Multi-source surveying and mapping data fusion GIS analysis and visualization system and method

Abstract

The invention discloses a GIS analysis and visualization system and method for multi-source surveying and mapping data fusion in the technical field of geographic information integrated processing and intelligent analysis, and the method comprises the steps: defining a unified space-time reference based on to-be-fused multi-source surveying and mapping data, and constructing a quantization dimension at least based on data collection time, collection equipment precision grade and original data resolution, calculating a basic quality index of each data unit through a quality index formula; and based on the established unified space-time reference, introducing weight mapping derived from the basic quality index, performing time resampling and space coordinate transformation on all data by minimizing a space-time calibration residual term, and outputting data registered to the same space-time framework. According to the method, dynamic credible expression is achieved through a full-process precision modeling mechanism, space-time dislocation, geometric offset and precision distortion caused by single data source or fixed threshold value superposition are avoided, and the requirements for unified precision presentation and quality self-interpretation after complex spatial data multi-scale superposition are met.

Description

Technical Field

[0001] This invention relates to a GIS analysis and visualization system and method for multi-source surveying and mapping data fusion, belonging to the field of geographic information integration processing and intelligent analysis technology. Background Technology

[0002] With the increasing variety of surveying sensors, inconsistent acquisition times, and amplified differences in resolution scales, traditional single-coordinate calibration and fixed-precision description methods can no longer support the fusion and computation needs of cross-temporal, cross-platform, and cross-precision data. GIS systems are increasingly exhibiting structural limitations such as inconsistent benchmarks, uncontrollable quality, and untraceable errors during data overlay, spatial statistics, dynamic updates, and multi-dimensional representation. To ensure that multi-source data possesses unified quality semantics and reliable fusion basis before entering the spatial computing and analysis stage, constructing a unified spatiotemporal benchmark, dynamic quality dimensions, spatial registration, and weighted fusion mechanisms has become a necessary trend for the fusion control of geographic information data systems.

[0003] Traditional GIS data processing methods often rely on static overlay algorithms or fixed threshold merging strategies based on a single data source. They lack mechanisms for synchronous quality modeling that addresses differences in data acquisition time spans, equipment accuracy levels, and spatial resolution. This results in the fusion process being dominated by absolute data or manual threshold judgments. By connecting the interface presentation layer, application service layer, and data platform, event-based management of power outage data is achieved, reducing outage frequency and optimizing service management on the power grid operation side. While this technical framework already possesses information aggregation, access, and output capabilities, its data source structure is singular. It lacks mechanisms for modeling accuracy differences between multi-source mapping data, controlling spatial fusion errors, and implementing dynamic weight registration. Therefore, it cannot meet the requirements for unified accuracy presentation and self-interpretation of quality after multi-scale overlay of complex spatial data. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of the prior art and provide a GIS analysis and visualization system and method for multi-source mapping data fusion. It achieves dynamic and reliable expression through a full-process accuracy modeling mechanism, avoiding spatiotemporal misalignment, geometric offset and accuracy distortion caused by single data source or fixed threshold superposition. It enables multi-source data to be fused under a unified spatiotemporal framework with quality weights, and synchronously displays the inherent accuracy labels in the final GIS visualization, meeting the requirements of unified accuracy presentation and quality self-interpretation after multi-scale superposition of complex spatial data.

[0005] To achieve the above objectives, the present invention is implemented using the following technical solution:

[0006] In a first aspect, the present invention provides a GIS analysis and visualization method for multi-source mapping data fusion, including:

[0007] A unified spatiotemporal benchmark is defined based on the multi-source mapping data to be fused, and a quantitative dimension is constructed based at least on the data acquisition time, the accuracy level of the acquisition equipment, and the original data resolution. The basic quality index of each data unit is calculated using the quality index formula.

[0008] Based on the established unified spatiotemporal benchmark, a weight mapping derived from the basic quality index is introduced. By minimizing the spatiotemporal calibration residual term, all data are resampled in time and transformed in spatial coordinates, and the data registered to the same spatiotemporal framework is output.

[0009] Based on the aforementioned basic quality index, a fusion quality evaluation function is constructed. After performing dynamic quality evaluation on each data unit, the dynamic quality evaluation results are used to identify and clean abnormal data units through a robust deviation identification function to obtain the remaining qualified data units.

[0010] The dynamic precision weight value of the remaining qualified data units is calculated using the dynamic quality assessment results, and the dynamic precision weight value is mapped to a dynamic precision weight matrix based on spatial continuity.

[0011] Using the dynamic precision weight matrix as weights, the remaining qualified data units are weighted and fused to generate a unified geospatial data layer, and the fusion result is smoothed by constructing a fusion stability function.

[0012] GIS spatial analysis is performed on the unified geospatial data layer. By constructing an analysis reliability backtracking function and an expression brightness adjustment function, the GIS spatial analysis results are correlated with a dynamic precision weight matrix for visualization.

[0013] Furthermore, the formula for the quality index is:

[0014]

[0015] in, Indicates the basic quality index; These are the weighting coefficients; This is the time decay parameter; This represents the difference between the data acquisition time and the fusion processing time. This represents the numerical result indicating the accuracy level of the data acquisition equipment. The baseline resolution factor; Indicates the resolution of the original data.

[0016] Furthermore, the spatiotemporal calibration residual term is:

[0017]

[0018] in, Represents the spatiotemporal calibration residual term; n represents the total number of data units; i represents the data unit; Mapping the relevant weights for the quality index; It is a spacetime transformation function; These are the original spatial coordinates; This is the original time position; To unify coordinates within a unified framework; To unify time and location within a unified framework; This is a time consistency adjustment factor.

[0019] Furthermore, the fusion quality evaluation function is:

[0020]

[0021] in, (i) represents the fusion quality evaluation function value of data unit i; i represents the data unit. The basic quality index value for data unit i; Weights are assigned to account for the impact of registration errors. These are the original spatial coordinates; To unify coordinates within a unified framework; This is the normalized spatial scale factor; Weights are assigned to account for the impact of time errors. This is the original time position; To unify the time and location within the framework.

[0022] Furthermore, the robust bias identification function is:

[0023]

[0024] in, Let i be the robust bias identification function for data unit i; i represents the data unit. (i) represents the fusion quality evaluation function value of data unit i; The mean of the global quality score; The standard deviation of the rating; This is the bias sensitivity coefficient.

[0025] Furthermore, the formula for calculating the dynamic precision weight value is as follows:

[0026]

[0027] in, Let i be the dynamic precision weight function value of data unit i; i represents the data unit. (i) represents the fusion quality evaluation function value of data unit i; For sensitive control coefficients; Indicates the time span between the current time and the time of data collection; The numerical value is used to express the accuracy level of the equipment; The baseline resolution factor; and These represent the maximum time span and the maximum equipment accuracy level corresponding to all data. This is the current data resolution.

[0028] Furthermore, the dynamic precision weight matrix is ​​as follows:

[0029]

[0030] in, (x, y) is the dynamic precision weight matrix at positions x and y; x and y represent position coordinates; n represents the total number of data units; i represents a data unit. Let i be the dynamic precision weight function value of data unit i; i represents the data unit. This is the spatial decay factor; For positions x, y and data units Distance representation corresponding to spatial location.

[0031] Secondly, this invention provides a GIS analysis and visualization system for multi-source mapping data fusion, including:

[0032] Benchmark and Quality Quantification Module: Define a unified spatiotemporal benchmark based on the multi-source mapping data to be fused, and construct a quantification dimension based at least on the data acquisition time, the accuracy level of the acquisition equipment, and the resolution of the original data. Calculate the basic quality index of each data unit using the quality index formula.

[0033] Spatiotemporal registration module: Based on the established unified spatiotemporal benchmark, a weight mapping derived from the basic quality index is introduced. By minimizing the spatiotemporal calibration residual term, all data are resampled in time and transformed in spatial coordinates, and the data registered to the same spatiotemporal framework is output.

[0034] Quality assessment and cleaning module: Based on the basic quality index, a fusion quality evaluation function is constructed. After dynamic quality assessment of each data unit, the dynamic quality assessment results are used to identify and clean abnormal data units through a robust deviation identification function to obtain the remaining qualified data units.

[0035] Dynamic weight matrix generation module: Calculates the dynamic precision weight value of the remaining qualified data units using the dynamic quality assessment results, and maps the dynamic precision weight value into a dynamic precision weight matrix based on spatial continuity;

[0036] Weighted fusion module: Using the dynamic precision weight matrix as weights, the remaining qualified data units are weighted and fused to generate a unified geospatial data layer, and the fusion result is smoothed by constructing a fusion stability function;

[0037] GIS Analysis and Visualization Module: Performs GIS spatial analysis on the unified geospatial data layer, and visualizes the GIS spatial analysis results by constructing an analysis reliability backtracking function and an expression brightness adjustment function, and correlates them with a dynamic precision weight matrix.

[0038] Thirdly, the present invention provides a GIS analysis and visualization device for multi-source mapping data fusion, including a processor and a storage medium;

[0039] The storage medium is used to store instructions;

[0040] The processor is configured to operate according to the instructions to perform the steps of the method according to any of the foregoing.

[0041] Fourthly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the methods described above.

[0042] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0043] This scheme establishes a unified spatiotemporal benchmark and constructs a quality quantification dimension, ensuring that multi-source mapping data have a consistent reference system before entering the fusion stage. This eliminates issues such as coordinate benchmark differences, misaligned acquisition times, and incomparable quality, achieving standardization and structured representation at the data foundation level. The scheme utilizes temporal alignment and spatial registration processes to construct a dual consistency correction mechanism, ensuring that all data form a strict mapping relationship under a unified coordinate framework and time scale. This avoids spatiotemporal drift, geometric mismatch, and scale conflicts caused by traditional multi-source overlay. Furthermore, this scheme employs multi-dimensional quality quantification and anomaly removal mechanisms to filter the credibility of all data, ensuring that the data ultimately entering the fusion process has clear quality boundaries and reliable indicators. This prevents error seeds from being amplified in subsequent calculations, thereby improving global accuracy, security, and fusion robustness.

[0044] Furthermore, this scheme introduces a dynamic precision weight matrix and maps it to a spatially continuous domain. This transforms data credibility from static labeling to a dynamic weight distribution structure with location sensitivity and time-responsiveness. This allows the fusion process to automatically identify high-precision dominant areas and low-precision compensation areas, significantly reducing fusion distortion. The scheme uses dynamic weights to drive weighted fusion and smoothing correction, achieving collaborative expression of multi-source data at the same spatial location based on credibility strength. This results not only have a consistent geometric structure but also unified precision semantics, ultimately forming a data layer that maintains continuity of expression and stability of precision under multi-scale, multi-time period, and multi-device input. This scheme traces the fusion results and precision weight system back to the GIS analysis and visualization stage, enabling transparent and reliable labeling of GIS spatial analysis results. Users can simultaneously understand the source precision, influence intensity, and error range when judging trends, hotspot distributions, and statistical patterns, thus achieving precision interpretability and result traceability across the entire chain from data input and fusion processing to analysis presentation. Attached Figure Description

[0045] The accompanying drawings, which form part of this specification, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0046] Figure 1 This is a flowchart illustrating the GIS analysis and visualization method for multi-source mapping data fusion provided in Embodiment 1 of the present invention. Detailed Implementation

[0047] The present invention will now be described in detail with reference to the accompanying drawings and embodiments. It should be noted that, unless otherwise specified, the embodiments and features described herein can be combined with each other.

[0048] The following detailed description is exemplary and intended to provide further detailed explanation of the invention. Unless otherwise specified, all technical terms used in this invention have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in this invention is for describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention.

[0049] Example 1:

[0050] Please see Figure 1 This embodiment proposes a GIS analysis and visualization method based on multi-source mapping data fusion, including the following steps:

[0051] Step one involves establishing a unified spatiotemporal benchmark and quality quantification dimensions. A unified geographic coordinate system and time benchmark are defined for all multi-source mapping data to be merged. To address inconsistencies in data accuracy, quantification dimensions are established, including at least the data acquisition time, the accuracy level of the acquisition equipment, and the original data resolution. It's important to note that step one serves as the foundation for establishing basic constraints in the scheme. Its key objective is to ensure that all multi-source mapping data to be merged have a consistent reference system and quantifiable quality attributes before entering the unified processing flow. To this end, a unified geographic coordinate system and time benchmark must first be defined, mapping data from different sources to the same spatial reference frame and the same time alignment scale, thereby eliminating systematic offsets caused by differences in coordinate systems, acquisition epochs, and time formats. After establishing the spatiotemporal benchmark, the quality dimensions are quantitatively modeled. These dimensions include the data acquisition time interval, equipment accuracy level, and original resolution, and are expressed in a computable manner, transforming quality attributes from a descriptive state to a measurable state. To achieve the above objectives, this embodiment introduces a basic quality index to quantify the basic quality of each data unit. The index is calculated by normalizing three key dimensions and combining them with an exponential decay coefficient, as shown in the following formula:

[0052]

[0053] in, Indicates the basic quality index; These are weighting coefficients, and the weighting ratios should be consistent with the subsequent fusion strategy. This is a time decay parameter used to describe the impact of timeliness on the reliability of spatial data; This represents the difference between the data acquisition time and the fusion processing time, reflecting timeliness constraints. This represents the numerical result of the accuracy level of the data acquisition device; the smaller the value, the higher the accuracy. This serves as the baseline resolution factor, used to avoid scale incomparability. This represents the original data resolution. This formula transforms quality attributes from discrete descriptions to continuously computable weighted expressions, ensuring consistency in quality quantification across data from different times, devices, and scales. The formula result will continue to participate in dynamic calculations as a foundational value for data reliability in subsequent steps, achieving a natural transition from quality description to the construction of a precision weight matrix. This establishes a unified constraint for temporal alignment and spatial registration in step two.

[0054] Step two involves performing time-space alignment and registration. Based on the unified time benchmark, all data are assigned precise timestamps, and spatial registration is performed using the unified geographic coordinate system to ensure all data are within the same spatiotemporal framework. It's important to note that step two is performed only after a unified spatiotemporal benchmark and quality quantification system have been established. The core significance of this step is to ensure that all participating mapping data achieves a strictly consistent reference correspondence in both the temporal and spatial dimensions, eliminating time drift, coordinate offset, and spatial scale misalignment between data points. First, based on the unified time benchmark provided in the previous stage, each data unit is assigned a precise timestamp, expressing the time position through continuous numerical representation. Data with different acquisition periods undergoes time resampling to ensure that data points in the time series are evenly distributed, preventing time discontinuities during the fusion process. After time dimension correction, spatial registration is further performed by projecting all data onto a unified geographic coordinate system to achieve spatial reference frame consistency. To address the issue of local spatial offsets between data from different sources, a spatiotemporal consistency fitting algorithm is introduced. This ensures that the registration results are not only unified in the coordinate frame but also finely corrected at the level of local spatial errors. Therefore, this embodiment introduces a spatiotemporal calibration residual term. The formula for iterative optimization is as follows:

[0055]

[0056] in, Represents the spatiotemporal calibration residual term; n represents the total number of data units; i represents the data unit; This is a weight mapping related to the quality index, used to give stronger constraints to high-quality data during spatial calibration. This is a spatiotemporal transformation function used to map the original data to a unified reference frame; These are the original spatial coordinates; This is the original time position; To unify coordinates within a unified framework; To unify time and location within a unified framework; This is a time consistency adjustment factor used to balance the coupling relationship between time correction error and spatial correction error. The significance of this formula lies in minimizing... This process achieves dual consistency of data across both the time axis and the spatial reference system, allowing higher-precision data to guide lower-quality data towards a unified framework while avoiding the accumulation of additional errors caused by coordinate transformation or time correction. Through iterative solutions, once the calibration residuals converge to a set threshold, all data achieves strict temporal alignment and spatial consistency mapping, providing a controllable starting point for subsequent data quality assessment and anomaly cleaning, and enabling step three to be carried out based on a trustworthy unified data system.

[0057] Step three involves multi-source data quality assessment and outlier cleaning. Based on the quality quantification dimensions established in Step one, the temporal freshness, acquisition accuracy, and spatial resolution of each data unit are quantitatively assessed. Outlier data units that significantly deviate from the assessment benchmark are then identified, removed, or marked. It should be noted that Step three is conducted on the basis of a unified spatiotemporal system and registration results. Its purpose is to quantitatively review the quality status of all data units, shifting the quality dimension from static description to dynamic screening. The processing first uses the quality quantification dimensions constructed in Step one to comprehensively score the timeliness, acquisition equipment accuracy, and spatial resolution of each data unit, ensuring comparability. Then, this quality score is correlated with the data performance after alignment in Step two. By calculating the spatiotemporal consistency deviation, a dimension of real data performance is added to the quality assessment process, ensuring that quality judgment is not only based on theoretical weights but also reflects spatial behavior. To achieve this goal, this embodiment constructs a fusion quality evaluation function. This function introduces registration error information on top of the original basic quality index, enabling the evaluation results to reflect the true performance status of the data. The formula is as follows:

[0058]

[0059] in, (i) represents the fusion quality evaluation function value of data unit i; i represents the data unit. The basic quality index value for data unit i; Weights are assigned to account for the impact of registration errors. These are the original spatial coordinates; To unify coordinates within a unified framework; This is the normalized spatial scale factor; The weighting for the impact of time errors is used to adjust the degree of influence of time errors; This is the original time position; To unify the temporal location within the framework, this formula is significant because it penalizes evaluation results when data exhibits offset or instability during registration, thus correlating quality performance with real-world spatial consistency. After quality quantification, anomaly identification is further performed by constructing anomaly detection boundaries by calculating the quality distribution probability thresholds for all data units. To enhance the adaptive capability of detection, this embodiment introduces a robust bias identification function. This is used to determine whether the current data should enter the removal or marking process, and the formula is as follows:

[0060]

[0061] in, Let i be the robust bias identification function for data unit i; i represents the data unit. (i) represents the fusion quality evaluation function value of data unit i; The mean of the global quality score; The standard deviation of the rating; This is the bias sensitivity coefficient, used to adjust the screening sensitivity. A value of 1 indicates that data unit i has deviated from the confidence threshold and entered the elimination or labeling range. A value of 0 indicates that data unit i has not deviated from the confidence threshold or has entered the elimination or labeling range. Through the above process, the data achieves quality stratification before fusion, ensuring that reliable data maintains its dominant role, marginal data is brought into a controlled state, and data that seriously deviates from the norm is eliminated or labeled, thus providing a stable input for the generation of the dynamic precision weight matrix in step four.

[0062] Step four involves generating a dynamic precision weight matrix. Based on the quality quantification assessment results from step three, a dynamic precision weight value is calculated for each qualified data unit. This weight value is jointly determined by the data acquisition time, equipment precision level, and resolution quantification results, forming a dynamic precision weight matrix associated with spatiotemporal location. It should be noted that step four uses the quality assessment results from step three as input and performs a dynamic precision weight assignment process on the data units that have passed the quality screening. This transforms data quality from being distinguished by fixed labels into a continuously calculable weight distribution, enabling the subsequent fusion process to possess difference sensitivity and spatiotemporal adaptability. Therefore, this embodiment incorporates the data acquisition time attenuation degree, the numerical representation of equipment precision level, and the resolution normalization factor into the weight calculation model, ensuring that the final weight reflects the synergistic influence of real-time performance, equipment reliability, and spatial detail capability. The dynamic weights are generated through a scoring-based mapping method and incorporate a soft suppression mechanism, allowing low-quality data that does not meet the rejection criteria to still participate, but in a weakly influential position. This embodiment constructs a dynamic precision weight function based on this principle. The formula is as follows:

[0063]

[0064] in, Let i be the dynamic precision weight function value of data unit i; i represents the data unit. (i) represents the fusion quality evaluation function value of data unit i, reflecting the overall credibility of the data unit; This is a sensitive control coefficient used to adjust the proportional relationship between the influence of time, equipment, and scale differences on the final weight; It indicates the span of time since the data collection began, reflecting the trend of timeliness decay; The numerical value is used to express the accuracy level of the equipment; The baseline resolution factor; and These represent the maximum time span and the maximum equipment accuracy level corresponding to all data, respectively, for normalized scale calibration. This represents the current data resolution. The significance of this formula is that data from older periods, with coarser scales, and lower device precision are proportionally suppressed in weighting calculations, while higher-quality data maintains its weight advantage, thus forming a continuously distributed, reliable strength structure. To further meet the needs of spatially heterogeneous feature representation, this embodiment maps single-point weights to a matrix expression based on spatial continuity. This ensures that weights not only belong to the data unit itself but also are associated with its local spatial location. A neighborhood propagation model is constructed using a kernel function to obtain a dynamic precision weight matrix. The formula is as follows:

[0065]

[0066] in, (x, y) is the dynamic precision weight matrix at positions x and y; x and y represent position coordinates; n represents the total number of data units; i represents a data unit. For positions x, y and data units Distance representation corresponding to spatial location; Let i be the dynamic precision weight function value of data unit i; i represents the data unit. The spatial attenuation factor influences the propagation of precision based on the proximity of neighbors. This matrix not only describes the numerical magnitude of the weights but also reflects their spatial continuity, ensuring that the data fusion process is no longer dependent on single-point reliability but is constrained by the overall spatial structure. Therefore, the dynamic precision weight matrix provides a direct basis for subsequent steps in weighted fusion and unified data layer generation.

[0067] Step 5 involves weighted fusion and generation of a unified data layer. Using the dynamic precision weight matrix generated in Step 4, weighted fusion calculations are performed on the multi-source mapping data processed in Steps 2 and 3 to generate a unified geospatial data layer that integrates multi-source information and maintains consistent internal precision evaluation. It should be noted that Step 5 fuses all selected data units based on the dynamic precision weight matrix. This ensures that the fusion result is not simply a numerical superposition, but rather a weighted integration of the differentiated and credible contributions of each data point based on spatiotemporal consistency, thereby generating a spatial data layer with a single benchmark system and unified precision semantics. The core of the fusion process lies in the dynamic precision weight matrix constructed in Step 4. As a spatial constraint, it guides each data unit to participate in the fusion with different influence intensities in different spatial neighborhoods, making the data contribution values ​​continuous rather than rigidly proportional, thereby avoiding fusion gaps caused by insufficient high-precision data coverage or abnormal local data resolution. To achieve this goal, this embodiment introduces a fusion mapping operator. After weight normalization for each spatial location, the data is accumulated and fused using the following formula:

[0068]

[0069] in, This is the fusion mapping operator at positions x and y; n represents the total number of data units; i represents the data unit. The dynamic precision weight matrix value of data unit i at position x and y is used to describe the confidence strength of the data unit at this position; x and y represent the position coordinate values. For data units The effective observations at locations x and y. This formula transforms the competitive relationship between multi-source data at the same spatial location into a collaborative relationship, where high-weighted data dominates the expression of the core structure, while low-weighted data fills in local information gaps, thus achieving a unified expression of spatial content. To address situations where available data is scarce and spans a large area, a continuous smoothing term is further introduced into the fusion result to construct a fusion stability function. The overall smoothness of the fusion surface is improved by suppressing local neighborhood differences, as shown in the following formula:

[0070]

[0071] in, Let x be the fusion stabilizing function at positions x and y; For the fusion mapping operator at positions x and y; To smooth the influence coefficients, the global fusion result should retain boundary clarity while suppressing abrupt changes in noise level gradients; The Laplacian operator at locations x and y is used to characterize the magnitude of local variation. Through a two-stage processing of weighted fusion and smoothing correction, the generated unified geospatial data layer maintains consistent expression logic even in situations with significant differences in data sources, collection time, and resolution scale, thus providing a stable data foundation for the GIS analysis and visualization in step six.

[0072] Step Six: Perform GIS analysis and visualization based on the unified data layer. Based on the unified geospatial data layer generated in Step Five, perform GIS spatial analysis, modeling, or statistical operations, and visualize the analysis results in relation to the inherent accuracy evaluation information of the unified data layer. It should be noted that Step Six uses the unified data layer as the computational basis, ensuring that the GIS spatial analysis results maintain quality transparency and accuracy recognition capabilities at the output stage. This allows analytical decisions to no longer rely solely on the result values ​​themselves, but to simultaneously understand the credible structure of the data. During analysis, spatial pattern recognition and structural modeling are first performed on the fused data. The weighted expressions formed in Steps Four and Five are then associated with the fused expressions as inseparable attributes, ensuring that each analysis output possesses both location scale and accuracy semantics. To achieve this goal, this embodiment introduces an analysis credibility backtracking function. This function compares and maps the sensitivity expression at each location based on the analysis results with the fusion weight matrix, as shown in the following formula:

[0073]

[0074] in, For the analysis of the reliable backtracking function at positions x and y; The output values ​​of the GIS spatial analysis results at location x and y; For the fusion mapping operator at positions x and y; Describe the local sensitivity gradient of the result to the input fused data; (x, y) is a dynamic precision weight matrix at positions x and y, used to transfer the reliability of the weights to the post-analysis state, so that the output shows both the trend and the degree of reliability. This calculation result makes spatial hotspots, feature extraction, and statistical aggregation no longer purely numerical expressions, but rather provides precision markers, allowing users to clearly identify the boundaries between high-precision analysis areas and unstable regions. To give the visualization process quantitative guidance, this embodiment further introduces an expression brightness adjustment function. In the output stage, the intensity and credibility of the results are simultaneously controlled, as shown in the following formula:

[0075]

[0076] in, Here is the brightness adjustment function expressed at positions x and y; The output values ​​of the GIS spatial analysis results at location x and y; The confidence enhancement coefficient is used to increase the brightness of the result in the confidence gain region, while maintaining suppression in the low weight region. This serves as a reliable backtracking function for analysis at locations x and y. The visualized imagery generated through this mapping process provides an intuitive reference for interpreting the accuracy of spatial results, ensuring a consistent propagation logic for spatial anomaly identification, risk mapping, and quality labeling. Ultimately, this visualization model enables the unified data layer to possess not only fusion consistency but also analytical interpretation consistency, and can support subsequent multi-dimensional spatial dynamic analysis, intelligent geographic modeling, and the extension of accuracy constraint feedback mechanisms.

[0077] Example 2:

[0078] The GIS analysis and visualization system for multi-source surveying and mapping data fusion can realize the GIS analysis and visualization method for multi-source surveying and mapping data fusion described in Example 1, including:

[0079] Benchmark and Quality Quantification Module: Define a unified spatiotemporal benchmark based on the multi-source mapping data to be fused, and construct a quantification dimension based at least on the data acquisition time, the accuracy level of the acquisition equipment, and the resolution of the original data. Calculate the basic quality index of each data unit using the quality index formula.

[0080] Spatiotemporal registration module: Based on the established unified spatiotemporal benchmark, a weight mapping derived from the basic quality index is introduced. By minimizing the spatiotemporal calibration residual term, all data are resampled in time and transformed in spatial coordinates, and the data registered to the same spatiotemporal framework is output.

[0081] Quality assessment and cleaning module: Based on the basic quality index, a fusion quality evaluation function is constructed. After dynamic quality assessment of each data unit, the dynamic quality assessment results are used to identify and clean abnormal data units through a robust deviation identification function to obtain the remaining qualified data units.

[0082] Dynamic weight matrix generation module: Calculates the dynamic precision weight value of the remaining qualified data units using the dynamic quality assessment results, and maps the dynamic precision weight value into a dynamic precision weight matrix based on spatial continuity;

[0083] Weighted fusion module: Using the dynamic precision weight matrix as weights, the remaining qualified data units are weighted and fused to generate a unified geospatial data layer, and the fusion result is smoothed by constructing a fusion stability function;

[0084] GIS Analysis and Visualization Module: Performs GIS spatial analysis on the unified geospatial data layer, and visualizes the GIS spatial analysis results by constructing an analysis reliability backtracking function and an expression brightness adjustment function, and correlates them with a dynamic precision weight matrix.

[0085] Example 3:

[0086] This invention also provides a GIS analysis and visualization device for multi-source surveying and mapping data fusion, which can realize the GIS analysis and visualization method for multi-source surveying and mapping data fusion described in Embodiment 1, including a processor and a storage medium;

[0087] The storage medium is used to store instructions;

[0088] The processor is configured to operate according to the instructions to perform the steps of the following method:

[0089] A unified spatiotemporal benchmark is defined based on the multi-source mapping data to be fused, and a quantitative dimension is constructed based at least on the data acquisition time, the accuracy level of the acquisition equipment, and the original data resolution. The basic quality index of each data unit is calculated using the quality index formula.

[0090] Based on the established unified spatiotemporal benchmark, a weight mapping derived from the basic quality index is introduced. By minimizing the spatiotemporal calibration residual term, all data are resampled in time and transformed in spatial coordinates, and the data registered to the same spatiotemporal framework is output.

[0091] Based on the aforementioned basic quality index, a fusion quality evaluation function is constructed. After performing dynamic quality evaluation on each data unit, the dynamic quality evaluation results are used to identify and clean abnormal data units through a robust deviation identification function to obtain the remaining qualified data units.

[0092] The dynamic precision weight value of the remaining qualified data units is calculated using the dynamic quality assessment results, and the dynamic precision weight value is mapped to a dynamic precision weight matrix based on spatial continuity.

[0093] Using the dynamic precision weight matrix as weights, the remaining qualified data units are weighted and fused to generate a unified geospatial data layer, and the fusion result is smoothed by constructing a fusion stability function.

[0094] GIS spatial analysis is performed on the unified geospatial data layer. By constructing an analysis reliability backtracking function and an expression brightness adjustment function, the GIS spatial analysis results are correlated with a dynamic precision weight matrix for visualization.

[0095] Example 4:

[0096] This invention also provides a computer-readable storage medium that implements the GIS analysis and visualization method for multi-source mapping data fusion as described in Embodiment 1. The medium stores a computer program that, when executed by a processor, performs the steps of the following method:

[0097] A unified spatiotemporal benchmark is defined based on the multi-source mapping data to be fused, and a quantitative dimension is constructed based at least on the data acquisition time, the accuracy level of the acquisition equipment, and the original data resolution. The basic quality index of each data unit is calculated using the quality index formula.

[0098] Based on the established unified spatiotemporal benchmark, a weight mapping derived from the basic quality index is introduced. By minimizing the spatiotemporal calibration residual term, all data are resampled in time and transformed in spatial coordinates, and the data registered to the same spatiotemporal framework is output.

[0099] Based on the aforementioned basic quality index, a fusion quality evaluation function is constructed. After performing dynamic quality evaluation on each data unit, the dynamic quality evaluation results are used to identify and clean abnormal data units through a robust deviation identification function to obtain the remaining qualified data units.

[0100] The dynamic precision weight value of the remaining qualified data units is calculated using the dynamic quality assessment results, and the dynamic precision weight value is mapped to a dynamic precision weight matrix based on spatial continuity.

[0101] Using the dynamic precision weight matrix as weights, the remaining qualified data units are weighted and fused to generate a unified geospatial data layer, and the fusion result is smoothed by constructing a fusion stability function.

[0102] GIS spatial analysis is performed on the unified geospatial data layer. By constructing an analysis reliability backtracking function and an expression brightness adjustment function, the GIS spatial analysis results are correlated with a dynamic precision weight matrix for visualization.

[0103] As is known from common technical knowledge, this invention can be implemented through other embodiments that do not depart from its spirit or essential characteristics. Therefore, the disclosed embodiments described above are merely illustrative and not exhaustive. All modifications within the scope of this invention or its equivalents are included in this invention.

[0104] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention 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.

[0105] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will 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 and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0106] 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.

[0107] 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.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A GIS analysis and visualization method based on multi-source surveying and mapping data fusion, characterized by: include: A unified spatiotemporal benchmark is defined based on the multi-source mapping data to be fused, and a quantitative dimension is constructed based at least on the data acquisition time, the accuracy level of the acquisition equipment, and the original data resolution. The basic quality index of each data unit is calculated using the quality index formula. Based on the established unified spatiotemporal benchmark, a weight mapping derived from the basic quality index is introduced. By minimizing the spatiotemporal calibration residual term, all data are resampled in time and transformed in spatial coordinates, and the data registered to the same spatiotemporal framework is output. Based on the aforementioned basic quality index, a fusion quality evaluation function is constructed. After performing dynamic quality evaluation on each data unit, the dynamic quality evaluation results are used to identify and clean abnormal data units through a robust deviation identification function to obtain the remaining qualified data units. The dynamic precision weight value of the remaining qualified data units is calculated using the dynamic quality assessment results, and the dynamic precision weight value is mapped to a dynamic precision weight matrix based on spatial continuity. Using the dynamic precision weight matrix as weights, the remaining qualified data units are weighted and fused to generate a unified geospatial data layer, and the fusion result is smoothed by constructing a fusion stability function. GIS spatial analysis is performed on the unified geospatial data layer. By constructing an analysis reliability backtracking function and an expression brightness adjustment function, the GIS spatial analysis results are correlated with a dynamic precision weight matrix for visualization.

2. The GIS analysis and visualization method for multi-source mapping data fusion according to claim 1, characterized in that, The formula for the quality index is: in, Indicates the basic quality index; These are the weighting coefficients; This is the time decay parameter; This represents the difference between the data acquisition time and the fusion processing time. This represents the numerical result indicating the accuracy level of the data acquisition equipment. The baseline resolution factor; Indicates the resolution of the original data.

3. The GIS analysis and visualization method for multi-source mapping data fusion according to claim 1, characterized in that, The spatiotemporal calibration residual term is: in, Represents the spatiotemporal calibration residual term; n represents the total number of data units; i represents the data unit; Mapping the relevant weights for the quality index; It is a spacetime transformation function; These are the original spatial coordinates; This is the original time position; To unify coordinates within a unified framework; To unify time and location within a unified framework; This is a time consistency adjustment factor.

4. The GIS analysis and visualization method for multi-source mapping data fusion according to claim 1, characterized in that, The fusion quality evaluation function is: in, (i) represents the fusion quality evaluation function value of data unit i; i represents the data unit. The basic quality index value for data unit i; Weights are assigned to account for the impact of registration errors. These are the original spatial coordinates; To unify coordinates within a unified framework; This is the normalized spatial scale factor; Weights are assigned to account for the impact of time errors. This is the original time position; To unify the time and location within the framework.

5. The GIS analysis and visualization method for multi-source mapping data fusion according to claim 1, characterized in that, The robust bias identification function is: in, Let i be the robust bias identification function for data unit i; i represents the data unit. (i) represents the fusion quality evaluation function value of data unit i; The mean of the global quality score; The standard deviation of the rating; This is the bias sensitivity coefficient.

6. The GIS analysis and visualization method for multi-source mapping data fusion according to claim 1, characterized in that, The formula for calculating the dynamic precision weight value is as follows: in, Let i be the dynamic precision weight function value of data unit i; i represents the data unit. (i) represents the fusion quality evaluation function value of data unit i; For sensitive control coefficients; Indicates the time span between the current time and the time of data collection; The numerical value is used to express the accuracy level of the equipment; The baseline resolution factor; and These represent the maximum time span and the maximum equipment accuracy level corresponding to all data. This is the current data resolution.

7. The GIS analysis and visualization method for multi-source mapping data fusion according to claim 1, characterized in that, The dynamic precision weight matrix is: in, (x, y) is the dynamic precision weight matrix at positions x and y; x and y represent position coordinates; n represents the total number of data units; i represents a data unit. Let i be the dynamic precision weight function value of data unit i; i represents the data unit. This is the spatial decay factor; For positions x, y and data units Distance representation corresponding to spatial location.

8. A GIS analysis and visualization system that integrates multi-source surveying and mapping data, characterized by: include: Benchmark and Quality Quantification Module: Define a unified spatiotemporal benchmark based on the multi-source mapping data to be fused, and construct a quantification dimension based at least on the data acquisition time, the accuracy level of the acquisition equipment, and the resolution of the original data. Calculate the basic quality index of each data unit using the quality index formula. Spatiotemporal registration module: Based on the established unified spatiotemporal benchmark, a weight mapping derived from the basic quality index is introduced. By minimizing the spatiotemporal calibration residual term, all data are resampled in time and transformed in spatial coordinates, and the data registered to the same spatiotemporal framework is output. Quality assessment and cleaning module: Based on the basic quality index, a fusion quality evaluation function is constructed. After dynamic quality assessment of each data unit, the dynamic quality assessment results are used to identify and clean abnormal data units through a robust deviation identification function to obtain the remaining qualified data units. Dynamic weight matrix generation module: Calculates the dynamic precision weight value of the remaining qualified data units using the dynamic quality assessment results, and maps the dynamic precision weight value into a dynamic precision weight matrix based on spatial continuity; Weighted fusion module: Using the dynamic precision weight matrix as weights, the remaining qualified data units are weighted and fused to generate a unified geospatial data layer, and the fusion result is smoothed by constructing a fusion stability function; GIS Analysis and Visualization Module: Performs GIS spatial analysis on the unified geospatial data layer, and visualizes the GIS spatial analysis results by constructing an analysis reliability backtracking function and an expression brightness adjustment function, and correlates them with a dynamic precision weight matrix.

9. A GIS analysis and visualization device for multi-source surveying and mapping data fusion, characterized in that: Including processor and storage media; The storage medium is used to store instructions; The processor is configured to operate according to the instructions to perform the steps of the method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the steps of the method according to any one of claims 1 to 7.

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