Natural resource data checking and cascading updating method and device driven by AI (Artificial Intelligence) and based on rule engine

Abstract

The invention discloses an AI-driven natural resource data checking and cascading updating method and device based on a rule engine, and the method comprises the steps: decoupling a process into four technical components: 1) a field operator library which provides an atomized checking and updating function; the rule engine serves as a configuration and analysis center of a configuration rule; 3) an AI auxiliary module which assists a user in generating a process framework and rule parameters; and 4) a process executor which is responsible for driving the process, scheduling operators and managing transactions. According to the method, an AI auxiliary module assists a user in defining a process template and generating rule parameters, and the rule parameters are written into a rule engine; and the process executor starts the process and starts a transaction, requests rule configuration from the rule engine according to steps, calls the operator library to execute operation, runs through the process of'checking-updating-re-checking ', and ensures the atomicity of cascade updating through a transaction mechanism. According to the invention, decoupling of execution and logic is realized, and the definition threshold of business rules is obviously reduced.

Description

Technical Field

[0001] This invention relates to the field of data processing, and more specifically, to an AI-driven method and apparatus for inspecting and cascading updating natural resource data based on a rule engine. Background Technology

[0002] The inspection and updating of natural resource data is a core aspect of data governance. Current technologies mainly face the following technical bottlenecks: 1) Traditional check update logic is either "hard-coded" in business application software or relies on complex rule engines (such as Drools). The former makes it difficult to adjust business rules (such as check thresholds); the latter, although it achieves decoupling, has an extremely high barrier to entry for writing rules (such as DRL scripts), and business experts cannot define and maintain them themselves.

[0003] 2) Fragmented processes and cumbersome configuration. The "checking" and "updating" processes for data are often separate. Business personnel need to manually build a complex workflow containing multiple steps (such as "check A first, then update B, then check C"), which is cumbersome to configure and prone to errors.

[0004] 3) Lack of intelligent assistance. Faced with a massive number of inspection operators and complex business processes, non-technical users find it difficult to build a scientific and efficient "inspect-update-re-inspect" process framework, and also find it difficult to describe their business needs with precise rule language.

[0005] 4) Cascading updates are difficult to implement. Natural resource data is highly interconnected. An update to one piece of data (such as the repair of plot A) often requires triggering a chain of updates to neighboring plot B or related table C (i.e., cascading updates). Existing technologies struggle to automate and handle such complex cascading effects in a transactional manner. Summary of the Invention

[0006] Based on the analysis of existing technologies, there is an urgent need in this field for a method that can decouple "execution" from "logic," allowing business personnel to define rule parameters and process frameworks in a low-threshold manner with the assistance of AI, and automatically connect the complete "check-update" process to achieve intelligent cascading updates.

[0007] This invention provides an AI-driven method for natural resource data inspection and cascading updates based on a rule engine, aiming to solve the problems of high rule configuration threshold, rigid process construction, lack of AI assistance, and difficulty in achieving automated cascading updates in existing technologies.

[0008] The core idea of ​​this invention is to decouple the "check-update" process into four core technical components: 1. Domain operator library: as the bottom layer, providing atomic check and update functions; 2. Rule engine: as the middle layer, responsible for storing and parsing configurable rules; 3. AI-assisted module: as the middle layer, assisting in the generation of process framework and rule parameters; 4. Process executor: as the top-level scheduler, driving process execution and managing database transactions.

[0009] Based on the above architecture, the first aspect of this invention provides an AI-driven method for natural resource data inspection and cascading update based on a rule engine, comprising: Responding to the user's high-level natural language intent, an AI-based process framework containing multiple nodes is automatically generated; For at least one node in the process framework, the user's natural language input is converted into parameterized configuration rules based on AI. The configuration rules are used to define the calling parameters of operators in the domain operator library and are stored in the rule configuration manager of the rule engine. The domain operator library is used to provide atomic checking and updating functions. Based on the generated process framework and parameterized configuration rules, operators in the domain operator library are invoked to initiate database transactions and check and cascade update the natural resource data.

[0010] In one implementation, in response to the user's high-level natural language intent, an AI-generated process framework containing multiple nodes is automatically generated, including: Based on the understanding of the user's high-level natural language intent and combined with a pre-trained natural resource data governance model, a process framework containing multiple nodes is automatically generated. The process framework includes nodes such as topology check, topology repair, attribute cascade update, and post-consistency check.

[0011] In one implementation, the domain operator library includes inspection operators and update operators, wherein the inspection operators include topology inspection operators and attribute inspection operators, and the update operators include topology correction operators and association update operators.

[0012] In one implementation, for at least one node in the process framework, AI is used to convert the user's natural language input into parameterized configuration rules, including: For nodes clicked by users and receiving natural language input from users, the system uses a large language model to understand user intent, automatically queries the domain operator library, and generates parameterized configuration rules.

[0013] In one implementation, the rule engine also includes a rule parser for retrieving parameterized configuration rules for the current node from the rule configuration manager, parsing them into a structured configuration object, and returning it to the process executor.

[0014] In one implementation, operators from the domain operator library are invoked according to the generated process framework and parameterized configuration rules to initiate a database transaction, and natural resource data is inspected and cascaded updated, including: Based on the nodes in the process framework, a request is sent to the rule parser of the rule engine so that the rule parser can retrieve the parameterized configuration rule of the current node based on the request and parse it into a structured configuration object and return it. Based on the structured configuration object, atomic operators in the domain operator library are invoked to perform operations.

[0015] In one implementation, based on the structured configuration object, invoking atomic operators from the domain operator library to perform operations includes: Determine if the current node is a successor node. If not, continue sending requests to the rule parser of the rule engine and perform corresponding operations based on the structured configuration object returned by the rule engine. If it is a successor node, initiate transaction commit or transaction rollback based on the execution result of the successor node.

[0016] Based on the same inventive concept, a second aspect of the present invention provides an AI-driven, rule-engine-based natural resource data inspection and cascading update device, comprising: The process framework generation module is used to respond to the user's high-level natural language intent and automatically generate a process framework containing multiple nodes based on AI. The configuration rule generation module is used to convert the user's natural language input into parameterized configuration rules based on AI for at least one node in the process framework. The configuration rules are used to define the calling parameters of operators in the domain operator library and are stored in the rule configuration manager of the rule engine. The domain operator library is used to provide atomic checking and updating functions. The inspection and cascading update module is used to call operators in the domain operator library according to the generated process framework and parameterized configuration rules, start database transactions, and inspect and cascade update natural resource data.

[0017] Based on the same inventive concept, a third aspect of the present invention provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the rule engine-based natural resource data inspection and cascading update method of the first aspect.

[0018] Based on the same inventive concept, a fourth aspect of the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, it implements the AI-driven rule engine-based natural resource data inspection and cascading update method of the first aspect. Compared with the prior art, the advantages and beneficial technical effects of the present invention are as follows: This invention provides an AI-driven, rule-engine-based method for inspecting and cascading updates of natural resource data. First, responding to the user's high-level natural language intent, an AI-generated process framework containing multiple nodes is automatically generated. Then, for at least one node in the process framework, the user's natural language input is converted into parameterized configuration rules using AI. Finally, based on the generated process framework and the parameterized configuration rules, operators from a domain operator library are invoked, initiating a database transaction to inspect and cascade updates the natural resource data. In this way, this invention decouples "process orchestration" (executor), "rule definition" (rule engine), and "atomic operations" (operator library) and introduces a transaction mechanism, realizing a secure and automated "inspect-update-re-inspect" cascading update process configured with AI assistance. This solves the problems of high rule configuration thresholds, rigid process construction, lack of AI assistance, and difficulty in achieving automated cascading updates in existing technologies. Attached Figure Description

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

[0020] Figure 1 This is a flowchart of an AI-driven, rule-engine-based natural resource data inspection and cascading update method in an embodiment of the present invention. Figure 2 This is a diagram illustrating the overall framework of the AI-driven, rule-engine-based natural resource data inspection and cascading update method in this invention. Figure 3 This is a flowchart of the "through-cascade" execution mechanism of the "process executor" module in this embodiment of the invention. Detailed Implementation

[0021] Example 1 This embodiment provides an AI-driven, rule-engine-based method for natural resource data inspection and cascading updates. Please refer to [link to relevant documentation]. Figure 1 ,include: S1: Responds to the user's high-level natural language intent and automatically generates a process framework containing multiple nodes based on AI.

[0022] S1 is executed by the AI-assisted module and includes: Based on an understanding of the user's high-level natural language intent and combined with a pre-trained natural resource data governance model, a process framework containing multiple nodes is automatically generated.

[0023] S2: For at least one node in the process framework, the user's natural language input is converted into parameterized configuration rules based on AI. The configuration rules are used to define the calling parameters of operators in the domain operator library and are stored in the rule configuration manager of the rule engine. The domain operator library is used to provide atomic checking and updating functions.

[0024] S2 is executed by the AI-assisted module, including: For nodes clicked by users and receiving natural language input from users, the system uses a large language model to understand user intent, automatically queries the domain operator library, and generates parameterized configuration rules.

[0025] S3: Based on the generated process framework and parameterized configuration rules, call the operators in the domain operator library, start database transactions, and check and cascade update the natural resource data.

[0026] S3 is executed by the process executor and includes: Based on the nodes in the process framework, a request is sent to the rule parser of the rule engine so that the rule parser can retrieve the parameterized configuration rule of the current node based on the request and parse it into a structured configuration object and return it. Based on the structured configuration object, atomic operators in the domain operator library are invoked to perform operations.

[0027] In practice, the operators in the domain operator library include check operators and update operators. Data cascading updates are achieved by calling relevant operators within a transaction. Specifically, during the execution of operations using atomic operators in the domain operator library, if the previous operator succeeds, the process executor receives the returned "modified facts" and then, based on the generated process framework, passes these "modified facts" as input to the next node. When the process reaches a post-check node, it determines whether to commit or rollback the database transaction based on the check results.

[0028] Specifically, the core idea of ​​this invention is to decouple the "inspection-update" process (inspection and cascading updates of natural resources) into four core technical components: 1. Domain Operator Library: The underlying layer, responsible for performing specific operations, such as topology checking, attribute updating, cascading repair, etc. Each operator is an atomic functional unit, providing atomic checking and updating functionality.

[0029] 2. Rule Engine: The middle layer, responsible for defining which operators to call and what parameters to use based on the configuration file (YAML / JSON), serving as the configuration and parsing center for configurable rules.

[0030] 3. AI-Assisted Module: This middleware layer assists non-technical business personnel in generating process frameworks and rule parameters. Technical personnel can also write their own process frameworks and rules.

[0031] 4. Process Executor: Top level, responsible for driving process execution, scheduling operators and managing transactions.

[0032] The following sections provide a detailed introduction to these four parts. First, the two main static core components, the operator library and the rule engine, are defined. Then, it explains how the AI-assisted module and the process executor dynamically collaborate to implement the method of this invention. For details, please refer to [link to relevant documentation]. Figure 2 .

[0033] Content 1: Domain Operator Library This invention's domain operator library is specifically designed to adapt to natural resource data (such as vectors, raster data, and topological relationships). The library's responsibility is to encapsulate atomic functions (Operators) and configurable parameters (Params), allowing business users to define rules through parameter configuration. The operator library can be divided into inspection operators and update operators. Specifically: a) Checking operators: The core of this type of operator is to return "True / False" or other data information. Its parameters are mainly used to define "what is an error".

[0034] Check_Topology (topology checking operator): Encapsulates algorithms for checking topological relationships such as overlap, gap, and sliver between patches.

[0035] Check_Attribute (Attribute Check Operator): Encapsulates an algorithm for checking whether a field value conforms to its value range, logic, and spatiotemporal relationships.

[0036] b) Update operators: The core of this type of operator is "to perform the modification", and its parameters are mainly used to define "how to fix" or "how to update".

[0037] Correct_Topology (topology correction operator): Encapsulates algorithms for automatically correcting topological errors, such as merging fragments and automatically trimming overlapping parts.

[0038] Update_Attribute (attribute update operator): encapsulates the algorithm for updating the attribute value of the current feature.

[0039] Update_Related (Associated Update Operator): Encapsulates the attributes of updated attribute association calculations and other levels of associated features.

[0040] Example table of parameters for check class operators

[0041] Content 2: Rule Engine The core innovation of this invention lies in the configurability of the rule engine. Users do not need to write complex rule judgment code; they only need to write judgment logic based on the parameters of the operator class (Content 1).

[0042] Specifically, the rule engine of this method is a parameterized rule configuration and parsing center. Its core responsibility is to manage and parse the configuration rules used to invoke operators and provide them to "Content 4: Process Executor" (described later). Its core components and functions are as follows: a) Rule Configuration Manager. This is the storage core of the rule engine. It acts as a central repository for configurable rule scripts (such as YAML / JSON format). It provides an interface that allows the "Content 3: AI Assist Module" (described later) or user interface to define, store, and manage these rules. An example of a YAML script for configuring rules in a data inspection and update process is as follows: Rule_Name: "Treat small areas of broken glass" # 1. Parameters corresponding to "checking" operators Check_Operator: "Check_Topology" Check_Params: type: "Sliver" max_area_threshold: 5.0 # 2. Parameters corresponding to "update class" operators Update_Operator: "Correct_Topology" Update_Params: strategy: "Merge_Largest_Neighbor" b) Rule Parser. This is the core logic component of the rule engine. When "Content 4: Process Executor" (described later) reaches a node (e.g., [Step 1: Topology Check]) and sends a request to the rule engine, the parser performs the following operations: Retrieve the corresponding rule script from the library of (a) based on the node ID; Parse the script (YAML / JSON) and convert it into a structured "configuration object" for operator execution; This "configuration object" is returned to the "process executor," which then calls the operator and the configuration object to perform operator calculations.

[0043] Example table of update class operator parameters

[0044] Content 3: AI Assisted Module The AI-assisted module (including a process framework generation module and a configuration-based rule generation module) addresses the issues of "fragmented processes, cumbersome configuration," and "high barriers to entry and lack of intelligent assistance" in the background technology. This module is responsible for generating process frameworks and rule parameters for users.

[0045] Step S1: AI-assisted process framework generation. This invention provides an AI-assisted module that, based on an understanding of the senior management's intent (e.g., "I want to perform data entry checks"), and combined with a pre-trained best practice model for natural resource data governance, automatically recommends a default process framework containing multiple steps for the business personnel. The AI-recommended framework follows a closed-loop logic of "check-update-recheck," as shown in the following example: [Step 1: Topology Check] -> [Step 2: Topology Repair] -> [Step 3: Attribute Cascade Update] -> [Step 4: Post-Consistency Check] This method also allows business users to define or modify the nodes and order of the process framework based on the recommended framework. This process framework is persistently stored as a process template (e.g., Workflow_T001) to ensure that the process framework can be reused in the future.

[0046] Step S2, AI-assisted rule parameter generation. At each key node of the process framework generated in S1, the AI-assisted module is responsible for converting the user's natural language intent into the parameter configuration required by "Content 2: Rule Engine".

[0047] When a business user clicks on a node (e.g., [Step 1: Topology Check]) and receives the user's natural language input (e.g., "I want to merge all fragments smaller than 5 square meters"), the large language model understands the intent, automatically queries the operator library API in (Content 1), and generates a "parameterized rule script" (parameterized configuration rules).

[0048] AI writes this configuration script (e.g., { "Operator": "Check_Topology", "Params": {"type":"Sliver", "max_area_threshold": 5.0}}) into the "Rule Configuration Manager" of "Component Two: Rule Engine" and associates it with the corresponding node of "Process Template" (Step 1 of Workflow_T001).

[0049] Content 4: Process Executor The method of this invention uses a process executor to connect the entire process. It is responsible for coordinating the invocation of the rule engine and operator library to achieve automated cascading updates with transactional characteristics. Specifically, it includes the following steps: Step S3, the execution and transaction mechanism of "through-cascading". Please refer to [link / reference]. Figure 3 The specific steps for implementing this mechanism include: Step S301: The executor starts and initiates a transaction. The process executor starts the process framework defined in S1 or the framework template selected by the business personnel, first running to the [Step 1: Topology Check] node. At the same time, the process executor initiates a database transaction, and all subsequent data modification operations of update operators (such as Correct_Topology, Update_Related) will be executed within this transaction.

[0050] Step S302, the executor requests rules. The process executor sends a request to "Content 2: Rule Engine": "Please give me the configuration for 'Step 1'".

[0051] Step S303, the engine parses the rules. The rule parser of the rule engine retrieves the script from [Step 1] from the rule configuration manager, parses it into a structured configuration object (e.g., { Operator: "Check_Topology", Params: ...}), and returns it to the process executor.

[0052] Step S304, the executor invokes the operator. The process executor receives the configuration object, invokes the operator in "Content 1: Operator Library" based on the Operator name (Check_Topology), and passes its Params({...}) as parameters. The "process executor" captures the execution result of the operator (e.g., "success", "failure", or the returned "modified facts").

[0053] Step S305 implements connection and cascading. If the operator execution in S304 fails (e.g., "Pre-topology check" fails), the "process executor" immediately aborts the process and jumps to S307 (transaction rollback); if the operator (e.g., the Correct_Topology operator) executes successfully and returns a "modified fact" (e.g., Fact_B_New), the "process executor" receives this fact and, according to the process framework defined in S1, passes it as input to the next node (e.g., [Step 2: Topology Repair]). The process executor repeats steps S302 to S304.

[0054] Step S306, Post-Check and Decision. When the process reaches the final "post-check" node (e.g., [Step 4: Post-Consistency Check]), the process executor calls the Check_Attribute operator. If the "post-check" fails (e.g., cascading updates cause the total metric to exceed the limit), the process executor immediately aborts the process and jumps to S307 (transaction rollback). If all nodes have successfully completed, the process enters S307 (transaction commit).

[0055] Step S307: Commit or rollback the transaction.

[0056] Transaction Commit: If the process (as described in S306) is executed completely and all "post-checks" pass, the process executor initiates a "Transaction Commit" (COMMIT) instruction to atomically and permanently save all data changes (including initial updates and all cascading updates) generated by all "update-type" operators since the start of the transaction in S301.

[0057] Transaction rollback: If the process is aborted in S305 or S306 due to the failure of any operator execution or the failure of the "post-check", the process executor initiates a "transaction rollback" (ROLLBACK) instruction, which automatically undoes all data modifications performed by update operators during this execution, ensuring that the database is restored to the state before the process execution and guaranteeing data consistency.

[0058] Regardless of whether a transaction is committed or rolled back, the "process templates" and "rule definitions" stored in S1 and S2 remain unaffected and are always persisted, waiting to be invoked again.

[0059] In this way, the present invention decouples the "process orchestration" (executor), "rule definition" (rule engine) and "atomic operation" (operator library), and introduces a transaction mechanism to realize a secure and automated "check-update-recheck" cascading update process configured with AI assistance.

[0060] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of this invention. The following example, using the cascading update of "fragment repair" and "associated parcel status" of a specific land parcel as an illustration, will explain in detail how the four core components (Contents 1 to 4) of this invention work collaboratively.

[0061] The objective of this embodiment is first to examine the "Land Category Pieces" layer, identify all fragments with an area less than 5 square meters, and automatically merge them into the adjacent piece with the largest area. Secondly, it performs cascading updates. Since the repair (topology change) of land category patches affects the integrity of parcel data, the "Parcel Status" field of all parcel elements associated with the repaired patches must be automatically updated to "Requires Review." Finally, a post-check is performed to ensure that after all repairs and updates are completed, no patch area becomes 0. The specific implementation process includes the following steps: 1. System Architecture and Component Preparation Reference Figure 2 (Method architecture diagram), the system in this embodiment includes: Content 1: Domain Operator Library: The Check_Topology, Correct_Topology, Update_Related, and Check_Attribute operators are predefined.

[0062] Content 2: Rule Engine: Includes a rule configuration manager and a rule parser.

[0063] Content 3: AI Assistance Module: For business personnel.

[0064] Content 4: Process Executor: Responsible for driving the entire process.

[0065] 2. AI-assisted configuration (steps S1 and S2) Step S1: The AI-assisted process framework generates an intent from a business user (non-technical user) to "Content 3: AI-assisted module": "I need to repair land parcel fragmentation and update the status of related land parcels." The AI-assisted module understands this intent and, based on the best practice model for natural resource data governance, automatically recommends a four-step process framework (stored as the process template Workflow_T002): [Node 1: Fragmentation Inspection] -> [Node 2: Fragmentation Repair] -> [Node 3: Parcel Status Cascade Update] -> [Node 4: Area Validity Post-Check] In step S2, AI assists business personnel in generating rule parameters or allows business personnel to configure parameters for each node of the process framework generated in S1. These parameter configurations (YAML / JSON) are written into the rule configuration manager of "Content 2: Rule Engine".

[0066] For the rule (Rule_001) of [Node 1: Fragmentation Inspection]: The business user inputs: "Find all fragmented areas smaller than 5 square meters." The AI ​​generates a configuration script, which is then linked to the Check_Topology operator. Check_Operator: "Check_Topology" Check_Params: type: "Sliver" max_area_threshold: 5.0 For the rule (Rule_002) of [Node 2: Fragment Repair]: The user inputs: "Merge the fragmentation into its largest neighbor convex plate." The AI ​​generates a configuration script, which is then linked to the Correct_Topology operator. Update_Operator: "Correct_Topology" Update_Params: strategy: "Merge_Largest_Neighbor" The rule (Rule_003) for [Node 3: Parcel Status Cascade Update] is as follows: The business user inputs: "Update the status of the associated land parcel to 'Requires Review'." AI generates a configuration script, which is then linked to the Update_Related (cascade core) operator. Update_Operator: "Update_Related" Update_Params: relation_type: "Contains" # Assuming the land parcel "contains" land use type patches target_layer: "Land Parcel Surface" update_expression: "SET Parcel Status = 'Requires Review'" For the rule (Rule_004) regarding [Node 4: Area Validity Post-Check]: AI or technical personnel configuration, associated with the Check_Attribute operator: Check_Operator: "Check_Attribute" Check_Params: field: "area of ​​the map patch" logic_expression: ">0" 3. Execution of “Connection-Cascade” (Step S3) Step S3: Business personnel activate the stored workflow template Workflow_T002. "Content 4: Workflow Executor" begins execution, and references... Figure 3 (Diagram of the "through-cascade" execution mechanism of process executors).

[0067] Step S301: Start and initiate a transaction. The process executor starts and runs to [Node 1: Fragmentation Check]. Simultaneously, a BEGIN TRANSACTION command is sent to the database to initiate a database transaction.

[0068] Steps S302-S304, execute node 1. The executor requests the configuration of [node 1] from "Content 2: Rule Engine". The rule parser of the rule engine retrieves and returns the configuration object of Rule_001. The executor calls the Check_Topology operator in "Content 1: Operator Library" and passes in the parameters {type: "Sliver", max_area_threshold: 5.0}. The operator is executed and returns a fact after the operator execution (e.g., Fact_Slivers_Found = [ID_001, ID_002]).

[0069] Step S305, execute node 2. The executor receives the post-execution facts Fact_Slivers_Found from node 1 and, according to the process framework, passes it to [node 2: fragment repair]. The executor repeats S302-S304, requesting the configuration of Rule_002 from the rule engine. The executor calls the Correct_Topology operator, passing in {strategy: "Merge_Largest_Neighbor"} and the list of IDs in Fact_Slivers_Found. The operator executes, modifying parcel IDs_001 and ID_002 in the current transaction (in memory or a temporary table), such as merging them into neighbor ID_003. The operator returns the new post-execution facts Fact_Parcels_Affected = [Parcel_A, Parcel_B], i.e., the list of affected parcels.

[0070] Step S305, cascading update, execute node 3. The executor receives the new post-execution fact Fact_Parcels_Affected returned by the operator and passes it to [Node 3: Parcel Status Cascading Update]. The executor repeats S302-S304, requesting the configuration of Rule_003 from the rule engine. The executor calls the Update_Related operator, passing in parameters (such as target_layer: "Parcel Table") and Fact_Parcels_Affected. The operator executes, updating the status of Parcels Parcel_A and Parcel_B to "Requires Review" in the current transaction. The cascading update is now complete.

[0071] Step S306, Post-check and Decision, Execute Node 4. The process executes to the final [Node 4: Area Validity Post-check]. The executor repeats S302-S304, requesting the configuration of Rule_004. The executor calls the Check_Attribute operator to check whether the area of ​​all affected patches is >0.

[0072] Step S307: Transaction Commit or Rollback. If the Check_Attribute operator of [Node 4] returns "Success" (all checks passed), the process executor initiates a "Transaction Commit" (COMMIT) instruction. The database permanently saves all changes: ID_001 and ID_002 are merged, and the status of Parcel_A and Parcel_B is updated to "Requires Review"; if the check of [Node 4] returns "Failure" (for example, a repair algorithm error causes the area of ​​ID_003 to become 0), the process executor immediately aborts and initiates a "Transaction Rollback" (ROLLBACK) instruction. The database automatically undoes all modifications executed: the merge of ID_001 and ID_002 is rolled back, and the status of Parcel_A and Parcel_B is restored to its original state. Data consistency is guaranteed.

[0073] This embodiment solves the problems of high threshold for rule configuration and fragmented processes by using an "AI-assisted module"; it automates the "check-update-recheck" process by combining a "process executor" and a "rule engine"; and it securely and atomically implements complex cascading updates through the Update_Related operator and transaction mechanism.

[0074] Example 2 Based on the same inventive concept, this embodiment discloses an AI-driven, rule-engine-based natural resource data inspection and cascading update device, comprising: The process framework generation module is used to respond to the user's high-level natural language intent and automatically generate a process framework containing multiple nodes based on AI. The configuration rule generation module is used to convert the user's natural language input into parameterized configuration rules based on AI for at least one node in the process framework. The configuration rules are used to define the calling parameters of operators in the domain operator library and are stored in the rule configuration manager of the rule engine. The domain operator library is used to provide atomic checking and updating functions. The inspection and cascading update module is used to call operators in the domain operator library according to the generated process framework and parameterized configuration rules, start database transactions, and inspect and cascade update natural resource data.

[0075] Among them, the process framework generation module and the configuration rule generation module belong to the AI-assisted module, while the inspection and cascading update module belongs to the process executor.

[0076] Since Embodiment 2 of this invention uses the same apparatus as the AI-driven rule engine-based natural resource data inspection and cascading update method described in Embodiment 1, those skilled in the art can understand the specific structure and variations of this apparatus based on the method introduced in Embodiment 1, and therefore will not elaborate further here. All apparatuses used in the method of Embodiment 1 of this invention fall within the scope of protection of this invention.

[0077] Example 3 Based on the same inventive concept, the present invention also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the AI-driven rule engine-based natural resource data inspection and cascading update method of Embodiment 1.

[0078] Since the computer-readable storage medium described in Embodiment 3 of this invention is the same computer-readable storage medium used in implementing the AI-driven rule engine-based natural resource data inspection and cascading update method in Embodiment 1 of this invention, those skilled in the art can understand the specific structure and variations of this computer-readable storage medium based on the method described in Embodiment 1 of this invention, and therefore will not be repeated here. All computer-readable storage media used in the method of Embodiment 1 of this invention fall within the scope of protection of this invention.

[0079] Example 4 Based on the same inventive concept, the present invention also provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the method described in Embodiment 1.

[0080] Since the computer device described in Embodiment 4 of this invention is the same computer device used to implement the AI-driven rule engine-based natural resource data inspection and cascading update method in Embodiment 1 of this invention, those skilled in the art can understand the specific structure and variations of this computer device based on the method described in Embodiment 1 of this invention, and therefore will not be repeated here. All computer devices used in the method of Embodiment 1 of this invention fall within the scope of protection of this invention.

[0081] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0082] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0083] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various modifications and variations to the embodiments of the invention without departing from the spirit and scope of the invention. Thus, if these modifications and variations of the embodiments of the invention fall within the scope of the claims of the invention and their equivalents, the invention also intends to include these modifications and variations.

Claims

1. An AI-driven method for inspecting and cascading updates of natural resource data based on a rule engine, characterized in that, include: Responding to the user's high-level natural language intent, an AI-based process framework containing multiple nodes is automatically generated; For at least one node in the process framework, the user's natural language input is converted into parameterized configuration rules based on AI. The configuration rules are used to define the calling parameters of operators in the domain operator library and are stored in the rule configuration manager of the rule engine. The domain operator library is used to provide atomic checking and updating functions. Based on the generated process framework and parameterized configuration rules, operators in the domain operator library are invoked to initiate database transactions and check and cascade update the natural resource data.

2. The AI-driven, rule-engine-based method for inspecting and cascading updating natural resource data as described in claim 1, characterized in that, Responding to the user's high-level natural language intent, an AI-based process framework containing multiple nodes is automatically generated, including: Based on the understanding of the user's high-level natural language intent and combined with a pre-trained natural resource data governance model, a process framework containing multiple nodes is automatically generated. The process framework includes nodes such as topology check, topology repair, attribute cascade update, and post-consistency check.

3. The AI-driven, rule-engine-based method for inspecting and cascading updating natural resource data as described in claim 1, characterized in that, The domain operator library includes inspection operators and update operators. The inspection operators include topology inspection operators and attribute inspection operators, and the update operators include topology correction operators and association update operators.

4. The AI-driven, rule-engine-based method for inspecting and cascading updating natural resource data as described in claim 1, characterized in that, For at least one node in the aforementioned process framework, AI is used to convert the user's natural language input into parameterized configuration rules, including: For nodes clicked by users and receiving natural language input from users, the system uses a large language model to understand user intent, automatically queries the domain operator library, and generates parameterized configuration rules.

5. The AI-driven, rule-engine-based method for inspecting and cascading updating natural resource data as described in claim 1, characterized in that, The rules engine also includes a rule parser, which retrieves the parameterized configuration rules for the current node from the rule configuration manager, parses them into a structured configuration object, and returns it to the process executor.

6. The AI-driven, rule-engine-based method for inspecting and cascading updating natural resource data as described in claim 5, characterized in that, Based on the generated process framework and parameterized configuration rules, operators in the domain operator library are invoked to initiate database transactions, and natural resource data is inspected and cascaded updated, including: Based on the nodes in the process framework, a request is sent to the rule parser of the rule engine so that the rule parser can retrieve the parameterized configuration rule of the current node based on the request and parse it into a structured configuration object and return it. Based on the structured configuration object, atomic operators in the domain operator library are invoked to perform operations.

7. The AI-driven, rule-engine-based method for inspecting and cascading updating natural resource data as described in claim 6, characterized in that... Based on the structured configuration object, operations are performed by invoking atomic operators from the domain operator library, including: Determine if the current node is a successor node. If not, continue sending requests to the rule parser of the rule engine and perform corresponding operations based on the structured configuration object returned by the rule engine. If it is a successor node, initiate transaction commit or transaction rollback based on the execution result of the successor node.

8. An AI-driven, rule-engine-based device for inspecting and cascading updating natural resource data, characterized in that, include: The process framework generation module is used to respond to the user's high-level natural language intent and automatically generate a process framework containing multiple nodes based on AI. The configuration rule generation module is used to convert the user's natural language input into parameterized configuration rules based on AI for at least one node in the process framework. The configuration rules are used to define the calling parameters of operators in the domain operator library and are stored in the rule configuration manager of the rule engine. The domain operator library is used to provide atomic checking and updating functions. The inspection and cascading update module is used to call operators in the domain operator library according to the generated process framework and parameterized configuration rules, start database transactions, and inspect and cascade update natural resource data.

9. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the AI-driven rule engine-based natural resource data inspection and cascading update method as described in any one of claims 1 to 7.

10. A computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the AI-driven rule engine-based natural resource data inspection and cascading update method as described in any one of claims 1 to 7.

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