Subcompartment change-oriented multi-source business data collaborative acquisition method

Abstract

The invention discloses a multi-source business data collaborative acquisition method for subcompartment change, and relates to the technical field of forest resource management, and the method comprises the following steps: S1, capturing subcompartment boundary, ownership and operation attribute change events in real time through a dynamic topology perception layer, and generating a dynamic topology map; according to the multi-source business data collaborative acquisition method oriented to the subcompartment change, a dynamic topology sensing mechanism and a standardized acquisition process are constructed, so that the problem of multi-source data splitting caused by frequent subcompartment change is solved, and real-time collaborative acquisition and consistency guarantee of multi-source heterogeneous data are realized. A dynamic topological graph accurately senses boundary adjustment and ownership change events, and a double-channel analyzer is combined to automatically map service rules into equipment instructions, so that the data association error rate is remarkably reduced; the bimodal verification mechanism effectively eliminates version conflicts and business rule contradictions through time sequence alignment and sandbox simulation, and improves the accuracy of forest resource assessment.

Description

Technical Field

[0001] The present invention relates to the technical field of forest resource management, and in particular to a multi-source business data collaborative collection method for small-scale changes. Background Art

[0002] In the dynamic renewal of forest land and forest resources, small forests, as the fundamental units of forest management, face dynamic behaviors such as boundary adjustments, ownership changes, and updates to management attributes that directly impact the accuracy and timeliness of resource data. Traditional forest resource monitoring relies on manual surveys combined with fixed-period remote sensing interpretation. However, with frequent changes in small forests, collaborative multi-source data collection faces significant challenges. For example, when a small forest boundary is redrawn due to natural succession or artificial planning, existing methods typically employ pre-defined static data collection rules that fail to dynamically detect changes in the forest topology. This leads to spatial and temporal disjunctions in multi-source data, including drone aerial photography, ground sensor networks, and manual inspections. Misalignment of satellite imagery of newly designated land parcels with soil moisture data collected by sensors within the old boundaries can lead to inaccurate forest stock calculations. Furthermore, if data access permissions between different management departments are not synchronized in a timely manner after a small forest ownership change, data collection conflicts can arise. For example, a management plan uploaded to the mobile app of the newly assigned entity may conflict with historical records in the database of the old responsible party. The essence of these problems lies in the fact that existing technologies lack the ability to respond in real time to the reconstruction of multi-source data network topology caused by dynamic changes in small classes. They are unable to automatically adapt to the spatial logical relationships after the changes at the data collection layer, and it is difficult to eliminate cross-source data conflicts caused by delayed changes at the business layer. Summary of the Invention

[0003] (1) Technical problems solved

[0004] In response to the shortcomings of the existing technology, the present invention provides a multi-source business data collaborative collection method for small class changes, which solves the topology adaptation and conflict resolution problems of real-time collaborative collection of multi-source data in dynamic small class scenarios.

[0005] (2) Technical solution

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a multi-source business data collaborative collection method for small class changes, comprising the following steps:

[0007] S1: The dynamic topology perception layer is used to capture the changes in sub-class boundaries, ownership and business attributes in real time, and generate a dynamic topology map; the dynamic topology perception layer accesses remote sensing image data, ground sensor network signals and manually annotated system logs to extract the characteristics of sub-class spatial boundary changes; it should be further explained that step S1 includes dynamic topology perception and map generation: real-time access to remote sensing image data, ground sensor network signals and manually annotated system logs, and constructs a dynamic topology map by analyzing the characteristics of sub-class boundary changes, ownership change signals and business attribute update events. The sub-class nodes are encoded as vertices in the spatiotemporal graph structure, the associated paths are defined as edges, and the weight coefficients represent the intensity of resource dependence between sub-classes. When boundary redrawing or ownership changes are detected, an incremental spatiotemporal graph neural network model is used to update the node embedding vector, reconstruct the topology map and mark the change event type;

[0008] S2: Build standardized collection processes for afforestation, logging, and disaster operations based on structured metadata templates that define operation type codes, spatial boundary coordinate ranges, time window thresholds, and monitoring indicator sets;

[0009] S3: Use the semantic-spatial dual-channel parser to map the structured metadata to the resource class data model, and generate a data collection instruction set including the drone aerial photography path, sensor sampling frequency and manual inspection tasks; it should be further explained that the dual-channel parsing and instruction generation include: using the semantic-spatial dual-channel parser, calling the forest management knowledge graph to parse the logical constraint relationship between business terms, and generating a list of data collection requirements. At the same time, based on the dynamic topology map, the spatial impact range of the business instructions is calculated to generate drone aerial photography path planning parameters and sensor network sampling rules. After integrating semantic requirements and spatial rules, the collection instruction set containing geographic fence constraints and time window restrictions is output and sent to drones, sensors and manual inspection terminals;

[0010] S4: Perform dual-modal verification of temporal consistency verification and business rule sandbox simulation on the collected multi-source data, filter out conflicting data and trigger resolution strategies; it should be further explained that dual-modal verification and conflict resolution include: performing temporal consistency verification on the collected multi-source data, that is: using a hybrid logic clock model to align the physical timestamp and the event logic sequence, and constructing a cross-device event causal graph to detect time paradoxes. When it is found that the time of the change of ownership of the small class is later than the data upload event of the new affiliated unit, the data version rollback is triggered. At the same time, the current business policy module is loaded in the virtualized environment to simulate the impact of the data to be synchronized on the forest carbon sink model. If the calculation result deviates from the historical trend threshold, it is marked as abnormal data. For data that fails to pass the verification, a resolution strategy is generated based on reinforcement learning and the policy library is updated;

[0011] S5: Update the forest resource records in the central database using the verified data according to the dynamic topology map. It should be further explained that dynamic data synchronization and updating includes: adjusting the central database index according to the dynamic topology map and establishing a resource transfer mapping relationship between the old and new sub-groups. Implement branch storage for version conflict data, retain the original version, and generate a difference report for manual review and merging. When a new business conflict pattern is detected, the conflict features are extracted to generate a policy patch. After offline training and updating, it is dynamically bound to the data acquisition channel to achieve continuous adaptation of the monitoring indicator set and equipment control parameters.

[0012] Preferably, the generation of the dynamic topology map in step S1 includes:

[0013] The class nodes are encoded as vertices in the spatiotemporal graph structure, the associated paths are defined as edges, and the weight coefficients represent the resource dependence strength between the classes.

[0014] When the remote sensing image features identify boundary redrawing or the sensor network detects ownership change signals, the incremental spatiotemporal graph neural network model is triggered to update the node embedding vector and reconstruct the topology map.

[0015] Preferably, the incremental spatiotemporal graph neural network model operates by extracting spatial texture features from the remote sensing image feature matrix through a convolutional layer and performing spatiotemporal alignment with the timestamp encoding of the sensor signal transition sequence. A multi-head attention mechanism is used to calculate dynamic association weights between nodes, generating a topological map with logical timestamps to accurately mark boundary adjustments, ownership changes, or attribute updates.

[0016] Preferably, the construction of the structured metadata template in step S2: includes:

[0017] Define a set of survival rate monitoring indicators for afforestation operations, including the frequency of drone multispectral image acquisition, soil moisture sensor threshold, and manual sampling verification ratio;

[0018] A set of ecological impact monitoring indicators is defined for logging operations, including canopy density zero verification rules, surface runoff sensor activation conditions, and stump diameter statistical requirements.

[0019] Preferably, the execution logic of the semantic-spatial dual-channel parser in step S3 includes: the semantic channel calls the forest management knowledge graph, parses the logical constraint relationship between business terms, and generates a data collection requirement list; the spatial channel calculates the spatial impact range of the business instructions based on the dynamic topology graph, generates drone aerial photography path planning parameters and sensor network sampling rules; after fusing the semantic requirements with the spatial rules, outputs a collection instruction set with geographic fence constraints and time window restrictions.

[0020] Preferably, the association rules of the forest management knowledge graph include: mapping "afforestation survival rate monitoring" to "UAV collection of near-infrared band images once a week" and "soil moisture sensor uploading data every two hours"; associating "clear-cutting operations" with "real-time monitoring of surface runoff sensors after felling" and "compensation calculation of surrounding small group canopy density after boundary redrawing."

[0021] It's important to further clarify the semantic-spatial parsing logic: The semantic channel uses the forest management knowledge graph to map "afforestation survival rate monitoring" to drone multispectral imagery and periodic soil moisture sensor monitoring, and links "clear-cutting operations" to real-time post-felling surface runoff monitoring and canopy density compensation calculations for surrounding small-scale plots. The spatial channel, based on the geofence boundaries of the topological map, generates the flight altitude and image overlap parameters for the drone's aerial photography path and sets the sampling rules for the sensor network.

[0022] Preferably, the bimodal verification in step S4 includes:

[0023] Timing consistency verification: A hybrid logical clock model is used to align the physical clocks of drones, sensors, and human terminals with the logical sequence of events, and a cross-device event causal graph is constructed to detect time paradoxes.

[0024] Business rule sandbox verification: This involves loading the current business policy module in a virtualized environment and simulating the impact of the data to be synchronized on the forest carbon sink model. Any carbon storage calculation results that deviate from historical trend thresholds are flagged as an anomaly. It should be further clarified that the business rule sandbox simulation involves loading the forest carbon sink model in a virtualized environment and substituting the afforestation survival rate data to be synchronized into the simulation. If the carbon storage forecast exceeds the historical fluctuation range, it is identified as an anomaly and a manual review process is triggered. Simultaneously, the post-logging ecological impact data is simulated as input to the surface runoff model to verify whether it meets the preset ecological protection threshold.

[0025] Preferably, the triggering resolution strategy, i.e., the execution of the conflict resolution strategy, includes:

[0026] Generate candidate resolution plans based on historical conflict resolution records, including data version rollback, dynamic permission reset, and device command resend;

[0027] The Markov decision process is used to evaluate the impact of each solution on the data consistency index and business compliance rate, select the optimal solution and update the strategy library.

[0028] It should be further clarified that conflict resolution strategy execution involves generating candidate solutions based on historical conflict resolution records, including data version rollback, dynamic permission reset, and device command resend. By evaluating the impact of each solution on the data consistency index and business compliance rate, the optimal solution is selected and the policy library is updated. For cross-domain permission conflicts caused by small class reorganizations, a dynamic role mapping mechanism is used to reallocate data access rights.

[0029] Preferably, the updating process of the central database in step S5 includes:

[0030] Adjust the data association index according to the dynamic topology map and establish the resource transfer mapping relationship between the new and old classes;

[0031] Data with conflicting versions is stored in branches, retaining the original version and generating a difference report for manual review. It should be further explained that dynamic database adaptation includes: the central database updates resource transfer mappings based on the topology map, for example, by proportionally allocating the storage volume data corresponding to a portion of the original sub-class area to the new sub-class. For conflicting versions of data due to timestamp conflicts, the original records are retained and difference annotations are generated, allowing for manual intervention and merging according to business rules.

[0032] Preferably, the dynamic adaptation of the standardized acquisition process includes: when a new business conflict pattern is detected, the conflict features are extracted to generate a policy patch and offline training and updating are carried out; the updated policy module is dynamically bound to the data acquisition channel, and the monitoring indicator set and the equipment control parameters are synchronously adjusted. It should be further explained that the construction of the standardized acquisition process includes: for afforestation, logging, and disaster operations, a structured metadata template including an operation type code, a spatial boundary coordinate range, a time window threshold, and a monitoring indicator set is defined. Through the business logic decoupling engine, the business rules are converted into independent policy modules, for example, the afforestation survival rate monitoring indicator is dynamically bound to drone image acquisition, soil moisture sensor monitoring, and manual sampling verification.

[0033] (3) Beneficial effects

[0034] The present invention provides a multi-source business data collaborative collection method for small class changes. It has the following beneficial effects:

[0035] (1) This collaborative multi-source business data collection method for small-scale changes overcomes the problem of multi-source data fragmentation caused by frequent small-scale changes by building a dynamic topology perception mechanism and standardized collection process, and achieves real-time collaborative collection and consistency assurance of multi-source heterogeneous data. The dynamic topology map accurately perceives boundary adjustments and ownership change events, and combined with a dual-channel parser, automatically maps business rules to device instructions, significantly reducing the data association error rate; the dual-modal verification mechanism effectively eliminates version conflicts and business rule contradictions through timing alignment and sandbox simulation, reducing data collection response speed to minutes and improving the accuracy of forest resource assessment.

[0036] (2) This collaborative multi-source business data collection method for small-scale changes breaks through the traditional static collection model and achieves intelligent adaptation of business data and resource small-scale in dynamic forest management scenarios, providing highly reliable data support for forest carbon sink accounting and ecological protection decision-making. Through the self-evolution of conflict resolution strategies and database dynamic indexing technology, manual intervention is reduced, helping forestry management departments to quickly respond to small-scale change events, improve resource supervision efficiency, and reduce planning deviations and economic losses caused by data lags. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a schematic diagram of the overall framework of the present invention;

[0038] Figure 2 This is a control logic timing diagram of the present invention. DETAILED DESCRIPTION

[0039] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0040] See also Figure 1 and Figure 2 The present invention provides a technical solution: a multi-source business data collaborative collection method for small class changes, comprising the following steps:

[0041] S1: The dynamic topology perception layer is used to capture the changes in sub-class boundaries, ownership and business attributes in real time, and generate a dynamic topology map; the dynamic topology perception layer accesses remote sensing image data, ground sensor network signals and manually annotated system logs to extract the characteristics of sub-class spatial boundary changes; it should be further explained that step S1 includes dynamic topology perception and map generation: real-time access to remote sensing image data, ground sensor network signals and manually annotated system logs, and constructs a dynamic topology map by analyzing the characteristics of sub-class boundary changes, ownership change signals and business attribute update events. The sub-class nodes are encoded as vertices in the spatiotemporal graph structure, the associated paths are defined as edges, and the weight coefficients represent the intensity of resource dependence between sub-classes. When boundary redrawing or ownership changes are detected, an incremental spatiotemporal graph neural network model is used to update the node embedding vector, reconstruct the topology map and mark the change event type;

[0042] S2: Build standardized collection processes for afforestation, logging, and disaster operations based on structured metadata templates. The templates define the operation type code, spatial boundary coordinate range, time window threshold, and monitoring indicator set.

[0043] S3: Use the semantic-spatial dual-channel parser to map structured metadata to the resource class data model, and generate a data collection instruction set that includes drone aerial photography paths, sensor sampling frequencies, and manual inspection tasks; it should be further explained that the dual-channel parsing and instruction generation include: using the semantic-spatial dual-channel parser, calling the forest management knowledge graph to parse the logical constraint relationship between business terms, and generating a list of data collection requirements. At the same time, based on the dynamic topology graph, the spatial impact range of the business instructions is calculated to generate drone aerial photography path planning parameters and sensor network sampling rules. After integrating semantic requirements and spatial rules, the collection instruction set containing geographic fence constraints and time window restrictions is output and sent to drones, sensors, and manual inspection terminals;

[0044] S4: Perform dual-modal verification of temporal consistency verification and business rule sandbox simulation on the collected multi-source data, filter out conflicting data and trigger resolution strategies; it should be further explained that dual-modal verification and conflict resolution include: performing temporal consistency verification on the collected multi-source data, that is: using a hybrid logic clock model to align the physical timestamp and the event logic sequence, and constructing a cross-device event causal graph to detect time paradoxes. When it is found that the time of the change of ownership of the small class is later than the data upload event of the new affiliated unit, the data version rollback is triggered. At the same time, the current business policy module is loaded in the virtualized environment to simulate the impact of the data to be synchronized on the forest carbon sink model. If the calculation result deviates from the historical trend threshold, it is marked as abnormal data. For data that fails to pass the verification, a resolution strategy is generated based on reinforcement learning and the policy library is updated;

[0045] S5: Update the forest resource records in the central database using the verified data according to the dynamic topology map. It should be further explained that dynamic data synchronization and updating includes: adjusting the central database index according to the dynamic topology map and establishing a resource transfer mapping relationship between the old and new sub-groups. Implement branch storage for version conflict data, retain the original version, and generate a difference report for manual review and merging. When a new business conflict pattern is detected, the conflict features are extracted to generate a policy patch. After offline training and updating, it is dynamically bound to the data acquisition channel to achieve continuous adaptation of the monitoring indicator set and equipment control parameters.

[0046] Step S1: generating a dynamic topology map includes:

[0047] The class nodes are encoded as vertices in the spatiotemporal graph structure, the associated paths are defined as edges, and the weight coefficients represent the resource dependence strength between the classes.

[0048] When the remote sensing image features identify boundary redrawing or the sensor network detects ownership change signals, the incremental spatiotemporal graph neural network model is triggered to update the node embedding vector and reconstruct the topology map.

[0049] The incremental spatiotemporal graph neural network model operates by extracting spatial texture features from the remote sensing image feature matrix through a convolutional layer and then performing spatiotemporal alignment with the timestamp encoding of the sensor signal transition sequence. A multi-head attention mechanism is then used to calculate dynamic association weights between nodes, generating a topological map with logical timestamps that accurately identifies events such as boundary adjustments, ownership changes, and attribute updates.

[0050] In the specific implementation process, the training and node embedding calculation of the incremental spatiotemporal graph neural network model include: dividing the historical small class change event dataset into spatiotemporal segments, each segment contains a remote sensing image feature matrix, a sensor signal sequence, and manually annotated labels. The boundary texture features in the image are extracted through the convolutional layer, and the timestamp encoding of the sensor signal is aligned in time and space to form the initial node feature vector. The multi-head attention mechanism is used to calculate the dynamic association weights between nodes, and the edge weights are dynamically updated according to the feature similarity of adjacent nodes and the time interval between events. A contrastive learning strategy is introduced during model training, and the node embedding representation is optimized through positive and negative sample pairs (normal change events and artificially constructed abnormal events), so that similar change events are clustered in the embedding space.

[0051] It should be further explained that during the implementation, the training and node embedding calculations of the incremental spatiotemporal graph neural network model involve data preprocessing steps, including: dividing the historical sub-class change event dataset into spatiotemporal segments according to time windows, such as every 24 hours. Each segment contains: a remote sensing image feature matrix, with boundary texture features extracted through a convolutional layer and normalized to a 256×256 pixel grayscale image; a sensor signal sequence, normalized to a timestamp-encoded time series signal with a sampling frequency of 1Hz; and manual labeling, including change type, impact scope, and associated sub-class ID. When constructing anomalous event samples, the spatiotemporal correlations of normal events are randomly disrupted, such as by combining boundary redrawing events with unrelated sensor signals.

[0052] The calculation process of node embedding vectors and the optimization mechanism of contrastive learning strategy include:

[0053] The node embedding vector calculation adopts the multi-head self-attention mechanism, the formula is: Embedding(v i )=Concatleft(Attention1(Q i , K i , V i ), ..., Attention h (Q i , K i , V i )right); where Q i , K i , V i They represent query, key, and value matrices respectively, which are generated by concatenating remote sensing features and sensor signals.

[0054] The contrastive learning loss function is designed as:

[0055]

[0056] Among them, z i , z j is the embedding vector of the positive sample pair, z k is the negative sample vector, and τ is the temperature coefficient.

[0057] The construction of the structured metadata template in step S2 includes:

[0058] Define a set of survival rate monitoring indicators for afforestation operations, including the frequency of drone multispectral image acquisition, soil moisture sensor threshold, and manual sampling verification ratio;

[0059] A set of ecological impact monitoring indicators is defined for logging operations, including canopy density zero verification rules, surface runoff sensor activation conditions, and stump diameter statistical requirements.

[0060] In step S3, the execution logic of the semantic-spatial dual-channel parser includes: the semantic channel calls the forest management knowledge graph, parses the logical constraint relationship between business terms, and generates a list of data collection requirements; the spatial channel calculates the spatial impact range of business instructions based on the dynamic topology graph, generates drone aerial photography path planning parameters and sensor network sampling rules; after fusing the semantic requirements with the spatial rules, it outputs a collection instruction set with geographic fence constraints and time window restrictions.

[0061] In the specific implementation process, the geo-fence generation rules of the dual-channel parser include: the spatial channel calculates the minimum enclosing rectangle as the basic range of the geo-fence based on the coordinates of the sub-class boundary in the dynamic topological map, and superimposes the terrain slope data DEM and vegetation cover density NDVI to correct the flight altitude. For example, the drone's aerial photography altitude is automatically increased in steep slope areas to ensure safety, and the image overlap rate is increased in high-density forest areas to improve recognition accuracy. The sensor sampling rules are calculated based on the business impact range: with the changed sub-class as the center, the monitoring frequency of the surrounding sensors is dynamically adjusted according to the ecological impact radius (such as logging operations within 20 times the tree height).

[0062] It should be further explained that, in the specific implementation process, the process of correcting UAV flight parameters using DEM and NDVI data is as follows:

[0063] Flight altitude adjustment rules: H final =H base +k1Slope+k2·NDVI,H base Base altitude: 50m; Slope: terrain slope (degrees), k1 = 0.5, m / degree; NDVI: vegetation cover index, k2 = -10, m / index. Reduce flight altitude in areas with high NDVI.

[0064] Image overlap rate setting: In high-density forest areas, if NDVI is greater than 0.6, the overlap rate is increased to 80%; in logging areas, if NDVI is less than 0.3, the overlap rate is reduced to 60%.

[0065] The association rules of the forest management knowledge graph include: mapping "afforestation survival rate monitoring" to "drone collection of near-infrared band images once a week" and "soil moisture sensor uploading data every two hours"; associating "clear-cutting operations" with "real-time monitoring of surface runoff sensors after logging" and "compensation calculation of canopy density in surrounding small groups after boundary redrawing."

[0066] It's important to further clarify the semantic-spatial parsing logic: The semantic channel uses the forest management knowledge graph to map "afforestation survival rate monitoring" to drone multispectral imagery and periodic soil moisture sensor monitoring, and links "clear-cutting operations" to real-time post-felling surface runoff monitoring and canopy density compensation calculations for surrounding small-scale plots. The spatial channel, based on the geofence boundaries of the topological map, generates the flight altitude and image overlap parameters for the drone's aerial photography path and sets the sampling rules for the sensor network.

[0067] During the specific implementation process, the construction and dynamic update mechanism of the forest management knowledge graph includes: based on forestry industry standards and historical business rule documents, a three-layer knowledge graph is constructed, including operation types, monitoring indicators, and equipment control logic. Among them: the operation type layer defines metadata templates for businesses such as afforestation, logging, and disasters; the monitoring indicator layer establishes association rules between business terms and sensor types and drone aerial photography parameters; and the equipment control layer maps monitoring indicators to operating instructions for specific equipment. When a new type of business conflict is detected, the semantic parsing engine extracts conflict features (such as permission conflicts and version conflicts), generates temporary nodes and inserts them into the knowledge graph, and solidifies and updates them after manual review.

[0068] It should be further explained that in the specific implementation process, the specific construction rules of the three-layer structure of the knowledge graph (job type layer, monitoring indicator layer, and equipment control layer) include:

[0069] Operation type layer: Based on forestry industry standards (such as the Technical Regulations for Forest Resource Planning and Design), define the JSON structure of the metadata template, such as: {"operation type": "afforestation", "monitoring indicators": ["survival rate", "soil moisture"], "equipment control": {"drone": "near-infrared imaging once a week", "sensor": "sampling every 2 hours"}}

[0070] Monitoring indicator layer: Business terms are mapped to equipment parameters through the semantic parsing engine. For example, "canopy density returns to zero" is mapped to "LiDAR point cloud density ≥ 100 points / ㎡".

[0071] Device control layer: Use a rule engine, such as Drools, to convert monitoring indicators into device instructions. For example, "surface runoff sensor activation" corresponds to "sampling frequency increased to 1 time per minute."

[0072] Conflict feature extraction and updating: When a conflict of authority is detected, conflict features are extracted, such as "the same class is marked as a jurisdiction by multiple units," a temporary node is generated, and linked to the "Conflict of Authority" category in the knowledge graph. After manual review, the update rules are solidified, such as adding a constraint: "Changes in ownership of the same class must occur at least 24 hours apart."

[0073] Step S4: The bimodal verification includes:

[0074] Timing consistency verification: A hybrid logical clock model is used to align the physical clocks of drones, sensors, and human terminals with the logical sequence of events, and a cross-device event causal graph is constructed to detect time paradoxes.

[0075] Business rule sandbox verification: This involves loading the current business policy module in a virtualized environment and simulating the impact of the data to be synchronized on the forest carbon sink model. Any carbon storage calculation results that deviate from historical trend thresholds are flagged as an anomaly. It should be further clarified that the business rule sandbox simulation involves loading the forest carbon sink model in a virtualized environment and substituting the afforestation survival rate data to be synchronized into the simulation. If the carbon storage forecast exceeds the historical fluctuation range, it is identified as an anomaly and a manual review process is triggered. Simultaneously, the post-logging ecological impact data is simulated as input to the surface runoff model to verify whether it meets the preset ecological protection threshold.

[0076] In the specific implementation process, the collaborative verification algorithm of the hybrid logical clock model and the business rule sandbox includes: assigning a hybrid logical clock to each data acquisition device, which includes a physical clock value and a logical event counter. When the device generates data, the physical timestamp and event serial number are recorded synchronously. In the verification stage, a cross-device event causal graph is constructed: if the logical serial number of event A is before event B, and there is a spatial correlation between the two (such as belonging to a small class), then A is determined to be the causal predecessor event of B. For detected time paradoxes (such as data of later events being collected first), the backtracking mechanism is triggered to realign the event stream. When the business rule sandbox loads the carbon sink calculation model, lightweight container technology is used to isolate the simulation environment, and the data to be verified is input into the model in parallel through shadow data stream technology. The deviation of the output result from the historical trend is compared, and an exception report is generated if it exceeds the preset threshold.

[0077] It should be further explained that, in the specific implementation process, the hybrid logical clock allocation rules and event causal graph construction logic include:

[0078] Hybrid logical clock distribution: Assign a logical clock C = (l, c) to each device, where l is the physical clock value and c is the logical counter. When a device generates an event, the logical clock is updated: c new =max(c current , c received )+1; and recorded as timestamp (l, c).

[0079] Event causal graph construction: If the logical sequence number of event A is c A <c B , and spatially associated, such as A and B belong to the same class X, then add an edge A→B to the causal graph. When a time paradox is detected, such as l A >l B But c A<c B , triggering the backtracking mechanism to realign the event flow.

[0080] Sandbox technology implementation: Docker container technology is used to isolate the sandbox environment and load a lightweight carbon sequestration model, such as a simplified version of the InVEST model. Shadow data streams transmit the data to be verified in parallel through Kafka message queues, and the differences between the sandbox output and the historical database are compared in real time.

[0081] In practice, the virtual sandbox's ecological impact simulation technology involves constructing a surface runoff simulation model within the sandbox, inputting post-harvest terrain data and rainfall forecasts to be verified, and calculating a runoff curve. If the peak flow exceeds the ecological protection threshold, the harvesting plan is deemed to pose a risk of soil erosion. Simultaneously, the carbon sink accumulation process in the afforestation area is simulated, and future carbon storage is predicted using tree species growth models. If the predicted value falls below historical levels for the same period, a manual review process is triggered.

[0082] Triggering the resolution strategy, that is, the execution of the conflict resolution strategy includes: generating candidate resolution solutions based on historical conflict resolution records, including data version rollback, dynamic permission reset, and device command resending; evaluating the impact weight of each solution on the data consistency index and business compliance rate through the Markov decision process, selecting the optimal solution and updating the strategy library.

[0083] It should be further clarified that conflict resolution strategy execution involves generating candidate solutions based on historical conflict resolution records, including data version rollback, dynamic permission reset, and device command resend. By evaluating the impact of each solution on the data consistency index and business compliance rate, the optimal solution is selected and the policy library is updated. For cross-domain permission conflicts caused by small class reorganizations, a dynamic role mapping mechanism is used to reallocate data access rights.

[0084] In practice, the training and deployment of a reinforcement learning conflict resolution strategy involves constructing a state space for the conflict resolution decision-making environment, encompassing conflict type, topology state, and business policy version. The action space encompasses operations such as data rollback, permission reset, and command reissue. The reward function is designed as: R = w1·DataConsistency + w2·ComplianceRate - w3·OperationCost. After generating a policy model through offline training, it is deployed as a lightweight inference engine. When a new conflict occurs, transfer learning techniques are used to reuse historical policy parameters and generate an optimized solution based on the real-time topology state.

[0085] It should be further explained that in the specific implementation process, the design of the state space, action space and reward function, as well as the application of transfer learning technology are as follows:

[0086] State space definition: S = {conflict type, topology state, service policy version}, for example:

[0087] Conflict type: permission conflict, coded as 0; version conflict, coded as 1;

[0088] Topology graph status: number of nodes, average edge weight;

[0089] Business policy version: MD5 hash value of the semantic rule base.

[0090] Reward function design formula:

[0091] R = 0.6·DataConsistency+0.3·ComplianceRate-0.1·OperationCost; the data consistency index is calculated through hash verification, the business compliance rate is evaluated based on the policy library matching degree, and the operation cost is quantified by the execution time (milliseconds) of the resolution action.

[0092] Transfer learning application: The pre-trained strategy model freezes some network layers, such as the feature extraction layer, on historical conflict datasets. For new conflicts, the fully connected layer parameters are fine-tuned, and an optimization plan is generated based on the real-time topology status.

[0093] In step S5, the updating process of the central database includes:

[0094] Adjust the data association index according to the dynamic topology map and establish the resource transfer mapping relationship between the new and old classes;

[0095] Data with conflicting versions is stored in branches, retaining the original version and generating a difference report for manual review. It should be further explained that dynamic database adaptation includes: the central database updates resource transfer mappings based on the topology map, for example, by proportionally allocating the storage volume data corresponding to a portion of the original sub-class area to the new sub-class. For conflicting versions of data due to timestamp conflicts, the original records are retained and difference annotations are generated, allowing for manual intervention and merging according to business rules.

[0096] In the specific implementation process, the mathematical model and implementation cases of dynamic adjustment of central database index include:

[0097] Define resource transfer mapping function: f(S old , S new )=α·AreaRatio+β·Attribute Weight; where, S old and S newThe α and β represent the spatial areas of the new and old subdivisions, respectively. These weight coefficients are dynamically adjusted based on the type of business. For example, afforestation business prioritizes area ratios, while felling business prioritizes tree attribute weights. When 30% of subdivision A is assigned to subdivision B, the stock volume data is segmented based on area ratios, and the biomass allocation is weighted based on tree species composition. Conflicting data branches are stored using version snapshot technology, preserving the timestamps and topological state of the original data. Difference reports automatically indicate the path of changes and the scope of impact.

[0098] It should be further explained that, in the specific implementation process, the dynamic adjustment logic of the weight coefficients α and β in the resource transfer mapping function includes:

[0099] Dynamic adjustment rules for weight coefficients: Afforestation business: α = 0.8, β = 0.2, focusing on area ratio; logging business: α = 0.3, β = 0.7, focusing on forest attribute weights, such as tree species value coefficient.

[0100] Biomass allocation calculation example: When 30% of the area of class A is assigned to class B: B =Accumulation A ×0.3×(α+β·(species value B / Tree species value A )).

[0101] The dynamic adaptation of the standardized collection process includes: when a new business conflict pattern is detected, the conflict characteristics are extracted to generate a policy patch and then updated through offline training; the updated policy module is dynamically bound to the data collection channel, and the monitoring indicator set and equipment control parameters are synchronously adjusted. It should be further explained that the construction of the standardized collection process includes: for afforestation, logging, and disaster relief businesses, a structured metadata template containing the operation type code, spatial boundary coordinate range, time window threshold, and monitoring indicator set is defined. Through the business logic decoupling engine, business rules are converted into independent policy modules. For example, the afforestation survival rate monitoring indicator is dynamically bound to drone image acquisition, soil moisture sensor monitoring, and manual sampling verification.

[0102] By building a dynamic topology perception mechanism and standardized collection processes, this invention overcomes the problem of multi-source data fragmentation caused by frequent changes in small groups, and achieves real-time collaborative collection and consistency assurance of multi-source heterogeneous data. The dynamic topology map accurately perceives boundary adjustments and ownership change events, and combined with a dual-channel parser, automatically maps business rules to device instructions, significantly reducing data association error rates. The bimodal verification mechanism effectively eliminates version conflicts and business rule contradictions through timing alignment and sandbox simulation, reducing data collection response speed to minutes and improving the accuracy of forest resource assessments.

[0103] This breaks through the traditional static data collection model and enables intelligent adaptation of business data to resource clusters in dynamic forest management scenarios, providing highly reliable data support for forest carbon sink accounting and ecological protection decision-making. Through self-evolving conflict resolution strategies and database dynamic indexing technology, manual intervention is reduced, helping forestry management departments quickly respond to cluster changes, improving resource supervision efficiency and reducing planning deviations and economic losses caused by data lags.

[0104] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article, or device comprising the element.

[0105] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A multi-source business data collaborative collection method for small class changes, characterized by: The following steps are involved: S1: The dynamic topology perception layer captures changes in sub-class boundaries, ownership, and operating attributes in real time to generate a dynamic topology map. The dynamic topology perception layer accesses remote sensing image data, ground sensor network signals, and manually annotated system logs to extract sub-class spatial boundary change characteristics. S2: Build standardized collection processes for afforestation, logging, and disaster operations based on structured metadata templates that define operation type codes, spatial boundary coordinate ranges, time window thresholds, and monitoring indicator sets; S3: Use a semantic-spatial dual-channel parser to map the structured metadata to a resource class data model, generating a data collection instruction set including the drone aerial photography path, sensor sampling frequency, and manual inspection tasks; S4: Performs dual-modal verification of time series consistency and business rule sandbox simulation on the collected multi-source data, filters conflicting data and triggers resolution strategies; S5: Update the forest resource records in the central database according to the dynamic topology map using the verified data.

2. The method for collaboratively collecting multi-source business data for small-class changes according to claim 1 is characterized by: The generation of the dynamic topology map in step S1 includes: The class nodes are encoded as vertices in the spatiotemporal graph structure, the associated paths are defined as edges, and the weight coefficients represent the resource dependence strength between the classes. When the remote sensing image features identify boundary redrawing or the sensor network detects ownership change signals, the incremental spatiotemporal graph neural network model is triggered to update the node embedding vector and reconstruct the topology map.

3. The method for collaboratively collecting multi-source business data for small-scale changes according to claim 2 is characterized by: The operation process of the incremental spatiotemporal graph neural network model includes: The remote sensing image feature matrix is passed through the convolution layer to extract the spatial texture features and is spatially aligned with the timestamp encoding of the sensor signal jump sequence; Based on the multi-head attention mechanism, the dynamic association weights between nodes are calculated, a topological map with logical timestamps is generated, and the change event type is marked as boundary adjustment, ownership change or attribute update.

4. The method for collaboratively collecting multi-source business data for small-class changes according to claim 3 is characterized by: The construction of the structured metadata template in step S2 includes: Define a set of survival rate monitoring indicators for afforestation operations, including the frequency of drone multispectral image acquisition, soil moisture sensor threshold, and manual sampling verification ratio; A set of ecological impact monitoring indicators is defined for logging operations, including canopy density zero verification rules, surface runoff sensor activation conditions, and stump diameter statistical requirements.

5. The method for collaboratively collecting multi-source business data for small-class changes according to claim 4 is characterized by: The execution logic of the semantic-spatial dual-channel parser in step S3 includes: The semantic channel calls the forest management knowledge graph, analyzes the logical constraints between business terms, and generates a list of data collection requirements; The spatial channel calculates the spatial impact range of business instructions based on the dynamic topology map, and generates the UAV aerial photography path planning parameters and sensor network sampling rules; After integrating semantic requirements with spatial rules, a collection instruction set with geo-fence constraints and time window restrictions is output.

6. The method for collaboratively collecting multi-source business data for small-class changes according to claim 5 is characterized by: The association rules of the forest management knowledge graph include: mapping "afforestation survival rate monitoring" to "drone collection of near-infrared band images once a week" and "soil moisture sensor uploading data every two hours"; and associating "clear-cutting operations" with "real-time monitoring of surface runoff sensors after logging" and "compensation calculation of canopy density in surrounding sub-groups after boundary redrawing." 7. The method for collaboratively collecting multi-source business data for small-class changes according to claim 6 is characterized by: The dual-modal verification in step S4 includes: Timing consistency verification: A hybrid logical clock model is used to align the physical clocks of drones, sensors, and human terminals with the logical sequence of events, and a cross-device event causal graph is constructed to detect time paradoxes. Business rule sandbox verification: Load the current business policy module in a virtualized environment to simulate the impact of the data to be synchronized on the forest carbon sink model. If the carbon storage calculation results deviate from the historical trend threshold, they are marked as abnormal.

8. The multi-source business data collaborative collection method for small class changes according to claim 7 is characterized by: The trigger resolution strategy, i.e., the execution of the conflict resolution strategy, includes: Generate candidate resolution plans based on historical conflict resolution records, including data version rollback, dynamic permission reset, and device command resend; The Markov decision process is used to evaluate the impact of each solution on the data consistency index and business compliance rate, select the optimal solution and update the strategy library.

9. The method for collaboratively collecting multi-source business data for small-class changes according to claim 8, characterized in that: The updating process of the central database in step S5 includes: Adjust the data association index according to the dynamic topology map and establish the resource transfer mapping relationship between the new and old classes; Implement branch storage for version-conflicting data, retain the original version, and generate a difference report for manual review.

10. The method for collaboratively collecting multi-source business data for small class changes according to claim 9, characterized in that: The dynamic adaptation of the standardized acquisition process includes: When a new business conflict pattern is detected, conflict features are extracted to generate policy patches and then trained and updated offline; Dynamically bind the updated policy module to the data acquisition channel and synchronously adjust the monitoring indicator set and device control parameters.

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