Water environment remote sensing monitoring and analysis method based on space-time data cube bearing

Abstract

The application discloses a water environment remote sensing monitoring and analyzing method based on a space-time data cube, relates to the technical field of data processing and remote sensing application, and responds to a calculation request to construct a discrete time sequence, locks a zoning topology and an algorithm service version, and generates a benchmark commitment identifier; detects a double-benchmark switching event, constructs an anti-fact view matrix based on a benchmark combination before and after the change, separates a cross-term effect of a quantized nonlinear coupling error therefrom, calculates a residual generated by an execution order of an exchange algorithm operator and a spatial aggregation operator to obtain a non-exchange drift fingerprint, generates an evidence chain label in combination with the cross-term effect, and finally assembles a monitoring and analyzing product package and performs associated solidification writing by using the benchmark commitment identifier. The application effectively solves the problem that a complex false mutation is difficult to decouple due to the dynamic evolution of double benchmarks of an algorithm and zoning in long-period monitoring, and ensures the benchmark consistency and verifiability of water environment monitoring data.

Description

Technical Field

[0001] This invention relates to the field of data processing and remote sensing application technology, specifically to a method for remote sensing monitoring and analysis of the water environment based on a spatiotemporal data cube. Background Technology

[0002] Current watershed ecological environment governance has gradually shifted from single-point chemical sampling and analysis to large-scale, long-sequence integrated space-air-ground remote sensing monitoring. To support refined environmental supervision and scientific decision-making, monitoring systems typically need to aggregate and process massive amounts of multi-source satellite imagery data. Quantitative inversion models are used to transform spectral signals into parameter products reflecting water conditions, and these pixel-level data are mapped to specific administrative divisions or watershed management units to generate statistical reports. Faced with the storage and computational pressure of massive heterogeneous spatiotemporal data, the industry widely adopts spatiotemporal data cube technology as its core architecture. This technology constructs a unified spatiotemporal grid system, organizing data from different times and sources into multi-dimensional array structures, thereby supporting efficient time-slice queries and spatial aggregation calculations. It has become the mainstream underlying technology for water environment monitoring systems.

[0003] In long-term water environment monitoring and retrospective analysis based on spatiotemporal data cubes, a common challenge arises from the dynamic evolution of operational and spatial benchmarks, leading to data consistency difficulties. Existing technologies struggle to meet the stringent data requirements of environmental law enforcement. Specifically, remote sensing water quality inversion models are not static. As measured samples accumulate and algorithms are optimized, operational systems frequently iterate model versions or reprocess historical data, resulting in semantic drift in monitoring values ​​for the same water body. Furthermore, the vector boundaries of governance units, which are statistical objects, can change due to administrative adjustments or engineering implementations, altering the sampling set during spatial aggregation and triggering variable surface area problems.

[0004] Furthermore, complex scenarios frequently arise within long-term analysis windows where algorithm version upgrades and spatial zoning adjustments occur simultaneously. Existing spatiotemporal data cube technologies typically assume these two processes are independent, supporting only calculations based on a single latest benchmark or simple linear corrections. This fails to effectively address the nonlinear cross-term effects resulting from their superposition. Consequently, the system cannot identify and separate complex pseudo-mutations caused by the coupling of changes in calculation rules and statistical ranges, making it difficult for regulators to distinguish whether fluctuations in monitoring data represent genuine changes in water environmental quality or systemic deviations in the data processing chain. Moreover, the lack of a rigorous mechanism for solidifying and replaying the algorithm and zoning benchmark combinations used in historical calculations often renders calculation results unreproducible when reviewing historical tasks due to updates to default backend configurations. This severely undermines the traceability and integrity of the evidence chain in environmental monitoring data. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a water environment remote sensing monitoring and analysis method based on spatiotemporal data cubes. This method solves the problems of data semantic inconsistency, difficulty in decoupling complex pseudo-mutations, and missing monitoring evidence chains caused by the dynamic evolution of algorithm versions and spatial zoning dual benchmarks during long-term monitoring.

[0006] To achieve the above objectives, the present invention provides the following technical solution: In response to the computation task request, a discrete time series is constructed based on remote sensing observation data in the spatiotemporal data cube. The zoning topology version and algorithm business version are locked for the time nodes in the discrete time series to encapsulate the benchmark commitment record. The benchmark commitment record is aggregated and calculated to generate a globally unique benchmark commitment identifier. The baseline commitment record is used to detect dual baseline switching events where the zoning topology version and the algorithm business version change simultaneously. When a dual baseline switching event is detected, a counterfactual view matrix is ​​constructed based on the cross combination of the zoning topology version and the algorithm business version before and after the change. The cross-term effect is separated based on the counterfactual view matrix. Define algorithm operators and spatial aggregation operators, calculate the residuals generated by the execution order of the algorithm operators and spatial aggregation operators to obtain non-commutative drift fingerprints, and combine the cross-term effect and non-commutative drift fingerprints to generate evidence chain labels; The monitoring and analysis product package is assembled based on the cross-term effect, non-exchange drift fingerprint and evidence chain tag. The monitoring and analysis product package is associated and solidified using the benchmark commitment identifier and written into the spatiotemporal data cube.

[0007] Compared with existing technologies, it has the following advantages: This proposed method for water environment remote sensing monitoring and analysis based on a spatiotemporal data cube effectively solves the technical challenge of decoupling complex pseudo-mutations caused by simultaneous changes in algorithm versions and spatial zoning in long-term water environment monitoring. It constructs a counterfactual view matrix encompassing the old baseline, updated algorithms, updated zoning, and both new baselines. Existing technologies typically only perform simple linear attribution when facing dual baseline switching, failing to identify the nonlinear errors resulting from the superposition of changes in calculation rules and statistical ranges. This invention utilizes differential operations on four parallel views to successfully quantitatively separate the cross-term effect from the total variance, decomposing the originally chaotic fluctuations in monitoring values ​​into clear algorithm page-turning main effects, zoning page-turning main effects, and coupling error components. This approach enables regulators to accurately determine whether data mutations stem from actual changes in water environment quality or systematic deviations in the data processing chain, thus avoiding erroneous administrative decisions caused by technical baseline adjustments and significantly improving the usability and attribution accuracy of the spatiotemporal data cube in complex business scenarios.

[0008] Furthermore, this invention introduces a non-exchangeable drift fingerprint and benchmark commitment mechanism to solve the problems of unreproducible conclusions and broken evidence chains caused by implicit updates of background parameters in historical backtracking of environmental monitoring data. By calculating the residual norm between the two paths of inversion followed by statistics and statistics followed by inversion, this scheme establishes a quantitative diagnostic index for evaluating the sensitivity of operator execution order. Combined with a globally unique identifier generated based on a collision-resistant hash algorithm and a write-protected storage strategy, it achieves a rigid binding between the monitoring results and the complete data spectrum and computing environment that generated the results. This not only ensures that every monitoring report can be rigorously verified at any point in time, but also provides rigorous and tamper-proof digital evidence support for environmental law enforcement by solidifying the difference measurement under counterfactual conditions, filling the gap in the industry for data quality diagnosis and traceability technology under dynamic benchmarks. Attached Figure Description

[0009] Figure 1 This is a schematic diagram of the method flow of the present invention.

[0010] Figure 2 This is a schematic diagram of the cross-term effect separation process of the present invention. Detailed Implementation

[0011] 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, and 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.

[0012] Please see Figure 1 This application provides a method for remote sensing monitoring and analysis of the water environment based on spatiotemporal data cubes; The method specifically includes the following steps: Step 1: The system receives the computation task request and parses it to obtain the analysis region A and the start and end times of the backtracking analysis time window. and And the indicator set K. The system is based on the spatiotemporal index of the spatiotemporal data cube, in the closed interval [ , The system retrieves all time points with valid remote sensing observation data, constructs a strictly monotonically increasing discrete time series T, and extracts the unique identifier of the reflectance raster R(t) corresponding to each time t in the series.

[0013] It should be noted that the analysis region A is a user-specified spatial polygonal object (such as an administrative division vector) used to limit the calculation scope. The index set K is a list of specific water environment parameter types involved in this calculation task (such as chlorophyll a concentration and cyanobacterial bloom area). The reflectance raster R(t) is preprocessed (such as orthorectified and atmospheric corrected) remote sensing image data of the water body. By constructing a discrete time series T, the system transforms the fuzzy continuous time request into a computer-executable discrete data frame sequence, establishing the time anchor point for subsequent benchmark matching.

[0014] For each time point t in sequence T, the system executes a pre-defined priority decision logic to filter and lock a unique zoning topology version G(t) and algorithm business version V(t) from the metadata database.

[0015] Specifically, to ensure the uniqueness and reproducibility of the benchmark selection, the system executes the following deterministic logic: First, if a version identifier is explicitly specified in the user request, the specified version is directly adopted. Second, if not specified, the system retrieves all candidate versions that are in effect at time point t. Third, if multiple effective versions exist, the system prioritizes the version with the closest release timestamp and no later than time point t to ensure the restoration of the true regulatory status at historical moments. Fourth, if multiple versions still exist after the above screening, for the zoning topology version G(t), the system calculates the spatial overlap between the set of governance units and the analysis region A in each candidate version, and selects the one with the largest overlap. The spatial overlap refers to the proportion of the geometric intersection area of ​​the spatial union of all governance units in the candidate version and the analysis region A to the total area of ​​the analysis region A, or the intersection-union ratio, i.e., the intersection area divided by the union area. For the algorithm business version V(t), the system selects the one with the highest quality control level in the metadata (e.g., prioritizing the refined reprocessing version).

[0016] It should be noted that the zoning topology version G(t) does not refer to a static map, but rather to a set of spatial definitions of water environment governance units effective at a specific historical period. It contains two layers of meaning: spatial geometric information—the geographic boundary polygon data of each governance unit (such as sub-basins or administrative villages); and topological relationship rules—the logical rules defining whether nesting, adjacency, or merging relationships exist between governance units (e.g., two sub-basins were merged into one assessment unit in 2020). Locking the zoning topology version G(t) ensures that subsequent spatial aggregation calculations can accurately reproduce the assessment criteria at that historical moment. Furthermore, the algorithm business version V(t) does not merely refer to the model name, but rather to a complete encapsulated object containing executable artifact identifiers (such as container image digests), parameter set configuration tables, and auxiliary data dependency lists. This priority decision logic eliminates the uncertainty caused by system configuration updates, ensuring that the spatial definitions invoked for the same historical moment t remain consistent regardless of when the calculation task is executed.

[0017] The system encapsulates the data hierarchy information of the locked zoning topology version G(t), algorithm business version V(t), and reflectivity raster R(t) in a structured manner, and generates a baseline commitment record for each time node in sequence T.

[0018] Specifically, the baseline commitment record is a snapshot entity describing the computational context. This record contains data lineage information that includes at least the source image identifier and the preprocessing link identifier. The system employs serialization technology to combine these discrete metadata fields into indivisible data entities, preventing the loss of historical computational context due to changes to individual records in the metadata database.

[0019] The system employs a collision-resistant hash algorithm to aggregate the task constraints and the baseline commitment records of the entire sequence, generating a fixed-length digital digest, i.e., the baseline commitment ID, denoted as H. The calculation formula is as follows: In the formula, A, , K constitutes the header information for hash calculation, ensuring that different query conditions generate different identifiers. This represents the set of baseline commitment records that are strictly ordered according to the time series T. The data hierarchy is defined by time point t. The hash is SHA256 or another related algorithm.

[0020] This step constructs the digital fingerprint of the computational task. The sequence dependency design in the formula ensures that any minor adjustment of the algorithm version, change of the boundary, or misalignment of the time sequence T at any point in time will cause drastic changes in the output H, thus strictly guaranteeing the uniqueness and tamper-proof nature of the computational benchmark.

[0021] The system uses H as the primary key to write a data structure containing a complete list of baseline commitment records into the metadata index of the spatiotemporal data cube, and marks the status of the entry as committed.

[0022] It should be noted that marking the status as committed means that the database layer implements write protection for the entry, prohibiting any update or deletion operations on the record's content. This mechanism realizes the transformation from volatile computational logic to persistent storage credentials, enabling H to serve as the sole trusted index for detecting dual-benchmark switching events and performing counterfactual replays in subsequent steps. This ensures that when reproducing this computation at any future time, the system can accurately retrieve the algorithm environment and space definition that are completely consistent with the current one.

[0023] In detail, the dual-benchmark switching event refers to the event occurring within the backtracking analysis time window. , Within a defined scope, the discrete-time series T exhibits a specific computational state characterized by both spatial definition heterogeneity and business logic heterogeneity. Specifically, this manifests as a set cardinality greater than or equal to 2 for both the zoning topology version G(t) and the algorithm business version V(t). This event corresponds to the coupling of spatial boundary changes in the governance unit and changes in the pixel generation rules for water quality inversion within the same analytical task. In the water environment monitoring scenario, spatial boundary changes imply pixel-level increases or decreases in the sampling set during zoning statistics, while changes in pixel generation rules imply an overall shift in the numerical distribution within the sampling set. When these two events overlap within the same time window, they generate nonlinear cross-term errors that cannot be eliminated by a single benchmark correction, leading to complex pseudo-mutations in the monitoring results. Therefore, defining and identifying this event is a prerequisite for achieving interpretability of monitoring data.

[0024] Specifically, this step, as the initialization phase of the method, aims to establish a baseline anchor point for the computation task. By using deterministic rules to lock in the dynamically changing space and business baseline, it solves the problem of historical computations being unreproducible due to the system defaulting to calling the latest configuration.

[0025] Step Two: Based on the baseline commitment ID (H) generated in Step One, the system retrieves the corresponding baseline commitment record list from the metadata index. The system iterates through this list, extracting the zoning topology version G(t) and algorithm business version V(t) corresponding to each time node t in the discrete time series T, constructing a time-ordered baseline state tuple sequence S. Simultaneously, the system deduplicates all zoning topology versions in sequence S, generating a zoning version set. All algorithm business versions are deduplicated to generate a set of algorithm versions. .

[0026] Specifically, S manifests as An ordered list. and These respectively represent the values ​​in this analysis window. , This is an enumerated set of all space definitions and algorithmic logic that have actually taken effect within the [database name]. This step transforms the time-series data into a set cardinality through set operations, providing quantified input for subsequent complexity determination.

[0027] It should be noted that the deduplication process here is not a simple string comparison, but rather a comparison based on the unique identifiers of metadata entities. For algorithm business version V(t), even if the model name is the same, if the parameter set configuration table or the version of the dependent auxiliary data is different, they are considered different entities and included in the set to ensure sensitivity to minor changes.

[0028] System calculation zone version set cardinality With Algorithm Version Set cardinality And perform the following Boolean logic decision to generate event flag E.

[0029] The decision logic is as follows: If E is true, the system determines that a dual-baseline switching event has occurred in this calculation task and activates the subsequent higher-order attribution process. If E is false, the system determines that this task is a regular single-baseline or unilateral change task and marks it as a normal processing mode.

[0030] Specifically, the core of this determination lies in identifying the risk of compound pseudo-mutations. Errors generated by both the spatial definition and the business rules will only couple with each other if and only if both change within the same window, forming a cross-term effect that cannot be eliminated by simple linear correction. Existing technologies typically ignore this coupling risk, while this solution, through this Boolean determination, precisely allocates high-risk, high-order computing resources to the scenarios where they are truly needed.

[0031] It should be noted that the threshold of 2 here is the minimum natural number required to determine if a change has occurred. When the cardinality is greater than or equal to 2, it means that there is at least one transition point on the timeline, making the semantics before and after no longer equivalent.

[0032] When the event flag E is true, the system performs a time-series scan on the baseline state tuple sequence S to identify the time nodes when the baseline state transitions and generate the page-turning boundary set B.

[0033] Specifically, the system compares two adjacent time points in chronological order. and The baseline state. If satisfied or Then the time point The corresponding changed state is marked as a page-turning boundary. The page-turning boundary set B contains a series of segmented description information, each segment defining that G(t) and V(t) remain constant within that time interval.

[0034] It should be noted that the page-turning boundary set B is the routing table for segmented playback in subsequent steps. By dividing a continuous time window into several sub-intervals with constant baselines, the system can decompose complex non-stationary sequences into several locally stationary processes, thus providing a precise time-slicing basis for constructing a counterfactual view.

[0035] The system will calculate the event flag E and the set of zone versions. Algorithm version collection The page-turning boundary set B is associated and encapsulated, and its index is written into the task context bound to H.

[0036] Specifically, this step establishes the input parameters for subsequent calculations. and This will be used in step three to select representative old and new benchmarks to construct the counterfactual matrix. B will guide the data cube in correctly segmenting and aggregating data when reading the reflectance raster R(t). By solidifying these intermediate results, the system ensures data consistency between the detection and analysis phases.

[0037] It should be noted that if E is false, the system can choose to directly output the statistical results based on the current benchmark, skipping the subsequent matrix construction steps. This achieves adaptive optimization of the system's computational efficiency while ensuring rigor, thus demonstrating the flexibility and resource-saving characteristics of this solution in engineering implementation.

[0038] Specifically, this step, as the triggering mechanism for intelligent diagnosis, aims to identify, based on the baseline commitment record established in step one, whether there are complex working conditions within the retrospective window that lead to a break in the chain of evidence, namely, the simultaneous change of spatial baseline and business baseline, and to provide accurate temporal segmentation basis for subsequent counterfactual playback.

[0039] Step 3: Please refer to Figure 2 The system uses the set of regional versions output in step two. With Algorithm Version Set Representative reference pairs are selected for constructing the matrix according to the time sequence logic.

[0040] Specifically, the system executes the following selection logic: In this process, the version with the earliest effective date is selected as the reference zoning benchmark, denoted as . The version with the latest effective date (or the latest current date) is selected as the current zoning benchmark, denoted as... Similarly, in In this process, the version with the earliest effective time is selected as the benchmark algorithm, denoted as . The version with the latest effective date is selected as the current algorithm baseline, denoted as . If the set contains more than two versions, the optimal version is selected based on the coverage of the main time period determined by the page-turning boundary set B, ensuring that... , This represents the initial state of the backtracking window, while , This indicates the end of the retrospective window or the current regulatory status.

[0041] It should be noted that establishing this step provides a standardized coordinate system for subsequent differential calculations. By locking the baseline states at both ends, the system can abstract the complex evolution within the entire backtracking window into a structure starting from... arrive The state transition.

[0042] Based on the selected benchmark pair, the system utilizes the operator orchestration capability of the spatiotemporal data cube to construct four lock benchmark operator chains in parallel for each time point t and index K in the discrete time series T, generating four parallel statistical views.

[0043] Specifically, these four views are: First View (View A): Locking Baseline Combination This is the old baseline view. This view reproduces the calculation results that would be obtained at historical time t without any updates.

[0044] Second view (View B): Locking reference combination This is a counterfactual view that updates only the algorithm baseline. This view simulates the calculation results if only the algorithm is upgraded but the original zoning definition is maintained.

[0045] Third View (View C): Locking Baseline Combination This is a counterfactual view that updates only the zoning baseline. This view simulates the calculation results if only the zoning is adjusted but the old algorithm is used.

[0046] Fourth view (View D): Locked reference combination This is the new baseline view, which represents the nominal result of the system's current default output.

[0047] System call algorithm operators With spatial aggregation operator Perform specific calculations to generate the governance unit statistical result Y. The four-view result set is denoted as: It should be noted that Views B and C are referred to as counterfactual views because the baseline combination they correspond to may never have actually occurred simultaneously in history (e.g., before the old district boundaries were abolished and the new algorithm was released). Existing techniques typically only calculate View A (archived data) or View D (latest recalculated data), and directly comparing D and A can lead to confusion regarding the source of error. This step introduces intermediate views B and C, thereby completing the logical chain and making single-variable controlled analysis possible.

[0048] The system performs difference operations on the four-view results across the same governance unit U, time t, and indicator K to separate the main effects of the algorithm, the main effects of zoning, and the cross-term effects. The calculation process is as follows: Main effect of algorithm page turning : The main effect of zoning page turning : Cross-term effect (i.e., the composite pseudo-mutation component): In the above formula, These are the statistical output values ​​for the corresponding view.

[0049] It should be noted that the cross-term effect The core issue lies in the non-linear coupling error between quantification zone changes and algorithm changes. For example, when the algorithm version... Adjustments to the cloud masking rules have altered the effective pixel set at the land-water boundary, while the zoning version... The watershed boundary line at that location was precisely adjusted, and the combined effect of these two factors leads to an additional drift in the statistical values. This drift cannot be attributed to either pure algorithmic improvement or pure zoning adjustment. This scheme accurately separates this component using the formula described above, preventing it from being misinterpreted as a true change in water quality.

[0050] The system will combine the four-view result set with the calculated three types of difference components ( , , The structured packaging is performed to generate a unique matrix view identifier view_matrix_id, which is then associated and stored with the baseline commitment ID H generated in step one.

[0051] Specifically, this step enables the persistence of intermediate calculation results. The stored data structure contains not only numerical values ​​but also metadata about the reference pair on which the matrix was generated. , , , This allows subsequent steps to directly use the matrix data for fingerprint calculation and evidence chain generation, eliminating the need to repeatedly perform costly image inversion and spatial aggregation operations, thus significantly improving the system's response efficiency.

[0052] Specifically, this step aims to resolve attribution ambiguity issues under dual-baseline switching conditions. When the event flag E output from step two is true, the system activates this step, generating a data structure containing the historical true state, the current nominal state, and the counterfactual intermediate state by constructing parallel computational links within the spatiotemporal data cube. The view matrix is ​​used to quantify and separate the cross-term effects generated by the coupling of spatial and business benchmarks.

[0053] Step 4: For each time point t in the time series T, the system abstracts the calculation process of water environment monitoring into two types of mathematical operators: algorithm operators. With spatial aggregation operator .

[0054] Specifically, algorithm operators This refers to the processing logic based on algorithmic business version v, whose function is to map the input reflectance raster R(t) to the water quality index raster P(t). This process typically involves nonlinear inversion model calculations. Spatial aggregation operator. This refers to the processing logic based on the zoning topology version g, which maps raster data to statistical values ​​on the governance unit vector set U. This process involves spatial overlay analysis and statistical summarization.

[0055] It should be noted that this operator abstraction transforms complex business processes into standard mathematical mapping relationships. In an ideal linear uncoupled system, these two types of operators should satisfy the commutative law, meaning that the order of operations does not affect the final result. However, in monitoring complex watersheds such as Erhai Lake, due to the nonlinearity of the inversion model and the existence of mixed pixels, the exchange of operator order often produces residuals, which are the mathematical expression of the system coupling risk.

[0056] The system is based on the current reference pair selected in step three. , For the reflectivity grid R(t), commutative residual calculation is performed to generate a non-commutative drift fingerprint, denoted as . The calculation formula is as follows: In the formula, This represents the standard processing flow, which involves first reversing the water quality and then calculating the average value, corresponding to the calculation logic of view D. This represents the counterfactual process after the order of exchange, which is to first calculate the average reflectance within the region (or generate the mean spectral vector), and then input the mean into the inversion model to calculate the water quality (at this time, it is regarded as a homogeneous region). The difference measure norm is taken as the absolute value or root mean square error.

[0057] It should be noted that, The core lies in quantifying the sensitivity of the algorithm model to spatial heterogeneity. If A significantly non-zero value indicates that the inversion algorithm exhibits strong nonlinearity in the current region or is extremely sensitive to boundary mixing pixels. In this case, small changes in the zoning boundary G and adjustments to the algorithm parameters V are highly susceptible to severe coupled oscillations, i.e., cross-terms. The risk is extremely high. (Based on calculations...) The system is able to evaluate the baseline coupling stability of the current computing task in a single frame without relying on historical data.

[0058] Cross-term effect of the output of step three in system synthesis The drift fingerprint calculated in this step Based on the preset attribution classification rules, an evidence chain label, denoted as L, is generated for each governance unit.

[0059] Specifically, the main effects of the system calculation algorithm Main effect of zoning and cross-term effects The absolute value of each of the three factors is calculated, and the sum of their absolute values ​​is taken as the total variance. If... The proportion of the total variance exceeds the first preset threshold, and If the value exceeds the second preset threshold, the system will label L as "cross-term dominant mutation," indicating to the user that the mutation is likely a spurious mutation. The largest proportion is marked as "algorithm update-driven". If The largest proportion is marked as "regional adjustment-driven". If the total variation is extremely small, it is marked as "baseline stable".

[0060] It should be noted that the first preset threshold is used to determine the dominance of the cross term in the variation, and can be set according to the business requirements for attribution accuracy, for example, a value of 50% (i.e., accounting for more than half) or one standard deviation; the second preset threshold is used to determine the significance of the operator's noncommutativity, and is usually set to the tolerance value of the system's calculation accuracy or the level of environmental background noise (e.g. ),when When the value is greater than the second preset threshold, it indicates that the current non-commutative residual has exceeded the allowable calculation error range and is significant.

[0061] It's important to note that the evidence chain label L serves as a bridge connecting underlying data computation with upper-level business decision-making. It transforms mathematical differences into understandable semantic conclusions. In regulatory scenarios, when decision-makers see the label "cross-term dominant mutation," they can immediately understand that the abnormal water quality data is not due to environmental deterioration, but rather a systematic bias caused by a dual change in the calculation benchmark.

[0062] The system will calculate the drift fingerprint The evidence chain tag L is written into the task metadata table, and a strong correlation index is established between it and the baseline commitment ID, i.e., H, and the view matrix identifier, view_matrix_id.

[0063] Specifically, this step ensures the persistence of quality assessment information. During future reviews or audits, the system can not only reproduce the calculation results but also simultaneously retrieve the quality diagnostic labels from that time, proving that the calculation was identified by the system as having high confidence or coupling risks at that time, thus perfecting the entire lifecycle evidence chain of monitoring data.

[0064] Specifically, this step aims to perform a deep structural characterization of the cross-term effects separated in step three. By calculating the non-commutativity of spatial aggregation and business inversion operations, a drift fingerprint is generated, and the mutation sources of the monitoring results are classified and characterized accordingly, forming a machine-readable chain of evidence labels.

[0065] Step 5: The system aggregates the baseline commitment ID (H) generated in Step 1 and the four-view differential components generated in Step 3. , , and the drift fingerprint generated in step four Based on the predefined evidence data model and the evidence chain label L, a structured product package object is generated.

[0066] Specifically, the product package object contains four core data blocks: The first data block is the baseline view data. The system defaults to selecting view D. The time series data, as the main data presented to the public, represents the monitoring results under the current nominal benchmark.

[0067] The second data block contains attribution explanation data. System encapsulation. The equal difference component is used to explain the source of abrupt changes in the monitoring curve.

[0068] The third data block contains quality diagnostic data. System encapsulation. L is used to indicate the confidence level of the current result and the potential system coupling risk.

[0069] The fourth data block contains source metadata. The system embeds H and the matrix view identifier generated in step three, and establishes an index pointer pointing to the original operator chain.

[0070] It's important to note that this step achieves a strong binding between data and interpretation. Existing technologies typically only output numerical monitoring results, making it impossible for users to determine the authenticity of data mutations. This solution, through atomic encapsulation, mandates that the output monitoring values ​​include their constituent components (actual changes and pseudo-mutation components), thereby completely eliminating the ambiguity of data semantics.

[0071] The system performs a hash operation on the assembled product package object to generate a globally unique product package identifier, `package_id`, and then serializes this object and writes it to the persistent storage layer of the spatiotemporal data cube. The calculation formula is as follows: Specifically, in the formula, H locks the input baseline, and view_matrix_id locks the intermediate calculation process. The final diagnostic conclusion was reached with L. The system established an index mapping relationship using package_id as the primary key. At the same time, the system updated the baseline commitment record generated in step one in the metadata index, appending package_id to the associated field of the record, completing the state loop from commitment to delivery.

[0072] It's important to note that the core of this step lies in constructing a write-once, self-destructing reverse anti-tampering mechanism. Once the product package is fixed, any reprocessing of the reflectivity grid R(t) or any update to the zoning G will generate a new H and a new package_id, without overwriting the previously generated package_id. This allows regulatory agencies to retrieve the monitoring conclusions and their corresponding interpretations at any point in the future by searching the old package_id, fully meeting the stringent requirements of environmental enforcement for a fixed chain of evidence.

[0073] Based on the fixed product package data, the system outputs the monitoring and analysis product package, namely the water environment monitoring report at the treatment unit level, to the user terminal through a visualization rendering engine for users to review and make decisions.

[0074] Specifically, when displaying the water quality change trend graph of treatment unit U, the system automatically overlays the time nodes marked with label L as "cross-term dominant mutations". The text shows the contribution ratio in a bar chart, with the message: "This mutation was detected to be significantly affected by the baseline switch. It is recommended to refer to the corrected trend."

[0075] It should be noted that this output method changes the traditional monitoring report's practice of only reporting results and not risks. It provides a more intuitive display. and The system empowers decision-makers without a technical background to quickly identify data traps, avoiding erroneous administrative decisions caused by data conflicts resulting from algorithm upgrades or zoning adjustments, and significantly improving the level of precision in water environment governance.

[0076] Specifically, this step aims to construct the final deliverable of the monitoring and analysis task by atomically packaging the counterfactual attribution conclusions generated in the previous steps with the original monitoring data and writing the data package back to the immutable storage area of ​​the spatiotemporal data cube to ensure the long-term validity and reproducibility of the evidence chain.

[0077] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for remote sensing monitoring and analysis of the water environment based on spatiotemporal data cubes, characterized in that, include: In response to the computation task request, a discrete time series is constructed based on remote sensing observation data in the spatiotemporal data cube. The zoning topology version and algorithm business version are locked for the time nodes in the discrete time series to encapsulate the benchmark commitment record. The benchmark commitment record is aggregated and calculated to generate a globally unique benchmark commitment identifier. The baseline commitment record is used to detect dual baseline switching events where the zoning topology version and the algorithm business version change simultaneously. When a dual baseline switching event is detected, a counterfactual view matrix is ​​constructed based on the cross combination of the zoning topology version and the algorithm business version before and after the change. The cross-term effect is separated based on the counterfactual view matrix. Define algorithm operators and spatial aggregation operators, calculate the residuals generated by the execution order of the algorithm operators and spatial aggregation operators to obtain non-commutative drift fingerprints, and combine the cross-term effect and non-commutative drift fingerprints to generate evidence chain labels; The monitoring and analysis product package is assembled based on the cross-term effect, non-exchange drift fingerprint and evidence chain tag. The monitoring and analysis product package is associated and solidified using the benchmark commitment identifier and written into the spatiotemporal data cube.

2. The method for remote sensing monitoring and analysis of water environment based on spatiotemporal data cubes according to claim 1, characterized in that, Locking the zoning topology version and algorithm business version, including: Retrieve candidate versions that are active at the specified time. Prioritize candidate versions with release timestamps that are closest to and no later than the specified time. If multiple candidate versions exist, for the zoning topology version, calculate the spatial overlap between the set of governance units and the analysis area in the candidate versions, and select the candidate version with the largest spatial overlap. For algorithm business versions, select the candidate version with the highest quality control level in the metadata.

3. The method for remote sensing monitoring and analysis of the water environment based on a spatiotemporal data cube as described in claim 1, characterized in that, include: Remote sensing data is in the form of reflectance gratings; The encapsulation benchmark commitment record includes the structured encapsulation of the zoning topology version, algorithm service version, and reflectivity raster data hierarchy information corresponding to the time node, wherein the data hierarchy information includes the source image identifier and the preprocessing link identifier. Generating a globally unique baseline commitment identifier involves using a collision-resistant hash algorithm to perform an aggregation operation on the constraints in the computation task request and the complete set of baseline commitment records arranged in chronological order, resulting in a digital digest.

4. The method for remote sensing monitoring and analysis of water environment based on spatiotemporal data cubes according to claim 1, characterized in that, The detection of dual-benchmark switching events where the topology version of the characterization zone and the business version of the algorithm change simultaneously includes: Extract all zoning topology versions and all algorithm business versions from the baseline commitment records to construct a zoning topology version set and an algorithm business version set, respectively. The cardinality of the statistical zoning topology version set and the cardinality of the algorithm business version set; If the cardinality of both the zoning topology version set and the algorithm business version set is greater than or equal to two, it is determined that a dual-baseline switching event has occurred, and a page-turning boundary set containing the time nodes of the baseline state transition is generated.

5. The method for remote sensing monitoring and analysis of water environment based on spatiotemporal data cubes according to claim 4, characterized in that, The counterfactual view matrix contains four parallel statistical views, including: Lock the oldest baseline view of the zoning topology version and the algorithm business version with the earliest effective time in the discrete time series; The counterfactual view of the algorithm baseline is updated only, locking the earliest effective zoning topology version and the latest effective algorithm business version in the discrete time series. The counterfactual view of the zoning benchmark is updated only when the latest effective zoning topology version and the earliest effective algorithm business version in the discrete time series are locked. A new dual-benchmark view is established, locking the latest effective zoning topology version and the latest effective algorithm business version in the discrete time series.

6. The method for remote sensing monitoring and analysis of water environment based on spatiotemporal data cubes according to claim 5, characterized in that, Separating the cross-term effect specifically involves performing a difference operation: Subtracting the statistics of the counterfactual view that only updates the zoning baseline and the counterfactual view that only updates the algorithm baseline from the statistics of the full new baseline view, and adding the statistics of the full old baseline view, we obtain the cross-term effect of the nonlinear coupling error between the zoning topology version change and the algorithm business version change.

7. The method for remote sensing monitoring and analysis of water environment based on spatiotemporal data cubes according to claim 1, characterized in that, include: Remote sensing data is in the form of reflectance gratings; The calculation of the residuals generated by the execution order of the exchange algorithm operator and the spatial aggregation operator to obtain the non-exchangeable drift fingerprint specifically includes: The first calculation path is constructed by first using algorithm operators to invert the reflectivity raster into an index raster, and then using spatial aggregation operators to statistically calculate the average index within the governance unit. A second calculation path is constructed. First, the average reflectance within the governance unit is statistically analyzed using spatial aggregation operators. Then, the average reflectance is inverted into an index value using algorithm operators. The difference norm between the output of the first computation path and the output of the second computation path is calculated to obtain the non-exchange drift fingerprint.

8. The method for remote sensing monitoring and analysis of water environment based on spatiotemporal data cubes according to claim 6, characterized in that, Generate evidence chain labels, including: The absolute difference between the counterfactual view that only updates the algorithm baseline and the old baseline view is calculated as the main effect of algorithm page turning, and the absolute difference between the counterfactual view that only updates the zoning baseline and the old baseline view is calculated as the main effect of zoning page turning. The sum of the absolute values ​​of the algorithm page-turning main effect, the zoning page-turning main effect, and the cross-term effect is used as the total variance. If the absolute value of the cross-term effect accounts for more than a first preset threshold in the total variance, and the non-exchange drift fingerprint is greater than a second preset threshold, then an evidence chain label marked as a cross-term-dominated mutation is generated.

9. The method for remote sensing monitoring and analysis of water environment based on spatiotemporal data cubes according to claim 8, characterized in that, Monitoring and analyzing the data structure of the product package, including: Time series as the main data in a fully dual new benchmark view; Cross-term effect, algorithmic page-turning main effect, and zoning page-turning main effect as attribution explanations of data; Non-exchangeable drift fingerprints and chain-of-evidence tags as quality diagnostic data; It serves as the baseline commitment identifier for traceability metadata and the view matrix identifier for indexing the counterfactual view matrix.

10. The method for remote sensing monitoring and analysis of water environment based on spatiotemporal data cubes according to claim 1, characterized in that, The monitoring and analysis product package is correlated and solidified using benchmark commitment identifiers and written into the spatiotemporal data cube, including: Perform a hash operation on the monitoring and analysis product package to generate a product package identifier; An index is created using the product package identifier as the primary key, and the monitored and analyzed product packages are written to the persistent storage layer. Mark the status of the stored entry as committed, and implement write protection on the entry in the committed state to prevent update or deletion operations.

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