremental data contains core dynamic semantic features, and the more the data volume does not exceed the processing capacity of heterogeneous resources, the more the scheduling latency can be maintained within a reasonable range, and the higher the efficiency of dynamic demand adaptation.

[0045] S300, based on the efficiency judgment of dynamic demand adaptation, whether to perform effective adaptive adjustment of demand adaptation, so as to achieve the optimal ratio between processing resources and dynamic demand, and avoid resource idleness and response oscillation caused by over-adjustment. If yes, then step S100 is re-executed after adjustment; otherwise, step S100 is executed directly. It can continuously and stably output high-quality and timely fused spatial information through adaptive negative feedback adjustment.

[0046] Furthermore, the specific steps for determining whether to execute effective adaptive control of demand adaptation are as follows: if the efficiency of dynamic demand adaptation is greater than or equal to the effective threshold of demand adaptation, then effective adaptive control of demand adaptation is not executed; otherwise, the conflict arbitration time-consuming single-threshold back pressure control mechanism and the incremental calculation acceleration ratio unidirectional control mechanism driven by the rule generation cycle are executed.

[0047] As further detailed, the specific steps for implementing the conflict arbitration time-consuming single-threshold backpressure control mechanism are as follows: It receives task requests from multiple task sources and injects the task requests into a dynamic task queue; it schedules tasks from the dynamic task queue and distributes them to at least one rule execution engine for processing; the rule execution engine contains multiple business rules with potentially overlapping execution conditions and executes rule conflict detection and arbitration logic during task processing.

[0048] During the process of the rule execution engine processing tasks, events that trigger rule conflict detection and arbitration due to multiple business rules making competing claims on the same task or task context are monitored and recorded in real time. For each rule conflict detection and arbitration event, the time taken from the detection of the conflict to the arbitration logic producing a deterministic ruling is collected and defined as the time consumed in a single rule conflict arbitration.

[0049] A negative feedback control relationship is established between dynamic demand adaptation efficiency, rule conflict detection and arbitration time, and the average arrival rate of the dynamic task queue, and arrival rate control instructions are generated based on this relationship. Specifically, the negative feedback control relationship is configured such that the rule conflict detection and arbitration time is negatively correlated with the average arrival rate of the dynamic task queue. The logic for generating the arrival rate control instructions is as follows: If the time taken for rule conflict detection and arbitration is less than or equal to the arbitration time threshold, an arrival rate maintenance instruction is generated to maintain the average arrival rate of the target dynamic task queue.

[0050] If the time taken for rule conflict detection and arbitration exceeds the arbitration time threshold, an arrival rate reduction instruction is generated; the arrival rate reduction instruction is used to reduce the target average arrival rate of the dynamic task queue.

[0051] It's important to understand that, firstly, the system receives task requests from multiple task sources and injects them into a dynamic task queue. Then, it schedules and distributes tasks to the rule execution engine for processing. This rule execution engine integrates multiple business rules with potentially overlapping execution conditions, inevitably leading to rule conflict detection and arbitration logic during task processing. This design directly addresses the core bottleneck issue of complex rule engines in decision-making. To optimize this process, the system monitors and records conflict arbitration events triggered in real-time during rule engine runtime due to competition from multiple business rules for the same task or context. For each event, the system precisely collects the time elapsed from conflict detection to the final arbitration logic output, defined as the time consumed in a single rule conflict arbitration. This time directly quantifies the efficiency of the rule engine in handling internal logical contradictions and making deterministic decisions, serving as a key indicator reflecting its core "decision load." Subsequently, the system establishes a clear negative feedback control relationship between the rule conflict detection and arbitration time and the average arrival rate of the dynamic task queue, and comprehensively considers the efficiency of dynamic demand adaptation to generate arrival rate control instructions: When the conflict arbitration time is less than or equal to the preset arbitration time threshold, it indicates that the rule engine decision-making is smooth and the load is moderate, and the system generates an arrival rate maintenance instruction. This operation maintains the current task input rate, enabling the system to stably exert its maximum processing throughput capacity while ensuring decision quality, avoiding unnecessary control interference. When the conflict arbitration time exceeds the arbitration time threshold, it indicates that the rule engine is facing high-intensity decision conflicts, and the arbitration logic may become a performance bottleneck. At this time, the system generates an arrival rate reduction instruction. The core function of this instruction is to implement "backpressure" control, by actively reducing the average task arrival rate of the dynamic task queue, reducing the flow of new tasks input to the rule engine, thereby "reducing pressure" on it. This directly prevents the risk of arbitration queue backlog, surge in overall task processing latency, and even engine crashes caused by continuous high conflict load, ensuring the service reliability and response stability of the system under high load.

[0052] In this embodiment, the entire control mechanism forms an intelligent buffer layer centered on the health of the rule engine's decisions: by transforming the time consumed by conflict arbitration related to the underlying business logic into a global rate control signal for the inflow of tasks from the upper layer, the system achieves a leap from "business decision bottleneck perception" to "system-level resource protection." This ensures that even in scenarios with exceptionally complex business rules and frequent conflicts, the system can maintain controllable latency and high availability through self-regulation, thereby meeting dynamic demand adaptation and efficiency requirements while guaranteeing the robustness of long-term operation.

[0053] It should be further explained that the specific steps of the unidirectional control mechanism for incremental computation speedup driven by the rule generation cycle are as follows: The rule generation behavior is monitored in real time, continuous rule generation time points are recorded, and the interval between adjacent time points is calculated and defined as the rule generation cycle. A positive dynamic mapping relationship is established between the rule generation cycle, the efficiency of dynamic demand adaptation, and the acceleration ratio of incremental fusion and update computation, and acceleration ratio adjustment instructions are generated. The positive dynamic mapping relationship is configured as follows: the rule learning and generation cycle is positively correlated with the acceleration ratio of incremental fusion and update computation.

[0054] If the rule learning and generation cycle is greater than or equal to the generation cycle threshold, then a speedup instruction is issued. The speedup instruction is used to improve the speedup of incremental fusion and update computation in exchange for a higher computational performance improvement.

[0055] If the rule learning and generation cycle is less than the generation cycle threshold, a speedup reduction instruction is issued. This speedup reduction instruction is used to reduce the speedup of incremental fusion and update computation.

[0056] It's important to understand that, firstly, the system monitors the behavior of the rule learning module in real time. By recording consecutive rule generation time points and calculating the time interval between adjacent points, it precisely defines the rule generation cycle. This metric directly reflects the evolution speed and stability of the underlying business logic, data patterns, or decision-making strategies, serving as a key indicator of whether the system is in a "steady state" or a "turbulent period." Subsequently, the system establishes a positive dynamic mapping relationship between the rule generation cycle and the efficiency of dynamic demand adaptation and the speedup ratio of incremental fusion and update computation, and generates speedup ratio adjustment instructions accordingly. The core logic is that when the rule learning and generation cycle is greater than or equal to a preset generation cycle threshold, it indicates that the rule system is in a slow-updating, relatively stable state. At this time, the system generates a speedup ratio increase instruction. This instruction drives the system to increase the speedup ratio used in incremental fusion and update computation, which means that more aggressive and efficient incremental optimization strategies can be enabled (such as expanding the incremental computation window, adopting more complex incremental algorithm variants, or enhancing the pre-computation cache). The technical effect is that, within a stable business logic window, it fully leverages the advantage of incremental computation in "avoiding full recalculation," maximizing the processing performance of data fusion and updates while significantly reducing computational resource consumption and latency, thus efficiently responding to dynamic demands for high throughput or low latency. Conversely, when the rule learning and generation cycle is less than the critical value of the generation cycle, it indicates that the rules are being frequently modified or rapidly iterated, and the business logic is in an unstable state. At this time, the system generates a speedup reduction instruction. This instruction drives the system to reduce the speedup ratio of incremental computation, that is, to adopt a more conservative and robust computation mode (such as narrowing the incremental window, increasing the frequency of full verification, or falling back to partial full computation). Its core effect is that, when business logic changes frequently, it prioritizes ensuring the absolute correctness of computation results and data consistency, avoiding large-scale data errors or expensive reconstruction costs caused by the invalidation of historical states or data assumptions on which incremental computation relies due to rule changes. Although this temporarily sacrifices some performance, it ensures the data reliability and service continuity of the system during periods of change, serving as a preventative measure against major data quality risks.

[0057] In this embodiment, the entire mechanism constructs a performance-reliability trade-off system based on the forward-looking judgment of rule stability. It breaks through the limitation of traditional incremental computing that only adjusts according to changes in data volume, and innovatively uses the evolution rhythm of business logic as a leading signal for adjusting computing strategies. Thus, it adaptively balances computing efficiency and data quality throughout the entire lifecycle of the system, ultimately supporting the continuous achievement of a high level of efficiency in adapting to dynamic requirements.

[0058] like Figure 3The diagram shown is a structural schematic of the multi-source spatial data and indicator integration device provided in this application embodiment. The device includes: a multi-source heterogeneous data dynamic semantic mapping module, an integration semantic consistency monitoring and control module, and a dynamic demand adaptation efficiency evaluation and resource control module. The multi-source heterogeneous data dynamic semantic mapping module is used to acquire raw data from at least two independent data sources, including spatial data and indicator spatial data. It parses the acquired raw data and, based on a preset semantic mapping rule set, dynamically associates and maps the parsed spatial data with the indicator data. The integration semantic consistency monitoring and control module is used to acquire integrated semantic data during the dynamic association and mapping process, quantify the degree of matching between multi-source spatial data and business indicators in the integration semantics, obtain the multi-source spatial data-indicator integration semantic consistency, and based on the multi-source spatial data-indicator integration semantics... The semantic consistency judgment determines whether to perform semantic consistency adaptive control of the indicator integration to adaptively balance computational overhead and control accuracy, avoiding control losses. If yes, demand adaptation data is obtained after control; otherwise, demand adaptation data is obtained directly. This data is used to quantify the effective response capability to the dual requirements of high-precision spatial modeling and real-time multi-source spatial data stream dynamic integration, obtaining the efficiency of dynamic demand adaptation. The dynamic demand adaptation efficiency evaluation and resource control module is used to determine whether to perform effective adaptive control of demand adaptation based on the efficiency of dynamic demand adaptation, so as to achieve the optimal ratio between processing resources and dynamic demands, avoiding resource idleness and response oscillation caused by over-control. If yes, step S100 is re-executed after control; otherwise, step S100 is executed directly. Through adaptive negative feedback adjustment, it can continuously and stably output high-quality and timely fused spatial information.

[0059] In this embodiment, the systematic optimization of the integration efficiency of multi-source spatial data and business indicators is achieved through the coordinated operation of three core modules. The multi-source heterogeneous data dynamic semantic mapping module first completes the precise conversion from raw data to a unified semantic expression, laying a highly consistent data foundation for the entire integration process. This directly determines the reliability and accuracy of subsequent analysis results. Based on this, the integration semantic consistency monitoring and control module plays the role of "quality perception and fine-tuning." It intelligently judges and executes semantic consistency adaptive control by quantifying the degree of semantic matching in real time. This process precisely balances the computational overhead of semantic enhancement with the accuracy improvement obtained, effectively avoiding resource waste or quality loss caused by excessive or insufficient semantic processing, ensuring that the system always operates in an optimal semantic integrity state. Furthermore, the dynamic demand adaptation efficiency assessment and resource control module, as the central hub of "resource allocation and global optimization," transforms the semantic quality status output by the previous module into a quantitative assessment of the system's responsiveness to dual business demands (high-precision modeling and real-time integration). Based on this assessment, this module dynamically decides whether to perform resource reallocation control. This mechanism ensures that limited computing resources are always adaptively matched to the most pressing business needs, fundamentally preventing idle processing capacity or system performance oscillations caused by resource mismatch. Ultimately, these three modules, through closed-loop linkage, constitute a robust and adaptive intelligent integrated system. It not only outputs high-quality, timely fused spatial information products, but also, under dynamic and ever-changing data environments and business demands, maintains a globally optimal balance between data processing accuracy, system operating efficiency, and resource utilization efficiency through continuous self-adjustment and negative feedback, achieving a leap from a static data processing pipeline to an intelligent adaptive engine.

[0060] A computing device, characterized in that it comprises: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 8.

[0061] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software, it can be implemented entirely or partially in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that integrates one or more available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid-state disk (SSD)). Where there is no conflict, the solutions in the above embodiments can be combined.

[0062] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0063] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to this application. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0064] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0065] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0066] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the scope and intent of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application is also intended to include such modifications and variations.

Claims

1. A method for integrating multi-source spatial data and indicators, characterized in that, Includes the following steps: S100: Obtain raw data from at least two independent data sources, the raw data including spatial data and indicator data spatial data; parse the obtained raw data; and dynamically associate and map the parsed spatial data with the indicator data based on a preset semantic mapping rule set. S200: Acquire integrated semantic data during the dynamic association and mapping process to quantify the degree of matching between multi-source spatial data and business indicators in integrated semantics, obtain the semantic consistency of multi-source spatial data-indicator integration, and determine whether to perform adaptive adjustment of indicator integration semantic consistency based on the semantic consistency of multi-source spatial data-indicator integration to adaptively balance computational overhead and adjustment accuracy and avoid adjustment loss. If yes, obtain demand adaptation data after adjustment; otherwise, directly obtain demand adaptation data to quantify the effective response capability to the dual requirements of high-precision spatial modeling and real-time dynamic integration of multi-source spatial data streams, and obtain the efficiency of dynamic demand adaptation. S300, based on the efficiency judgment of dynamic demand adaptation, whether to perform effective adaptive adjustment of demand adaptation, so as to achieve the optimal ratio between processing resources and dynamic demand, and avoid resource idleness and response oscillation caused by over-adjustment. If yes, then step S100 is re-executed after adjustment; otherwise, step S100 is executed directly. It can continuously and stably output high-quality and timely fused spatial information through adaptive negative feedback adjustment.

2. The multi-source spatial data and index integration method as described in claim 1, characterized in that, The specific steps for obtaining the semantic consistency of multi-source spatial data-indicator integration are as follows: The integrated semantic data includes metrics-data mappability, spatiotemporal benchmark alignment time, and alignment task parallelization speedup ratio. The mapping rate correction factor compensates for the ratio of the index-data mapping rate to the mapping rate reference value, thus obtaining the mapping rate influence component; The alignment time correction factor compensates for the ratio of the alignment time reference value to the alignment time of the spatiotemporal reference, thus obtaining the alignment time influence component; The speedup correction factor compensates for the ratio of the parallelization speedup of the alignment task to the speedup reference value, thus obtaining the speedup influence component. By coupling the components affecting mappability, alignment time, and speedup, we obtain the semantic consistency of multi-source spatial data-indicator integration. The specific steps for determining whether to perform adaptive adjustment based on the semantic consistency of the integrated indicators are as follows: If the semantic consistency of multi-source spatial data-indicator integration is greater than or equal to the semantic consistency threshold, then the adaptive adjustment of semantic consistency of indicator integration will not be executed; otherwise, the adaptive semantic granularity tuning mechanism and the benchmark drift-conflict resolution delay coupling adaptive threshold adjustment mechanism will be executed.

3. The method for integrating multi-source spatial data and indicators as described in claim 2, characterized in that, The specific steps for implementing the adaptive semantic granularity tuning mechanism are as follows: Receive source data and map the source data to target data based on a preset initial mapping rule set; wherein the initial mapping rule set contains at least one mapping rule with a specific granularity. Monitor and statistically analyze the semantic ambiguity redundancy of each rule in the initial mapping rule set during the mapping process. Based on the semantic ambiguity redundancy and the semantic consistency of multi-source spatial data-indicator integration, generate granular adjustment instructions for the initial mapping rule set, specifically: If the semantic ambiguity redundancy is less than or equal to the redundancy baseline lower limit, a granularity refinement instruction is generated. The granularity refinement instruction is used to drive the following operations: splitting a current mapping rule into sub-rules; adding additional semantic verification conditions or context filters to existing rules. If the semantic ambiguity redundancy is greater than or equal to the redundancy baseline upper limit, a granularity coarsening instruction is generated. The granularity coarsening instruction is used to drive the following operation: merging mapping rules with consistent output results into a more general rule. If the semantic ambiguity redundancy is within the redundancy benchmark interval, a granularity maintenance instruction is generated to keep the granularity of the current mapping rule set unchanged. The redundancy benchmark interval represents the open interval formed by the lower limit of the redundancy benchmark and the upper limit of the redundancy benchmark. The granularity adjustment instruction is executed to perform corresponding addition, deletion, splitting, and merging operations on the initial mapping rule set to form an optimized new mapping rule set for subsequent data mapping tasks.

4. The method for integrating multi-source spatial data and indicators as described in claim 2, characterized in that, The specific steps of the reference drift-conflict resolution time-delay coupling adaptive thresholding mechanism are as follows: Acquire multi-source spatiotemporal data streams, and align and fuse the multi-source spatiotemporal data based on a preset spatiotemporal reference; during the alignment and fusion process, monitor the spatiotemporal reference drift between the multi-source spatiotemporal data; when the spatiotemporal reference drift exceeds a preset correction delay threshold, trigger a spatiotemporal reference drift correction process, specifically: Real-time monitoring and recording of events that trigger conflict resolution rules; based on each conflict resolution rule event, calculating the time elapsed from its detection to its resolution by the conflict resolution rule, which is defined as the conflict resolution rule adaptation delay; A dynamic mapping relationship is established between the spatiotemporal reference drift correction delay threshold, the conflict resolution rule adaptation delay, and the semantic consistency of multi-source spatial data-indicator integration, and a threshold adjustment instruction is generated based on the dynamic mapping relationship; wherein, the dynamic mapping relationship is configured such that the conflict resolution rule adaptation delay and the spatiotemporal reference drift correction delay threshold are negatively correlated.

5. The multi-source spatial data and index integration method as described in claim 4, characterized in that, The specific logic for generating the threshold adjustment instruction is as follows: If the adaptation delay of the conflict resolution rule is higher than the critical upper limit of the adaptation delay, a threshold reduction instruction is generated to reduce the correction delay threshold. If the adaptation delay of the conflict resolution rule is lower than the critical lower limit of the adaptation delay, a threshold adjustment instruction is generated to increase the correction delay threshold. If the adaptation delay of the conflict resolution rule is within the critical interval of the adaptation delay, a threshold maintenance instruction is generated to keep the modified delay threshold unchanged. The critical interval of the adaptation delay represents the closed interval formed by the lower critical limit of the adaptation delay and the upper critical limit of the adaptation delay. Execute the threshold adjustment instruction to dynamically update the corrected delay threshold; The updated spatiotemporal reference drift correction delay threshold is applied to subsequent spatiotemporal reference drift monitoring and correction.

6. The method for integrating multi-source spatial data and indicators as described in claim 1, characterized in that, The required adaptation data includes: integrated semantically consistent valid values, heterogeneous resource collaborative scheduling latency, and the proportion of incremental data volume. The consistent validity correction factor compensates for the ratio of the consistent validity value to the consistent validity reference value of the integrated semantics, thus obtaining the consistent validity influence component; The scheduling delay correction factor is used to compensate for the ratio of the scheduling delay reference value to the scheduling delay of heterogeneous resource collaborative scheduling, thus obtaining the scheduling delay impact component. The volume proportion correction factor compensates for the ratio of the incremental data volume proportion to the volume proportion reference value, thus obtaining the volume proportion influence component. By coupling the consistent and effective impact component, the scheduling delay impact component, and the volume proportion impact component, the dynamic demand adaptation efficiency is obtained. The specific steps for determining whether effective adaptive control for demand adaptation should be performed are as follows: If the efficiency of dynamic demand adaptation is greater than or equal to the effective threshold of demand adaptation, then the effective adaptive control of demand adaptation will not be executed. Otherwise, the conflict arbitration time-consuming single-threshold back pressure control mechanism and the incremental calculation speedup ratio unidirectional control mechanism driven by the rule generation cycle will be executed.

7. The method for integrating multi-source spatial data and indicators as described in claim 6, characterized in that, The specific steps of the single-threshold backpressure control mechanism for conflict arbitration execution time are as follows: The system receives task requests from multiple task sources and injects the task requests into a dynamic task queue; it schedules tasks from the dynamic task queue and distributes them to at least one rule execution engine for processing; the rule execution engine contains multiple business rules with potentially overlapping execution conditions and executes rule conflict detection and arbitration logic during task processing. During the process of the rule execution engine processing tasks, events that trigger rule conflict detection and arbitration are monitored and recorded in real time; For each rule conflict detection and arbitration event, the time consumed in a single rule conflict arbitration is collected; A negative feedback control relationship is established between dynamic demand adaptation efficiency, rule conflict detection and arbitration time, and the average arrival rate of the dynamic task queue, and an arrival rate control instruction is generated based on this relationship; wherein, the negative feedback control relationship is configured such that the rule conflict detection and arbitration time is negatively correlated with the average arrival rate of the dynamic task queue; the specific logic for generating the arrival rate control instruction is as follows: If the time taken for rule conflict detection and arbitration is less than or equal to the arbitration time threshold, an arrival rate maintenance instruction is generated to maintain the average arrival rate of the target dynamic task queue unchanged. If the time taken for rule conflict detection and arbitration exceeds the arbitration time threshold, an arrival rate reduction instruction is generated; the arrival rate reduction instruction is used to reduce the target average arrival rate of the dynamic task queue.

8. The method for integrating multi-source spatial data and indicators as described in claim 6, characterized in that, The specific steps of the unidirectional control mechanism for incremental computation speedup driven by the rule generation cycle are as follows: The rule generation behavior is monitored in real time, continuous rule generation time points are recorded, and the interval between adjacent time points is calculated and defined as the rule generation cycle. A positive dynamic mapping relationship is established between the rule generation cycle, the efficiency of dynamic demand adaptation, and the acceleration ratio of incremental fusion and update calculations, and an acceleration ratio adjustment instruction is generated. The positive dynamic mapping relationship is configured such that the rule learning and generation cycle is positively correlated with the acceleration ratio of incremental fusion and update calculations. If the rule learning and generation cycle is greater than or equal to the generation cycle threshold, a speedup instruction is generated. The speedup instruction is used to improve the speedup of incremental fusion and update computation in exchange for a higher computational performance improvement. If the rule learning and generation cycle is less than the generation cycle threshold, a speedup reduction instruction is generated. The speedup reduction instruction is used to reduce the speedup of incremental fusion and update calculations.

9. A multi-source spatial data and index integration device, employing the multi-source spatial data and index integration method as described in any one of claims 1-8, characterized in that, It includes a multi-source heterogeneous data dynamic semantic mapping module, an integrated semantic consistency monitoring and control module, and a dynamic demand adaptation efficiency evaluation and resource control module: The multi-source heterogeneous data dynamic semantic mapping module is used to obtain raw data from at least two independent data sources, the raw data including spatial data and indicator data spatial data, parse the obtained raw data, and dynamically associate and map the parsed spatial data with the indicator data based on a preset semantic mapping rule set. The integrated semantic consistency monitoring and control module is used to acquire integrated semantic data during the dynamic association and mapping process, quantify the degree of matching between multi-source spatial data and business indicators in integrated semantics, obtain the integrated semantic consistency of multi-source spatial data and indicators, and determine whether to perform adaptive control of indicator integrated semantic consistency based on the integrated semantic consistency of multi-source spatial data and indicators, so as to adaptively balance the computational overhead and control accuracy and avoid control loss. If yes, the demand adaptation data is acquired after control; if no, the demand adaptation data is acquired directly to quantify the effective response capability to the dual requirements of high-precision spatial modeling and real-time dynamic integration of multi-source spatial data streams, and obtain the efficiency of dynamic demand adaptation. The dynamic demand adaptation efficiency assessment and resource regulation module is used to determine whether to perform effective adaptive regulation based on the efficiency of dynamic demand adaptation, so as to achieve the optimal ratio between processing resources and dynamic demand, and avoid resource idleness and response oscillation caused by over-regulation. If yes, step S100 is re-executed after regulation; otherwise, step S100 is executed directly. It can continuously and stably output high-quality and timely fused spatial information through adaptive negative feedback adjustment.

10. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 8.

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