Report information processing and geographic location push method based on similar case matching

Abstract

The present invention provides a method for processing report information and pushing geographic location based on similar case matching; it relates to the technical field of insurance case processing. The present application matches a target claim case similar to the current case from the insurance case library by similar case matching, and provides a process file of the target claim case to construct the claim task of the current case. The present invention uses comparative learning to associate the report data with the insurance rules and regulations, and configures the legal provisions involved in the case in the report data. The present invention performs similarity matching of claim cases from three aspects: the claim case event, the law involved in the claim case, and the insurance rules involved in the claim case. The matching result considers the semantics of the text describing the event, as well as the law and insurance rules behind the event, so that the matched target claim case is more accurate, and the claim task constructed based on the process file of the target claim case is more in line with the requirements of the current claim case.

Description

Technical Field

[0001] The present invention relates to the technical field of insurance case processing, and in particular to a method for processing case report information and pushing geographic location based on similar case matching. Background Art

[0002] With the rapid development of information technology, intelligent services have penetrated every aspect of society. In the fields of public security and financial insurance, the goal has always been to quickly and accurately respond to and process user reports, improve claims service efficiency and satisfaction, and effectively utilize and redevelop claims client resources.

[0003] Because the traditional insurance claims process relies on manual processing, requiring time for information entry, review, and scheduling, response times are long and inefficient, making it difficult to achieve emergency responses and leading to decreased customer satisfaction. Furthermore, the vast amount of historical claims data is not fully utilized, relying on expert experience, making it difficult to extract valuable information from this large amount of data based on actual circumstances and unable to support real-time decision-making. With the development of natural language processing technology, similar case matching methods based on text similarity analysis have been used in insurance case processing. By matching similar cases, similar cases can be found in historical claims, and the claims process of historical cases can be used to assist in the current case's claims. However, existing case similarity matching methods mostly analyze semantic similarity from the perspective of event facts. This analysis is limited to semantic similarity and fails to consider the underlying legal and insurance regulations underlying the event. Legal information and insurance regulations are essential factors in the claims process. This results in a large range of matched historical cases, resulting in poor claims assistance. Summary of the Invention

[0004] In order to solve the above technical problems or at least partially solve the above technical problems, the present invention provides a method for processing report information and pushing geographic location based on similar case matching.

[0005] In a first aspect, the present invention provides a method for processing case report information and pushing geographic location information based on similar case matching, comprising:

[0006] For different types of insurance, a corresponding insurance case library is constructed. The insurance case library stores insurance claim cases and the process documents corresponding to each claim case. The claim cases in the insurance case library are labeled with pre-set legal and insurance rules at the sentence level.

[0007] Collect claim reporting data from claimants and configure corresponding reporting times; the reporting data includes the reporter's information, insurance type, geographic location of the incident, incident time, and case description; the case description includes the relationship between the parties involved, the detailed process of the case, the determination of case responsibility, and the legal provisions involved;

[0008] The second text encoder and the first text encoder trained by comparative learning are used to match the case report data with the corresponding insurance rules within the scope of the insurance rule library;

[0009] Label the legal provisions and insurance rules in the case report data;

[0010] Using prompt words to control the language model that has been fine-tuned for similar case matching tasks, the target claim case with the highest similarity score is matched from the insurance case database based on the case report data labeled with legal provisions and insurance rules. The process file of the selected target claim case is used to construct the claim task of the current case.

[0011] The geographical information of the claim settlement task of the current case is displayed based on the geographical location information of the crime scene.

[0012] Furthermore, the process of adding pre-defined legal and insurance rules labels to claim cases at the sentence level includes:

[0013] Organize the laws and insurance rules in the corresponding fields for different types of insurance; represent the articles in the laws and insurance rules with different numbers, and form different lists corresponding to the numbers of the articles in the laws and insurance rules as legal tags and insurance rule tags;

[0014] For claims cases, the content is analyzed sentence by sentence, and corresponding legal tags and insurance rules tags are added according to the legal provisions or insurance rules involved in each sentence, so that the case facts in the claims case contain in-depth legal and insurance rules information.

[0015] Furthermore, after obtaining the report data, the report data of the claim customer is structured according to the claim case format in the insurance case library.

[0016] Furthermore, the second text encoder and the first text encoder are trained by contrastive learning, and the process includes:

[0017] Construct contrastive learning training set;

[0018] The second text encoder is built based on the Lawbert model fine-tuned for insurance rules, and the first text encoder is built based on the Bert model. The last Transformer layer of the Lawbert model and the Bert model are used as the adjustable parameters in the comparative learning process, and the parameters of the remaining Transformer layers are fixed.

[0019] The second text encoder extracts the text features of any claim case sentence, and the first text encoder extracts the text features of the insurance rules and regulations in the positive and negative pairs of the claim case sentence in the contrastive learning training set:

[0020] Text features of case sentences:

[0021] ,in, For the first text encoder, For the claim case sentence;

[0022] Text features of insurance rules and regulations in the claim case sentences:

[0023] ,in, For the second text encoder, Sentences for claims cases The insurance rules and regulations are directly in place;

[0024] Text features of the insurance rules clauses in all negative alignments of the case sentences:

[0025] ,in, Sentences for claims cases The kth negative pair insurance rule clause;

[0026] The objective function in the contrastive learning process is as follows:

[0027] ;

[0028] Where D is the total number of positive and negative pairs of case sentences, To find the cosine similarity function, is the exponential function of the natural logarithm, is the learnable temperature coefficient;

[0029] The objective function is used to constrain the adjustment of the parameters of the last Transformer layer of the second text encoder and the first text encoder so that within the scope of the contrastive learning training set, the similarity of the text features extracted from the positive pairs by the second text encoder and the first text encoder is maximized, and the similarity of the text features extracted from the negative pairs is minimized.

[0030] Furthermore, based on the correspondence between the cases in the insurance case library and the insurance rule labels in the cases, the matching relationship between the sentences in the cases and the insurance rule clauses is determined, and the mutually matching case sentences and insurance rule clauses form positive training data for contrastive learning; the insurance rules are randomly changed so that the case sentences and insurance rule clauses no longer match, thereby obtaining negative training data for contrastive learning; the case sentences are expanded using the GPT model while ensuring that the semantics remain unchanged; the corresponding insurance rules after expansion are still the insurance rules corresponding to the original sentences, thereby obtaining expanded positive training data and negative training data.

[0031] Furthermore, the process of fine-tuning the language model includes:

[0032] Randomly selecting a portion of various claim cases in the insurance case database as matching case sources, manually selecting the claim cases most relevant to the matching case sources from the remaining claim cases as matching results, and constructing a matching training set using the matching case sources and the matching results;

[0033] The language model is trained to select predicted matching results from the remaining claim cases based on the matching case sources. The language model parameters are fine-tuned using the information cross entropy loss between the predicted matching results and the actual matching results in the matching training set as the loss function.

[0034] Furthermore, the trained second text encoder and the first text encoder are packaged into a Docker image, and the Docker image is deployed as an API service for configuring insurance rules and regulations. By calling the deployed API service for insurance rules and regulations, the report data is matched with the corresponding insurance rules and regulations involved in the case; the trained language model is packaged into a Docker image, and the language model is deployed as a claim case matching API service. By calling the deployed claim case matching API service, the current claim case is processed in real time to construct a claim task.

[0035] Furthermore, the geographical information display of the current case's claim settlement task is realized based on the geographical location information of the crime scene, including: the front end obtains the geographical location information of the crime scene in the report data from the back end, obtains the report timestamp, and obtains the reporter information;

[0036] Map visualization based on the geographic location of the crime scene:

[0037] Set a claim task icon at the location where the map was issued on the visual map;

[0038] The first action on the claim task icon triggers navigation, navigating from the current location to the incident location corresponding to the claim task;

[0039] In the visual map, a description card of the claim task that can be displayed or hidden is configured. The second operation on the claim task icon triggers the display or hiding of the description card. The description card is designed through the UI to provide the reporting time, reporter information, and links to obtain case status and process files. An information export button is set in the description card. Click the information export button to obtain the reporting time, location of the incident, reporter information, and links to obtain case status and process files of the claim task.

[0040] In the second aspect, the present invention provides a device for processing report information and pushing geographic location based on similar case matching, including: interconnected back-end and front-end devices, the back-end and front-end devices contain at least one processing unit, the processing unit and the storage unit are interconnected through a bus unit, the storage unit stores a computer program, and when the computer program is executed by the processing unit, the method for processing report information and pushing geographic location based on similar case matching is implemented.

[0041] In a third aspect, the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method for processing report information and pushing geographic location based on similar case matching.

[0042] The above technical solution provided by the embodiment of the present invention has the following advantages compared with the prior art:

[0043] This application will match target claim cases similar to the current case from the insurance case library by similar case matching, and provide the process file of the target claim case to construct the claim task of the current case. The present invention performs similarity matching of claim cases from three aspects: claim case events, claims case-related laws, and claims case-related insurance rules. The matching results take into account the semantics of the text describing the event, as well as the laws and insurance rules behind the event, so that the matched target claim case is more accurate, and the claim task constructed based on the process file of the target claim case is more in line with the requirements of the current claim case.

[0044] This application realizes the geographical information display of the claims task of the current case based on the geographical location information of the crime scene, and more intuitively displays the claims task status and processing results on the front end. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0047] Figure 1 A flowchart of a method for processing case report information and pushing geographic location based on similar case matching provided by an embodiment of the present invention;

[0048] Figure 2 A flowchart for adding preset legal and insurance rule labels to a claim case at the sentence level, provided by an embodiment of the present invention;

[0049] Figure 3 A flowchart of training a second text encoder and a first text encoder through contrastive learning provided by an embodiment of the present invention;

[0050] Figure 4 A flowchart of fine-tuning a language model to better adapt it to the task of matching similar claims cases, provided in an embodiment of the present invention;

[0051] Figure 5 A schematic diagram of a device for processing report information and pushing geographic location based on similar case matching provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0052] To make the objectives, technical solutions, and advantages of the embodiments of the present invention more clear, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts shall fall within the scope of protection of the present invention.

[0053] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0054] Example 1

[0055] like Figure 1As shown in the figure, the present invention implements a method for processing case report information and pushing geographic location information based on similar case matching. This method utilizes a separate front-end and back-end design architecture. The back-end is responsible for collecting case report data and matching similar cases. It then constructs the current case's claim settlement task based on the claims process files of the matched similar cases. The front-end displays the current case's claim settlement task in a GIS format based on the geographic location of the crime scene.

[0056] The process of the present invention comprises:

[0057] Configure an insurance case library in the backend. This application will match target claims cases similar to the current case from the insurance case library through similar case matching, and provide the process file of the target claims case to build the claims task of the current case.

[0058] During implementation, a corresponding insurance case library is constructed for different types of insurance. This library stores insurance claims cases and the corresponding process documents for each claim case. Claim cases in the library are tagged with pre-set legal and insurance rules at the sentence level.

[0059] like Figure 2 As shown in Figure 2, the process of adding pre-set legal and insurance rule labels to claim cases at the sentence level includes:

[0060] Predefine legal and insurance rule labels based on the laws and insurance rules. Specifically, organize the laws and insurance rules for different types of insurance. Use different numbers to represent the articles in the laws and insurance rules. Create lists of corresponding numbers for the articles in the laws and insurance rules as legal and insurance rule labels.

[0061] For claims cases, the content is analyzed sentence by sentence, and corresponding legal tags and insurance rules tags are added according to the legal provisions or insurance rules involved in each sentence, so that the case facts in the claims case contain in-depth legal and insurance rules information.

[0062] Collect claim reporting data from claimants and configure corresponding reporting times. This reporting data includes the reporter, insurance type, geographic location, time of occurrence, and case description. The case description includes the relationships between the individuals involved, the detailed course of the case, the determination of responsibility, and the legal provisions involved.

[0063] During implementation, claimant report data is captured through the insurance call center API or the insurance reporting system API. When a claimant successfully submits their report, a timestamp representing the time of submission is assigned based on the local time of the insurance call center API or the insurance reporting system API. To improve claim case matching, the claimant report data is structured according to the claim case format in the insurance case database.

[0064] The second and first text encoders are deployed on the backend, along with the insurance rule base. The trained second and first text encoders are then compared and learned to match the case report data with the relevant insurance rules within the insurance rule base. The trained second and first text encoders are packaged into a Docker image and deployed as an API service for configuring insurance rules. The deployed API service is then called to match the case report data with the relevant insurance rules.

[0065] In the specific implementation process, Figure 3 As shown, the second text encoder and the first text encoder are trained by contrastive learning, and the process includes:

[0066] A contrastive learning training set is constructed. Based on the correspondence between cases in the insurance case database and the insurance rule labels within the cases, the matching relationship between the case sentences and the insurance rule clauses is determined. The matching case sentences and insurance rule clauses form the positive training data for contrastive learning. The insurance rules are randomly altered to eliminate the matching between the case sentences and the insurance rule clauses, thereby obtaining the negative training data for contrastive learning. The case sentences are expanded using the GPT model while ensuring semantic consistency. The corresponding insurance rules after expansion remain the same as those of the original sentences. This results in expanded positive and negative training data.

[0067] The second text encoder is constructed based on the Lawbert model fine-tuned for insurance rules, and the first text encoder is constructed based on the Bert model. The last Transformer layer of the Lawbert model and the Bert model are used as the adjustable parameters in the comparative learning process, and the parameters of the remaining Transformer layers are fixed.

[0068] The second text encoder extracts the text features of any case sentence, and the first text encoder extracts the text features of the insurance rules in the positive and negative pairs of the case sentence in the contrastive learning training set:

[0069] Text features of case sentences:

[0070] ,in, For the first text encoder, For the claim case sentence;

[0071] Text features of insurance rules and regulations in the claim case sentences:

[0072] ,in, For the second text encoder, Sentences for claims cases The insurance rules and regulations are directly in place;

[0073] Text features of the insurance rules clauses in all negative alignments of the case sentences:

[0074] ,in, Sentences for claims cases The kth negative pair insurance rule clause;

[0075] The objective function in the contrastive learning process is as follows:

[0076] ;

[0077] Where D is the total number of positive and negative pairs of case sentences, To find the cosine similarity function, is the exponential function of the natural logarithm, is the learnable temperature coefficient.

[0078] The objective function is used to constrain the adjustment of the parameters of the last Transformer layer of the second text encoder and the first text encoder so that within the scope of the contrastive learning training set, the similarity of the text features extracted from the positive pairs by the second text encoder and the first text encoder is maximized, and the similarity of the text features extracted from the negative pairs is minimized.

[0079] When the trained second text encoder and the first text encoder are deployed at the back end to match the case report data with the corresponding insurance rules and clauses involved in the case, the insurance rules and clauses are selected within the scope of the insurance rule library.

[0080] Label the legal provisions and insurance rules in the report data; during the specific implementation process, label the legal provisions and insurance rules in the insurance data according to the construction method of the legal labels and insurance rules labels of this application.

[0081] The prompt word control language model is used to match the target claim case with the largest similarity score from the insurance case database according to the report data labeled with legal provisions and insurance rules.

[0082] During the specific implementation process, the language model is fine-tuned to make it better suited to the task of matching similar claims cases, such as Figure 4 As shown, the process includes:

[0083] Randomly select a portion of the various claims cases in the insurance case database as matching case sources. Hide the insurance rule and legal labels of the matching case sources, and manually select the claims cases most relevant to the matching case sources from the remaining claims cases as matching results. Use the matching case sources and matching results to construct a matching training set.

[0084] The language model is trained to select predicted matching results from the remaining claim cases based on the matching case sources. The information cross entropy loss between the predicted matching results and the actual matching results in the matching training set is used as the loss function to fine-tune the language model parameters to better match similar claim cases.

[0085] Use the process files of the selected target claim cases to build the claim task of the current case.

[0086] During the specific implementation process, the trained language model is packaged into a Docker image, and the language model is deployed as a claim case matching API service. By calling the deployed claim case matching API service, the current claim case is processed in real time to build a claim task.

[0087] The geographical information display of the current case's claim settlement task is realized based on the geographical location information of the crime scene, including: the front end obtains the geographical location information of the crime scene in the report data from the back end, obtains the report timestamp, and obtains the reporter information.

[0088] Map visualization based on the geographical location of the crime scene, including:

[0089] Set a claim task icon at the map issuance location on the visual map.

[0090] Navigation is triggered by the first operation on the claim task icon, and navigation is performed from the current location to the crime scene corresponding to the claim task.

[0091] In the visual map, a description card of the claim task that can be displayed or hidden is configured. The second operation on the claim task icon triggers the display or hiding of the description card. The description card is designed through the UI to provide the reporting time, reporter information, and links to obtain case status and process files. An information export button is set in the description card. Click the information export button to obtain the reporting time, location of the incident, reporter information, and links to obtain case status and process files of the claim task.

[0092] During implementation, the duration of a report is graded, for example, reports within 10 minutes are classified as level 1, reports within 20 minutes are classified as level 2, and so on. The reporting time is calculated based on the report time and the current time of the claim task. Different borders or background colors are assigned to the description card based on the reporting time level to more clearly distinguish the importance of claims tasks.

[0093] Example 2

[0094] See Figure 5 As shown, an embodiment of the present invention provides a device for processing report information and pushing geographic location based on similar case matching, including: interconnected back-end and front-end devices, the back-end and front-end devices including at least one processing unit, the processing unit and storage unit being interconnected via a bus unit, the storage unit being a computer-readable storage medium that can be used to store software programs, computer executable programs, and modules, such as the software programs, computer executable programs, and modules corresponding to a method for processing report information and pushing geographic location based on similar case matching in an embodiment of the present invention. The processing unit implements the above-mentioned method for processing report information and pushing geographic location based on similar case matching by running the software programs, computer executable programs, and modules stored in the storage unit, including:

[0095] For different types of insurance, a corresponding insurance case library is constructed. The insurance case library stores insurance claim cases and the process documents corresponding to each claim case. The claim cases in the insurance case library are labeled with pre-set legal and insurance rules at the sentence level.

[0096] Collect claim reporting data from claimants and configure corresponding reporting times; the reporting data includes the reporter's information, insurance type, geographic location of the incident, incident time, and case description; the case description includes the relationship between the parties involved, the detailed process of the case, the determination of case responsibility, and the legal provisions involved;

[0097] The second text encoder and the first text encoder trained by comparative learning are used to match the case report data with the corresponding insurance rules within the scope of the insurance rule library;

[0098] Label the legal provisions and insurance rules in the case report data;

[0099] Using prompt words to control the language model that has been fine-tuned for similar case matching tasks, the target claim case with the highest similarity score is matched from the insurance case database based on the case report data labeled with legal provisions and insurance rules. The process file of the selected target claim case is used to construct the claim task of the current case.

[0100] The geographical information of the claim settlement task of the current case is displayed based on the geographical location information of the crime scene.

[0101] Of course, the computer program stored in the storage unit of the device for processing report information and pushing geographic location based on similar case matching provided by an embodiment of the present invention is not limited to the method operations described above, and can also execute related operations in the method for processing report information and pushing geographic location based on similar case matching provided by any embodiment of the present invention.

[0102] Example 3

[0103] An embodiment of the present invention provides a computer-readable storage medium storing a computer program. When the computer program is executed, the method for processing case report information and pushing geographic location information based on similar case matching is implemented, including:

[0104] For different types of insurance, a corresponding insurance case library is constructed. The insurance case library stores insurance claim cases and the process documents corresponding to each claim case. The claim cases in the insurance case library are labeled with pre-set legal and insurance rules at the sentence level.

[0105] Collect claim reporting data from claimants and configure corresponding reporting times; the reporting data includes the reporter's information, insurance type, geographic location of the incident, incident time, and case description; the case description includes the relationship between the parties involved, the detailed process of the case, the determination of case responsibility, and the legal provisions involved;

[0106] The second text encoder and the first text encoder trained by comparative learning are used to match the case report data with the corresponding insurance rules within the scope of the insurance rule library;

[0107] Label the legal provisions and insurance rules in the case report data;

[0108] Using prompt words to control the language model that has been fine-tuned for similar case matching tasks, the target claim case with the highest similarity score is matched from the insurance case database based on the case report data labeled with legal provisions and insurance rules. The process file of the selected target claim case is used to construct the claim task of the current case.

[0109] The geographical information of the claim settlement task of the current case is displayed based on the geographical location information of the crime scene.

[0110] A computer-readable storage medium provided by an embodiment of the present invention stores a computer program that is not limited to the method operations described above, but can also execute related operations in a method for processing report information and pushing geographic location based on similar case matching provided by any embodiment of the present invention.

[0111] In the embodiments provided by the present invention, it should be understood that the disclosed structures and methods can be implemented in other ways. For example, the structural embodiments described above are merely illustrative. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interface, structure or unit, which can be electrical, mechanical or other forms.

[0112] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0113] In addition, the functional units in the various embodiments of the present invention may be integrated into a single processing unit, each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0114] The foregoing description is intended only to provide specific embodiments of the present invention, which will enable those skilled in the art to understand and implement the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown herein, but is intended to be accorded the widest scope consistent with the principles and novel features claimed herein.

Claims

1. A method for processing case report information and pushing geographic location based on similar case matching, characterized in that: include: For different types of insurance, a corresponding insurance case library is constructed, wherein the insurance case library stores insurance claim cases and process files corresponding to each claim case; Claim cases in the insurance case database are labeled with pre-set legal and insurance rules at the sentence level; Collect claim reporting data from claimants and configure corresponding reporting times; the reporting data includes the reporter's information, insurance type, geographic location of the incident, incident time, and case description; the case description includes the relationship between the parties involved, the detailed process of the case, the determination of case responsibility, and the legal provisions involved; The second text encoder and the first text encoder trained by contrastive learning match the report data with the corresponding insurance rules and regulations involved in the case within the scope of the insurance rule library; wherein the second text encoder and the first text encoder are trained by contrastive learning, and the process includes: constructing a contrastive learning training set; constructing the second text encoder based on the Lawbert model fine-tuned for the insurance rules, and constructing the first text encoder based on the BERT model; using the last Transformer layer of the Lawbert model and the BERT model as the part with adjustable parameters in the contrastive learning process, and fixing the parameters of the remaining Transformer layers; extracting the text features of any claim case sentence by the second text encoder, and extracting the text features of the insurance rules and regulations of the claim case sentence in the positive and negative pairs in the contrastive learning training set by the first text encoder; adjusting the parameters of the last Transformer layer of the second text encoder and the first text encoder by constraining the objective function so that the similarity of the text features extracted from the positive pairs by the second text encoder and the first text encoder is maximized, and the similarity of the text features extracted from the negative pairs is minimized within the scope of the contrastive learning training set; Label the legal provisions and insurance rules in the case report data; Using prompt words to control the language model that has been fine-tuned for similar case matching tasks, the target claim case with the highest similarity score is matched from the insurance case database based on the case report data labeled with legal provisions and insurance rules. The process file of the selected target claim case is used to construct the claim task of the current case. The geographical information of the claim settlement task of the current case is displayed based on the geographical location information of the crime scene.

2. The method for processing case report information and pushing geographic location information based on similar case matching according to claim 1, characterized in that: The process of adding pre-defined legal and insurance rules labels to claim cases at the sentence level includes: Organize the laws and insurance rules in the corresponding fields for different types of insurance; represent the articles in the laws and insurance rules with different numbers, and form different lists corresponding to the numbers of the articles in the laws and insurance rules as legal tags and insurance rule tags; For claims cases, the content is analyzed sentence by sentence, and corresponding legal tags and insurance rules tags are added according to the legal provisions or insurance rules involved in each sentence, so that the case facts in the claims case contain in-depth legal and insurance rules information.

3. The method for processing case report information and pushing geographic location based on similar case matching according to claim 1, characterized in that: After obtaining the report data, the report data of the claim customer will be structured according to the claim case format in the insurance case library.

4. The method for processing case report information and pushing geographic location based on similar case matching according to claim 1, characterized in that: The second text encoder extracts the text features of any claim case sentence: ,in, For the first text encoder, For the claim case sentence; The first text encoder extracts the text features of the insurance rules and regulations in the positive and negative pairs of the claim case sentence in the contrastive learning training set: Text features of insurance rules and regulations in the claim case sentences: ,in, For the second text encoder, Sentences for claims cases The insurance rules and regulations are directly in place; Text features of the insurance rules clauses in all negative alignments of the case sentences: ,in, Sentences for claims cases The kth negative pair insurance rule clause; The objective function in the contrastive learning process is as follows: ； Where D is the total number of positive and negative pairs of case sentences, To find the cosine similarity function, is the exponential function of the natural logarithm, is the learnable temperature coefficient.

5. The method for processing case report information and pushing geographic location based on similar case matching according to claim 1, characterized in that: According to the correspondence between the cases in the insurance case library and the insurance rule labels in the cases, the matching relationship between the sentences in the cases and the insurance rule provisions is determined, and the mutually matching case sentences and insurance rule provisions form positive training data for contrastive learning; the insurance rules are randomly changed so that the case sentences and insurance rule provisions no longer match, thereby obtaining negative training data for contrastive learning; the case sentences are expanded using the GPT model while ensuring that the semantics remain unchanged; the corresponding insurance rules after expansion are still the insurance rules corresponding to the original sentences, thereby obtaining expanded positive training data and negative training data.

6. The method for processing case report information and pushing geographic location based on similar case matching according to claim 1, characterized in that: The process of fine-tuning a language model involves: Randomly selecting a portion of various claim cases in the insurance case database as matching case sources, manually selecting the claim cases most relevant to the matching case sources from the remaining claim cases as matching results, and constructing a matching training set using the matching case sources and the matching results; The language model is trained to select predicted matching results from the remaining claim cases based on the matching case sources. The language model parameters are fine-tuned using the information cross entropy loss between the predicted matching results and the actual matching results in the matching training set as the loss function.

7. The method for processing case report information and pushing geographic location based on similar case matching according to claim 1, characterized in that: The trained second text encoder and the first text encoder are packaged into a Docker image, and the Docker image is deployed as an API service for configuring insurance rules and regulations. By calling the deployed API service for insurance rules and regulations, the report data is matched with the corresponding insurance rules and regulations involved in the case; the trained language model is packaged into a Docker image, and the language model is deployed as a claim case matching API service. By calling the deployed claim case matching API service, the current claim case is processed in real time to build a claim task.

8. The method for processing case report information and pushing geographic location based on similar case matching according to claim 1, characterized in that: Based on the geographic location of the crime scene, the geographical information of the current claim settlement task is displayed, including: obtaining the geographic location of the crime scene in the report data from the backend, obtaining the report timestamp, and obtaining the reporter information; Map visualization based on the geographic location of the crime scene: Set a claim task icon at the location where the map was issued on the visual map; The first action on the claim task icon triggers navigation, navigating from the current location to the incident location corresponding to the claim task; In the visual map, a description card of the claim task that can be displayed or hidden is configured. The second operation on the claim task icon triggers the display or hiding of the description card. The description card is designed through the UI to provide the reporting time, reporter information, and links to obtain case status and process files. An information export button is set in the description card. Click the information export button to obtain the reporting time, location of the incident, reporter information, and links to obtain case status and process files of the claim task.

9. A device for processing report information and pushing geographic location based on similar case matching, characterized in that: include: The back-end and front-end devices include at least one processing unit, and the processing unit and the storage unit are interconnected through a bus unit. The storage unit stores a computer program. When the computer program is executed by the processing unit, the method for processing report information and pushing geographic location based on similar case matching as described in any one of claims 1-8 is implemented.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for processing case report information and pushing geographic location based on similar case matching as described in any one of claims 1 to 8 is implemented.

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