A method for asset evaluation and operation analysis of spatial global data

Abstract

This invention relates to the field of data asset management technology, and in particular to a method for asset valuation and operational analysis of spatial whole-domain data. The method involves collecting spatial whole-domain data and ownership-related data; integrating and governing multi-source heterogeneous data through data quality assessment; constructing a spatiotemporal hybrid index tree and performing online performance evaluation and structural adjustment; extracting the multi-dimensional features of the spatiotemporal hybrid index tree to obtain a whole-domain data asset value index; generating data ownership certificates through blockchain anchoring; and using a data operational analysis decision engine to perform supply and demand matching modeling and operational analysis decision output, thereby improving the accuracy of data asset valuation.

Description

Technical Field

[0001] This invention relates to the field of data asset management technology, and in particular to a method for asset-based assessment and operational analysis of spatial whole-domain data. Background Technology

[0002] As a crucial production factor and strategic resource, spatial data increasingly demands asset management and operational analysis. Current spatial data management primarily relies on static storage and basic query services, leading to the following problems: First, the lack of a systematic asset evaluation system makes it difficult to quantify the value of spatial data resources. Second, the lack of a reliable mechanism for verifying data ownership makes it difficult to resolve ownership disputes during data asset circulation. Third, the lack of closed-loop optimization capabilities in operational analysis results in a lack of data support for data asset operational decisions. Existing systems lack the capacity for full-chain asset management, from collection, governance, value assessment, ownership confirmation to operational analysis, resulting in low efficiency in the transformation of spatial data from resources to assets.

[0003] Chinese patent application CN117932619A discloses a method for visualized asset management and security operation of industrial internet enterprises. It focuses on asset detection and security strategy adjustment, and solves the problems of visualized management and vulnerability handling. However, it still does not involve dynamic optimization of index performance for spatial full-domain data, lacks a structural adjustment mechanism for spatiotemporal data query efficiency, and cannot support efficient asset assessment and operation analysis.

[0004] Chinese patent application CN108090835A discloses a system and method for assessing the value of urban data assets. It calculates the value of data assets through a cost-benefit model, but the asset value assessment still deviates from spatial characteristics. It does not consider spatial scarcity dimensions such as geographical coverage, spatial resolution, and spatial uniqueness, nor does it introduce time-activity assessment, resulting in a distortion of the quantification of spatial data asset value.

[0005] Chinese patent application CN119250910A discloses a data asset valuation system and method based on system dynamics. The system assesses value by adjusting coefficients of multiple subsystems. However, it still suffers from a disconnect between ownership verification and operational analysis, and lacks a reliable ownership definition mechanism for data lineage tracing and blockchain verification. This results in operational analysis lacking reliable ownership support and inaccurate asset valuation. Summary of the Invention

[0006] In view of this, the present invention provides a method for asset valuation and operational analysis of spatial global data, in order to overcome the problems of structural adjustment lacking indexing performance, asset value assessment being divorced from spatial characteristics, and operational analysis lacking credible ownership support and inaccurate data asset valuation caused by the disconnect between ownership verification and operational analysis in the existing technology.

[0007] Specifically, the present invention is achieved through the following technical solution: A method for asset valuation and operational analysis of spatial global data provided by the present invention includes: Step S1: Collect spatial data and ownership-related data. Step S2: Perform fusion and governance on the spatial global data to obtain a standardized spatial global dataset; Step S3: Construct a spatiotemporal hybrid index tree based on the standardized spatial global dataset, evaluate its online performance, obtain the online performance evaluation results, and adjust the structure of the spatiotemporal hybrid index tree based on the online performance evaluation results; Step S4: Obtain the global data asset value index based on the spatiotemporal hybrid index tree; Step S5: Generate a data ownership certificate based on the ownership-related data; Step S6: Output operational analysis decisions based on data ownership certificates and the overall data asset value index.

[0008] Optionally, in step S7, the operational analysis results are obtained based on the operational analysis decision, and the rules for the integration governance process are updated based on the operational analysis results, and the parameters for the structural adjustment process are optimized.

[0009] Optionally, in step S2, the spatial data is fused and processed, and the specific steps are as follows: Step S21: Perform coordinate system transformation on the spatial global data to obtain unified coordinate data; Step S22: Standardize the format of the unified coordinate data to obtain standard format data; Step S23: Perform semantic alignment mapping on the format standard data to obtain semantically aligned data; Step S24: Perform quality defect repair on the semantic alignment data to obtain quality repaired data; Step S25: Perform duplicate recording disambiguation on the quality repair data to obtain a deduplicated dataset, and use the deduplicated dataset as a standardized spatial global dataset.

[0010] Optionally, in step S3, the spatiotemporal hybrid index tree is constructed based on the standardized spatial global dataset, and the specific steps are as follows: Step S31: Calculate the spatiotemporal boundaries of the standardized spatial global dataset to obtain a spatiotemporal range cube; Step S32: Perform octree spatial partitioning on the spatiotemporal range cube to obtain spatial voxel units; Step S33: Discretize the spatial voxel unit in time intervals to obtain spatiotemporal voxel nodes; Step S34: Perform Hilbert curve encoding on the spatiotemporal voxel nodes to obtain the space-filling curve index code C; Step S35: Construct a B+ tree hierarchical structure based on the space-filling curve index code C, and use the B+ tree hierarchical structure as a spatiotemporal hybrid index tree.

[0011] Optionally, in step S3, the online performance is evaluated, specifically including: monitoring the query response latency of the spatiotemporal hybrid index tree to obtain the average query latency value D; monitoring the storage space occupancy rate of the spatiotemporal hybrid index tree to obtain the space utilization value U; monitoring the concurrent access throughput of the spatiotemporal hybrid index tree to obtain the concurrent processing capability value T; and calculating the online performance evaluation result E based on the average query latency value D, the space utilization value U, and the concurrent processing capability value T.

[0012] Optionally, in step S3, the online performance evaluation result E is compared with a preset performance threshold E0, the compliance status of the online performance evaluation result is judged based on the comparison result, and the spatiotemporal hybrid index tree is structurally adjusted based on the judgment result, wherein: When E < E0, the online performance evaluation result is determined to be non-compliant, and the spatiotemporal hybrid index tree is structurally adjusted: the spatiotemporal hybrid index tree node filling rate f is adjusted according to the node filling strategy; When E≥E0, the online performance evaluation result is deemed to be in compliance, and no structural adjustment is made to the spatiotemporal hybrid index tree.

[0013] Optionally, in step S4, the index coverage of the spatiotemporal hybrid index tree is measured to obtain a spatial coverage breadth value A; the data update frequency of the spatiotemporal hybrid index tree is statistically analyzed to obtain a timeliness freshness value F; the query access popularity of the spatiotemporal hybrid index tree is statistically analyzed to obtain a demand activity value H; the data association complexity of the spatiotemporal hybrid index tree is calculated to obtain a network effect value N; and the global data asset value index V is calculated based on the spatial coverage breadth value A, the timeliness freshness value F, the demand activity value H, and the network effect value N.

[0014] Optionally, in step S5, a hash digest is calculated on the ownership-related data to obtain a data fingerprint Hf, a digital signature is performed on the data fingerprint Hf to obtain a signature digest S, and the signature digest S is encapsulated with the ownership subject's public key K, timestamp information Ts, and evidence storage height B to obtain a data ownership certificate C.

[0015] Optionally, in step S6, the data ownership certificate C is verified to obtain the ownership validity status R; the global data asset value index V is classified to obtain the value level label G; the data assets are screened for access based on the ownership validity status R and the value level label G to obtain the operable data asset pool P; the operable data asset pool P is modeled for demand matching to obtain the supply and demand matching degree matrix M; and the operation analysis decision Dr is output based on the supply and demand matching degree matrix M.

[0016] Optionally, in step S7, feedback is collected on the actual execution effect of the operational analysis decision Dr to obtain a decision accuracy value Acc. The decision accuracy value Acc is compared with the expected target Acc0. Based on the comparison result, the status of the rule update requirement is judged, and based on the judgment result, the process of rule updating in the integration governance process and the process of parameter optimization in the structural adjustment process are performed, wherein: When Acc < Acc0, the rule update requirement is determined to be "update required". The rule update process and the structural adjustment process are then optimized. When Acc ≥ Acc0, the rule update requirement is determined to be no need to update. No rule update is performed during the integration governance process, and no parameter optimization is performed during the structural adjustment process. Attached Figure Description

[0017] Figure 1 A flowchart illustrating the method for asset-based assessment and operational analysis of spatial global data provided in this embodiment of the invention; Figure 2 A schematic diagram of the fusion governance process provided in an embodiment of the present invention; Figure 3 This is a schematic diagram of the spatiotemporal hybrid index tree construction process provided in an embodiment of the present invention; Figure 4 This is a schematic diagram illustrating the process of generating and analyzing data ownership certificates and making operational decisions, as provided in an embodiment of the present invention. Detailed Implementation

[0018] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] In related technologies, spatial data management in urban environmental monitoring scenarios often adopts static storage and basic query services, which are difficult to cope with complex needs such as multi-source heterogeneous data fusion, dynamic index optimization, asset value quantification, credible ownership verification, and closed-loop operational decision-making. This results in low efficiency in the conversion of data assets from resources to capital and a lack of data support for operational decisions. Therefore, it is necessary to construct methods that can structurally adjust index performance, conduct asset value assessment in accordance with spatial characteristics, and establish credible ownership verification to improve the accuracy of credible ownership support for operational analysis and data asset assessment.

[0020] This embodiment provides a method for asset valuation and operational analysis of spatial global data. The method collects spatial global data and ownership-related data to establish a foundation for linking data assets with ownership relationships, thus providing a reliable data source guarantee for subsequent asset valuation operations. It also integrates and governs multi-source heterogeneous data through data quality assessment to eliminate differences in coordinate systems, data formats, semantic definitions, and quality levels, thereby providing a high-quality data foundation for subsequent index construction and value assessment. Furthermore, it constructs a spatiotemporal hybrid index tree and performs online performance evaluation and structural adjustments to dynamically optimize the index structure according to data distribution and access patterns, thereby increasing the response efficiency of spatial data queries and indexing. The system improves storage space utilization, thereby enhancing the accuracy of data asset assessment. It also extracts a global data asset value index through multi-dimensional feature extraction using a spatiotemporal hybrid index tree, facilitating the quantification of data asset value and providing a scientific basis for data pricing and transactions. Furthermore, it generates data ownership certificates through blockchain anchoring, establishing an immutable data ownership evidence system to ensure clear ownership and secure transactions during data asset circulation, providing credible ownership support for operational analysis. Finally, it utilizes a data operational analysis decision engine to model supply and demand matching and output operational analysis decisions, updating rules and optimizing parameters based on operational analysis results to establish a closed-loop management system for the entire data asset lifecycle, thereby continuously improving the accuracy of data asset assessment.

[0021] See Figure 1 This invention provides a method for asset valuation and operational analysis of spatial global data, including: Step S1: Collect spatial data and ownership-related data. Step S2: Perform fusion and governance on the spatial global data to obtain a standardized spatial global dataset; Step S3: Construct a spatiotemporal hybrid index tree based on the standardized spatial global dataset, evaluate its online performance, obtain the online performance evaluation results, and adjust the structure of the spatiotemporal hybrid index tree based on the online performance evaluation results; Step S4: Obtain the global data asset value index based on the spatiotemporal hybrid index tree; Step S5: Generate a data ownership certificate based on the ownership-related data; Step S6: Output operational analysis decisions based on data ownership certificates and the overall data asset value index; Step S7: Obtain the operational analysis results based on the operational analysis decision, update the rules for the integration governance process based on the operational analysis results, and optimize the parameters for the structural adjustment process.

[0022] In this embodiment, step S1 involves collecting spatial data across the entire domain. Specifically, this embodiment collects land cover data using satellite remote sensing sensors, topographic data using aerial photogrammetry equipment, real-time environmental perception data using ground-based IoT sensor nodes, and crowd activity trajectory data using mobile terminal positioning services. The land cover data, topographic data, real-time environmental perception data, and crowd activity trajectory data are then used as spatial data across the entire domain.

[0023] In this embodiment, the satellite remote sensing sensor refers to a detection device mounted on a satellite platform for acquiring electromagnetic radiation information of the Earth's surface, including optical imaging satellites, synthetic aperture radar satellites, and multispectral imaging satellites. It acquires surface cover data through a spaceborne pushbroom imager. The aerial photogrammetry equipment refers to a measurement system mounted on an aerial platform for acquiring high-resolution surface images and three-dimensional point clouds, including fixed-wing UAVs, multi-rotor UAVs, and airborne lidar systems. The ground sampling distance is at the centimeter level. In this embodiment, a five-lens oblique photography camera and lidar combined system are selected as the main acquisition device for terrain and landform data. The ground IoT sensor node refers to an intelligent sensing terminal deployed on the ground for real-time monitoring of environmental elements, including meteorological monitoring stations, water quality monitoring buoys, traffic flow detectors, and environmental monitoring micro-stations. The sampling frequency is at the minute to hour level. The mobile terminal positioning service refers to a service system that provides location calculation for mobile intelligent terminals based on the Global Navigation Satellite System, including GPS positioning for smartphones, vehicle navigation positioning, and positioning for smart wearable devices. The positioning accuracy is at the meter level.

[0024] In this embodiment, step S1 involves collecting ownership-related data.

[0025] In this embodiment, the ownership-related data refers to a set of metadata used to prove the ownership relationship of data assets, including data source identifier, collection timestamp, spatial reference coordinate system, and ownership subject information. The data source identifier refers to the coded information used to uniquely identify the source of data production, including satellite platform number, sensor model, aerial photography zoning code, or equipment serial number. The collection timestamp refers to the time mark that records the time of data observation, which is uniformly marked by Coordinated Universal Time with millisecond precision. The spatial reference coordinate system refers to the reference frame that defines the geometric benchmark of the data, such as the geodetic coordinate system. The ownership subject information refers to the identity identifier that records the data owner, such as the organization code and natural person identity information.

[0026] In this embodiment, the ownership-related data is obtained by reading the factory configuration of the acquisition device, such as a satellite; the acquisition timestamp is obtained by reading a network time protocol server; the spatial reference coordinate system is obtained according to the data format specifications set by the acquisition device, such as a geodetic coordinate system; and the ownership subject information is read from the digital certificate of the data producer.

[0027] Figure 2 This is a schematic diagram of the integrated governance process provided by the present invention, such as... Figure 2 As shown, in step S1, spatial data and ownership-related data are collected to establish a basis for the association between data assets and ownership relationships, thereby providing a reliable data source guarantee for subsequent assetization operations.

[0028] In this embodiment, the fusion and governance of the spatial global data in step S2 includes the following specific steps: Step S21: Perform coordinate system transformation on the spatial global data to obtain unified coordinate data; Step S22: Standardize the format of the unified coordinate data to obtain standard format data; Step S23: Perform semantic alignment mapping on the format standard data to obtain semantically aligned data; Step S24: Perform quality defect repair on the semantic alignment data to obtain quality repaired data; Step S25: Perform duplicate recording disambiguation on the quality repair data to obtain a deduplicated dataset, and use the deduplicated dataset as a standardized spatial global dataset.

[0029] In this embodiment, the coordinate system transformation refers to the process of projecting the coordinates of the entire spatial data to the same reference coordinate system. In this embodiment, the spatial reference is unified by the seven-parameter Bursa model, and the optimal solution of the seven parameters is obtained by solving the least squares method to obtain unified coordinate data.

[0030] In this embodiment, the format standardization process refers to the process of converting heterogeneous data formats into a unified standard format. In this embodiment, vector data is structured and encapsulated using the GeoJSON standard to obtain standard format data. The GeoJSON standard refers to a geographic data exchange format specification based on JavaScript object representation.

[0031] In this embodiment, the semantic alignment mapping refers to the process of establishing semantic equivalence relationships between different data sources. In this embodiment, semantic equivalence relationships between cross-source data elements are established through ontology alignment technology to obtain semantically aligned data. The ontology alignment technology refers to a technical method for achieving semantic integration through formal ontology concept matching.

[0032] In this embodiment, the quality defect repair refers to the process of identifying and correcting quality problems in semantic alignment data. In this embodiment, outliers in semantic alignment data are identified through spatial autocorrelation analysis, and outliers in semantic alignment data are repaired through Kriging to obtain quality-repaired data.

[0033] In this embodiment, the despecification of duplicate records refers to the process of identifying and eliminating duplicate data records. This embodiment generates attribute fingerprints using SimHash, which stands for SimilarityHash. Deduplication is achieved through joint matching of spatial location hash and attribute fingerprint. This embodiment does not limit the input attributes for generating attribute fingerprints; those skilled in the art can freely choose according to actual needs. Exemplary key attribute combinations include: data source identifier, sensor type, observation element code, and data quality level. The Hamming distance Hd of the location hash is compared with 0, and the Hamming distance Fd of the attribute fingerprint is compared with a preset attribute distance threshold Fd0. The duplicate record status is determined based on the comparison results. When Hd=0 and Fd<Fd0, the duplicate record is determined to be duplicated, the record with the latest timestamp is retained and the historical version information is merged. Otherwise, retain the current record; The preset attribute distance threshold Fd0 is determined using the Hamming distance empirical method, resulting in Fd0=3, thus obtaining the deduplicated dataset.

[0034] In this embodiment, in step S2, multi-source heterogeneous data is fused and governed through data quality assessment in order to eliminate differences in coordinate system, data format, semantic definition and quality level, thereby providing a high-quality data foundation for subsequent index construction and value assessment.

[0035] Figure 3 This is a schematic diagram of the spatiotemporal hybrid index tree construction process provided by the present invention, as shown below. Figure 3As shown, in step S3, the spatiotemporal hybrid index tree is constructed based on the standardized spatial global dataset. The specific steps are as follows: Step S31: Calculate the spatiotemporal boundaries of the standardized spatial global dataset to obtain a spatiotemporal range cube; Step S32: Perform octree spatial partitioning on the spatiotemporal range cube to obtain spatial voxel units; Step S33: Discretize the spatial voxel unit in time intervals to obtain spatiotemporal voxel nodes; Step S34: Perform Hilbert curve encoding on the spatiotemporal voxel nodes to obtain the space-filling curve index code C; Step S35: Construct a B+ tree hierarchical structure based on the space-filling curve index code C, and use the B+ tree hierarchical structure as a spatiotemporal hybrid index tree.

[0036] In this embodiment, the spatiotemporal boundary calculation refers to the process of determining the spatiotemporal range of the standardized spatial global dataset. This includes calculating the bounding rectangle of the spatial range of the standardized spatial global dataset to obtain the minimum longitude Lmin, maximum longitude Lmax, minimum latitude Bmin, and maximum latitude Bmax; statistically analyzing the start and end times of the time range to obtain the earliest time Tmin and the latest time Tmax; and combining the spatial and time ranges to construct a four-dimensional spatiotemporal range cube. In this embodiment, the spatial dimension is set to latitude and longitude coordinates, and the time dimension is set to Unix timestamps. The resulting four-dimensional spatiotemporal range cube covers the global range and the past ten-year time span. The full name of the Unix timestamp is Unix Timestamp.

[0037] In this embodiment, the octree spatial partitioning refers to a spatial indexing method that recursively subdivides a three-dimensional space into eight sub-cubes. In this embodiment, through a recursive subdivision strategy, the spatiotemporal range cube is bisected along the midpoints of each dimension, generating eight sub-cubes as initial spatial voxel units. The data point density ρ within each spatial voxel unit is calculated, and ρ is compared with a preset density threshold ρ0. Based on the comparison result, the subdivision state is judged, and the unit is recursively subdivided based on the judgment result. Wherein: When ρ > ρ0, the subdivision state is determined to be that subdivision is required. The subdivision of the unit is continued recursively until the subdivision state is that subdivision is not required. When ρ≤ρ0, the subdivision state is determined to be no subdivision required, the recursion is stopped and the current unit is taken as a spatial voxel unit; The process of calculating the data point density ρ within a spatial voxel unit is as follows: ρ = N / V, where N is the number of data points contained in the unit, obtained by traversing all data records within the unit and statistically analyzing them; V is the spatial volume corresponding to the unit, which is equal to the product of the longitude range, latitude range, and elevation range covered by the octree node; the preset density threshold ρ0 is calculated by collecting a predetermined number of historical spatial datasets, such as 1000, and calculating the data point density of all non-empty spatial voxel units in the historical spatial datasets, taking the 75th percentile as ρ0, resulting in ρ0 = 1000 data points per cubic kilometer.

[0038] In this embodiment, the time interval discretization refers to the process of dividing the continuous time domain in the spatial voxel unit into discrete time segments. In this embodiment, the spatial region corresponding to the octagonal leaf node is divided at equal intervals according to the time dimension to generate time slices. The equal intervals are dynamically adjusted according to the data update frequency. In this embodiment, static terrain data is processed through annual slices, and dynamic monitoring data is processed through daily slices. The spatial voxel unit and time slices are combined to generate spatiotemporal voxel nodes. Each spatiotemporal voxel node records the spatiotemporal range, data pointer, and statistical information.

[0039] In this embodiment, the Hilbert curve encoding refers to a method of mapping multidimensional coordinates to one-dimensional codes using a space-filling curve. The process is as follows: the three-dimensional spatial coordinates are encoded using 10th-order Hilbert encoding to generate a 30-bit binary code Cs, and the time dimension is encoded using linear encoding to generate a 20-bit binary code Ct. These are combined to form the space-filling curve index code C.

[0040] In this embodiment, the B+ tree hierarchical structure refers to a multi-level index system built based on the B+ tree data structure. In this embodiment, a disk-resident balanced tree is built with the space-filling curve index code C as the key value, the node size is set to 8KB, the fill rate is set to 70%, and the B+ tree hierarchical structure is used as a spatiotemporal hybrid index tree.

[0041] In this embodiment, in step S3, the online performance is evaluated, specifically including: monitoring the query response latency of the spatiotemporal hybrid index tree to obtain the average query latency value D; monitoring the storage space occupancy rate of the spatiotemporal hybrid index tree to obtain the space utilization value U; monitoring the concurrent access throughput of the spatiotemporal hybrid index tree to obtain the concurrent processing capability value T; and calculating the online performance evaluation result E based on the average query latency value D, the space utilization value U, and the concurrent processing capability value T.

[0042] In this embodiment, the query response latency refers to the time interval from the issuance of a query request to the return of a response. This embodiment deploys a probe component on the index server to record the time interval from the arrival of a query request to the return of a response, and calculates the average value within the sampling period as the average query latency value D. The sampling period is set to 5 minutes. The storage space utilization rate refers to the ratio of the actual space occupied by the index file to the theoretical data volume. In this embodiment, the ratio of the actual occupied space Vs to the theoretical data volume Vt is used as the space utilization rate value U. The actual occupied space refers to the physical storage capacity actually occupied by the spatiotemporal hybrid index tree on the storage medium. The theoretical data volume refers to the sum of the original data sizes of all valid data records in the index without considering any index structure overhead. This embodiment records and reads the actual occupied space and the theoretical data volume through system operation logs. The concurrent access throughput refers to the number of concurrent query requests successfully processed per unit time. In this embodiment, a stress testing tool simulates multi-user concurrent queries, and the number of query requests successfully processed per unit time is used as the concurrent processing capacity value T. The online performance evaluation... The estimated result E is calculated using a weighted normalization method, defined as E = w1 × (1 / Dn) + w2 × (1 / Un) + w3 × Tn, where Dn is the normalized value of the average query latency D, Un is the normalized value of the space utilization U, and Tn is the normalized value of the concurrent processing capability T. In this embodiment, the average query latency D, space utilization U, and concurrent processing capability T are normalized using a max-min normalization method, with w1 being the first weighting coefficient, w2 the second weighting coefficient, and w3 the third weighting coefficient, and w1 + w2 + ... w3=1, w1, w2, and w3 are determined by expert scoring. In this embodiment, w1=0.4, w2=0.3, and w3=0.3 are set. The expert scoring method refers to the process of inviting domain experts to independently score the first weight coefficient, the second weight coefficient, and the third weight coefficient. This embodiment does not limit the specific method of expert scoring. Those skilled in the art can freely choose according to actual needs, such as pushing the window through the cloud, and the domain experts input the values ​​of the first weight coefficient, the second weight coefficient, and the third weight coefficient through the window.

[0043] In this embodiment, in step S3, the online performance evaluation result E is compared with the preset performance threshold E0. Based on the comparison result, the compliance status of the online performance evaluation result is judged, and the spatiotemporal hybrid index tree is structurally adjusted based on the judgment result, wherein: When E < E0, the online performance evaluation result is determined to be non-compliant, and the spatiotemporal hybrid index tree is structurally adjusted: the spatiotemporal hybrid index tree node filling rate f is adjusted according to the node filling strategy; When E≥E0, the online performance evaluation result is deemed to be in compliance, and no structural adjustment is made to the spatiotemporal hybrid index tree.

[0044] In this embodiment, the preset performance threshold E0 is determined using a service quality level agreement (SQLA) method. This includes determining the upper limit of query response time, the upper limit of storage cost budget, and the upper limit of concurrent user scale based on the business scenario. The weighted combination of these three upper limits is used as the preset performance threshold E0, resulting in E0 = 0.6. The upper limit of query response time is determined according to the SQLA requirements of the business scenario, such as a requirement of less than 500 milliseconds for real-time monitoring. The upper limit of storage cost budget is calculated by back-calculating the hardware procurement cost and available budget, such as a cost of no more than 0.1 yuan / month per GB. The upper limit of concurrent user scale is estimated through system capacity planning, such as an expected maximum number of concurrent users of 200. The online performance evaluation result is categorized as either compliant or non-compliant.

[0045] In this embodiment, the spatiotemporal hybrid index tree node fill rate refers to the ratio of used space in the spatiotemporal hybrid index tree node to the total space. The process of adjusting the spatiotemporal hybrid index tree node fill rate f in this embodiment includes: The space utilization value U and the average query latency value D are compared with the utilization threshold U0 and the latency threshold D0, respectively. Based on the comparison results, the node filling strategy is output, where: When U < U0 and D < D0, output the node filling strategy: reduce the node filling rate of the spatiotemporal hybrid index tree to 60%; Otherwise, output the node filling strategy: increase the spatiotemporal hybrid index tree node filling rate to 80%; The utilization threshold U0 is calculated by collecting storage space utilization data within one week of normal system operation, and the 75th percentile is used as the threshold, resulting in U0=60%. The latency threshold D0 is calculated by taking 1.2 times the 95th percentile of the query response latency during peak business periods, such as 10:00-12:00 per day, as the tolerance limit, resulting in D0=300 milliseconds.

[0046] In this embodiment, the positive correlation mechanism between the node fill rate f and the query response latency D is as follows: an increase in the node fill rate f leads to an increase in the number of data records stored in the spatiotemporal hybrid index tree node, increasing the range of data sequentially scanned within the node, thereby increasing the query response latency D; the negative correlation mechanism between the node fill rate f and the space utilization U is as follows: an increase in the node fill rate f reduces the frequency of B+ tree node splitting, reducing the total number of nodes and node pointer overhead, thereby improving the space utilization U; the nonlinear mechanism between the node fill rate f and the concurrent processing capability T is as follows: an increase in the node fill rate f reduces the height of the spatiotemporal hybrid index tree, reducing the number of disk accesses, and improving the concurrent processing capability T.

[0047] In this embodiment, in step S3, a spatiotemporal hybrid index tree is constructed and online performance evaluation and structural adjustment are performed to enable dynamic optimization of the index structure according to data distribution and access patterns, thereby improving the accuracy of data asset evaluation.

[0048] Figure 4 This invention provides a flowchart illustrating the data ownership certificate generation and operational analysis decision-making process, as shown below. Figure 4 As shown, in step S4, the index coverage of the spatiotemporal hybrid index tree is measured to obtain the spatial coverage breadth value A; the data update frequency of the spatiotemporal hybrid index tree is statistically analyzed to obtain the timeliness freshness value F; the query access popularity of the spatiotemporal hybrid index tree is statistically analyzed to obtain the demand activity value H; the data association complexity of the spatiotemporal hybrid index tree is calculated to obtain the network effect value N; and the global data asset value index V is calculated based on the spatial coverage breadth value A, the timeliness freshness value F, the demand activity value H, and the network effect value N.

[0049] In this embodiment, the spatial coverage breadth refers to the size of the geographical area covered by the index. Based on the geographical area covered by the index, the spatial coverage breadth is graded and assigned values ​​according to administrative division levels, such as 1.0 for global coverage, 0.8 for continental coverage, 0.6 for national coverage, 0.4 for provincial coverage, and 0.2 for city-level and below coverage. The timeliness refers to the frequency and timeliness of data updates. Based on the time difference between the most recent update time and the current time in the spatiotemporal hybrid index tree, the timeliness is scored in segments, such as: 1.0 for updates within 24 hours, 0.8 for updates within 7 days, 0.6 for updates within 30 days, 0.4 for updates within 6 months, and 0.2 for updates more than 6 months. The demand activity refers to the frequency of user queries and access. Based on the total number of user queries per unit time... In this embodiment, the query access count pn over the past 24 hours is counted, and the demand activity H is assigned a value according to the following intervals: 1.0 when pn ≥ 10000 times, 0.8 when 1000 ≤ pn < 9999 times, 0.6 when 100 < pn ≤ 999 times, 0.4 when 10 < pn ≤ 99 times, and 0.2 when pn ≤ 10 times. The network effect refers to the value gain brought by data association. In this embodiment, the data entity is classified according to the number of other data nodes associated with it. The direct connection count bm of each data asset in the spatiotemporal hybrid index tree is counted, and assigned a value according to the following intervals: 1.0 when bm ≥ 100 associated nodes, 0.8 when 50 ≤ bm < 99, 0.6 when 10 < bm ≤ 49, 0.4 when 1 ≤ bm ≤ 9, and 0.2 when bm = 0.

[0050] In this embodiment, the value index V of the whole domain data asset is calculated by a multi-index weighted synthesis method, which sets V=wA×A+wF×F+wH×H+wN×N, where wA is the weight of spatial coverage breadth, wF is the weight of timeliness and freshness, wH is the weight of demand activity, wN is the weight of network effect, and wA+wF+wH+wN=1. The weights of each index are determined by the analytic hierarchy process.

[0051] In this embodiment, in step S4, the global data asset value index is obtained by multidimensional feature extraction of the spatiotemporal hybrid index tree, so as to quantify the value of data assets and provide a scientific basis for data pricing and trading.

[0052] In this embodiment, in step S5, a hash digest is calculated on the ownership-related data to obtain a data fingerprint Hf, a digital signature is performed on the data fingerprint Hf to obtain a signature digest S, and the signature digest S is encapsulated with the ownership subject's public key K, timestamp information Ts, and evidence storage height B to obtain a data ownership certificate C.

[0053] In this embodiment, the hash digest calculation refers to the process of mapping an input of arbitrary length to a fixed-length output through a hash function. In this embodiment, the SHA-256 algorithm is used to generate a 256-bit hash value as a data fingerprint Hf from the ownership-related data. The digital signature refers to a method of verifying identity and integrity by signing the data fingerprint using asymmetric encryption technology. In this embodiment, the elliptic curve digital signature algorithm is used, with the secp256k1 curve parameter selected, and the private key Sk of the ownership subject is used to sign the data fingerprint Hf to generate a signature digest S. The data ownership certificate is a digital certificate that proves the ownership relationship of data assets. In this embodiment, it is encoded using the X.509 digital certificate standard. The data ownership certificate includes a version number, serial number, signature algorithm identifier, issuer name, validity period, subject name, subject public key information, extension items, and signature value. The extension items include a data fingerprint field, a spatiotemporal range field, a storage height field, and a quality level field.

[0054] In this embodiment, the evidence storage height refers to the storage location identifier on the blockchain. In this embodiment, it is obtained through the blockchain anchoring method. The blockchain anchoring method refers to submitting the signature digest S to a consortium blockchain network composed of Np institutional nodes. The Np institutional nodes include data provider nodes, data operator nodes, third-party auditing institution nodes, and regulatory agency nodes. Np=4 is set. The consortium blockchain network adopts the PBFT consensus mechanism. Based on the preset security assumption: when the number of Byzantine nodes fv satisfies fv<Np / 3, the consensus security is guaranteed. The block generation cycle is 3 seconds. After the transaction is confirmed, the block height of the transaction package block is obtained as the consortium blockchain evidence storage height B1. The block header hash value of the consortium blockchain network is periodically anchored to the public chain, and the public chain transaction hash is obtained as the public chain anchoring proof B2. The consortium blockchain evidence storage height B1 and the public chain anchoring proof B2 are combined to obtain the evidence storage height B.

[0055] The method for submitting a data digest to a blockchain network to obtain proof of evidence involves submitting the signature digest S to a consortium blockchain network, where a consortium blockchain is a blockchain network jointly managed by multiple institutions. In this embodiment, the PBFT consensus mechanism is used, with a block generation cycle of 3 seconds. After transaction confirmation, the block height of the transaction package block is obtained as the proof height B, and the proof height B is written into the extension field of the data ownership certificate.

[0056] In this embodiment, in step S5, a data ownership certificate is generated by blockchain anchoring to establish an immutable data ownership evidence storage system, thereby ensuring clear ownership and transaction security in the circulation of data assets and providing credible ownership support for operational analysis.

[0057] In this embodiment, in step S6, the data ownership certificate C is verified to obtain the ownership validity status R, the global data asset value index V is classified to obtain the value level label G, the data assets are screened for access based on the ownership validity status R and the value level label G to obtain the operable data asset pool P, the operable data asset pool P is modeled for demand matching to obtain the supply and demand matching degree matrix M, and the operation analysis decision Dr is output based on the supply and demand matching degree matrix M.

[0058] In this embodiment, the trusted verification refers to the process of verifying the authenticity and validity of a digital certificate. The trusted verification in this embodiment includes: comparing the version number Nv, signature algorithm identifier Sa, and certificate validity period Vt with the standard version number Nv0, standard algorithm identifier Sa0, and current time Tc, respectively; and outputting the ownership validity status R based on the comparison results, wherein: When Nv=Nv0, Sa=Sa0, and Vt>Tc, the validity will be output as the ownership validity state R; Otherwise, output invalid as the ownership validity state R; The standard version number Nv0 is determined according to the X.509 standard version convention, resulting in Nv0=3. The standard algorithm identifier Sa0 is determined according to the digital signature algorithm standard convention, resulting in Sa0 being secp256k1. The current time Tc is obtained through the system clock.

[0059] In this embodiment, the value level label refers to a classification identifier representing the value level of data assets. Based on the asset value index V, the value level label is divided into five levels: strategic level when V ≥ 0.8, core level when 0.6 ≤ V < 0.8, important level when 0.4 ≤ V < 0.6, general level when 0.2 ≤ V < 0.4, and basic level when V < 0.2. The admission screening refers to the process of adding data assets with valid ownership and V ≥ 0.4 to the operational data asset pool. The demand matching modeling refers to establishing… The process of matching data asset supply with user demand is described in this embodiment using a collaborative filtering algorithm. The process is as follows: construct the feature vector Fd of the data asset and the feature vector Fq of the user's query intent, calculate the cosine similarity S of the two feature vectors as the supply-demand matching degree, set S = (Fd·Fq) / (|Fd|×|Fq|), and generate the supply-demand matching degree matrix M. The output of the operation analysis decision Dr includes recommending the optimal data asset combination based on the supply-demand matching degree S, predicting the market demand trend of data assets, formulating differentiated pricing strategies, and identifying potential data product development directions.

[0060] In this embodiment, in step S6, the data operation analysis and decision engine performs supply and demand matching modeling and operation analysis decision output to accurately output operation analysis decisions and improve the efficiency of operation analysis.

[0061] In this embodiment, in step S7, feedback is collected on the actual execution effect of the operational analysis decision Dr to obtain the decision accuracy value Acc. The decision accuracy value Acc is compared with the expected target Acc0. Based on the comparison result, the status of the rule update requirement is judged, and based on the judgment result, the process of rule updating and the process of structural adjustment are optimized. When Acc < Acc0, the rule update requirement is determined to be "update required". The rule update process and the structural adjustment process are then optimized. When Acc ≥ Acc0, the rule update requirement is determined to be no need to update. No rule update is performed during the integration governance process, and no parameter optimization is performed during the structural adjustment process.

[0062] In this embodiment, the feedback collection refers to the process of collecting and measuring the actual effect of the operational analysis decision Dr after its execution. In this embodiment, the decision accuracy value Acc is calculated by statistically analyzing the number of accurately matched data assets Nm and the total number of recommended data assets Nt, and Acc is set to Nm / Nt.

[0063] In this embodiment, the decision accuracy value Acc refers to a quantitative indicator of the degree of matching between the operational analysis decision Dr's recommended results and the actual results. The value ranges from 0 to 1, and the larger the value, the higher the accuracy of the operational analysis decision.

[0064] In this embodiment, the expected target Acc0 refers to the critical threshold for determining the rule update requirement state. The expected target Acc0 is determined based on the minimum acceptable decision accuracy rate as the expected target according to the business operation quality requirements, resulting in Acc0=0.85. The rule update requirement state refers to the binary state of determining whether the system rules need to be updated based on the comparison result between the decision accuracy rate value Acc and the expected target Acc0, including whether the update is required and whether the update is not required.

[0065] In this embodiment, the rule update includes: performing median filtering on the deduplicated dataset to obtain a filtered deduplicated dataset, and outputting the filtered deduplicated dataset as a standardized spatial global dataset.

[0066] In this embodiment, the parameter optimization includes: optimizing the spatiotemporal hybrid index tree node filling rate f through golden section search, setting the feasible range of the spatiotemporal hybrid index tree node filling rate f as [0.5, 0.9], and the precision threshold as 0.02.

[0067] In this embodiment, in step S7, rules are updated and parameters are optimized based on the operational analysis results in order to establish a closed-loop management system for the entire lifecycle of data assets, thereby continuously improving the accuracy of data asset assessment.

[0068] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for assetized evaluation and operational analysis of spatial universe data, characterized by, include: Step S1: Collect spatial data and ownership-related data. Step S2: Perform fusion and governance on the spatial global data to obtain a standardized spatial global dataset; Step S3: Construct a spatiotemporal hybrid index tree based on the standardized spatial global dataset, evaluate its online performance, obtain the online performance evaluation results, and adjust the structure of the spatiotemporal hybrid index tree based on the online performance evaluation results; Step S4: Obtain the global data asset value index based on the spatiotemporal hybrid index tree; Step S5: Generate a data ownership certificate based on the ownership-related data; Step S6: Output operational analysis decisions based on data ownership certificates and the overall data asset value index.

2. The method of claim 1, wherein, The method further includes step S7, which involves acquiring the operational analysis results based on the operational analysis decision, updating the rules for the integration governance process based on the operational analysis results, and optimizing the parameters for the structural adjustment process.

3. The method of claim 1, wherein, In step S2, the spatial data is fused and processed, and the specific steps are as follows: Step S21: Perform coordinate system transformation on the spatial global data to obtain unified coordinate data; Step S22: Standardize the format of the unified coordinate data to obtain standard format data; Step S23: Perform semantic alignment mapping on the format standard data to obtain semantically aligned data; Step S24: Perform quality defect repair on the semantic alignment data to obtain quality repaired data; Step S25: Perform duplicate recording disambiguation on the quality repair data to obtain a deduplicated dataset, and use the deduplicated dataset as a standardized spatial global dataset.

4. The method of claim 3, wherein, In step S3, the spatiotemporal hybrid index tree is constructed based on the standardized spatial global dataset. The specific steps are as follows: Step S31: Calculate the spatiotemporal boundaries of the standardized spatial global dataset to obtain a spatiotemporal range cube; Step S32: Perform octree spatial partitioning on the spatiotemporal range cube to obtain spatial voxel units; Step S33: Discretize the spatial voxel unit in time intervals to obtain spatiotemporal voxel nodes; Step S34: Perform Hilbert curve encoding on the spatiotemporal voxel nodes to obtain the space-filling curve index code C; Step S35: Construct a B+ tree hierarchical structure based on the space-filling curve index code C, and use the B+ tree hierarchical structure as a spatiotemporal hybrid index tree.

5. The method for assetized evaluation and operational analytics of spatial universe data as claimed in claim 1 wherein, In step S3, the online performance is evaluated, specifically including: monitoring the query response latency of the spatiotemporal hybrid index tree to obtain the average query latency value D; monitoring the storage space occupancy rate of the spatiotemporal hybrid index tree to obtain the space utilization value U; monitoring the concurrent access throughput of the spatiotemporal hybrid index tree to obtain the concurrent processing capability value T; and calculating the online performance evaluation result E based on the average query latency value D, the space utilization value U, and the concurrent processing capability value T.

6. The method of assetized evaluation and operational analysis of spatial universe data according to claim 5, wherein, In step S3, the online performance evaluation result E is compared with the preset performance threshold E0. Based on the comparison result, the compliance status of the online performance evaluation result is judged, and the spatiotemporal hybrid index tree is structurally adjusted according to the judgment result, wherein: When E < E0, the online performance evaluation result is determined to be non-compliant, and the spatiotemporal hybrid index tree is structurally adjusted: the spatiotemporal hybrid index tree node filling rate f is adjusted according to the node filling strategy; When E≥E0, the online performance evaluation result is deemed to be in compliance, and no structural adjustment is made to the spatiotemporal hybrid index tree.

7. The method for assetized evaluation and operational analytics of spatial universe data as claimed in claim 1 wherein, In step S4, the index coverage of the spatiotemporal hybrid index tree is measured to obtain the spatial coverage breadth value A; the data update frequency of the spatiotemporal hybrid index tree is statistically analyzed to obtain the timeliness freshness value F; the query access popularity of the spatiotemporal hybrid index tree is statistically analyzed to obtain the demand activity value H; the data association complexity of the spatiotemporal hybrid index tree is calculated to obtain the network effect value N; and the global data asset value index V is calculated based on the spatial coverage breadth value A, the timeliness freshness value F, the demand activity value H, and the network effect value N.

8. The method for assetized evaluation and operational analytics of spatial universe data as claimed in claim 1 wherein, In step S5, a hash digest is calculated on the ownership-related data to obtain a data fingerprint Hf. The data fingerprint Hf is digitally signed to obtain a signature digest S. The signature digest S is then encapsulated with the ownership subject's public key K, timestamp information Ts, and evidence storage height B to obtain a data ownership certificate C.

9. The method for asset-based assessment and operational analysis of spatial full-domain data according to claim 8, characterized in that, In step S6, the data ownership certificate C is verified to obtain the ownership validity status R. The global data asset value index V is classified into levels to obtain the value level label G. Data assets are screened for access based on the ownership validity status R and the value level label G to obtain an operable data asset pool P. Demand matching modeling is performed on the operable data asset pool P to obtain the supply and demand matching degree matrix M. Operational analysis decision Dr is output based on the supply and demand matching degree matrix M.

10. The method for asset valuation and operational analysis of spatial full-domain data according to claim 2, characterized in that, In step S7, feedback is collected on the actual execution effect of the operational analysis decision Dr to obtain the decision accuracy value Acc. The decision accuracy value Acc is compared with the expected target Acc0. Based on the comparison result, the status of the rule update requirement is judged, and based on the judgment result, the rule update process and the parameter optimization process of the integrated governance process and the structural adjustment process are optimized, wherein: When Acc < Acc0, the rule update requirement is determined to be "update required". The rule update process and the structural adjustment process are then optimized. When Acc ≥ Acc0, the rule update requirement is determined to be no need to update. No rule update is performed during the integration governance process, and no parameter optimization is performed during the structural adjustment process.

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