A rural wisdom management platform and data processing method

Abstract

This application discloses a smart rural management platform and data processing method, belonging to the field of computer technology. The technical solution provided by the embodiments of this application realizes unified semantic understanding of multi-source heterogeneous data, eliminating the phenomenon of data silos; it reveals deep causal relationships between entity states through time-series pattern mining, supporting forward-looking inference of the evolution path of complex events; the closed-loop optimization mechanism enables the decision-making system to have adaptive evolution capabilities, thereby solving to a certain extent the technical problems of insufficient data integration, lack of causal inference, and lack of feedback optimization in decision execution in rural management.

Description

Technical Field

[0001] This application relates to the field of computer technology, and in particular to a smart rural management platform and data processing method. Background Technology

[0002] Rural governance is an important component of the modernization of the national governance system, and its management effectiveness is directly related to the sustainable development and stability of rural areas. With the popularization of technologies such as the Internet of Things and big data, rural management is gradually transforming from a traditional manual model to a digital model.

[0003] However, existing rural management solutions have significant limitations. First, various data sources, such as IoT sensors, government systems, and geographic information systems, are independent and vary in format. Existing systems struggle to achieve effective cross-modal fusion and unified semantic understanding, resulting in severe "data silos" and an inability to construct a dynamic digital image that accurately reflects rural entities and their inherent relationships. Second, existing methods are mostly limited to recording past events or passively responding based on simple rules, lacking the ability to mine deep causal relationships between entities from time-series data and failing to proactively extrapolate and predict the evolutionary paths of complex events. Finally, even when preliminary decisions are generated, their execution often relies on manual scheduling, lacking a closed-loop decision-making mechanism that integrates intelligent contingency plan generation, automatic resource coordination, and execution feedback optimization.

[0004] Therefore, existing technologies are insufficient to support in-depth understanding of rural operational status, proactive early warning, and adaptive decision-making. There is an urgent need for a new data processing method that can integrate data fusion, causal discovery, intelligent inference, and closed-loop optimization. Summary of the Invention

[0005] This application provides a smart rural management platform and data processing method, which can, to some extent, solve the problems of insufficient data integration, lack of causal inference, and lack of feedback optimization in decision-making and execution in rural management. The technical solution is as follows:

[0006] On the one hand, a data processing method is provided, the method comprising:

[0007] Acquire multi-source heterogeneous data from IoT sensors, government systems, manual reporting terminals, and geospatial information systems; perform fusion processing on the multi-source heterogeneous data to generate a rural digital twin containing rural entities and corresponding semantic associations;

[0008] Based on the rural digital twin, the state changes of the rural entities are monitored, and the correlation between entity states is identified through time-series pattern mining to generate a composite event rule set containing triggering conditions and event propagation paths; based on the event propagation paths, multi-step deduction is performed to generate deduction and prediction results containing event development trajectories.

[0009] The real-time data stream of the rural digital twin is matched with the composite event rule set; when the match is successful, the inference and prediction results are called, and the decision is optimized by combining the AI ​​prediction model to generate a decision plan, and the relevant resources are executed through the task collaboration engine.

[0010] Collect task execution results and actual event development data, compare them with the inference and prediction results, and perform collaborative optimization of the semantic associations in the rural digital twin, the composite event rule set, and the parameters of the AI ​​prediction model based on the comparison results.

[0011] On the one hand, a smart rural management platform is provided, the platform comprising:

[0012] The acquisition module is used to acquire multi-source heterogeneous data from IoT sensors, government systems, manual reporting terminals, and spatial geographic information systems; and to perform fusion processing on the multi-source heterogeneous data to generate a rural digital twin containing rural entities and corresponding semantic associations.

[0013] The monitoring module is used to monitor the state changes of the rural entities based on the rural digital twin, identify the correlation between entity states through time-series pattern mining, and generate a composite event rule set containing triggering conditions and event propagation paths; and perform multi-step deduction based on the event propagation paths to generate deduction and prediction results containing event development trajectories.

[0014] The matching module is used to match the real-time data stream of the rural digital twin with the composite event rule set; when the matching is successful, the inference and prediction results are called, and the AI ​​prediction model is combined to optimize the decision, generate a decision plan, and drive the execution of relevant resources through the task collaboration engine.

[0015] The optimization module is used to collect task execution results and actual event development data, compare them with the inference and prediction results, and perform collaborative optimization of the semantic associations in the rural digital twin, the composite event rule set, and the parameters of the AI ​​prediction model based on the comparison results.

[0016] On one hand, a computer device is provided, the computer device including one or more processors and one or more memories, the one or more memories storing at least one computer program, the computer program being loaded and executed by the one or more processors to implement the data processing method.

[0017] On one hand, a computer-readable storage medium is provided, wherein at least one computer program is stored in the computer program, which is loaded and executed by a processor to implement the data processing method.

[0018] On one hand, a computer program product or computer program is provided, which includes program code stored in a computer-readable storage medium. A processor of a computer device reads the program code from the computer-readable storage medium and executes the program code, causing the computer device to perform the above-described data processing method. Attached Figure Description

[0019] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0020] Figure 1 This is a schematic diagram of the implementation environment of a data processing method provided in an embodiment of this application;

[0021] Figure 2 This is a flowchart of a data processing method provided in an embodiment of this application;

[0022] Figure 3 This is a flowchart of another data processing method provided in an embodiment of this application;

[0023] Figure 4 This is a flowchart of yet another data processing method provided in the embodiments of this application;

[0024] Figure 5 This is a flowchart of another data processing method provided in the embodiments of this application;

[0025] Figure 6 This is a flowchart of another data processing method provided in the embodiments of this application;

[0026] Figure 7 This is a flowchart of another data processing method provided in the embodiments of this application;

[0027] Figure 8 This is a schematic diagram of the structure of a rural smart management platform provided in an embodiment of this application;

[0028] Figure 9 This is a schematic diagram of the structure of a server provided in an embodiment of this application. Detailed Implementation

[0029] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings.

[0030] In this application, the terms "first," "second," etc., are used to distinguish identical or similar items with essentially the same function. It should be understood that there is no logical or temporal dependency between "first," "second," and "n," nor are they limited in quantity or execution order.

[0031] Artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain better results.

[0032] Machine learning (ML) is a multidisciplinary field involving probability theory, statistics, approximation theory, convex analysis, and algorithm complexity theory. It specifically studies how computers can simulate or implement human learning behavior to acquire new knowledge or skills, and reorganize existing knowledge sub-models to continuously improve their performance. Machine learning is the core of artificial intelligence and the fundamental way to endow computers with intelligence; its applications span all areas of artificial intelligence.

[0033] IoT sensors: Physical devices deployed in rural environments to automatically collect data from the physical world, such as ambient temperature and humidity, water quality parameters, security videos, vehicle locations, etc., and are the main source of real-time data for the platform.

[0034] Governmental systems refer to existing information systems used by various government departments to process daily government affairs and store business data, such as population management, social security, and administrative approval systems, which provide authoritative structured business data for the platform.

[0035] Spatial Geographic Information System: A system used to collect, manage, analyze, and display data related to geospatial location, providing the platform with spatial information such as rural terrain, landforms, buildings, and infrastructure.

[0036] Rural digital twins are dynamic virtual models constructed in virtual space by integrating multi-source data, corresponding to physical rural entities. They include not only the static attributes of entities (such as houses, rivers, and equipment), but also their state data and dynamic semantic and weighted relationships between entities.

[0037] Event propagation path: When an event (cause) occurs, its impact, through the relationships between entities, triggers one or more subsequent events (effects) in sequence according to time order and causal logic, forming a complete chain or network path.

[0038] Composite event rule set: A set of predefined rules that can be used for pattern matching. Each rule describes how one or more primary events (triggering conditions) can trigger a complex high-level event, consisting of multiple sub-events arranged according to a specific event propagation path, when certain spatiotemporal and logical relationships are satisfied.

[0039] Event trajectory: A dynamic description of the complete process of a complex event from its occurrence and transmission to its final result; it is a concrete instantiation of the event transmission path in the time dimension.

[0040] The inference and prediction results are a set of possible future event trajectories calculated through simulation based on the current state and the set of rules for complex events. Each trajectory is usually accompanied by an estimated probability of occurrence.

[0041] Task collaboration engine: A core software component responsible for decomposing the optimized decision plan into specific atomic task sequences, and intelligently and dynamically allocating these tasks to the most suitable relevant resources for execution based on dynamic resource profiles.

[0042] Related resources: In the context of rural management, entities such as personnel, vehicles, equipment, and computing units that can be dispatched to perform specific tasks.

[0043] Atomic task sequence: A series of indivisible or clearly defined execution units obtained by the task collaboration engine after parsing the macro-level decision-making plan. For example, "dispatch personnel A to location B to inspect equipment C" is an atomic task.

[0044] Cross-modal: refers to data originating from different types (such as sensor values, government documents, and geographic images).

[0045] Spatiotemporal alignment: unifying data from different sources onto a consistent temporal and spatial coordinate reference.

[0046] Semantic enhancement processing: By using models (such as semantic encoders) to understand the unified meaning behind different data and extracting features that can represent that meaning, modal differences are eliminated and standardized data with semantic consistency is formed.

[0047] Time-series causal discovery algorithms: a class of algorithms specifically designed for analyzing time-series data. Their goal is not simply correlation, but to identify whether a cause-effect relationship exists between variables and to determine the strength and direction of this relationship.

[0048] Causal relationship mining: The process of automatically finding and identifying potential causal relationship pairs from massive amounts of time-series data using time-series causal discovery algorithms.

[0049] Statistical significance: A statistical concept used to determine the probability that a causal relationship found in causal relationship mining is not caused by random fluctuations, but rather by a real possibility. It is usually measured using indicators such as the p-value; a relationship is considered "significant" if it exceeds a preset threshold.

[0050] Direct causal relationship identification: In complex association networks, distinguish whether the relationship between two variables is a direct causal relationship or an indirect relationship generated through other intermediate variables.

[0051] Analytical proxy: An intermediate variable or alternative indicator introduced in the identification of direct causal relationships to help isolate and confirm direct causal drivers.

[0052] Direct causal relationship: refers to a situation where a change in one variable directly causes a change in another variable, without the need for a third variable to mediate.

[0053] Topology: In this embodiment of the application, it refers to the network graph structure formed by the event propagation path, including the connection method of nodes (event states) and edges (causal propagation relationships).

[0054] Causal strength value: A quantitative representation of the degree of influence of the identified causal relationship. The larger the value, the stronger the driving force of the cause on the effect.

[0055] Timing Simulation Engine: A computational module responsible for simulating the dynamic evolution of events in chronological order.

[0056] Monte Carlo methods are a class of computational methods that obtain numerical results through extensive random sampling. In this scheme, they are used to simulate various random possibilities in the evolution of events.

[0057] Random walk algorithm: A mathematical model that simulates paths, used here to generate a large number of possible event evolution paths on a probabilistic event propagation graph by randomly selecting the direction of movement based on the weights (causal strength values) of the edges.

[0058] Multi-step temporal evolution: Simulates the process of an event advancing step by step into the future along the event propagation path, involving multiple time steps.

[0059] Candidate event trajectory set: The set of all possible event trajectories generated after running multiple random walks through a time-series simulation engine.

[0060] Stability assessment trajectory: This evaluates the development trajectory of candidate events, examining whether the causal strength values ​​of the paths traversed by the trajectory are stable. Trajectories with excessive fluctuations are considered to have poor reliability and low robustness.

[0061] Multi-dimensional quantitative evaluation: A trajectory is comprehensively scored based on multiple different indicators (such as probability of occurrence, stability, and topological importance).

[0062] Trajectory stability coefficient: A quantitative indicator used to measure the robustness of an event's trajectory, typically calculated based on the variance or fluctuation of causal strength values ​​along the path.

[0063] Topological importance score: A quantitative metric used to measure the importance of key nodes traversed by a trajectory within the overall rural digital twin network.

[0064] Historical projection accuracy data: This is statistical information on the accuracy of past projections and predictions compared with actual results. It is used to provide feedback for optimizing future projection and selection models.

[0065] Convergence detection algorithm: an algorithm used to judge and select the final stable and reliable result from the subset of candidate trajectories, avoiding the selection of unstable paths that may diverge even though they have a high probability.

[0066] Pareto Front Sorting Algorithm: A multi-objective decision optimization method. It is used to find a set of "non-dominated solutions" (i.e., Pareto optimal solutions) among multiple conflicting objectives (such as maximizing utility, minimizing risk, and minimizing cost), where improvements in any one objective lead to the deterioration of at least one other objective.

[0067] Dynamic resource profile: A comprehensive digital description of relevant resources, consisting of static attributes and dynamic indicators.

[0068] Static attributes: Relatively fixed attributes of resources, such as skill qualifications, department, default location, etc.

[0069] Dynamic metrics: Real-time changes in resource status, such as current GPS location, workload, and device health status.

[0070] Pareto Front Search Algorithm: Similar to the Pareto Front Screening Algorithm, it is a specific algorithm used to solve multi-objective collaborative optimization functions and find the equilibrium optimization parameter set.

[0071] Incremental learning mechanism: a machine learning technique that allows a model to continuously update and optimize itself using new data (such as feedback comparison results) without forgetting old knowledge, rather than training from scratch each time.

[0072] It should be noted that the information (including but not limited to user device information, user personal information, etc.), data (including but not limited to data used for analysis, data stored, data displayed, etc.) and signals involved in this application are all authorized by the user or fully authorized by all parties, and the collection, use and processing of related data must comply with the relevant laws, regulations and standards of the relevant countries and regions.

[0073] Figure 1 This is a schematic diagram illustrating the implementation environment of a data processing method provided in this application embodiment. See also... Figure 1 This implementation environment may include node 110 and server 140.

[0074] Node 110 is connected to server 140 via a wireless or wired network. Optionally, node 110 may be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. Node 110 has applications installed and running that support data processing.

[0075] Server 140 is a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms. Server 140 can provide background services for applications running on node 110. The rural smart management platform provided in this application embodiment is deployed on server 140.

[0076] In traditional rural management techniques, the semantic fragmentation of multi-source heterogeneous data leads to the widespread phenomenon of data silos, making it difficult to construct dynamic digital images of rural entities and their inherent relationships. Simultaneously, the deep causal relationships between entity states in time-series data cannot be effectively mined, and the evolutionary paths of complex events lack forward-looking projection capabilities. Furthermore, the decision-making and execution processes rely on manual scheduling, failing to form a closed-loop mechanism for intelligent contingency plan generation, automatic resource coordination, and execution feedback optimization. Specifically, the format differences and independent storage of data sources such as IoT sensors, government systems, and geographic information systems create obstacles to cross-modal fusion, hindering unified semantic understanding. Time-series pattern analysis is limited to surface statistical features, lacking a quantitative identification mechanism for causal relationships between entities, resulting in the inability to systematically define event transmission paths. In the decision-making process, task assignment and resource coordination are manually led, and the comparison and optimization of projection and prediction results with actual execution data are not incorporated into the feedback process. This limits the system's ability to deeply understand the rural operational situation, results in insufficient timeliness of proactive early warnings, and lacks adaptability in decision execution.

[0077] For example, in rural flood control emergency management scenarios, real-time water level data collected by IoT sensors, villager household registration information stored in government systems, hazard descriptions submitted by manual reporting terminals, and terrain elevation data provided by geospatial information systems are isolated from each other. Existing technologies cannot achieve cross-modal spatiotemporal alignment and semantic enhancement, resulting in the inability to accurately map the dynamic correlation between water level changes and villagers' evacuation needs. When an abnormal rise in water level is detected, the system can only trigger a simple alarm based on a preset threshold, but it cannot identify statistically significant causal relationships between rainfall, river siltation, and road damage from historical time-series data, and therefore cannot deduce the possible multi-step propagation trajectory and impact range of the flood. In the decision-making stage, the formulation of emergency plans and the dispatch of rescue resources rely entirely on the experience of management personnel, and the task coordination process lacks an automated engine, resulting in response delays and resource allocation imbalances.

[0078] If the aforementioned technical issues are not resolved, the situational awareness of the rural management system will remain at the level of static data recording for a long time, making it impossible to proactively warn of potential risks; the unpredictability of the event evolution process will lead to a lack of scientific basis for decision-making plans and persistently low execution efficiency; the lack of a closed-loop optimization mechanism will prevent the system from dynamically adjusting semantic association weights, event rule trigger thresholds, and prediction model parameters based on actual execution results, ultimately hindering the modernization process and sustainable development capacity of rural governance.

[0079] To address this, this application proposes a data processing method, see [link to relevant documentation]. Figure 2 Taking the server as the executing entity as an example, the following steps are included.

[0080] 201. Acquire multi-source heterogeneous data from IoT sensors, government systems, manual reporting terminals, and geospatial information systems;

[0081] 202. Perform fusion processing on multi-source heterogeneous data to generate a rural digital twin containing rural entities and corresponding semantic associations;

[0082] 203. Based on rural digital twins, monitor the state changes of rural entities, identify the correlation between entity states through time-series pattern mining, and generate a composite event rule set containing triggering conditions and event propagation paths;

[0083] 204. Perform multi-step deduction based on the event propagation path to generate deduction and prediction results containing the event development trajectory;

[0084] 205. Match the real-time data stream of the rural digital twin with the composite event rule set;

[0085] 206. When a match is successful, the inference and prediction results are invoked, and the AI ​​prediction model is combined to optimize the decision, generate a decision plan, and drive the execution of relevant resources through the task collaboration engine.

[0086] 207. Collect task execution results and actual event development data, compare them with the inference and prediction results, and optimize the semantic associations, composite event rule sets, and AI prediction model parameters in the rural digital twin based on the comparison results.

[0087] In practical applications, a rural digital twin refers to a virtual model that can dynamically map rural entities and their semantic relationships. This can be achieved using ontology-based semantic modeling methods, such as defining entity types and relationships using OWL and storing semantic relationships through RDF triples; or constructing a network structure of nodes and edges using relational databases combined with semantic annotation, primarily to achieve unified semantic representation and dynamic updates of multi-source data. Furthermore, time-series pattern mining refers to the process of identifying relationships from time-series data of rural entity states. This can be achieved using statistical methods such as Granger causality tests to analyze causal relationships between time series; or using time-series clustering algorithms to discover state change patterns, primarily to form the basis for generating composite event rule sets. The composite event rule set refers to a set of rules defining triggering conditions and event propagation paths. It can be managed using rule engines such as Drools, for example, by describing event logic through condition-action rules; or by automatically generating rules based on decision tree models, primarily to support event prediction and deduction. Therefore, multi-step deduction refers to the process of generating event development trajectories based on event propagation paths. This can employ discrete event simulation methods, such as gradually deducing event state changes through state machine models; or utilize stochastic simulation methods to generate multiple possible paths, primarily to provide forward-looking prediction results. Furthermore, collaborative optimization refers to the process of adjusting system parameters based on comparison results. This can employ iterative learning methods, such as updating model parameters by minimizing prediction errors; or dynamically adjusting rule thresholds based on feedback mechanisms. This is primarily to achieve adaptive evolution of the system, which is also known as a rural smart management platform. This application, through the collaborative work of the above technical features, solves the problems of severe data silos, the inability to mine deep causal relationships between entities for complex event deduction, and the lack of closed-loop decision-making mechanisms in rural management. Specifically, the acquisition of multi-source heterogeneous data covers multiple dimensions of information, including rural physical environment, administration, human feedback, and geospatial data, avoiding the limitations of a single data source in fully depicting rural entities; the generation of rural digital twins eliminates data silos and achieves unified semantic understanding; temporal pattern mining reveals statistically significant correlations between entity states, overcoming the shortcomings of traditional methods that can only passively respond; multi-step inference enables forward-looking prediction of the development path of complex events; decision optimization and resource execution mechanisms avoid the inefficiency of manual scheduling; and collaborative optimization mechanisms enable the system to continuously improve based on actual execution feedback. Therefore, this embodiment provides a rural governance method that integrates data fusion, causal discovery, intelligent inference, and closed-loop optimization.

[0088] This method involves a data processing workflow in the field of rural management. First, multi-source heterogeneous data is acquired, originating from IoT sensors, government systems, manual reporting terminals, and geospatial information systems. Specifically, IoT sensors can be soil moisture monitoring equipment and meteorological stations deployed in farmland areas; government systems provide household registration and land registration data; manual reporting terminals receive on-site information submitted by grassroots staff via mobile terminals; and geospatial information systems contribute high-precision topographic and administrative division data. Further, the above data is fused to generate a rural digital twin containing rural entities and their corresponding semantic associations. This process eliminates time offsets and coordinate differences between data sources through cross-modal spatiotemporal alignment and uses semantic enhancement technology to map physical entities (such as farmland and roads) and their attributes (such as soil moisture and traffic flow) into nodes with unified representations. Simultaneously, a dynamically evolving semantic association network between nodes is constructed, enabling the digital twin to reflect changes in entity status and their mutual influences in real time. Based on this digital twin, the state changes of rural entities are continuously monitored. By mining temporal patterns, the correlations between entity states are identified, generating a composite event rule set containing triggering conditions and event propagation paths. Specifically, in flood disaster early warning scenarios, the temporal correlation between water level sensor data and road closure records is analyzed, identifying "continuous rise in river water level exceeding the warning line" as the triggering condition. The "intensity of the impact of water level change magnitude on the probability of traffic disruption" is quantified as an event propagation path, forming executable composite event rules. Furthermore, multi-step extrapolation is performed based on the event propagation path, generating a prediction result containing the event's development trajectory. This extrapolation process constructs a probabilistic event propagation model based on the causal strength quantified in the rule set, simulating the evolution path of the event state in multiple time steps using a random walk algorithm, outputting a predicted trajectory containing the states and probabilities of occurrence of key nodes. When the real-time data stream of the rural digital twin successfully matches the composite event rule set, the inference and prediction results are invoked. Combined with the AI ​​prediction model, decision optimization is performed to generate a decision plan, which is then driven by a task coordination engine to execute relevant resources. For example, the task coordination engine can dynamically assign flood control material dispatching tasks to emergency vehicles closest to the danger point and with transportation capabilities, achieving precise resource coordination. Finally, the task execution results and actual event development data are collected and compared with the inference and prediction results. Based on the comparison results, the semantic associations, composite event rule sets, and AI prediction model parameters within the rural digital twin are collaboratively optimized. This optimization process quantifies the deviation between the predicted trajectory and the actual trajectory at key nodes, simultaneously adjusting the semantic association weights, rule trigger thresholds, and model parameters to adapt the system to the dynamically changing rural environment.

[0089] Thus, this method achieves unified semantic understanding of multi-source heterogeneous data, eliminating the phenomenon of data silos; it reveals deep causal relationships between entity states through temporal pattern mining, supporting forward-looking inference of the evolution path of complex events; and the closed-loop optimization mechanism enables the decision-making system to have adaptive evolution capabilities, thereby solving to some extent the technical problems of insufficient data integration, lack of causal inference, and lack of feedback optimization in decision execution in rural management.

[0090] Specifically, in some of the embodiments described above in this application, a method for fusing multi-source heterogeneous data to generate a rural digital twin is proposed. However, in its implementation, due to the large modal differences and inconsistent spatiotemporal benchmarks among multi-source data such as IoT sensors, government systems, manual reporting terminals, and spatial geographic information systems, there is a lack of effective cross-modal alignment and semantic unification mechanisms. As a result, the generated digital twin cannot accurately depict the dynamic semantic relationships between rural entities, which in turn affects the accuracy of subsequent monitoring of changes in rural status and the reliability of event transmission paths.

[0091] To address this, this application further proposes a method for fusing multi-source heterogeneous data to generate a rural digital twin containing rural entities and their corresponding semantic relationships. (See [link to relevant documentation]). Figure 3 In order to execute the action, with the server as the executing entity, the following steps are included.

[0092] 301. Perform cross-modal spatiotemporal alignment and semantic enhancement processing on multi-source heterogeneous data. The processing includes extracting unified spatiotemporal and semantic features from each data source through a semantic encoder, eliminating modal differences based on cross-modal learning, and generating a semantically aligned standardized data stream.

[0093] 302. Based on a pre-defined rural entity knowledge graph, perform entity-relation joint extraction and disambiguation on standardized data streams to construct a dynamic semantic network with entities as nodes and multi-dimensional semantic associations with confidence weights as edges.

[0094] 303. Map the real-time monitoring data of entity status to the corresponding entity nodes in the dynamic semantic network to update the dynamic attributes of the entity nodes, and dynamically optimize the weight of semantic association in the dynamic semantic network based on the co-occurrence and causal patterns of entity status in the real-time data stream to obtain a rural digital twin.

[0095] Cross-modal spatiotemporal alignment and semantic enhancement refers to the process of unifying the representation of heterogeneous data sources. This can be achieved using deep learning-based feature alignment networks or cross-modal embedding mapping techniques. Specifically, it can extract spatiotemporal semantic features through a pre-trained multimodal encoder model. The aim is to eliminate the expression differences between different modalities such as text, images, and time series data, ensuring that the data is aligned in a unified semantic space. The semantic encoder can be understood as the core component of feature extraction. It can be implemented using generalized structures such as the Transformer architecture or convolutional neural networks. Specifically, it can train model parameters through self-supervised learning tasks. The aim is to abstract spatiotemporal context features related to rural entities from the original data. The dynamic semantic network can be understood as the topological structure of knowledge representation. It can be implemented using graph databases or in-memory graph computing frameworks. Specifically, it can store the dynamic state of entities through node attributes and record semantic association weights through edge attributes. The aim is to build a scalable rural entity relationship model to support subsequent real-time updates and inference.

[0096] Specifically, the proposed solution first performs cross-modal spatiotemporal alignment and semantic enhancement processing on multi-source heterogeneous data. A semantic encoder converts time-series data collected by IoT sensors, structured records from government systems, text descriptions from manually reported terminals, and vector information from spatial geographic information systems into feature vectors of a unified dimension, eliminating spatiotemporal reference offsets caused by heterogeneous data sources. Based on this, entity-relationship joint extraction and disambiguation are performed on the standardized data stream using a pre-defined rural entity knowledge graph. Domain knowledge constrains the entity recognition process, avoiding ambiguity issues in isolated data sources, and constructing a dynamic semantic network with entities as nodes and confidence weights quantifying multi-dimensional semantic associations. Subsequently, real-time monitoring data is mapped to network nodes to update dynamic attributes, and the weights of semantic association edges are dynamically adjusted based on the co-occurrence frequency and causal patterns of entity states in the data stream, ensuring the network continuously reflects the real-world association patterns between rural entities. This process, through a phased data fusion mechanism, ensures strict semantic consistency in the conversion process from raw heterogeneous data to a high-fidelity digital twin, avoiding feature fragmentation issues, while simultaneously achieving adaptive optimization of semantic associations.

[0097] As a specific implementation method, the solution of this application is implemented as follows: In the scenario of rural farmland environmental monitoring, the soil moisture time-series data collected by IoT sensors, the crop planting information recorded by the government system, the description of pests and diseases input by the manual reporting terminal, and the plot boundary data provided by the spatial geographic information system are first converted into a unified feature vector by a semantic encoder. Among them, the soil moisture data uses sliding window encoding to generate a spatiotemporal feature sequence, and the planting information extracts semantic features through a text embedding model. Subsequently, based on a preset agricultural knowledge graph, entity-relation extraction is performed on the standardized data stream to identify entity nodes such as "farmland plots", "crops", and "weather stations", and semantic association edges such as "located in" and "affected by climate" are established. At the same time, duplicate entities are merged through a disambiguation algorithm. Finally, the real-time updated soil moisture data is mapped to the corresponding farmland plot nodes. When soil moisture and meteorological data are detected to co-occur frequently in a continuous time series, the weight value of the "affected by climate" association edge is automatically increased, thereby dynamically optimizing the semantic network.

[0098] Through the above technical solutions, this application effectively solves the semantic inconsistency problem when constructing rural digital twins from multi-source heterogeneous data, ensuring the accurate representation of rural entities and their dynamic relationships by the digital twin. Specifically, the cross-modal spatiotemporal alignment mechanism eliminates modal differences between data sources, enabling heterogeneous data to be expressed in a unified semantic space; the entity-relationship joint extraction and disambiguation process utilizes domain knowledge to constrain the identification results, avoiding association errors caused by entity ambiguity; and the real-time update mechanism of the dynamic semantic network enables the semantic association weights to adapt to changes in the rural environment, thus providing a high-fidelity foundation for subsequent rural status monitoring and event propagation path deduction, significantly improving the practicality and reliability of digital twins in rural governance scenarios.

[0099] Specifically, in some of the embodiments described above in this application, a composite event rule set containing triggering conditions and event propagation paths is proposed to monitor changes in the state of rural entities and support event inference and prediction. However, in its implementation, the existing technology has difficulty in effectively identifying deep temporal causal relationships between entity states from multi-source heterogeneous time-series data of rural digital twins, resulting in the inability to accurately distinguish between statistical correlation and real causal relationship. This leads to fuzzy definitions of triggering conditions for composite event rules and distorted quantification of event propagation paths, ultimately affecting the reliability of inference and prediction results and the adaptability of decision-making plans.

[0100] To address this, this application further proposes a method based on rural digital twins to monitor state changes of rural entities, identify relationships between entity states through time-series pattern mining, and generate a composite event rule set containing triggering conditions and event propagation paths. (See [link to relevant documentation]). Figure 4 Taking the server as the executing entity as an example, the following steps are included.

[0101] 401. Based on the rural digital twin, extract the state time series data of rural entities and the semantic associations between entities to construct a multimodal time series graph. The node attributes contain the multi-dimensional state time series sequence of the entity, and the edge attributes contain the type and strength of the association between entities.

[0102] 402. Input the multimodal temporal graph into the temporal graph neural network (T-GNN) model, aggregate the spatiotemporal features of nodes and the neighborhood of the multimodal temporal graph through the message passing mechanism, and learn the dynamic representation vector of the node state.

[0103] 403. Based on dynamic representation vectors, a temporal causal discovery algorithm is used to analyze the temporal causal relationships between different entity state sequences, identify statistically significant causal event patterns, and calculate their causal strength and confidence.

[0104] 404. Abstract the causal event patterns in which the causal relationship strength exceeds the preset threshold and the confidence level meets the requirements into composite event rules.

[0105] Among them, the triggering condition of the compound event rule is defined by the causal event pattern, and the event transmission path of the compound event rule is quantitatively represented by the development trajectory of the result event and the corresponding causal intensity.

[0106] In practical applications, multimodal temporal graphs refer to data models that integrate temporal data of rural entity states with semantic associations into structured graph representations. These can be implemented using graph databases or temporal graph structures, aiming to uniformly represent the dynamic changes of entities and their interrelationships. The Temporal Graph Neural Network (T-GNN) model can be understood as a deep learning architecture specifically designed for processing temporal graph data. It can be implemented using variants of graph neural networks based on gated recurrent units or attention mechanisms, aiming to capture complex spatiotemporal dependency patterns between nodes. Specifically, dynamic representation vectors refer to high-dimensional feature representations of entity states learned through neural networks. These can be implemented using vector embedding techniques, aiming to condense... The system integrates information on the historical state of an entity and its neighborhood influence. Furthermore, time-series causal discovery algorithms can be understood as statistical inference methods used to identify causal relationships in time-series data. Specifically, they can be implemented using Granger causality tests or information-theoretic-based causal inference methods, aiming to distinguish between true causal associations and superficial correlations. Causal event patterns refer to statistically significant causal sequences of entity state changes, which can be obtained by filtering based on causal strength thresholds, aiming to provide quantifiable event association rules. Finally, composite event rules can be understood as decision rules abstracted from causal event patterns, specifically represented as a mapping relationship between triggering conditions and transmission paths, aiming to support event deduction and decision optimization.

[0107] Specifically, the proposed solution first extracts the state time-series data of rural entities and the semantic relationships between entities based on rural digital twins, constructing a multimodal time-series graph. This graph integrates discrete multi-source data into a unified topological structure, where node attributes record the multi-dimensional state change trajectory of entities, and edge attributes quantify the type and strength of the relationship. Then, the multimodal time-series graph is input into a time-series graph neural network (T-GNN) model. Through a message passing mechanism, the spatiotemporal features of the node itself and its neighborhood are aggregated to generate a dynamic representation vector of the node state. This vector integrates comprehensive information on the entity's historical state and the influence of network interactions. Next, a time-series causal discovery algorithm is applied based on the dynamic representation vector to analyze the time-series causal relationships between different entity state sequences. Reliable causal event patterns are identified through statistical significance testing, and their causal strength and confidence are quantified. Finally, causal event patterns with causal relationship strength exceeding a preset threshold and confidence levels meeting requirements are selected and abstracted into composite event rules. The triggering conditions are precisely defined by the causal event pattern, and the event propagation path is quantitatively represented by the development trajectory of the resulting event and the causal strength, thus forming a computable basis for event deduction.

[0108] As a specific implementation method, the solution of this application is implemented as follows: In the scenario of rural flood disaster early warning, the multimodal time series graph can be specifically constructed as a graph structure with meteorological stations, river water level sensors, and farmland humidity sensors as nodes. The node attributes include time series data sequences such as rainfall, water level, and soil moisture, and the edge attributes represent the geographical proximity or hydrological correlation strength. After processing the graph, the time series graph neural network (T-GNN) model outputs the dynamic representation vector of each node. The time series causal discovery algorithm identifies the causal event pattern of "continuous heavy rainfall leading to river water level rise and thus causing farmland waterlogging" based on these vectors, and calculates its causal strength and confidence. This pattern is abstracted into a composite event rule, and the triggering condition is defined as rainfall exceeding a preset threshold and lasting for a long time. The event transmission path is quantitatively represented as the dynamic correlation between the water level rise trend and the risk of waterlogging.

[0109] Through the above scheme, this application can accurately identify deep temporal causal relationships between entity states from multi-source heterogeneous time-series data of rural digital twins, effectively distinguish between statistical correlation and real causal relationship, make the triggering conditions of composite event rules clearly defined and the event transmission path accurately quantified, thereby improving the reliability of inference and prediction results and the adaptability of decision-making plans.

[0110] In some of the embodiments described above in this application, a temporal causal discovery algorithm based on dynamic representation vectors is proposed to analyze the temporal causal relationships between entity states in order to identify causal event patterns. However, in its implementation, directly applying the temporal causal discovery algorithm makes it difficult to ensure that the identified causal relationships are statistically significant, cannot effectively identify the direct causal driving relationship between entity states, and the quantification of causal strength and confidence is inaccurate, which leads to the risk of misjudgment in the generated composite event rule set, affecting the reliability of subsequent event inference and decision optimization.

[0111] To address this, this application further proposes a time-series causal discovery algorithm based on the aforementioned dynamic representation vector to analyze the time-series causal relationships between different entity state sequences, identify statistically significant causal event patterns, and calculate their causal strength and confidence level. This includes: mining causal relationships from the dynamic representation vector; selecting statistically significant candidate causal relationship pairs from entity nodes in the multimodal time-series graph based on a preset significance level threshold; identifying direct causal relationships between the selected candidate causal relationship pairs by introducing the observed state of the result entity in the candidate causal relationship pair in subsequent time series as an analysis proxy; quantifying the causal effect strength and confidence level of the direct causal driving relationship by calculating the predictive contribution of the result entity state to the result entity state to determine the causal strength; and generating causal event patterns with confidence levels based on repeated sampling to evaluate the confidence interval of the strength value.

[0112] Among them, the dynamic representation vector refers to the state representation generated by aggregating the spatiotemporal features of nodes through a time-series graph neural network. It can be implemented by embedding transformation of node attribute sequences or weighted aggregation of neighborhood features, with the aim of capturing the dynamic evolution law of entity state; the significance level threshold refers to the critical value used to judge the statistical reliability of causal association. It can be implemented by p-value test or Bayesian factor threshold, with the aim of filtering random noise interference in multimodal time-series graphs; the analytical surrogate refers to the time-series observation data used to distinguish direct causal relationships. It can be implemented by state snapshots within a sliding time window or lagged variable sequences, with the aim of eliminating the indirect path influence caused by mediating variables; the predictive contribution refers to the index that quantifies the degree of influence of the causal entity on the result entity. It can be implemented by feature importance scoring or gradient backpropagation attribution, with the aim of establishing the correlation between causal strength and predictive ability; and repeated sampling refers to the method of evaluating the stability of estimation through statistical resampling techniques. It can be implemented by bootstrap sampling or cross-validation resampling, with the aim of quantifying the uncertainty range of causal strength estimation.

[0113] Specifically, this application's scheme first mines causal relationships in dynamic representation vectors based on a preset significance level threshold. By setting strict statistical criteria to screen candidate causal association pairs, it effectively filters random noise and weak correlation interference in multimodal time series graphs, focusing on relationships between entities with practical significance. Subsequently, by introducing the observed states of the result entities in subsequent time series within candidate causal association pairs as analytical proxies, it identifies direct causal relationships. Utilizing the dynamic evolution characteristics of time series data, it accurately distinguishes between direct causal drivers and indirect path influences, avoiding erroneous attributions due to mediating variables in traditional methods. Based on this, it quantifies the causal effect strength by calculating the predictive contribution of the result entity's state to the result entity's state, ensuring that the strength calculation not only reflects correlation but also embodies actual predictive value. Finally, based on repeated sampling, it evaluates the confidence intervals of causal strength values, generating causal event patterns with confidence levels, providing a reliable statistical basis for setting thresholds for subsequent composite event rules.

[0114] As a specific implementation method, this application is implemented as follows in the environmental monitoring scenario of a rural digital twin: When monitoring rural water pollution events, the system first extracts the dynamic representation vectors of water quality sensor nodes and surrounding farmland nodes from the multimodal time series map, and selects candidate causal association pairs between statistically significant water quality parameters and crop growth status based on a significance level threshold of 0.05; then, it introduces the observation data of crop growth status in the subsequent three time series as an analysis proxy to identify the direct causal driving relationship between dissolved oxygen content in water and chlorophyll content in crops; by calculating the gradient contribution of dissolved oxygen change to chlorophyll prediction, the causal strength value is determined to be 0.78; finally, the bootstrap method is used to repeatedly sample 1000 times to evaluate the 95% confidence interval of this strength value as [0.72, 0.84], generating a causal event pattern of "water quality deterioration → crop yield reduction" with confidence.

[0115] Through the above technical solutions, this application ensures the statistical significance of causal association identification, effectively identifies the essential driving mechanism between entity states, achieves accurate quantification of causal strength and confidence, significantly reduces the risk of misjudgment of composite event rule sets, and thus improves the reliability of event inference and decision optimization in rural governance scenarios.

[0116] In some of the embodiments described above in this application, a multi-step extrapolation based on the event propagation path is proposed to generate extrapolation prediction results containing the event development trajectory. However, in this process, only the causal relationship between entity states can be identified, but the event evolution cannot be extrapolated in multiple time series. Furthermore, there is a lack of an evaluation mechanism for the stability and reliability of the prediction trajectory, which makes it difficult for the generated prediction results to accurately reflect the dynamic process of event development and to provide a reliable forward-looking basis for decision-making.

[0117] To address this, this application further proposes a multi-step extrapolation based on the event propagation path to generate extrapolation and prediction results that include the event's development trajectory. See [link to relevant documentation]. Figure 5 Taking the server as the executing entity as an example, the following steps are included.

[0118] 501. Based on the topological structure of the event propagation path and the quantified causal strength value on the path, construct a probabilistic event propagation graph. The nodes in the probabilistic event propagation graph represent potential event states, and the weight of the directed edges is defined by the causal strength value.

[0119] 502. Input the probabilistic event propagation graph into the time series simulation engine, and simulate the multi-step time series evolution of the event state on the probabilistic event propagation graph through the random walk algorithm based on the Monte Carlo method, generate a set of candidate event development trajectories containing multiple possible paths, and calculate the occurrence probability of each trajectory based on the cumulative path weight.

[0120] 503. Perform trajectory convergence analysis and screening on the candidate event development trajectory set, evaluate the robustness of the trajectory based on the stability of the causal strength value in the path, and screen the key trajectories with an occurrence probability higher than the preset probability and robustness that meet the preset conditions to obtain the inference and prediction results.

[0121] In practical applications, a probabilistic event propagation graph refers to transforming the event propagation path into a graph structure model with probabilistic characteristics. This can be implemented using a directed weighted graph data structure, where nodes represent potential event states and edge weights are quantified by causal strength values. Its purpose is to transform abstract causal relationships into a computable probabilistic model, avoiding the prediction rigidity problem caused by relying solely on static rules in traditional methods. The time-series simulation engine can be understood as a computational module specifically designed to simulate dynamic time-series processes. It can be implemented using an event-driven discrete-time simulation framework, aiming to provide a configurable environment for simulating multi-step time-series evolution of event states. Specifically, the Monte Carlo-based random walk algorithm uses random sampling techniques to simulate the evolution path of events on the probabilistic event propagation graph. This can be implemented using Markov chain Monte Carlo methods or importance sampling techniques. Its purpose is to effectively handle the uncertainty in event evolution through extensive random sampling, generating multiple possible development paths and calculating the cumulative probability of occurrence for each path. Trajectory convergence analysis and screening can be understood as the process of evaluating and selecting the quality of generated candidate trajectories. This can be achieved using a multi-threshold screening mechanism based on trajectory stability coefficient and occurrence probability. The purpose is to ensure the reliability and applicability of the final inference and prediction results in dynamic environments and avoid prediction deviations caused by data fluctuations or model errors.

[0122] Specifically, the proposed solution first constructs a probabilistic event propagation graph based on the topological structure of the event propagation path and quantified causal strength values, transforming abstract causal relationships into a structured probabilistic model. Then, this probabilistic event propagation graph is input into a time-series simulation engine, and a multi-step time-series evolution simulation is performed using a Monte Carlo-based random walk algorithm. A set of candidate event development trajectories containing multiple possible paths is generated through extensive random sampling, and the probability of occurrence of each trajectory is calculated. Finally, trajectory convergence analysis and screening are performed on the candidate event development trajectory set. The robustness of the trajectories is evaluated based on the stability of the causal strength values ​​in the paths, and key trajectories are selected in conjunction with probability thresholds. This phased processing flow ensures a complete technical chain from causal relationship modeling to dynamic evolution simulation to reliable trajectory selection, enabling the system to capture the multiple possibilities of event evolution in complex rural environments while guaranteeing the reliability and foresight of the prediction results.

[0123] As a specific implementation method, the solution of this application is implemented as follows: In the scenario of rural flood disaster early warning, firstly, a probabilistic event propagation graph is constructed based on historical monitoring data. Nodes represent different water level levels and the state of disaster-stricken areas, and the weight of directed edges is quantitatively defined by the causal strength between rainfall and water level changes. Then, the probabilistic event propagation graph is input into a time series simulation engine, and the Monte Carlo random walk algorithm is used to simulate possible water level change paths in the next 24 hours, generating a set containing multiple candidate development trajectories. Finally, convergence analysis is performed on these candidate trajectories, and the key trajectories with high probability of occurrence and good causal strength stability are selected as the final prediction results, which are used to guide the scheduling of flood control resources.

[0124] Through the above technical solutions, this application can realize multi-step temporal extrapolation of changes in the state of rural entities, effectively solving the technical problem that it can only identify causal relationships between entity states but cannot perform multi-step extrapolation of event evolution; at the same time, through trajectory convergence analysis and screening mechanism, the stability and reliability of the predicted trajectory are ensured, so that the generated extrapolation prediction results can accurately reflect the dynamic process of event development, providing a reliable forward-looking basis for rural governance decision-making.

[0125] Specifically, in some of the embodiments described above in this application, trajectory convergence analysis and screening of candidate event development trajectory sets are proposed to obtain inference and prediction results. However, in its implementation, screening based solely on occurrence probability and robustness may ignore the multi-dimensional characteristics of the trajectory, such as the topological importance of key nodes. Furthermore, the static screening model cannot dynamically adjust the weights based on historical accuracy, which may result in the selected key trajectories being inaccurate or not robust enough, affecting the reliability of subsequent decisions.

[0126] To this end, this application further proposes to perform trajectory convergence analysis and screening on the candidate event trajectory set, evaluate the robustness of the trajectory based on the stability of the causal strength value in the path, and screen key trajectories with an occurrence probability higher than a preset probability and robustness meeting preset conditions, to obtain the inference and prediction results, including:

[0127] Each trajectory in the candidate event development trajectory set is quantitatively evaluated in multiple dimensions. The comprehensive occurrence probability of the trajectory is calculated based on the cumulative causal strength of the path. The trajectory stability coefficient is calculated based on the variance of the causal strength values ​​of each node in the trajectory path. At the same time, the topological importance score of the key entity nodes passed through by the trajectory in the rural digital twin is determined.

[0128] The comprehensive occurrence probability, trajectory stability coefficient, and topological importance score of each trajectory are input into a multi-objective optimization screening model. Multi-objective collaborative optimization calculations are performed to screen out a subset of candidate trajectories that achieve non-dominated solutions in multiple evaluation dimensions, including occurrence probability, stability, and topological importance.

[0129] Based on historical projection accuracy data, the weight allocation of each evaluation dimension in the multi-objective optimization screening model is dynamically adjusted, and the convergence detection algorithm is used to determine the optimal key event development trajectory from the candidate trajectory subset as the projection prediction result.

[0130] In practical applications, the trajectory stability coefficient is a quantitative indicator that measures the stability of an event's trajectory in a dynamic environment. It can be implemented using statistical variance calculation methods, specifically by analyzing the fluctuation of causal strength values ​​of each node in the trajectory path. Its purpose is to capture the fluctuation characteristics of causal strength in temporal evolution and avoid misjudgments of stability caused by relying solely on the probability of occurrence. The topological importance score can be understood as the structural influence index of key entity nodes in the dynamic semantic network of the rural digital twin. It can be implemented using graph theory algorithms such as node degree centrality, proximity centrality, or betweenness centrality. Its purpose is to quantify the positional value of entities in the network and ensure that trajectories of high-influence nodes are given priority. Specifically, a non-dominated solution refers to a candidate trajectory in multi-objective optimization where no other solution is superior to it in all evaluation dimensions. It can be identified using the Pareto front search algorithm, aiming to balance conflicting optimization objectives and avoid overlooking trajectories with excellent performance in some dimensions through simple threshold screening. Furthermore, the convergence detection algorithm is a mechanism to verify the consistency of a trajectory in multiple iterations. It can be implemented based on the frequency of trajectory recurrence or path similarity thresholds, aiming to ensure the long-term reliability of the finally selected trajectory.

[0131] Specifically, the proposed solution first performs a multi-dimensional quantitative evaluation of each trajectory in the candidate event development trajectory set, obtaining the trajectory's comprehensive occurrence probability, trajectory stability coefficient, and topological importance score. The comprehensive occurrence probability reflects the overall likelihood through the cumulative path causality strength value, the trajectory stability coefficient quantifies dynamic environmental adaptability through variance calculation, and the topological importance score characterizes the structural value of key entity nodes. Subsequently, these evaluation indicators are input into a multi-objective optimization screening model, and collaborative optimization calculations are performed to screen a subset of non-dominated candidate trajectories that do not exhibit significant disadvantages in multiple dimensions of occurrence probability, stability, and topological importance, forming preliminary screening results. Based on this, the weight allocation of each evaluation dimension is dynamically adjusted according to historical projection accuracy data, enabling the screening mechanism to adaptively optimize based on actual prediction performance. Finally, a convergence detection algorithm is used to determine the optimal key event development trajectory from the candidate trajectory subset. By verifying the consistency of the trajectory in multiple projections, the long-term reliability of the projection prediction results is ensured, thus forming a complete trajectory screening closed loop.

[0132] As a specific embodiment, the solution of this application is implemented as follows: In a rural flood disaster early warning scenario, the system analyzes the set of candidate event development trajectories. The system calculates the comprehensive probability of occurrence for each trajectory and quantifies the overall probability by accumulating the causal strength value on the path. Simultaneously, it calculates the trajectory stability coefficient based on the variance of the causal strength value of each node in the trajectory path; if the variance is lower than a preset threshold, it is considered stable. The system also determines the topological importance score of key entity nodes such as reservoirs and rivers, and uses a node degree centrality algorithm to evaluate their connectivity influence in the dynamic semantic network. Subsequently, these indicators are input into a multi-objective optimization screening model to select a subset of candidate trajectories that achieve non-dominated solutions in terms of occurrence probability, stability, and topological importance. Based on historical projection accuracy data, the weights of each evaluation dimension are dynamically adjusted; for example, when historical data shows that topological importance has a significant impact on prediction accuracy, its weight ratio is increased. Finally, through a convergence detection algorithm, the optimal trajectory with a path similarity higher than a threshold in multiple projections is selected as the projection prediction result.

[0133] Through the above technical solution, this application achieves multi-dimensional and accurate screening of the development trajectory of candidate events, ensuring comprehensive optimization of the prediction results in terms of occurrence probability, stability and topological importance, effectively avoiding trajectory omission or misjudgment caused by single-dimensional screening, thereby improving the reliability and adaptability of subsequent decision-making plans.

[0134] In some of the embodiments described above in this application, the method of calling the inference and prediction results, combining them with the AI ​​prediction model for decision optimization, generating decision plans, and driving the execution of relevant resources through a task collaboration engine is proposed. However, in the implementation process, the decision input fails to effectively integrate the multi-dimensional dynamic influencing factors of the event development trajectory, and the resource allocation mechanism lacks adaptability to real-time state changes. This results in the generation of decision plans relying too much on static rules, making it impossible to achieve multi-objective collaborative optimization in concurrent event scenarios, thereby causing low resource scheduling efficiency and poor decision execution effect.

[0135] To address this, this application further proposes to utilize the inference and prediction results, combine them with AI prediction models for decision optimization, generate decision-making plans, and drive the execution of relevant resources through a task collaboration engine. See [link to relevant documentation]. Figure 6 Taking the server as the executing entity as an example, the following steps are included.

[0136] 601. Based on the event development trajectory and its probability of occurrence in the inference and prediction results, and combined with the dynamic status data of the affected entities in the rural digital twin, construct a multi-dimensional decision input vector. The multi-dimensional decision input vector includes the influence degree of key nodes of the trajectory, the real-time load status of resources, and environmental constraints.

[0137] 602. Input the multi-dimensional decision input vector into the AI ​​prediction model, simulate the execution effect of different decision strategies in concurrent event scenarios through multi-agent deep reinforcement learning algorithm, generate a set of alternative decision plans with the optimization objectives of maximizing comprehensive benefits, minimizing resource consumption and controllable risks, and calculate the expected utility value and confidence score for each decision plan.

[0138] 603. Based on the expected utility value and confidence score of each decision plan in the alternative decision plan set, the optimal decision plan is selected by Pareto front screening algorithm, and the optimal decision plan is parsed into an atomic task sequence by the task collaboration engine.

[0139] 604. Based on the real-time location, capability matching degree, and load balancing requirements in the resource dynamic profile, dynamically assign atomic tasks in the atomic task sequence to the optimal resource combination for execution. The resource dynamic profile is composed of the static attributes and dynamic indicators of the resource.

[0140] In practical applications, multi-dimensional decision input vectors refer to data structures that integrate the dynamic influencing factors of an event's trajectory. These can be implemented using vector space models or tensor representations, aiming to comprehensively capture multi-dimensional dynamic information such as the influence of key trajectory nodes, real-time resource load status, and environmental constraints, thus avoiding decision blind spots caused by a single data source. Meanwhile, multi-agent deep reinforcement learning algorithms can be understood as optimization mechanisms that simulate the interactive behaviors of multiple decision-making agents. They can be implemented based on Markov decision process frameworks or distributed Q-learning architectures, aiming to optimize overall benefits, resource consumption, and [other factors] through dynamic exploration of the policy space. Risk control and other multi-objective conflicts; specifically, the Pareto front screening algorithm is a multi-objective optimization method for identifying non-dominated solution sets. It can be implemented using evolutionary algorithms such as NSGA-II or MOEA / D. Its purpose is to screen decision plans that are not inferior in terms of expected utility value and confidence score, avoiding decision bias caused by prioritizing a single indicator; in addition, resource dynamic profiling can be understood as a comprehensive model representing the state of resources. It can be constructed by integrating a static attribute database and a dynamic indicator stream processor. Its purpose is to reflect the real-time location, capability matching degree and load balance status of resources, providing an adaptive basis for task assignment.

[0141] Specifically, the proposed solution first constructs a multi-dimensional decision input vector based on the event development trajectory and probability of occurrence in the prediction results, combined with the dynamic state data of the affected entities in the rural digital twin, so that the input vector fully covers the dynamic dimensions of the event's impact. Then, this vector is input into an AI prediction model, and a multi-agent deep reinforcement learning algorithm is used to simulate the execution effect of different decision strategies in concurrent event scenarios. This generates a set of alternative decision plans through multi-objective optimization and quantifies their expected utility value and confidence score. On this basis, the Pareto frontier screening algorithm is used to select the optimal decision plan from the set of alternative plans, and the task collaboration engine parses it into a sequence of executable atomic tasks. Finally, according to the real-time state parameters in the resource dynamic profile, the atomic tasks are dynamically assigned to the optimal resource combination for execution, forming a complete closed-loop process from decision input construction, strategy simulation optimization to task execution assignment. In this process, multi-dimensional decision input vectors provide comprehensive and dynamic basis for AI prediction models, multi-agent deep reinforcement learning algorithms generate high-quality contingency plans through multi-objective collaborative optimization, Pareto front screening mechanism ensures the balance of contingency plan selection, and the linkage between task collaboration engine and dynamic resource profile ensures the adaptability of task allocation to real-time state changes, thereby solving to some extent the problems of insufficient decision input fusion and rigid resource allocation.

[0142] As a specific implementation method, the solution of this application is implemented as follows: In rural flood control emergency scenarios, based on the flood evolution trajectory and its probability of occurrence in the simulation and prediction results, combined with dynamic data such as river water level and dam status in the rural digital twin, a multi-dimensional decision input vector is constructed, including the inundation impact of key nodes, the load status of rescue vehicles, and meteorological constraints. This vector is input into an AI prediction model, and a multi-agent deep reinforcement learning algorithm based on a distributed deep Q-network is used to simulate the execution effect of different evacuation route planning strategies under concurrent emergencies in multiple areas, generating a set of alternative decision plans including evacuation efficiency, material consumption, and safety risk assessment. The Pareto frontier screening algorithm is used to select the plan with the best comprehensive utility, and the task collaboration engine parses it into atomic task sequences such as personnel transfer and material allocation. Based on the dynamic profile of resources such as rescue vehicles and personnel (integrating vehicle GPS location, personnel skills and qualifications, and current task load), spatiotemporal accessibility calculation and capability matching degree evaluation are used to dynamically assign atomic tasks to the optimal resource combination for execution. For example, medical rescue tasks are preferentially assigned to mobile medical units with emergency rescue qualifications and nearby locations.

[0143] Through the above technical solutions, this application achieves the effective integration of decision input with multi-dimensional dynamic influencing factors of event development trajectory, and the adaptive adjustment of resource allocation mechanism to real-time state changes. Thus, in concurrent event scenarios, it achieves multi-objective collaborative optimization of comprehensive benefits, resource consumption and risk control, and significantly improves resource scheduling efficiency and decision execution effect.

[0144] In some of the embodiments described above in this application, atomic tasks are dynamically assigned based on the dynamic profile of resources to execute decision-making plans. However, in the implementation process, the existing assignment method lacks a fine-grained adaptation mechanism to the dynamic changes in resource status, and cannot effectively cope with the risks of abnormal resource status or task timeout, resulting in insufficient reliability of task execution and low resource utilization.

[0145] In this regard, this application further proposes the following steps:

[0146] The resource requirement attributes of each atomic task in the atomic task sequence are analyzed. The resource requirement attributes include task type, processing time requirements, required professional skills, and data access permissions.

[0147] The resource requirement attributes are matched with the real-time location, processing capacity, current load and skill qualifications recorded in the resource dynamic profile in a multi-dimensional matching degree calculation to generate an initial task resource matching scheme.

[0148] Based on the initial matching scheme for task resources, a dynamic task resource affinity matrix is ​​constructed. The matrix element values ​​of the dynamic task resource affinity matrix are jointly quantified and determined by the spatiotemporal reachability cost derived from the real-time location of the resource, the capability matching weight, and the load balancing coefficient.

[0149] A multi-objective constrained programming algorithm is used to solve the dynamic task resource affinity matrix, with the optimization objectives of maximizing overall execution efficiency and minimizing resource idle rate, to generate the optimal resource combination allocation scheme;

[0150] The optimal resource combination allocation scheme is executed through a real-time fault-tolerant scheduling engine, and resource status changes and task execution progress are continuously monitored during task execution.

[0151] When an abnormal resource status or task timeout risk is detected, the task is reassigned in real time based on a preset dynamic reconfiguration strategy.

[0152] Among them, resource requirement attributes refer to the resource condition framework required for task execution, which can be implemented using a task type classification system, a time constraint quantification model, and a skill qualification mapping table. Its purpose is to accurately characterize the essential requirements of the task to avoid resource mismatch. Multi-dimensional matching degree calculation can be understood as a quantitative method for comprehensively evaluating the adaptability of tasks and resources. Specifically, it can be implemented using a weighted vector similarity algorithm or a fuzzy matching rule engine, aiming to integrate spatiotemporal constraints with the feasibility of load state improvement solutions. The dynamic task-resource affinity matrix is ​​a mathematical model representing the task-resource adaptation relationship. Its matrix element values ​​can be based on a spatiotemporal reachability cost function, a capability matching degree scoring system, and a load balancing adjustment factor. Dynamic generation aims to transform abstract assignment logic into computable quantifiable relationships; multi-objective constrained programming algorithms can be understood as mathematical solutions that simultaneously optimize multiple conflicting objectives, specifically implemented using Pareto optimality search or linear weighted programming, with the goal of balancing task timeliness and resource utilization; real-time fault-tolerant scheduling engines refer to task execution monitoring systems with anomaly detection capabilities, which can be implemented based on event-driven architecture or microservice containerization technology, with the goal of continuously capturing execution deviations; dynamic reconfiguration strategies can be understood as a pre-defined set of task reallocation rules, specifically including anomaly detection thresholds, reconfiguration triggering conditions, and resource reallocation logic, with the goal of ensuring task continuity.

[0153] Specifically, the proposed solution uses the resource requirement attributes of atomic task sequences as the starting point for allocation, transforming key dimensions such as task type and timeliness requirements into structured inputs to accurately reflect the essential needs of the tasks. This structured input is then compared with a dynamic resource profile using multi-dimensional matching calculations. Dynamic indicators such as real-time location and load status are used to quantify the capability matching degree, generating an initial matching scheme. Based on this, a dynamic task-resource affinity matrix is ​​constructed, with matrix element values ​​determined by spatiotemporal reachability cost, capability matching weight, and load balancing coefficients, transforming the allocation logic into a quantifiable mathematical relationship. A multi-objective constrained programming algorithm is then used to solve this matrix, achieving a balance between overall execution efficiency and resource idle rate, generating the optimal allocation scheme. This scheme is executed by a real-time fault-tolerant scheduling engine and continuously monitors resource status and task progress, forming a dynamic perception closed loop. When resource anomalies or timeout risks are detected, task re-allocation is triggered in real-time based on a preset dynamic reconfiguration strategy, upgrading the allocation mechanism from static allocation to closed-loop adaptive allocation.

[0154] As a specific implementation method, the solution of this application is implemented as follows: The real-time fault-tolerant scheduling engine can be specifically implemented as a containerized scheduling system based on Kubernetes, which continuously monitors the CPU utilization, network latency, and task completion status indicators of resource nodes; the dynamic reconfiguration strategy can include an automatic task reassignment mechanism when the resource load continuously exceeds a preset threshold or the task progress lags behind the expected trajectory; in the rural flood control decision-making scenario, when it is detected that an emergency vehicle's location information is abnormal due to road damage, the system immediately calls the dynamic reconfiguration strategy to reassign the material transportation task originally assigned to the vehicle to nearby available rescue teams, and dynamically adjusts the task path planning according to real-time traffic data.

[0155] Through the above scheme, this application realizes the refined adaptation of the task assignment mechanism to the dynamic changes in resource status, effectively responds to the risks of abnormal resource status or task timeout, significantly improves the reliability of task execution and resource utilization, and ensures the stable execution of decision-making plans in complex rural environments.

[0156] In practical applications, when existing technologies optimize the semantic associations, composite event rule sets, and AI prediction model parameters within a rural digital twin based on comparison results, adjusting parameters in only a single dimension or optimizing individual components in isolation may lead to a decline in overall system performance, conflicts in the optimization process, or low convergence efficiency. There is a lack of a multi-objective collaborative mechanism that can coordinate semantic association weights, rule triggering conditions, and model parameters to ensure the balance of the optimization process and the stability of the system.

[0157] To address this, this application further proposes a collaborative optimization of the semantic relationships within the rural digital twin, the composite event rule set, and the parameters of the AI ​​prediction model based on the comparison results. (See [link to relevant documentation]). Figure 7 Taking the server as the executing entity as an example, the following steps are included:

[0158] 701. Based on the comparison results, quantify the multi-dimensional error index between the inference and prediction results and the actual development data of the event. The multi-dimensional error index includes the timing deviation of the event occurrence, the difference in the state of key nodes, and the consistency of the event transmission path.

[0159] 702. Identify error sources based on the multi-dimensional error indicators, generate a weight adjustment strategy for the semantic association, a trigger condition correction strategy for the composite event rule set, and an update strategy for the AI ​​prediction model parameters, forming an optimization target list;

[0160] 703. Using the aforementioned list of optimization objectives as input, construct a multi-objective collaborative optimization function, wherein the multi-objective collaborative optimization function simultaneously constrains the adjustment range of semantic association weights, the correction range of compound event rule triggering conditions, and the update step size of AI prediction model parameters;

[0161] 704. Solve the multi-objective collaborative optimization function using the Pareto front search algorithm to generate a set of equilibrium optimization parameters, which includes new weights for semantic association, new thresholds for the composite event rule set, and new parameters for the model.

[0162] 705. An incremental learning mechanism is used to apply the balanced optimization parameter set to the dynamic semantic network of the rural digital twin, the composite event rule set, and the AI ​​prediction model, dynamically updating the semantic association weights, rule thresholds, and model parameters.

[0163] Among them, the multi-dimensional error index refers to a quantitative system used to comprehensively evaluate the difference between the predicted results and the actual data. It can be implemented using statistical methods or machine learning models, aiming to capture the spatiotemporal characteristics and structural differences of prediction bias, avoiding the limitations of single-index evaluation. The optimization target list can be understood as a unified task list integrating semantic association adjustment, rule correction, and model update requirements. Specifically, it can be generated based on error contribution analysis, aiming to integrate scattered optimization targets into collaborative tasks, ensuring that the optimization directions of each component are consistent. The multi-objective collaborative optimization function refers to a mathematical expression that simultaneously constrains multiple optimization objectives. It can be constructed using weighted summation or constraint optimization methods, aiming to prevent system oscillations and ensure the controllability of the optimization process by setting adjustment boundaries. The Pareto front search algorithm can be understood as a multi-objective optimization solution mechanism, specifically implemented using evolutionary algorithms or mathematical programming methods, aiming to find the optimal balance between semantic association accuracy, rule triggering reliability, and model prediction accuracy. The balanced optimization parameter set refers to a set containing optimized parameter values, which can be represented as a parameter vector or configuration file, aiming to provide a complete parameter scheme for system updates. Incremental learning mechanism refers to the method of gradually updating the model without forgetting historical knowledge. Specifically, it can be implemented by online learning or fine-tuning technology. Its purpose is to ensure the continuity and adaptability of system updates and avoid performance fluctuations caused by full updates.

[0164] Specifically, the proposed solution first quantifies multi-dimensional error indicators between the predicted results and the actual development data of the event, comprehensively capturing the spatiotemporal characteristics and structural differences of the prediction deviation. Then, based on these error indicators, it identifies the sources of error, generates adjustment strategies for semantic association weights, rule triggering conditions, and model parameters, and forms a unified list of optimization objectives. On this basis, it constructs a multi-objective collaborative optimization function that simultaneously constrains the adjustment magnitude of each optimization objective, and solves this function using the Pareto front search algorithm to generate a balanced optimization parameter set. Finally, it employs an incremental learning mechanism to apply the optimized parameter set to various components of the system, achieving dynamic parameter updates. This complete closed-loop process from error quantification to parameter updates ensures that semantic association adjustment, rule correction, and model updates are mutually responsive, avoiding global performance degradation caused by local optimization. Simultaneously, by constraining the adjustment magnitude and using Pareto optimization, it guarantees the controllability and balance of the optimization process.

[0165] As a specific implementation method, the solution of this application is implemented in the rural flood control scenario as follows: When an abnormal increase in rainfall is detected, the system generates flood projection prediction results based on the rural digital twin; after the actual event occurs, the actual water level data is collected and compared with the projection prediction results; based on the comparison results, multi-dimensional error indicators such as event occurrence time sequence deviation, key node state differences, and event transmission path consistency are quantified; based on these error indicators, the inaccurate estimation of river siltation is identified as the main source of error; adjustment strategies for the semantic weights associated with river entities and water levels, correction strategies for flood warning rule triggering conditions, and update strategies for hydrological prediction model parameters are generated; a multi-objective collaborative optimization function is constructed and a balanced optimization parameter set is generated by solving it; finally, the semantic association weights, warning rule thresholds, and prediction model parameters in the digital twin are updated through an incremental learning mechanism.

[0166] Through the above technical solution, this application can coordinate the optimization process of semantic association weights, rule triggering conditions and model parameters, avoid the system imbalance caused by single-dimensional adjustment, ensure the balance of the optimization process and the stability of the system, thereby improving the adaptability and decision-making accuracy of the rural digital twin system in dynamic environments.

[0167] In some of the embodiments described above in this application, a collaborative optimization of the semantic associations, composite event rule sets, and AI prediction model parameters within the rural digital twin is proposed based on the comparison results. However, in its implementation, the error source identification lacks a precise tracing mechanism, and the generated optimization strategy draft has the risk of cross-strategy effect conflicts or mutual cancellation, which makes it impossible to achieve multi-objective balanced adjustment of the optimization parameter set, affecting the stability and reliability of the overall optimization effect.

[0168] To address this, this application further proposes identifying error sources based on multi-dimensional error indicators, generating weight adjustment strategies for semantic associations, trigger condition correction strategies for composite event rule sets, and parameter update strategies for AI prediction models, forming an optimization target list, including:

[0169] Based on multi-dimensional error indicators, error source analysis is performed on the dynamic semantic network of the rural digital twin. The directed graph traversal algorithm searches in the reverse direction of the event propagation path in the prediction results to locate key entity nodes and semantic association edges whose contribution to the multi-dimensional error indicators exceeds the preset threshold, and calculates the specific contribution value of each key entity node and semantic association edge.

[0170] Based on the calculated contribution values, draft strategies for adjusting weights for semantic associations, correcting trigger conditions for composite event rule sets, and updating AI prediction model parameters are generated. Among them, the weight adjustment magnitude of semantic association edges is positively correlated with the contribution values ​​calculated on the association edges. The correction amount of the trigger conditions for composite event rules is determined by quantifying the state error of the key entity nodes involved in the rule. The update direction of AI prediction model parameters is determined by the deviation gradient between the model prediction output and the actual event development data at the key nodes.

[0171] Cross-strategy synergy tests were conducted on the generated draft weight adjustment strategy, draft trigger condition correction strategy, and draft model parameter update strategy. The test process included: simulating the system state changes after the execution of each strategy draft in a dynamic semantic network, and detecting whether there are effect conflicts or mutual cancellations between different strategy drafts.

[0172] The multi-objective Pareto optimization method is used to redistribute the weights and recalibrate the adjustment magnitudes of conflicting policy drafts, generating a list of optimization objectives.

[0173] In practical applications, error source analysis refers to the process of locating the source of error on a dynamic semantic network. This can be achieved using directed graph traversal algorithms such as depth-first search or breadth-first search, aiming to accurately identify key nodes and edges that significantly contribute to prediction errors. The contribution value quantifies the impact of key entity nodes and semantically related edges on multi-dimensional error indicators. Its calculation can be based on error propagation models or sensitivity analysis methods, aiming to provide a quantitative basis for strategy generation. The draft weight adjustment strategy refers to a preliminary adjustment plan for the weights of semantically related edges. The adjustment range can be proportional to the contribution value, aiming to ensure that weight adjustments strictly match the degree of error impact. The draft trigger condition correction strategy refers to a preliminary correction plan for the trigger conditions of composite event rules. The correction amount can be linearly or nonlinearly quantized based on the state error of key entity nodes, with the aim of improving rule adaptability; the draft model parameter update strategy refers to the preliminary update plan for the parameters of the AI ​​prediction model, and its update direction can be determined by gradient descent based on the bias gradient, with the aim of guiding the parameters to optimize in the direction of reducing prediction error; cross-policy synergy test refers to the process of verifying the compatibility of different policy drafts, which can be implemented in a simulation environment and the system state changes can be observed, with the aim of discovering policy conflicts in advance; the multi-objective Pareto optimization method refers to the method of finding non-dominated solution sets among multiple optimization objectives, which can be implemented using evolutionary algorithms such as NSGA-II, with the aim of balancing the adjustment of semantic association weights, rule thresholds and model parameters.

[0174] Specifically, the proposed solution first performs error source analysis on a dynamic semantic network based on multi-dimensional error indicators. A directed graph traversal algorithm is used to reverse-search and locate key entity nodes and semantically related edges along the event propagation path, and their contribution values ​​are calculated. Subsequently, three types of policy drafts are generated based on the contribution values: the adjustment magnitude of the semantically related weight adjustment policy draft is positively correlated with the contribution value; the correction amount of the composite event rule triggering condition correction policy draft is determined based on the quantification of the state error of key entity nodes; and the update direction of the AI ​​prediction model parameter update policy draft is determined by the deviation gradient at key nodes. Next, the system state changes after the policy drafts are executed are simulated in the dynamic semantic network to detect whether there are effect conflicts between different policy drafts. Finally, a multi-objective Pareto optimization method is used to redistribute weights and calibrate the adjustment magnitudes of conflicting policy drafts, generating a list of optimization objectives. The entire process, through a closed-loop mechanism of error source tracing, policy generation, collaborative verification, and optimization calibration, ensures that each optimization stage is closely connected, thereby achieving multi-objective balanced adjustment.

[0175] As a specific implementation method, in a rural flood disaster early warning scenario, when a significant deviation is found between the predicted river water level and the actual event development data, the system performs error source analysis on a dynamic semantic network. Using a depth-first search algorithm, it traces the event propagation path backward to locate a semantic association edge with a high contribution value between the water level monitoring node and the dam status node. Based on this contribution value, the system generates a draft strategy for adjusting the semantic association weight, increasing the weight of this edge by 15%; simultaneously, it generates a draft strategy for correcting the triggering conditions of the composite event rules, adjusting the water level threshold from 5 meters to 4.8 meters; and it generates a draft strategy for updating the AI ​​prediction model parameters, adjusting the weight coefficients of relevant features. Subsequently, the system tests the draft strategies in a simulation environment and finds a slight conflict between the weight adjustment and rule correction. Using a multi-objective Pareto optimization method, the adjustment magnitude is redistributed, ultimately generating a list of optimization objectives, increasing the semantic association weight by 12%, and adjusting the water level threshold to 4.9 meters.

[0176] Through the above technical solutions, this application achieves precise collaborative optimization of the rural digital twin, the composite event rule set, and the AI ​​prediction model. The error tracing mechanism accurately locates key error sources, avoiding blind adjustments; the strategy draft generation mechanism ensures that the adjustment magnitude strictly matches the degree of error impact; cross-strategy synergy testing identifies strategy conflicts in advance; and the multi-objective Pareto optimization method achieves a balance between multi-dimensional optimization objectives. Ultimately, the optimized parameter set can reliably and stably improve the prediction accuracy and decision-making effectiveness of the rural digital twin system, solving the optimization failure problems caused by inaccurate error location and strategy conflicts during the optimization process.

[0177] All of the above-mentioned optional technical solutions can be combined in any way to form the optional embodiments of this application, and will not be described in detail here.

[0178] Figure 8 This is a schematic diagram of the structure of a rural smart management platform provided in an embodiment of this application. See also... Figure 8 The platform includes:

[0179] The acquisition module 801 is used to acquire multi-source heterogeneous data from IoT sensors, government systems, manual reporting terminals, and geospatial information systems. It then fuses this multi-source heterogeneous data to generate a rural digital twin containing rural entities and their corresponding semantic relationships.

[0180] The monitoring module 802 is used to monitor the state changes of rural entities based on the rural digital twin, identify the correlation between entity states through time-series pattern mining, and generate a composite event rule set containing triggering conditions and event propagation paths. Based on the event propagation paths, multi-step inference is performed to generate inference and prediction results containing the event development trajectory.

[0181] The matching module 803 is used to match the real-time data stream of the rural digital twin with the composite event rule set. When a match is successful, the inference and prediction results are invoked, and the AI ​​prediction model is combined to optimize the decision, generate a decision plan, and drive the execution of relevant resources through the task collaboration engine.

[0182] The optimization module 804 is used to collect task execution results and actual event development data, compare them with the inference and prediction results, and perform collaborative optimization of semantic associations, composite event rule sets and AI prediction model parameters in the rural digital twin based on the comparison results.

[0183] It should be noted that the rural smart management platform provided in the above embodiments is only illustrated by the division of the above functional modules when processing data. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer equipment can be divided into different functional modules to complete all or part of the functions described above. In addition, the rural smart management platform and the data processing method embodiments provided in the above embodiments belong to the same concept, and their specific implementation process can be found in the method embodiments, which will not be repeated here.

[0184] Figure 9This is a schematic diagram of a server structure provided in an embodiment of this application. The server 900 can vary significantly due to different configurations or performance. It may include one or more Central Processing Units (CPUs) 901 and one or more memories 902. The one or more memories 902 store at least one computer program, which is loaded and executed by the one or more processors 901 to implement the methods provided in the various method embodiments described above. Of course, the server 900 may also have wired or wireless network interfaces, a keyboard, and input / output interfaces for input and output. The server 900 may also include other components for implementing device functions, which will not be elaborated upon here.

[0185] In an exemplary embodiment, a computer-readable storage medium is also provided, such as a memory including a computer program that can be executed by a processor to perform the data processing method described above. For example, the computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a compact disc read-only memory (CD-ROM), magnetic tape, floppy disk, and optical data storage device, etc.

[0186] In an exemplary embodiment, a computer program product or computer program is also provided, which includes program code stored in a computer-readable storage medium. The processor of a computer device reads the program code from the computer-readable storage medium and executes the program code, causing the computer device to perform the data processing method described above.

[0187] In some embodiments, the computer program involved in the present application embodiments may be deployed and executed on a computer device, or executed on multiple computer devices located in one location, or executed on multiple computer devices distributed in multiple locations and interconnected through a communication network. Multiple computer devices distributed in multiple locations and interconnected through a communication network may constitute a blockchain system.

[0188] Those skilled in the art will understand that all or part of the steps of the above embodiments can be implemented by hardware or by a program instructing related hardware. The program can be stored in a computer-readable storage medium, such as a read-only memory, a disk, or an optical disk.

[0189] The above are merely optional embodiments of this application and are not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A data processing method, characterized by, The method includes: Acquire multi-source heterogeneous data from IoT sensors, government systems, manual reporting terminals, and geospatial information systems; perform fusion processing on the multi-source heterogeneous data to generate a rural digital twin containing rural entities and corresponding semantic associations; Based on the rural digital twin, the state time-series data of rural entities and the semantic associations between entities are extracted to construct a multimodal time-series graph. Node attributes contain multi-dimensional state time-series sequences of entities, and edge attributes contain the type and strength of associations between entities. The multimodal time-series graph is input into a time-series graph neural network model. Through a message passing mechanism, the spatiotemporal features of nodes and the neighborhood of the multimodal time-series graph are aggregated to learn dynamic representation vectors of node states. Based on these dynamic representation vectors, a time-series causal discovery algorithm is used to analyze the time-series causal associations between different entity state sequences, identify statistically significant causal event patterns, and calculate their causal strength and confidence. Causal event patterns whose causal relationship strength exceeds a preset threshold and whose confidence meets the requirements are abstracted into composite event rules, resulting in a set of composite event rules containing triggering conditions and event propagation paths. The triggering conditions of the composite event rules are defined by the causal event patterns, and the event propagation paths of the composite event rules are quantified by the development trajectory of the resulting event and the corresponding causal strength. Based on the event propagation path, a multi-step deduction is performed to generate a deduction and prediction result containing the event development trajectory; The real-time data stream of the rural digital twin is matched with the composite event rule set. When a match is successful, a multi-dimensional decision input vector is constructed based on the event development trajectory and its probability of occurrence in the inference and prediction results, combined with the dynamic state data of the affected entities in the rural digital twin. The multi-dimensional decision input vector includes the influence degree of key trajectory nodes, real-time resource load status, and environmental constraints. The multi-dimensional decision input vector is input into an AI prediction model, and the execution effect of different decision strategies in concurrent event scenarios is simulated through a multi-agent deep reinforcement learning algorithm to maximize comprehensive benefits and resource consumption. With the optimization objectives of minimizing resource consumption and ensuring risk controllability, a set of alternative decision plans is generated, and the expected utility value and confidence score of each decision plan are calculated. Based on the expected utility value and confidence score of each decision plan in the set of alternative decision plans, the optimal decision plan is selected using the Pareto front screening algorithm, and the optimal decision plan is parsed into a sequence of atomic tasks using a task collaboration engine. According to the real-time location, capability matching degree, and load balancing requirements in the resource dynamic profile, the atomic tasks in the atomic task sequence are dynamically assigned to the optimal resource combination for execution. The resource dynamic profile is composed of the static attributes and dynamic indicators of the resources. Collect task execution results and actual event development data, compare them with the inference and prediction results, and perform collaborative optimization of the semantic associations in the rural digital twin, the composite event rule set, and the parameters of the AI ​​prediction model based on the comparison results.

2. The method of claim 1, wherein, The process of fusing the multi-source heterogeneous data to generate a rural digital twin containing rural entities and corresponding semantic associations includes: The multi-source heterogeneous data is subjected to cross-modal spatiotemporal alignment and semantic enhancement processing. The processing includes extracting unified spatiotemporal and semantic features from each data source through a semantic encoder, eliminating modal differences based on cross-modal learning, and generating a semantically aligned standardized data stream. Based on a pre-defined rural entity knowledge graph, entity-relationship joint extraction and disambiguation are performed on the standardized data stream to construct a dynamic semantic network with entities as nodes and multi-dimensional semantic associations with confidence weights as edges. The real-time monitoring data of the entity status is mapped to the corresponding entity node in the dynamic semantic network to update the dynamic attributes of the entity node. Based on the co-occurrence and causal patterns of the entity status in the real-time data stream, the weight of the semantic association in the dynamic semantic network is dynamically optimized to obtain the rural digital twin.

3. The method according to claim 1, characterized in that, Based on the dynamic representation vector, a temporal causal discovery algorithm is used to analyze the temporal causal relationships between different entity state sequences, identify statistically significant causal event patterns, and calculate their causal strength and confidence, including: Causal correlation mining is performed on the dynamic representation vector, and candidate causal correlation pairs with statistical significance are selected from the entity nodes of the multimodal time series graph based on a preset significance level threshold. The selected candidate causal relationship pairs are directly causally identified. By introducing the observed state of the result entity in the candidate causal relationship pair in subsequent time series as an analysis agent, the direct causal driving relationship between the entity states is identified. The causal effect strength and confidence level of the direct causal driving relationship are quantified by calculating the predictive contribution of the cause entity state to the effect entity state, and the causal strength is determined. Based on repeated sampling, the confidence interval of the intensity value is evaluated, and a causal event pattern with confidence level is generated.

4. The method according to claim 1, characterized in that, The multi-step deduction based on the event propagation path to generate a deduction and prediction result containing the event development trajectory includes: Based on the topology of the event propagation path and the quantified causal strength value on the path, a probabilistic event propagation graph is constructed. The nodes in the probabilistic event propagation graph represent potential event states, and the weights of the directed edges are defined by the causal strength value. The probabilistic event propagation graph is input into the time series simulation engine. Through a random walk algorithm based on the Monte Carlo method, the multi-step time series evolution of the event state on the probabilistic event propagation graph is simulated to generate a set of candidate event development trajectories containing multiple possible paths. The probability of occurrence based on the cumulative path weight is calculated for each trajectory. The candidate event trajectory set is subjected to trajectory convergence analysis and screening. The robustness of the trajectory is evaluated based on the stability of the causal strength value in the path. Key trajectories with a probability of occurrence higher than a preset probability and robustness meeting preset conditions are selected to obtain the inference and prediction results.

5. The method according to claim 4, characterized in that, The process involves performing trajectory convergence analysis and screening on the candidate event trajectory set, evaluating the robustness of the trajectory based on the stability of the causal strength value in the path, and screening key trajectories with an occurrence probability higher than a preset probability and robustness meeting preset conditions to obtain the inference and prediction results, including: A multi-dimensional quantitative evaluation is performed on each trajectory in the candidate event development trajectory set. The comprehensive occurrence probability of the trajectory is calculated based on the cumulative path causal strength. The trajectory stability coefficient is calculated based on the variance of the causal strength values ​​of each node in the trajectory path. At the same time, the topological importance score of the key entity nodes passed by the trajectory in the rural digital twin is determined. The comprehensive occurrence probability, trajectory stability coefficient, and topological importance score of each trajectory are input into a multi-objective optimization screening model. Multi-objective collaborative optimization calculations are performed to screen out a subset of candidate trajectories that achieve non-dominated solutions in multiple evaluation dimensions, including occurrence probability, stability, and topological importance. Based on historical projection accuracy data, the weight allocation of each evaluation dimension in the multi-objective optimization screening model is dynamically adjusted, and the optimal key event development trajectory is determined from the candidate trajectory subset using a convergence detection algorithm as the projection prediction result.

6. The method according to claim 1, characterized in that, The step of dynamically assigning atomic tasks in the atomic task sequence to the optimal resource combination for execution based on the real-time location, capability matching degree, and load balancing requirements in the resource dynamic profile includes: The resource requirement attributes of each atomic task in the atomic task sequence are analyzed. The resource requirement attributes include task type, processing time requirements, required professional skills, and data access permissions. The resource requirement attributes are matched with the real-time location, processing capacity, current load and skill qualifications recorded in the resource dynamic profile in a multi-dimensional matching degree calculation to generate an initial task resource matching scheme. Based on the initial matching scheme for task resources, a dynamic task resource affinity matrix is ​​constructed. The matrix element values ​​of the dynamic task resource affinity matrix are jointly quantified and determined by the spatiotemporal reachability cost derived from the real-time location of the resource, the capability matching weight, and the load balancing coefficient. A multi-objective constrained programming algorithm is used to solve the dynamic task resource affinity matrix, with the optimization objectives of maximizing overall execution efficiency and minimizing resource idle rate, to generate the optimal resource combination allocation scheme; The optimal resource combination allocation scheme is executed through a real-time fault-tolerant scheduling engine, and resource status changes and task execution progress are continuously monitored during task execution. When an abnormal resource status or task timeout risk is detected, the task is reassigned in real time based on a preset dynamic reconfiguration strategy.

7. The method according to claim 1, characterized in that, The step of collaboratively optimizing the semantic associations within the rural digital twin, the composite event rule set, and the parameters of the AI ​​prediction model based on the comparison results includes: Based on the comparison results, a multi-dimensional error index is quantified between the inference and prediction results and the actual development data of the event. The multi-dimensional error index includes the event occurrence time sequence deviation, the difference in the state of key nodes, and the consistency of the event transmission path. Based on the multi-dimensional error indicators, the sources of error are identified, and a weight adjustment strategy for the semantic association, a trigger condition correction strategy for the composite event rule set, and an update strategy for the AI ​​prediction model parameters are generated to form an optimization target list. Using the aforementioned list of optimization objectives as input, a multi-objective collaborative optimization function is constructed. This multi-objective collaborative optimization function simultaneously constrains the adjustment range of semantic association weights, the correction range of compound event rule triggering conditions, and the update step size of AI prediction model parameters. The multi-objective collaborative optimization function is solved by Pareto front search algorithm to generate a set of equilibrium optimization parameters, which includes new weights for semantic association, new thresholds for composite event rule sets, and new parameters for the model. An incremental learning mechanism is used to apply the balanced optimization parameter set to the dynamic semantic network of the rural digital twin, the composite event rule set, and the AI ​​prediction model, dynamically updating the semantic association weights, rule thresholds, and model parameters.

8. A smart rural management platform, characterized in that, The platform includes: The acquisition module is used to acquire multi-source heterogeneous data from IoT sensors, government systems, manual reporting terminals, and spatial geographic information systems; and to perform fusion processing on the multi-source heterogeneous data to generate a rural digital twin containing rural entities and corresponding semantic associations. The monitoring module is used to extract the state time-series data of rural entities and the semantic relationships between entities based on the rural digital twin, and construct a multimodal time-series graph. Node attributes contain multi-dimensional state time-series sequences of entities, and edge attributes contain the type and strength of relationships between entities. The multimodal time-series graph is input into a time-series graph neural network model, which aggregates the spatiotemporal features of nodes and the neighborhood of the multimodal time-series graph through a message passing mechanism to learn dynamic representation vectors of node states. Based on these dynamic representation vectors, a time-series causal discovery algorithm is used to analyze the time-series causal relationships between different entity state sequences. The system identifies statistically significant causal event patterns and calculates their causal strength and confidence level. Causal event patterns whose causal relationship strength exceeds a preset threshold and whose confidence level meets the requirements are abstracted into composite event rules, resulting in a set of composite event rules containing triggering conditions and event propagation paths. The triggering conditions of the composite event rules are defined by the causal event patterns, and the event propagation paths of the composite event rules are quantified by the development trajectory of the resulting event and its corresponding causal strength. Based on the event propagation paths, multi-step deduction is performed to generate deduction and prediction results containing the event development trajectory. The matching module is used to match the real-time data stream of the rural digital twin with the composite event rule set. When a match is successful, based on the event development trajectory and its probability of occurrence in the inference and prediction results, and combined with the dynamic state data of the affected entities in the rural digital twin, a multi-dimensional decision input vector is constructed. The multi-dimensional decision input vector includes the influence degree of key trajectory nodes, real-time resource load status, and environmental constraints. The multi-dimensional decision input vector is input into the AI ​​prediction model, and the execution effect of different decision strategies in concurrent event scenarios is simulated through a multi-agent deep reinforcement learning algorithm to maximize comprehensive benefits. With the optimization objectives of minimizing resource consumption and ensuring risk controllability, a set of alternative decision plans is generated, and the expected utility value and confidence score of each decision plan are calculated. Based on the expected utility value and confidence score of each decision plan in the set of alternative decision plans, the optimal decision plan is selected using the Pareto front screening algorithm, and the optimal decision plan is parsed into a sequence of atomic tasks using a task collaboration engine. According to the real-time location, capability matching degree, and load balancing requirements in the resource dynamic profile, the atomic tasks in the atomic task sequence are dynamically assigned to the optimal resource combination for execution. The resource dynamic profile is composed of the static attributes and dynamic indicators of the resources. The optimization module is used to collect task execution results and actual event development data, compare them with the inference and prediction results, and perform collaborative optimization of the semantic associations in the rural digital twin, the composite event rule set, and the parameters of the AI ​​prediction model based on the comparison results.

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