Claim settlement analysis method and device, computer equipment and storage medium

Abstract

The embodiment of the invention belongs to the field of financial science and technology, and relates to a claim analysis method and device, computer equipment and a storage medium, and the method comprises the following steps: obtaining a multi-modal claim data set; performing feature extraction and feature fusion on the multi-modal claim settlement data set to obtain claim settlement case feature representation; performing model training based on the claim settlement case feature representation to obtain a case classification model; obtaining a current claim settlement case, and performing type distinguishing on the current claim settlement case according to the case classification model to obtain a current case type; acquiring corresponding historical claim amount data according to the current case type, and performing amount distribution calculation according to the historical claim amount data to obtain claim amount distribution characteristics; and constructing a claim settlement amount prediction model based on the claim settlement amount distribution characteristics, and inputting the current claim settlement case into the claim settlement amount prediction model to obtain the claim settlement amount of the case. According to the invention, accurate amount prediction of the claim settlement case can be realized, so that the claim settlement case processing accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, specifically to the field of financial technology, and in particular to a claims analysis method, apparatus, computer equipment and storage medium. Background Art

[0002] In the field of claims case analysis, time series data of claims amounts constitute an important basis for insurance companies' risk management, financial planning and customer service decisions. However, these data often show extremely complex fluctuation patterns and trend changes, which poses a great challenge to accurate analysis. As a key information carrier that records the evolution of claims amounts over time, time series data has inherent characteristics including long-term trends, cyclical fluctuations, seasonal changes and possible impacts of emergencies. These elements are intertwined and together shape a complex picture of fluctuations in claims amounts.

[0003] Traditional time series decomposition methods, such as moving average method and exponential smoothing method, can reveal certain trends and cyclical components in the data to a certain extent, but their analytical capabilities are insufficient when faced with the superposition effect of multiple modes of claim amount data. These methods often find it difficult to accurately distinguish and depict the slow evolution of long-term trends, the regularity of cyclical fluctuations, and the specific forms of seasonal changes, resulting in deviations in the analysis results and the inability to provide accurate risk assessment and business guidance for insurance companies.

[0004] In addition, the diversity of claim case types further complicates the analysis. Different types of claims have very different distribution characteristics of the amount of claims, ranging from minor vehicle scratches to serious natural disaster losses. This significant difference in the amount of distribution requires that the anomaly detection algorithm must be highly flexible and adaptive, and be able to automatically adjust the threshold according to different case types to accurately identify potential abnormal claims. However, existing single anomaly detection algorithms often lack this adaptive capability, resulting in a significant reduction in the accuracy and efficiency of anomaly identification in the context of diverse case types. Summary of the invention

[0005] The purpose of the embodiments of the present application is to propose a claims analysis method, apparatus, computer equipment and storage medium to solve the problem of being unable to accurately and effectively determine the claim amount of a claims case.

[0006] In order to solve the above technical problems, the present application embodiment provides a claims analysis method, which adopts the following technical solutions:

[0007] Acquire claim case information, pre-process the claim case information, and obtain a multimodal claim data set;

[0008] Performing feature extraction and feature fusion on the multimodal claims data set to obtain a feature representation of the claims case;

[0009] Performing model training based on the claim case feature representation to obtain a case classification model;

[0010] Obtaining a current claim case, and classifying the current claim case according to the case classification model to obtain the current case type;

[0011] Obtaining corresponding historical claim amount data according to the current case type, and performing amount distribution calculation based on the historical claim amount data to obtain claim amount distribution characteristics;

[0012] A claim amount prediction model is constructed based on the claim amount distribution characteristics, and the current claim case is input into the claim amount prediction model to obtain the case claim amount.

[0013] Furthermore, the step of obtaining the claim case information, preprocessing the claim case information, and obtaining a multimodal claim data set specifically includes:

[0014] Obtaining a claim case number, and extracting the claim case information from a database according to the claim case number, wherein the claim case information includes case text information, case image information, and case audio information;

[0015] Performing word segmentation and stop word removal on the case text information to obtain standard case text information;

[0016] De-noising and normalizing the case image information to obtain standard case image information;

[0017] Performing noise reduction and endpoint detection processing on the case audio information to obtain standard case audio information;

[0018] The standard case text information, the standard case image information, and the standard case audio information are integrated into the multimodal claims data set.

[0019] Furthermore, the multimodal claims data set includes standard case text information, standard case image information, and standard case audio information. The step of extracting and fusing features of the multimodal claims data set to obtain a feature representation of the claims case specifically includes:

[0020] Inputting the standard case text information into a preset language processing model for feature extraction to obtain a case text feature vector;

[0021] Extracting features of the standard case image information based on a preset convolutional neural network to obtain a case image feature vector;

[0022] Extracting Mel-frequency cepstral coefficients from the standard case audio information, and using the extracted Mel-frequency cepstral coefficients as the case audio feature vector;

[0023] The case text feature vector, the case image feature vector, and the case audio feature vector are fused to obtain the claim case feature representation.

[0024] Furthermore, the step of fusing the case text feature vector, the case image feature vector, and the case audio feature vector to obtain the claim case feature representation specifically includes:

[0025] Obtain text feature vector weights, image feature vector weights, and audio feature vector weights;

[0026] Performing weighted fusion on the case text feature vector, the case image feature vector, and the case audio feature vector according to the text feature vector weight, the image feature vector weight, and the audio feature vector weight to obtain a fused feature vector;

[0027] The fused feature vector is subjected to feature extraction based on a preset convolutional neural network to obtain a feature representation of the claim case.

[0028] Furthermore, the step of obtaining the current claim case, classifying the current claim case according to the case classification model, and obtaining the current case type specifically includes:

[0029] Obtaining a current case number, and extracting a current claim case from a database according to the current case number;

[0030] Performing feature extraction and feature fusion on the current claim case to obtain a feature representation of the current case;

[0031] The feature representation of the current case is input into the case classification model to obtain the current case type.

[0032] Furthermore, the step of obtaining corresponding historical claim amount data according to the current case type, and performing amount distribution calculation according to the historical claim amount data to obtain claim amount distribution characteristics specifically includes:

[0033] Extracting the corresponding historical claim amount data from the database according to the current case type;

[0034] Preprocessing the historical claim amount data to obtain a valid historical claim amount;

[0035] Calculate the average of the valid historical claims amounts, and determine the average level of claims amounts for the current case type based on the calculated average;

[0036] Calculating the variance of the valid historical claim amount, and determining the variance as the degree of dispersion of the claim amount of the current case type;

[0037] Calculate the quantiles of the valid historical claim amounts, and characterize the claim amount distribution characteristics of the current case type according to the quantiles;

[0038] The average level of claim amount, the degree of dispersion of claim amount and the distribution characteristics of claim amount are taken as the distribution characteristics of claim amount.

[0039] Furthermore, the step of constructing a claim amount prediction model based on the claim amount distribution characteristics specifically includes:

[0040] Obtaining the timestamp information of the current claim case, extracting historical case information of the same period from the database according to the timestamp information, and analyzing the claim characteristics of the historical case information of the same period to obtain the characteristics of the claim amount of the same period;

[0041] Extracting historical case information from a database according to the current case type, and analyzing the claim characteristics of the historical case information to obtain the characteristics of the claim amounts of the same type;

[0042] The claim amount prediction model is constructed based on the claim amount distribution characteristics, the claim amount characteristics of the same period, and the claim amount characteristics of the same type.

[0043] In order to solve the above technical problems, the embodiment of the present application further provides a claims analysis device, which adopts the following technical solution:

[0044] An information processing module, used to obtain claim case information, pre-process the claim case information, and obtain a multimodal claim data set;

[0045] A feature processing module, used for performing feature extraction and feature fusion on the multimodal claims data set to obtain a feature representation of the claims case;

[0046] A model training module, used for performing model training based on the feature representation of the claim case to obtain a case classification model;

[0047] A case classification module is used to obtain the current claim case, classify the current claim case according to the case classification model, and obtain the current case type;

[0048] A feature extraction module is used to obtain the corresponding historical claim amount data according to the current case type, and perform amount distribution calculation based on the historical claim amount data to obtain claim amount distribution characteristics;

[0049] The amount prediction module is used to build a claim amount prediction model based on the claim amount distribution characteristics, and input the current claim case into the claim amount prediction model to obtain the case claim amount.

[0050] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0051] A computer device comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the claim analysis method as described in any one of the above when executing the computer-readable instructions.

[0052] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0053] A computer-readable storage medium having computer-readable instructions stored thereon, wherein the computer-readable instructions, when executed by a processor, implement the steps of the claim analysis method as described in any one of the above items.

[0054] Compared with the prior art, the embodiment of the present application has the following beneficial effects: the embodiment obtains claim case information, pre-processes the claim case information, and obtains a multimodal claim data set; extracts and fuses features of the multimodal claim data set to obtain a claim case feature representation; performs model training based on the claim case feature representation to obtain a case classification model; obtains the current claim case, and classifies the current claim case according to the case classification model to obtain the current case type; obtains the corresponding historical claim amount data according to the current case type, and calculates the amount distribution according to the historical claim amount data to obtain the claim amount distribution characteristics; constructs a claim amount prediction model based on the claim amount distribution characteristics, and inputs the current claim case into the claim amount prediction model to obtain the case claim amount. This effectively realizes accurate claim case amount prediction to improve the accuracy of claim case processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0056] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0057] Figure 2A flow chart of an embodiment of a claims analysis method according to the present application;

[0058] Figure 3 yes Figure 2 A flowchart of a specific implementation of step S10;

[0059] Figure 4 yes Figure 2 A flowchart of a specific implementation of step S20;

[0060] Figure 5 yes Figure 4 A flowchart of a specific implementation of step S204;

[0061] Figure 6 yes Figure 2 A flowchart of a specific implementation of step S40;

[0062] Figure 7 yes Figure 2 A flowchart of a specific implementation of step S50;

[0063] Figure 8 yes Figure 2 A flowchart of a specific implementation of step S60;

[0064] Fig. 9 is a schematic structural diagram of an embodiment of a claims analysis device according to the present application;

[0065] Fig.10 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0067] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor does it refer to non-related or alternative embodiments that are mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0068] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0069] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0070] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0071] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to a laptop computer 1011, a tablet computer 1012 or a mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0072] The server 103 may be a server that provides various services, such as a background server that provides support for a web page displayed on the terminal device 101 .

[0073] It should be noted that the claims analysis method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the claims analysis device is generally set in the server / terminal device.

[0074] It should be understood that Figure 1The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0075] Continue to refer Figure 2 , shows a flow chart of an embodiment of a method for claim analysis according to the present application. The method for claim analysis comprises the following steps:

[0076] Step S10, obtaining claim case information, preprocessing the claim case information, and obtaining a multimodal claim data set;

[0077] In this embodiment, the claim case information refers to the relevant information data of the insurance claim case of the insurance business, and the claim case information includes basic case information, specific case details, insured person information, case-related records, etc., wherein the basic case information includes the case number, reporting time, reporter information, etc., the specific case details include the time, location, cause of the accident, and the loss involved (such as personal injury, property loss, etc.), the insured person information includes the insured person's identity information, contact information, occupation and other personal background information, and the case-related records include reporting materials, investigation reports, medical certificates, legal documents, etc. The multimodal claim data set is a collection of claim data of multiple modes. In this embodiment, the multimodal claim data set includes text information, image information, and audio information of the claim case.

[0078] Step S20, performing feature extraction and feature fusion on the multimodal claims data set to obtain a feature representation of the claims case;

[0079] In this embodiment, the claim case feature representation is a feature representation generated by integrating the case text feature vector, the case image feature vector, and the case audio feature vector. The claim case feature representation is used to characterize the overall characteristics of the claim case.

[0080] Step S30, performing model training based on the claim case feature representation to obtain a case classification model;

[0081] In this embodiment, the model training step includes: according to the claim case feature representation, using a support vector machine algorithm to train a first case classification model; if the performance of the first case classification model does not meet the preset requirements, using a random forest algorithm to train a second case classification model; if the performance of the second case classification model still does not meet the preset requirements, using a deep neural network algorithm to train a third case classification model; through cross-validation, evaluating the performance of the first case classification model, the second case classification model and the third case classification model, and selecting the model with the best performance as the case classification model.

[0082] Step S40, obtaining a current claim case, and classifying the current claim case according to the case classification model to obtain the current case type;

[0083] In this embodiment, the current claim case refers to a case that currently needs to be analyzed for claims to obtain a claim amount. Feature extraction and feature fusion are performed on the current claim case to obtain a current case feature representation that represents the overall features of the current claim case. The current case feature representation is input into a case classification model for case classification processing to obtain an accurately identified current case type.

[0084] Step S50, obtaining corresponding historical claim amount data according to the current case type, and performing amount distribution calculation according to the historical claim amount data to obtain claim amount distribution characteristics;

[0085] In this embodiment, the historical claim amount data refers to the specific values ​​of the claim amounts involved in the historical claim cases of the same type (such as auto insurance, medical insurance, critical illness insurance, etc.) as the current claim case, and is mainly used to reflect the historical claim amount distribution of such cases, including statistical indicators such as the average claim amount, the highest claim amount, and the lowest claim amount. The claim amount distribution characteristics are representative information obtained by calculating the amount distribution of historical claim amount data. The claim amount distribution characteristics include the average claim amount level, the claim amount dispersion, and the claim amount distribution characteristics. The claim amount distribution characteristics can effectively help the model grasp the claim amount law corresponding to the case.

[0086] Step S60, constructing a claim amount prediction model based on the claim amount distribution characteristics, and inputting the current claim case into the claim amount prediction model to obtain the case claim amount.

[0087] In this embodiment, the claim amount prediction model is a model used to predict the claim amount of a case. The claim amount prediction model can adopt a random forest model. The random forest model is an integrated learning model based on multiple decision trees. It improves the prediction accuracy by voting or averaging to obtain the accurate claim amount of the case.

[0088] In this embodiment, by obtaining claim case information, preprocessing the claim case information to obtain a multi-modal claim data set; extracting and fusing features from the multi-modal claim data set to obtain a claim case feature representation; training a model based on the claim case feature representation to obtain a case classification model; obtaining a current claim case, distinguishing the type of the current claim case according to the case classification model to obtain the current case type; obtaining corresponding historical claim amount data according to the current case type, and calculating the amount distribution based on the historical claim amount data to obtain the characteristics of the claim amount distribution; constructing a claim amount prediction model based on the characteristics of the claim amount distribution, and inputting the current claim case into the claim amount prediction model to obtain the claim amount of the case. Thus, it effectively realizes accurate prediction of the claim amount of the claim case to improve the accuracy of claim case processing.

[0089] Reference Figure 3 , in some optional implementation manners of this embodiment, step S10 includes the following steps:

[0090] Step S101, obtain a claim case number, and extract the claim case information from the database according to the claim case number, where the claim case information includes case text information, case image information, and case audio information;

[0091] In this embodiment, the claim case number can be obtained by inputting in the system, and traversing and matching queries are performed in the database through the claim case number to effectively obtain the corresponding claim case information. The case text information includes the information of the applicant and the insured, accident description, medical expense list, diagnosis certificate, and reporting time; the case image information includes accident scene photos, medical certificate photos, and other relevant photos (such as identity photos of the applicant and the insured, etc., for identity verification); the case audio information includes accident scene recordings and call recordings.

[0092] Step S102, perform word segmentation and stop word removal processing on the case text information to obtain standard case text information;

[0093] In this embodiment, word segmentation is the process of splitting a continuous text string into individual words or phrases, and a word segmentation tool can be used to perform word segmentation on the case text information. Stop word removal refers to removing words that frequently appear in the text but contribute little to the meaning of the text, such as "de", "le", "zai", etc. The corresponding stop words can be compared and removed from the case text information through a preset stop word list, such as the Harbin Institute of Technology stop word list, the Baidu stop word list, etc.

[0094] Step S103, perform denoising and normalization processing on the case image information to obtain standard case image information;

[0095] In this embodiment, denoising refers to removing noise from an image to improve the clarity and quality of the image, and the case image information can be denoised by using a Gaussian filtering denoising algorithm. Normalization refers to adjusting the pixel values ​​of an image corresponding to the case image information to a certain range for subsequent processing or analysis, and processing the case image information into uniform image information by normalizing the pixel values ​​of the image to a range between 0 and 1 or between 0 and 255.

[0096] Step S104, performing noise reduction and endpoint detection processing on the case audio information to obtain standard case audio information;

[0097] In this embodiment, the noise reduction process refers to removing the noise component in the audio signal to improve the clarity and intelligibility of the audio. The noise reduction process can be performed by spectrum subtraction or a noise reduction algorithm based on deep learning. The endpoint detection process refers to accurately identifying the start and end positions of a valid speech segment from the audio signal for subsequent speech processing or analysis. The endpoint detection process can be implemented by an energy-based endpoint detection algorithm, which identifies the speech segment by calculating the short-time energy of the audio signal.

[0098] Step S105: Integrate the standard case text information, the standard case image information, and the standard case audio information into the multimodal claims data set.

[0099] In this embodiment, a multimodal claims data set is obtained by storing standard case text information, standard case image information, and standard case audio information in a pre-created data set folder.

[0100] This embodiment obtains the claim case number, extracts the claim case information from the database according to the claim case number, wherein the claim case information includes case text information, case image information, and case audio information; performs word segmentation and stop word removal on the case text information to obtain standard case text information; performs denoising and normalization on the case image information to obtain standard case image information; performs denoising and endpoint detection on the case audio information to obtain standard case audio information; and integrates the standard case text information, the standard case image information, and the standard case audio information into the multimodal claim data set. This effectively realizes a standard and effective multimodal claim data set based on the claim case information acquisition, so as to facilitate subsequent feature extraction and feature fusion processing.

[0101] refer to Figure 4 In some optional implementations of this embodiment, step S20 includes the following steps:

[0102] Step S201, inputting the standard case text information into a preset language processing model for feature extraction to obtain a case text feature vector;

[0103] In this embodiment, the preset language processing model can adopt a Transformer model, such as a BERT model, which converts the standard case text information into a word vector by using word embedding technology (BERT's embedding layer), and then inputs the word vector into the preset language processing model to obtain a case text feature vector.

[0104] Step S202, extracting features of the standard case image information based on a preset convolutional neural network to obtain a case image feature vector;

[0105] In this embodiment, the preset convolutional neural network is a pre-trained convolutional neural network, and the preset convolutional neural network includes an input layer, a convolutional layer, a pooling layer, a fully connected layer, and an output layer, wherein the input layer is responsible for receiving preprocessed text data, the convolutional layer is used to extract local features in the text, the pooling layer is used to reduce the feature dimension, reduce the amount of calculation, and retain important information at the same time, the fully connected layer is used to map the extracted features to the target space, and the output layer can be designed as a classifier or feature extractor according to task requirements. Feature extraction is performed on standard case image information through the preset convolutional neural network, so as to accurately extract the case image feature vector.

[0106] Step S203, extracting Mel-frequency cepstral coefficients from the standard case audio information, and using the extracted Mel-frequency cepstral coefficients as the case audio feature vector;

[0107] In this embodiment, MFCC (Mel-frequency cepstral coefficient) is an audio feature extraction method based on the auditory characteristics of the human ear, which can effectively capture the characteristics of audio signals. The Mel-frequency cepstral coefficient extraction of standard case audio information includes the following steps: performing a fast Fourier transform (FFT) on each audio frame of the standard case audio information to convert the time domain signal into a frequency domain signal; passing the spectrum obtained by FFT through a set of triangular filters uniformly distributed on the Mel scale to obtain a Mel spectrum; calculating the logarithmic energy of the output of each Mel filter; performing a discrete cosine transform on the logarithmic energy spectrum, and taking the first N coefficients after the transform (usually 12 or 13) as MFCC coefficients; combining the MFCC coefficients of each audio frame to form an MFCC sequence; and selecting the entire MFCC sequence as the case audio feature vector.

[0108] Step S204, fusing the case text feature vector, the case image feature vector, and the case audio feature vector to obtain the claim case feature representation.

[0109] In this embodiment, the fusion method of the case text feature vector, the case image feature vector, and the case audio feature vector adopts weighted summation, by assigning different weights to each feature vector and then performing weighted summation to obtain an overall claim case feature representation.

[0110] This embodiment extracts features from the standard case text information based on a preset convolutional neural network to obtain a case text feature vector; inputs the standard case image information into a preset language processing model for feature extraction to obtain a case image feature vector; extracts Mel-frequency cepstral coefficients from the standard case audio information, and uses the extracted Mel-frequency cepstral coefficients as a case audio feature vector; and fuses the case text feature vector, the case image feature vector, and the case audio feature vector, thereby effectively obtaining a claim case feature representation that characterizes the overall characteristics of the claim case, to facilitate subsequent model training processing.

[0111] refer to Figure 5 In some optional implementations of this embodiment, step S204 includes the following steps:

[0112] Step S2041, obtaining text feature vector weights, image feature vector weights, and audio feature vector weights;

[0113] In this embodiment, the text feature vector weight, image feature vector weight, and audio feature vector weight are the weight information corresponding to the case text feature vector, case image feature vector, and case audio feature vector, respectively. The text feature vector weight, image feature vector weight, and audio feature vector weight can be set and adjusted based on heuristic methods (such as based on feature vector dimension, modal importance, etc.).

[0114] Step S2042, performing weighted fusion on the case text feature vector, the case image feature vector, and the case audio feature vector according to the text feature vector weight, the image feature vector weight, and the audio feature vector weight to obtain a fused feature vector;

[0115] In this embodiment, the weights corresponding to each feature vector are multiplied by the feature vector to obtain a weighted feature vector, and then the weighted feature vectors are added and summed to achieve weighted fusion to obtain a fused feature vector after fusing multiple feature vectors.

[0116] Step S2043, performing feature extraction on the fused feature vector based on a preset convolutional neural network to obtain a feature representation of the claim case.

[0117] In this embodiment, feature extraction can be implemented based on a pre-trained CNN (such as VGG, ResNet, etc.) model. The fused feature vector is used as the input of the CNN model, and the high-level features in the fused feature vector are extracted through the CNN's convolutional layer, pooling layer, and fully connected layer structures to obtain a feature representation of the claim case.

[0118] This embodiment obtains the weight of the text feature vector, the weight of the image feature vector, and the weight of the audio feature vector; performs weighted fusion on the case text feature vector, the case image feature vector, and the case audio feature vector according to the weight of the text feature vector, the weight of the image feature vector, and the weight of the audio feature vector to obtain a fused feature vector; performs feature extraction on the fused feature vector based on a preset convolutional neural network to effectively obtain a claim case feature representation that fuses the case text feature vector, the case image feature vector, and the case audio feature vector, so as to provide reliable training data for subsequent case classification model training processing.

[0119] Continue to refer Figure 6 In some optional implementations of this embodiment, step S40 includes the following steps:

[0120] Step S401, obtaining the current case number, and extracting the current claim case from the database according to the current case number;

[0121] In this embodiment, the current case number is information corresponding to the current claim case. By using the current case number as a query condition, a traversal query is performed in the database, and the queried information is extracted to obtain the current claim case.

[0122] Step S402, extracting and fusing features of the current claim case to obtain a feature representation of the current case;

[0123] In this embodiment, the current claim case includes the corresponding current claim case text information, the current claim case image information, and the current claim case audio information. The feature extraction and feature fusion of the current claim case are the same as the feature extraction and feature fusion method performed on the multimodal claim data set mentioned above. By performing feature extraction and feature fusion on the current claim case, the current case feature representation that characterizes the overall characteristics of the current claim case can be effectively obtained.

[0124] Step S403: input the feature representation of the current case into the case classification model to obtain the current case type.

[0125] In this embodiment, the current case features are input into the case classification model to determine the case type corresponding to the current claim case. The case type is formed based on the characteristics and feature classification of the case. The case types may include traffic accident claims, medical health claims, property damage claims, life insurance claims, accidental injury claims, travel insurance claims, liability insurance claims, and other special claims (including pet insurance, legal expenses insurance, credit insurance and other special types of insurance claims).

[0126] This embodiment obtains the current case number, extracts the current claim case from the database according to the current case number; performs feature extraction and feature fusion on the current claim case to obtain the feature representation of the current case; inputs the feature representation of the current case into the case classification model, thereby effectively obtaining the current case type corresponding to the current claim case, so as to facilitate the subsequent extraction of historical claim amount data.

[0127] Continue to refer Figure 7 In some optional implementations of this embodiment, step S50 includes the following steps:

[0128] Step S501, extracting the corresponding historical claim amount data from the database according to the current case type;

[0129] In this embodiment, the current case type is used as a query condition to perform a traversal query in the database, and the queried data is extracted to obtain historical claim amount data.

[0130] Step S502, pre-processing the historical claim amount data to obtain a valid historical claim amount;

[0131] In this embodiment, preprocessing includes outlier processing and missing value processing. Outliers refer to values ​​that significantly deviate from most data points. They may be caused by data entry errors, measurement errors, or extreme events. Outliers can be detected through statistical methods, such as Z-score, IQR (interquartile range), etc., and the detected outliers are then deleted, replaced, or retained to form a preprocessing of the historical claims amount data.

[0132] Step S503, calculating the mean of the valid historical claim amounts, and determining the average level of the claim amount of the current case type according to the calculated mean;

[0133] In this embodiment, the mean calculation formula is: mean = (sum of all valid claim amounts) / (number of valid claim amounts). The mean calculation formula is used to calculate the mean of valid historical claim amounts to obtain the average level of claim amounts for the current case type.

[0134] Step S504, performing variance calculation on the valid historical claim amount, and determining the variance as the claim amount dispersion degree of the current case type;

[0135] In this embodiment, variance is a statistic that measures the degree of data dispersion. It represents the average of the squares of the differences between the data points and the mean. The variance calculation formula is: variance = Σ[(x-mean)^2] / n, where x is the amount data point of the valid historical claim amount, mean is the average of the valid historical claim amount, and n is the number of data points of the valid historical claim amount. The larger the variance, the higher the degree of dispersion of the claim amount, that is, the greater the difference between the data points; the smaller the variance, the lower the degree of dispersion of the claim amount, that is, the smaller the difference between the data points. The variance of the valid historical claim amount is used to represent the degree of dispersion of the claim amount of the current case type.

[0136] Step S505, calculating the quantiles of the valid historical claim amounts, and describing the claim amount distribution characteristics of the current case type according to the quantiles;

[0137] In this embodiment, the quantile is the value at a specific position after a set of data is sorted from small to large. Common quantiles include the 25th percentile (lower quartile), the 50th percentile (median), the 75th percentile (upper quartile), etc. In spreadsheet software (such as Excel), the built-in PERCENTILE function can be used to calculate the value of a specific percentile to obtain the quantile. Characterizing the distribution characteristics of the claim amount according to the quantile includes the distribution form: by comparing the values ​​of different quantiles, the distribution form of the claim amount can be preliminarily determined. For example, if the median is much lower than the upper quartile and much higher than the lower quartile, it may indicate that the claim amount has a large volatility; Central tendency: The median, as the center position of the distribution, can reflect the central tendency of the claim amount. If the median is low, it means that most claims are low; conversely, if the median is high, it means that most claims are high; Dispersion: By comparing the difference between different quantiles (such as the interquartile range, which is the difference between the upper quartile and the lower quartile), the dispersion of the claim amounts can be evaluated. The larger the difference, the higher the dispersion of the claim amounts; the smaller the difference, the lower the dispersion of the claim amounts; Outlier detection: Extremely low or high quantiles (such as the 1st percentile or the 99th percentile) can help identify outliers or extreme cases in the claim amounts.

[0138] Step S506, taking the average level of claim amount, the dispersion degree of claim amount, and the distribution characteristics of claim amount as the claim amount distribution characteristics.

[0139] In this embodiment, data integration is performed on the average level of claim settlement amount, the degree of dispersion of claim settlement amount, and the distribution characteristics of claim settlement amount to obtain the distribution characteristics of claim settlement amount.

[0140] This embodiment calculates the degree of influence of the characteristics of the crop growth monitoring data set on the crop growth status assessment result according to a preset feature importance analysis method; extracts a preset influence degree threshold to determine whether the characteristic influence degree is greater than or equal to the influence degree threshold; if the characteristic influence degree value is greater than or equal to the influence degree threshold, the characteristic corresponding to the characteristic influence degree value is used as the key influence factor; if the characteristic influence degree value is less than the influence degree threshold, the characteristic corresponding to the characteristic influence degree value is used as a non-key influence factor; the crop growth monitoring data set is screened according to the key influence factors to obtain a key influence data set; a key time series data set is constructed according to the key influence data set; the key time series data set is feature extracted according to the time series analysis method to obtain a growth trend feature vector; the growth trend feature vector and the crop growth status assessment result are input into a pre-trained key growth assessment model, thereby effectively obtaining a growth trend prediction result that accurately predicts crop growth, so as to facilitate subsequent crop growth claims analysis.

[0141] Continue to refer Figure 8 In some optional implementations of this embodiment, step S60 includes the following steps:

[0142] Step S601, obtaining the timestamp information of the current claim case, extracting historical case information of the same period from the database according to the timestamp information, and analyzing the claim characteristics of the historical case information of the same period to obtain the characteristics of the claim amount of the same period;

[0143] In this embodiment, the timestamp information is obtained by querying the timestamp automatically generated when the current claim case is submitted, and the timestamp information is used as a query condition to traverse and query in the database to obtain the historical case information corresponding to the current claim case. In this embodiment, the historical case information corresponds to the case time range, which is initially set to one month and can be adjusted accordingly according to actual conditions. The claim characteristics analysis of historical case information includes statistical distribution: calculating the average, median, maximum, minimum and other statistics of the claim amount of historical cases in the same period to understand the distribution of the claim amount; trend analysis: drawing a trend chart of the claim amount over time to observe whether there is an obvious fluctuation or trend; classification analysis: classifying the case according to the claim type or claim reason, calculating the claim amount of each type of case respectively, and comparing and analyzing. Based on the above analysis results, the claim characteristics of historical cases in the same period are summarized. For example, it can be pointed out which types of cases have higher claim amounts, which reasons cause the claim amount to fluctuate greatly, etc., so as to obtain the characteristics of the claim amount in the same period.

[0144] Step S602, extracting historical case information from the database according to the current case type, and analyzing the claim characteristics of the historical case information to obtain the characteristics of the claim amounts of the same type;

[0145] In this embodiment, the current case type is used as a query condition to perform a traversal query in the database to obtain the historical case information corresponding to the current case type. The claim characteristics analysis of historical case information includes claim amount statistics: calculating the average, median, maximum, minimum and other statistics of the same type of claim amounts to understand the distribution of the claim amounts, and drawing a frequency distribution histogram or box plot of the claim amounts to intuitively display the distribution form and discreteness of the claim amounts; claim amount trend analysis: if the historical case data contains information at multiple time points, a trend chart of the claim amount over time can be drawn to observe whether there are obvious fluctuations or trends, and analyze the reasons for the claim amount trend, such as policy changes, market environment changes, customer behavior changes, etc.; claim amount and case characteristics correlation analysis: analyze the correlation between the claim amount and case characteristics (such as claim reasons, case complexity, claim amount, etc.), and use tools such as scatter plots, line graphs or correlation matrices for visual analysis to reveal potential correlations and trends; claim amount characteristics summary: based on the above analysis, summarize the characteristics of the same type of claim amounts. For example, it can point out which claims reasons lead to higher claims amounts, which case characteristics are significantly correlated with claims amounts, etc., in order to obtain the characteristics of claims amounts in the same period.

[0146] Step S603, constructing the claim amount prediction model according to the claim amount distribution characteristics, the claim amount characteristics of the same period, and the claim amount characteristics of the same type.

[0147] In this embodiment, the claim amount prediction model adopts a random forest model, and by adjusting the parameters of the random forest model, such as the number of trees, the maximum depth, the minimum number of samples, etc., and using the claim amount distribution characteristics, the claim amount characteristics of the same period, and the claim amount characteristics of the same type as training data, multiple training subsets are generated through bootstrap sampling, and a decision tree is trained for each training subset. During the training process, nodes are split by randomly selecting feature subsets to increase the diversity of the model, thereby effectively constructing an initial claim amount prediction model. And through cross-validation and feature importance evaluation, the features that contribute most to the claim amount prediction are identified, and the feature importance evaluation results and cross-validation results are obtained. Finally, according to the feature importance evaluation results and cross-validation results, the parameters of the random forest are adjusted, such as increasing or decreasing the number of trees, adjusting the maximum depth, etc., to optimize the performance of the model and obtain a claim amount prediction model.

[0148] This embodiment obtains claims analysis environmental information; determines the risk status based on the claims analysis environmental information, the crop growth status assessment results, and the growth trend prediction results to obtain the risk status type; extracts the corresponding risk warning model from the database based on the risk status type; inputs the claims analysis environmental information, the crop growth status assessment results, and the growth trend prediction results into the risk warning model, thereby effectively obtaining risk warning information for accurate claims analysis based on the crop growth status assessment results and the growth trend prediction results, so as to facilitate risk avoidance based on the risk warning information.

[0149] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0150] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0151] Further references Fig. 9 , as a response to the above Figure 1 In order to realize the method shown in the figure, the present application provides an embodiment of a claims analysis device, and the device embodiment is Figure 1 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0152] like Fig. 9 As shown, the claim analysis device 700 described in this embodiment includes: an information processing module 701, a feature processing module 702, a model training module 703, a case classification module 704, a feature extraction module 705, and an amount prediction module 706. Among them:

[0153] The information processing module 701 is used to obtain claim case information, pre-process the claim case information, and obtain a multimodal claim data set;

[0154] A feature processing module 702 is used to extract and fuse features of the multimodal claims data set to obtain a feature representation of the claims case;

[0155] A model training module 703 is used to perform model training based on the claim case feature representation to obtain a case classification model;

[0156] The case classification module 704 is used to obtain the current claim case, classify the current claim case according to the case classification model, and obtain the current case type;

[0157] The feature extraction module 705 is used to obtain the corresponding historical claim amount data according to the current case type, and perform amount distribution calculation based on the historical claim amount data to obtain claim amount distribution characteristics;

[0158] The amount prediction module 706 is used to construct a claim amount prediction model based on the claim amount distribution characteristics, and input the current claim case into the claim amount prediction model to obtain the case claim amount.

[0159] By adopting the above-mentioned claims analysis device, this embodiment can obtain claims case information, pre-process the claims case information, and obtain a multimodal claims data set; perform feature extraction and feature fusion on the multimodal claims data set to obtain a claims case feature representation; perform model training based on the claims case feature representation to obtain a case classification model; obtain the current claims case, classify the current claims case according to the case classification model, and obtain the current case type; obtain the corresponding historical claims amount data according to the current case type, and calculate the amount distribution according to the historical claims amount data to obtain the claims amount distribution characteristics; construct a claims amount prediction model based on the claims amount distribution characteristics, and input the current claims case into the claims amount prediction model to obtain the case claim amount. This effectively realizes accurate claims case amount prediction to improve the accuracy of claims case processing.

[0160] To solve the above technical problems, the present application also provides a computer device. Fig.10 , Fig.10 This is a basic structural block diagram of the computer device in this embodiment.

[0161] The computer device 8 includes a memory 81, a processor 82, and a network interface 83 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 8 with components 81-83, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0162] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0163] The memory 81 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 81 can be an internal storage unit of the computer device 8, such as a hard disk or memory of the computer device 8. In other embodiments, the memory 81 can also be an external storage device of the computer device 8, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the computer device 8. Of course, the memory 81 can also include both the internal storage unit of the computer device 8 and its external storage device. In this embodiment, the memory 81 is generally used to store the operating system and various application software installed on the computer device 8, such as computer-readable instructions of the claim analysis method, etc. In addition, the memory 81 can also be used to temporarily store various types of data that have been output or are to be output.

[0164] The processor 82 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 82 is generally used to control the overall operation of the computer device 8. In this embodiment, the processor 82 is used to run computer-readable instructions stored in the memory 81 or process data, such as computer-readable instructions for running the claim analysis method.

[0165] The network interface 83 may include a wireless network interface or a wired network interface. The network interface 83 is generally used to establish a communication connection between the computer device 8 and other electronic devices.

[0166] By adopting the above-mentioned computer equipment, this embodiment can obtain claim case information, pre-process the claim case information, and obtain a multimodal claim data set; perform feature extraction and feature fusion on the multimodal claim data set to obtain a claim case feature representation; perform model training based on the claim case feature representation to obtain a case classification model; obtain the current claim case, classify the current claim case according to the case classification model, and obtain the current case type; obtain the corresponding historical claim amount data according to the current case type, and calculate the amount distribution according to the historical claim amount data to obtain the claim amount distribution characteristics; construct a claim amount prediction model based on the claim amount distribution characteristics, and input the current claim case into the claim amount prediction model to obtain the case claim amount. This effectively realizes accurate claim case amount prediction to improve the accuracy of claim case processing.

[0167] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the claim analysis method as described above.

[0168] By adopting the above-mentioned computer-readable storage medium, this embodiment can obtain claim case information, pre-process the claim case information, and obtain a multimodal claim data set; perform feature extraction and feature fusion on the multimodal claim data set to obtain a claim case feature representation; perform model training based on the claim case feature representation to obtain a case classification model; obtain the current claim case, classify the current claim case according to the case classification model, and obtain the current case type; obtain the corresponding historical claim amount data according to the current case type, and calculate the amount distribution according to the historical claim amount data to obtain the claim amount distribution characteristics; construct a claim amount prediction model based on the claim amount distribution characteristics, and input the current claim case into the claim amount prediction model to obtain the case claim amount. This effectively realizes accurate claim case amount prediction to improve the accuracy of claim case processing.

[0169] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0170] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A claims analysis method, characterized in that: The steps include: Acquire claim case information, pre-process the claim case information, and obtain a multimodal claim data set; Performing feature extraction and feature fusion on the multimodal claims data set to obtain a feature representation of the claims case; Performing model training based on the claim case feature representation to obtain a case classification model; Obtaining a current claim case, and classifying the current claim case according to the case classification model to obtain the current case type; Obtaining corresponding historical claim amount data according to the current case type, and performing amount distribution calculation based on the historical claim amount data to obtain claim amount distribution characteristics; A claim amount prediction model is constructed based on the claim amount distribution characteristics, and the current claim case is input into the claim amount prediction model to obtain the case claim amount.

2. The claim analysis method according to claim 1, characterized in that: The step of obtaining the claim case information, preprocessing the claim case information, and obtaining a multimodal claim data set specifically includes: Obtaining a claim case number, and extracting the claim case information from a database according to the claim case number, wherein the claim case information includes case text information, case image information, and case audio information; Performing word segmentation and stop word removal on the case text information to obtain standard case text information; De-noising and normalizing the case image information to obtain standard case image information; Performing noise reduction and endpoint detection processing on the case audio information to obtain standard case audio information; The standard case text information, the standard case image information, and the standard case audio information are integrated into the multimodal claims data set.

3. The claim analysis method according to claim 1, characterized in that: The multimodal claims data set includes standard case text information, standard case image information, and standard case audio information. The step of performing feature extraction and feature fusion on the multimodal claims data set to obtain a feature representation of the claims case specifically includes: Inputting the standard case text information into a preset language processing model for feature extraction to obtain a case text feature vector; Extracting features of the standard case image information based on a preset convolutional neural network to obtain a case image feature vector; Extracting Mel-frequency cepstral coefficients from the standard case audio information, and using the extracted Mel-frequency cepstral coefficients as the case audio feature vector; The case text feature vector, the case image feature vector, and the case audio feature vector are fused to obtain the claim case feature representation.

4. The claim analysis method according to claim 3, characterized in that: The step of fusing the case text feature vector, the case image feature vector, and the case audio feature vector to obtain the claim case feature representation specifically includes: Obtain text feature vector weights, image feature vector weights, and audio feature vector weights; Performing weighted fusion on the case text feature vector, the case image feature vector, and the case audio feature vector according to the text feature vector weight, the image feature vector weight, and the audio feature vector weight to obtain a fused feature vector; The fused feature vector is subjected to feature extraction based on a preset convolutional neural network to obtain a feature representation of the claim case.

5. The claim analysis method according to claim 1, characterized in that: The step of obtaining the current claim case and classifying the current claim case according to the case classification model to obtain the current case type specifically includes: Obtaining a current case number, and extracting a current claim case from a database according to the current case number; Performing feature extraction and feature fusion on the current claim case to obtain a feature representation of the current case; The feature representation of the current case is input into the case classification model to obtain the current case type.

6. The claim analysis method according to claim 1, characterized in that: The step of obtaining the corresponding historical claim amount data according to the current case type, and performing amount distribution calculation according to the historical claim amount data to obtain claim amount distribution characteristics specifically includes: Extracting the corresponding historical claim amount data from the database according to the current case type; Preprocessing the historical claim amount data to obtain a valid historical claim amount; Calculate the average of the valid historical claims amounts, and determine the average level of claims amounts for the current case type based on the calculated average; Calculating the variance of the valid historical claim amount, and determining the variance as the degree of dispersion of the claim amount of the current case type; Calculate the quantiles of the valid historical claim amounts, and characterize the claim amount distribution characteristics of the current case type according to the quantiles; The average level of claim amount, the degree of dispersion of claim amount and the distribution characteristics of claim amount are taken as the distribution characteristics of claim amount.

7. The claim analysis method according to claim 1, characterized in that: The step of constructing a claim amount prediction model based on the claim amount distribution characteristics specifically includes: Obtaining the timestamp information of the current claim case, extracting historical case information of the same period from the database according to the timestamp information, and analyzing the claim characteristics of the historical case information of the same period to obtain the characteristics of the claim amount of the same period; Extracting historical case information from a database according to the current case type, and analyzing the claim characteristics of the historical case information to obtain the characteristics of the claim amounts of the same type; The claim amount prediction model is constructed based on the claim amount distribution characteristics, the claim amount characteristics of the same period, and the claim amount characteristics of the same type.

8. A claims analysis device, characterized in that: include: An information processing module, used to obtain claim case information, pre-process the claim case information, and obtain a multimodal claim data set; A feature processing module, used for performing feature extraction and feature fusion on the multimodal claims data set to obtain a feature representation of the claims case; A model training module, used for performing model training based on the feature representation of the claim case to obtain a case classification model; A case classification module is used to obtain the current claim case, classify the current claim case according to the case classification model, and obtain the current case type; A feature extraction module is used to obtain the corresponding historical claim amount data according to the current case type, and perform amount distribution calculation based on the historical claim amount data to obtain claim amount distribution characteristics; The amount prediction module is used to build a claim amount prediction model based on the claim amount distribution characteristics, and input the current claim case into the claim amount prediction model to obtain the case claim amount.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the claim analysis method according to any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the claim analysis method according to any one of claims 1 to 7.

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