An insurance claim settlement and verification system and method based on a rule engine and an intelligent model

Abstract

An insurance claim settlement and verification system and method based on a rule engine and an intelligent model, wherein the system comprises a settlement module, a screening module, a rule determination module, an intelligent fee deduction module, an automatic submission process module, a log tracking module and a configuration management module; the method comprises: 1. case settlement; 2. case screening; 3. rule determination; 4. fee deduction; 5. claim settlement submission; the application realizes dynamic determination logic that can be hot loaded through a rule engine, and introduces a large language model black swan model to realize intelligent decision-making, combined with the gender, age, disease diagnosis and drug details of the insured in the case, to intelligently identify the list of irrelevant and exempted medications, thereby completing automatic fee deduction, automatic re-settlement and claim settlement submission, realizing the transition of the case from manual review to intelligent claim settlement, and significantly improving the business processing efficiency, consistency and intelligent level.

Description

Technical Field

[0001] This invention relates to the field of insurance claims automation and intelligent decision-making, specifically to an insurance claims settlement system and method based on a rule engine and intelligent model. Background Technology

[0002] In the traditional insurance claims process, the claims review stage plays a crucial role, serving as a bridge between the preceding and subsequent stages. Its purpose is to verify the legality, reasonableness, and compliance of the results entered and calculated in the earlier stages. However, most current claims systems still rely on manual review or simple automated verification methods based on fixed logic configurations. For medical claims, which involve multi-dimensional information such as drug lists, treatment items, medical insurance catalogs, scope of liability, and doctor-patient relationships, claims adjusters need to compare, judge, and enter each item one by one. This process is time-consuming and highly susceptible to subjective factors, making it difficult to establish a unified standard.

[0003] Current automated claims processing technologies mostly employ static rule-based methods such as threshold configuration and key field matching, involving simple filtering and judgment based on dimensions like amount ranges, number of images, and insurance type. These solutions typically use hard-coded logic control; when business rules change or policies are adjusted, the system requires manual coding and redeployment, lacking flexible scalability and hot updates. Furthermore, existing solutions lack semantic understanding capabilities based on artificial intelligence models, failing to accurately identify and determine liability for complex medication details and disease matching relationships. Therefore, their automation level is limited, and their intelligence is insufficient, particularly in scenarios involving medical expense exclusion, liability determination, and cost control.

[0004] The shortcomings of existing technologies are mainly reflected in three aspects: First, the rule system is rigid and it is difficult to support complex business differentiation needs and to respond quickly to changes in regulatory policies or insurance types; second, there is a lack of intelligent judgment mechanism, which makes it impossible to understand and identify the details of claims settlement at the semantic level; third, there is a disconnect between claims settlement and cost control, resulting in process fragmentation, frequent manual intervention, and low efficiency of automated systems.

[0005] Therefore, it is essential to propose an insurance claims settlement system and method based on rule engines and intelligent models. Summary of the Invention

[0006] The purpose of this invention is to provide an insurance claims settlement system and method based on a rule engine and an intelligent model. By constructing an automated claims settlement system and method driven by a "rule engine and intelligent model", a high degree of automation, dynamic configuration and intelligence of the insurance claims settlement process can be achieved, so as to solve the existing technical defects and unmet technical requirements.

[0007] To achieve the above objectives, the present invention provides the following technical solution: an insurance claims settlement system based on a rule engine and an intelligent model, comprising: The claims processing module receives claims data uploaded by users and claims after fee removal sent by the intelligent fee removal module. First, it processes the claims data uploaded by users. Then, it sends the data of claims that have been processed but not yet filed to the filtering module. Next, it re-processes and calculates the compensation amount for the claims after fee removal sent by the intelligent fee removal module. Finally, it sends the cases that have been re-processed and calculated to the automatic submission process module. At the same time, it sends all the processing content to the log traceability module. The filtering module receives case data that has been processed but not yet claimed from the claims settlement module, filters it, generates a dataset to be automatically claimed and determined, and then sends the dataset to be automatically claimed and determined to the rules engine determination module. The rule engine judgment module receives the dataset to be automatically assessed for claims sent by the filtering module, stores the structured data in the dataset, outputs the claims assessment conclusion of the case based on the rule execution results of the matching conditions, and then sends the cases that meet the conditions for automatic claims assessment to the intelligent fee elimination module. In this application, it is necessary to further explain that the automated claims assessment is based on a rules engine and is mainly used to determine whether a case meets the conditions for automated review. Cases that meet the conditions can be automatically removed and submitted, while cases that do not meet the conditions need to be manually removed and reviewed.

[0008] The intelligent fee removal module receives cases that meet the conditions for automated claims settlement from the rule engine judgment module. This module relies on the Black Goose model to identify and reason about cases that meet the conditions for automated claims settlement, output the results and corresponding reasons, and then automatically perform fee removal operations based on the output content. The fee-removed cases are then sent back to the claims settlement module, and the claims settlement details are sent to the log traceability module. The automatic submission process module receives cases from the claims settlement module that have completed re-settlement and compensation amount calculation, performs automatic claims settlement on the case, and submits the case execution claims settlement result into the database and status flow. If the claims settlement process is abnormal, the case is returned to the manual claims settlement procedure and the reason for the abnormality is sent to the log traceability module. The log tracing module receives claim details from the claim calculation module and the intelligent fee elimination module, the reasons for abnormal claims process sent by the automatic submission process module, and relevant configuration snapshots sent by the configuration management module, and stores and records all received information.

[0009] The configuration management module is electrically connected to the claims calculation module, the filtering module, the rule engine judgment module, the intelligent fee elimination module, and the automatic submission process module, and is used to uniformly manage claims rules, model parameters, and flow restriction strategies.

[0010] An insurance claims settlement method based on a rule engine and intelligent model includes the following steps: I. Case Settlement 1.1) The claims processing module receives user-uploaded claims data and the claims data after fee removal sent by the intelligent fee removal module. 1.2) The claims processing module processes the data of user-uploaded claims (initial processing), and then sends the data of cases that have been processed but not yet filed to the filtering module. 1.3) The claims processing module recalculates and calculates the compensation amount for cases that have been deducted from the intelligent deducting module (calculate after deducting), and then sends the processed cases to the automatic submission process module. 1.4) Send the settlement details from steps 1.2) and 1.3) to the log traceability module; In this application, it is necessary to further explain that the reconciliation details (initial reconciliation and reconciliation after elimination) include key processes, changes in cost items, and the final responsibility results pushed to the log traceability module for subsequent auditing.

[0011] II. Case Screening 2.1) Receive case data from the claims settlement module that has been settled but not yet filed, sent by the claims settlement module; 2.2) The case data in step 2.1) is filtered through the filtering module to generate a dataset for automated claims assessment; 2.3) Send the dataset to be automatically processed for claims assessment to the rule engine's assessment module; III. Rule Determination 3.1) The rule engine judgment module receives the dataset to be automatically assessed for claims from the filtering module; 3.2) and store the structured data in the dataset to be automatically assessed for claims; 3.3) Then, based on the results of the rule execution according to the matching conditions, output the claim settlement conclusion for the case; 3.4) Send cases that meet the conditions for automated claims processing to the intelligent fee elimination module; IV. Cost Exclusion 4.1) The intelligent fee elimination module receives cases that meet the conditions for automated claims settlement from the rule engine judgment module; 4.2) It identifies and infers cases that meet the conditions for automated claims settlement, and outputs the results and corresponding reasons; 4.3) Then, automatically perform the cost removal operation based on the output content; 4.4) and send the cases after deducting fees back to the claims settlement module, and send the claims settlement details to the log traceability module; V. Claim Submission 5.1) Receive cases from the claims settlement module that have completed re-settlement and compensation amount calculation through the automatic submission process module; 5.2) The case will be automatically processed for settlement, and the case will be submitted for settlement results and status updates.

[0012] Preferably, in step 2.2), the specific method for generating the dataset to be automatically assessed for claims is as follows: the case data in step 2.1) is filtered by the status field and business type to form the dataset to be automatically assessed for claims.

[0013] Preferably, the specific content of step 3.2) is as follows: 3.2.1) The Drools engine loads the currently valid claims settlement rule package; 3.2.2) The Drools rule engine stores structured data such as case information, invoice details, and insurance configurations through a reasoning mechanism based on facts and rules. Preferably, in step 3.3), the claim settlement conclusion includes types such as "automatic claim settlement is possible", "manual claim settlement is required", or "details need to be excluded".

[0014] Preferably, the specific content of step 4.2) is as follows: send a request to the Black Goose model as input using the core parameters of the case. The Black Goose model automatically identifies and reasons through semantic understanding and medical knowledge, generates two types of output results, and outputs the corresponding reasons.

[0015] Preferably, the specific content of step 5.2) is as follows: 5.2.1) After the fee elimination and recalculation are completed, the system automatically triggers the claims submission service to submit the case execution claims results into the database and track the status. 5.2.2) If an abnormality is encountered during the automatic claims settlement process, the case will be automatically rolled back to the manual claims settlement status, and the log traceability module will record the cause of the abnormality and related configuration snapshots.

[0016] Preferably, in step 4.2), the core parameters of the case include the gender, age, disease diagnosis information, and drug list of the person involved in the accident.

[0017] Preferably, in step 4.2), the two types of output results are: one is an "irrelevant medication list" and the other is a "disclaimer medication list".

[0018] Preferably, in step 5.2.2), abnormal situations include rule engine execution failure, model timeout, and exceptions returned by the calculation module.

[0019] Compared with the prior art, the beneficial effects of the present invention are: 1. This application implements hot-loadable dynamic judgment logic through a rules engine and introduces the Black Goose model of a large language model to achieve intelligent decision-making. By combining the insured's gender, age, disease diagnosis and drug details in the case, it can intelligently identify irrelevant drugs and exclusion drugs, thereby completing automatic deduction of fees, automatic recalculation and submission of claims, realizing the transformation of cases from manual review to intelligent claims settlement, and significantly improving business processing efficiency, consistency and intelligence level. Attached Figure Description

[0020] Figure 1 This is a partial logic diagram of the system in this invention; Figure 2 This is a partial logic diagram of the system in this invention; Figure 3 This is a partial logic diagram of the system adapted to this invention; Detailed Implementation The following will refer to the appendices in the embodiments of the present invention. Figure 1-3 The technical solutions in the embodiments of the present invention are clearly and completely described herein. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0021] Please see Figure 1-3 Embodiments of the present invention: Example: like Figure 1-3 As shown: An insurance claims processing system based on a rule engine and intelligent model includes: The claims processing module receives claims data uploaded by users and claims after fee removal sent by the intelligent fee removal module. First, it processes the claims data uploaded by users. Then, it sends the data of claims that have been processed but not yet filed to the filtering module. Next, it re-processes and calculates the compensation amount for the claims after fee removal sent by the intelligent fee removal module. Finally, it sends the cases that have been re-processed and calculated to the automatic submission process module. At the same time, it sends all the processing content to the log traceability module. The filtering module receives case data that has been processed but not yet claimed from the claims settlement module, filters it, generates a dataset to be automatically claimed and determined, and then sends the dataset to be automatically claimed and determined to the rules engine determination module. The rule engine judgment module receives the dataset to be automatically assessed for claims sent by the filtering module, stores the structured data in the dataset, outputs the claims assessment conclusion of the case based on the rule execution results of the matching conditions, and then sends the cases that meet the conditions for automatic claims assessment to the intelligent fee elimination module. The intelligent fee removal module receives cases that meet the conditions for automated claims settlement from the rule engine judgment module. This module relies on the Black Goose model to identify and reason about cases that meet the conditions for automated claims settlement, output the results and corresponding reasons, and then automatically perform fee removal operations based on the output content. The fee-removed cases are then sent back to the claims settlement module, and the claims settlement details are sent to the log traceability module. Intelligent fee elimination automatically removes and deducts the detailed expenses of a case. This process is fully automated, replacing manual calculation and verification in the fee control and elimination process.

[0022] Specifically, the Black Goose model identifies and infers the diseases and medication details in case invoices, and outputs the disclaimers or irrelevant medications in the details and the corresponding reasons.

[0023] The subsequent process will automatically perform the fee removal operation based on the output content, and send the fee removal details (including the reason for removal, amount changes, etc.) to the log traceability module. The case after fee removal is completed is fed back to the claims settlement module to recalculate the final compensation result after fee removal.

[0024] The automatic submission process module receives cases from the claims settlement module that have completed re-settlement and compensation amount calculation, performs automatic claims settlement on the case, and submits the case execution claims settlement result into the database and status flow. If the claims settlement process is abnormal, the case is returned to the manual claims settlement procedure, the reason for the abnormality is sent to the log traceability module, and the abnormality information is sent to the configuration management module. The log tracing module receives claim details from the claim calculation module and the intelligent fee elimination module, the reasons for abnormal claims process sent by the automatic submission process module, and relevant configuration snapshots sent by the configuration management module, and stores and records all received information.

[0025] In this application, the log tracing module forms an auditable link.

[0026] The configuration management module is electrically connected to the claims calculation module, the filtering module, the rule engine judgment module, the intelligent fee elimination module, and the automatic submission process module, and is used to uniformly manage claims rules, model parameters, and flow restriction strategies.

[0027] In this embodiment, the system manages the claims settlement rules, model parameters and flow control strategies in a unified manner through the configuration center, and is equipped with configuration snapshot and log recording mechanisms to facilitate subsequent traceability, quality inspection and model optimization.

[0028] In the diagram, the recalculation module can be understood as part of the calculation module.

[0029] An insurance claims settlement method based on rule engine and intelligent model ( Figure 1-3 The combined diagram forms the overall logical schematic diagram applicable to this method (it has been broken down due to its length), including the following steps: I. Case Settlement 1.1) The claims processing module receives user-uploaded claims data and the claims data after fee removal sent by the intelligent fee removal module. 1.2) The claims processing module processes the data of user-uploaded claims and then sends the data of cases that have been processed but not yet filed to the filtering module. 1.3) The claims processing module recalculates the claims and compensation amounts of the cases after the fees have been removed from the intelligent fee removal module, and then sends the processed cases to the automatic submission process module. 1.4) Send the settlement details from steps 1.2) and 1.3) to the log traceability module; II. Case Screening 2.1) Receive case data from the claims settlement module that has been settled but not yet filed, sent by the claims settlement module; 2.2) The case data in step 2.1) is filtered through the filtering module to generate a dataset for automated claims assessment; The specific method for generating the dataset for automated claims assessment is as follows: the case data in step 2.1) is filtered by the status field and business type to form the dataset for automated claims assessment.

[0030] 2.3) Send the dataset to be automatically processed for claims assessment to the rule engine's assessment module; III. Rule Determination 3.1) The rule engine judgment module receives the dataset to be automatically assessed for claims from the filtering module; 3.2) and store the structured data in the dataset to be automatically assessed for claims; 3.2.1) The Drools engine loads the currently valid claims settlement rule package; 3.2.2) The Drools rule engine stores structured data such as case information, invoice details, and insurance configurations through a fact- and rule-based reasoning mechanism.

[0031] 3.3) Then, based on the results of the rule execution according to the matching conditions, output the claim settlement conclusion for the case; Specifically, the claims assessment conclusions include types such as "automatic claims assessment is possible", "manual claims assessment is required", or "details need to be excluded".

[0032] 3.4) Cases that meet the conditions for automated claims processing will be sent to the intelligent fee elimination module, while those that do not meet the conditions will be reviewed manually; In this embodiment, the introduction of the Drools rule engine allows business rules to be stored and managed independently in DSL form, making rule management highly flexible and scalable. Business personnel can maintain rule definitions through central control configuration, achieving hot loading and immediate effect of rules without redeploying code. Through the linkage between the rule engine and central control configuration, the system can dynamically pull rule configurations according to different policies, insurers, and policyholders, achieving a high degree of customizability of claims settlement rules. During execution, the Drools engine combines case-level and detail-level data to perform multi-level condition matching, layered verification, and comprehensive judgment, thereby determining whether manual review is required and whether it meets the requirements of the automatic submission process. This mechanism greatly improves rule reusability and the accuracy of claims settlement decisions, supports rapid response to new rules, and reduces the cost of manual intervention.

[0033] IV. Cost Exclusion 4.1) The intelligent fee elimination module receives cases that meet the conditions for automated claims settlement from the rule engine judgment module; 4.2) It identifies and infers cases that meet the conditions for automated claims settlement, and outputs the results and corresponding reasons; Specifically, the core parameters of the case are sent to the Black Goose model as input. The Black Goose model automatically identifies and reasons through semantic understanding and medical knowledge, generating two types of output results and corresponding reasons. The core parameters of the case include the gender, age, disease diagnosis information, and drug details of the person involved in the accident. The two types of output results are: one is an "irrelevant drug list" and the other is a "disclaimer of liability drug list". It can be understood that the Black Goose model identifies and reasons about the diseases and drug details of the case documents, and outputs the disclaimer of liability or irrelevant drug details and the corresponding reasons.

[0034] 4.3) Then, automatically perform the cost removal operation based on the output content; 4.4) and send the cases after deducting fees back to the claims settlement module, and send the claims settlement details to the log traceability module; In this embodiment, the Black Goose model is trained based on semantic data in the insurance claims field, and has the ability to understand and reason about medical texts.

[0035] V. Claim Submission 5.1) Receive cases from the claims settlement module that have completed re-settlement and compensation amount calculation through the automatic submission process module; 5.2) The case will be automatically processed for settlement, and the case will be submitted for settlement results and status updates.

[0036] 5.2.1) After the fee elimination and recalculation are completed, the system automatically triggers the claims submission service to submit the case execution claims results into the database and track the status. 5.2.2) If an anomaly is encountered during the automatic claims settlement process, the case will be automatically rolled back to the manual claims settlement status, and the log traceability module will record the cause of the anomaly and related configuration snapshots to achieve closed-loop traceability and manual intervention. The anomalies include rule engine execution failure, model timeout, and anomaly returned by the claims settlement module.

[0037] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the scope of the invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

[0038] The overall logic of this embodiment can be understood as follows: after the case data is input and judged by the rule engine and intelligent model, the claim settlement result and fee deduction details are output, and then the case status is updated by the automatic submission module, thus forming a traceable, fully automated closed loop.

[0039] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can also be appropriately combined to form other embodiments that can be understood by those skilled in the art.

Claims

1. An insurance claims settlement system based on a rule engine and intelligent model, characterized in that, include: The claims processing module receives claims data uploaded by users and claims after fee removal sent by the intelligent fee removal module. First, it processes the claims data uploaded by users. Then, it sends the data of claims that have been processed but not yet filed to the filtering module. Next, it re-processes and calculates the compensation amount for the claims after fee removal sent by the intelligent fee removal module. Finally, it sends the cases that have been re-processed and calculated to the automatic submission process module. At the same time, it sends all the processing content to the log traceability module. The filtering module receives case data that has been processed but not yet claimed from the claims settlement module, filters it, generates a dataset to be automatically claimed and determined, and then sends the dataset to be automatically claimed and determined to the rules engine determination module. The rule engine judgment module receives the dataset to be automatically assessed for claims sent by the filtering module, stores the structured data in the dataset, outputs the claims assessment conclusion of the case based on the rule execution results of the matching conditions, and then sends the cases that meet the conditions for automatic claims assessment to the intelligent fee elimination module. The intelligent fee removal module receives cases that meet the conditions for automated claims settlement from the rule engine judgment module. This module relies on the Black Goose model to identify and reason about cases that meet the conditions for automated claims settlement, output the results and corresponding reasons, and then automatically perform fee removal operations based on the output content. The fee-removed cases are then sent back to the claims settlement module, and the claims settlement details are sent to the log traceability module. The automatic submission process module receives cases from the claims settlement module that have completed re-settlement and compensation amount calculation, performs automatic claims settlement on the case, and submits the case execution claims settlement result into the database and status flow. If the claims settlement process is abnormal, the case is returned to the manual claims settlement procedure and the reason for the abnormality is sent to the log traceability module. The log tracing module receives claim details from the claim calculation module and the intelligent fee elimination module, the reasons for abnormal claims process sent by the automatic submission process module, and relevant configuration snapshots sent by the configuration management module, and stores and records all received information. The configuration management module is electrically connected to the claims calculation module, the filtering module, the rule engine judgment module, the intelligent fee elimination module, and the automatic submission process module, and is used to uniformly manage the claims settlement rules, model parameters, and flow restriction strategies.

2. An insurance claims settlement method based on a rule engine and intelligent model, characterized by the following steps: I. Case Settlement 1.1) The claims processing module receives user-uploaded claims data and the claims data after fee removal sent by the intelligent fee removal module. 1.2) The claims processing module processes the data of user-uploaded claims and then sends the data of cases that have been processed but not yet filed to the filtering module. 1.3) The claims processing module recalculates the claims and compensation amounts of the cases after the fees have been removed from the intelligent fee removal module, and then sends the processed cases to the automatic submission process module. 1.4) Send the settlement details from steps 1.2) and 1.3) to the log traceability module; II. Case Screening 2.1) Receive case data from the claims settlement module that has been settled but not yet filed, sent by the claims settlement module; 2.2) The case data in step 2.1) is filtered through the filtering module to generate a dataset for automated claims assessment; 2.3) Send the dataset to be automatically processed for claims assessment to the rule engine's assessment module; III. Rule Determination 3.1) The rule engine judgment module receives the dataset to be automatically assessed for claims from the filtering module; 3.2) and store the structured data in the dataset to be automatically assessed for claims; 3.3) Then, based on the results of the rule execution according to the matching conditions, output the claim settlement conclusion for the case; 3.4) Send cases that meet the conditions for automated claims processing to the intelligent fee elimination module; IV. Cost Exclusion 4.1) The intelligent fee elimination module receives cases that meet the conditions for automated claims settlement from the rule engine judgment module; 4.2) It identifies and infers cases that meet the conditions for automated claims settlement, and outputs the results and corresponding reasons; 4.3) Then, automatically perform the cost removal operation based on the output content; 4.4) and send the cases after deducting fees back to the claims settlement module, and send the claims settlement details to the log traceability module; V. Claim Submission 5.1) Receive cases from the claims settlement module that have completed re-settlement and compensation amount calculation through the automatic submission process module; 5.2) The case will be automatically processed for settlement, and the case will be submitted for settlement results and status updates.

3. The insurance claims settlement method based on rule engine and intelligent model according to claim 2, characterized in that, in step 2.2), the specific method for generating the dataset to be automatically settled is: filtering the case data in step 2.1) by status field and business type to form the dataset to be automatically settled.

4. The insurance claims settlement method based on a rule engine and intelligent model according to claim 3, characterized in that the specific content of step 3.2) is as follows: 3.2.1) The Drools engine loads the currently valid claims settlement rule package; 3.2.2) The Drools rule engine stores structured data such as case information, invoice details, and insurance configurations through a fact- and rule-based reasoning mechanism.

5. The insurance claims settlement method based on a rule engine and intelligent model according to claim 4, characterized in that, In step 3.3), the claims settlement conclusion includes types such as "automatic claims settlement is possible", "manual claims settlement is required", or "details need to be excluded".

6. The insurance claims settlement method based on a rule engine and intelligent model according to claim 5, characterized in that, The specific content of step 4.2) is as follows: send a request to the Black Goose model as input using the core parameters of the case. The Black Goose model automatically identifies and reasons through semantic understanding and medical knowledge, generates two types of output results, and outputs the corresponding reasons.

7. The insurance claims settlement method based on a rule engine and intelligent model according to claim 6, characterized in that, The specific content of step 5.2) is as follows: 5.2.1) After the fee elimination and recalculation are completed, the system automatically triggers the claims submission service to submit the case execution claims results into the database and track the status. 5.2.2) If an abnormality is encountered during the automatic claims settlement process, the case will be automatically rolled back to the manual claims settlement status, and the log traceability module will record the cause of the abnormality and related configuration snapshots.

8. The insurance claims settlement method based on a rule engine and intelligent model according to claim 6, characterized in that, In step 4.2), the core parameters of the case include the gender, age, medical diagnosis information, and detailed list of drugs of the person involved in the accident.

9. The insurance claims settlement method based on a rule engine and intelligent model according to claim 8, characterized in that, In step 4.2), there are two types of output results: one is "list of irrelevant medications" and the other is "list of medications exempt from liability".

10. The insurance claims settlement method based on a rule engine and intelligent model according to claim 7, characterized in that, In step 5.2.2), abnormal situations include rule engine execution failure, model timeout, and exceptions returned by the calculation module.

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