Automobile insurance claim settlement method, automobile insurance claim settlement device, electronic device and storage medium

Abstract

The present application provides a method, device, and storage medium for settling auto insurance claims, and relates to the field of financial technology. The method comprises extracting claim features from an auto insurance policy, a first historical driving behavior during a first driving period, a first historical location, and a first historical driving environment to obtain an initial hidden claim state; extracting claim features from the auto insurance policy, a second historical driving behavior during a second driving period, a second historical location, a second historical driving environment, and the initial hidden claim state to obtain a target hidden claim state; and determining a claim category for a target vehicle based on the target hidden claim state. The claim category indicates whether a claim is approved or rejected, thereby improving the efficiency of auto insurance claims.

Description

Technical Field

[0001] The present application relates to the field of financial technology, and in particular to a method for settling automobile insurance claims, an automobile insurance claims device, an electronic device, and a storage medium. Background Art

[0002] When a vehicle is involved in a traffic accident and involves an auto insurance claim, it goes through multiple stages, including accident reporting, on-site investigation, claim document submission, vehicle assessment, and claim approval. These stages are complex and time-consuming, resulting in low claims processing efficiency. Therefore, improving the efficiency of auto insurance claims processing has become a pressing issue. Summary of the Invention

[0003] The main purpose of the embodiments of the present application is to propose a car insurance claims method, a car insurance claims device, an electronic device and a storage medium, aiming to improve the efficiency of car insurance claims.

[0004] To achieve the above objectives, a first aspect of an embodiment of the present application provides a method for settling auto insurance claims, the method comprising:

[0005] Obtaining automobile insurance claim data of a target vehicle; the automobile insurance claim data includes an automobile insurance policy and a driving record, the driving record including a first driving period, a first historical driving behavior, a first historical location, and a first historical driving environment of the target vehicle during the first driving period, a second driving period, and a second historical driving behavior, a second historical location, and a second historical driving environment of the target vehicle during the second driving period; the second driving period being after the first driving period;

[0006] Extracting claim features from the auto insurance policy, the first historical driving behavior, the first historical location, and the first historical driving environment to obtain an initial hidden claim state;

[0007] performing claim feature extraction on the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the initial claim hidden state to obtain a target claim hidden state;

[0008] The claim category of the target vehicle is determined according to the target claim hidden state; the claim category is used to indicate whether the claim is approved or rejected.

[0009] In some embodiments, extracting claim features from the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the initial claim hidden state to obtain a target claim hidden state includes:

[0010] Performing feature splicing on the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the initial claim hidden state to obtain an original claim splicing feature;

[0011] Obtaining first impact degree data of the initial claim hidden state on the target claim hidden state according to the original claim splicing feature;

[0012] determining a reference hidden state according to the first impact degree data and the initial claim hidden state;

[0013] determining a first candidate hidden state based on the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the original claim splicing feature;

[0014] The target claim hidden state is determined according to the first impact degree data, the reference hidden state and the first candidate hidden state.

[0015] In some embodiments, obtaining first impact degree data of the initial claim hidden state on the target claim hidden state based on the original claim splicing feature includes:

[0016] Performing linear mapping on the original claim splicing features to obtain candidate claim splicing features;

[0017] The candidate claim splicing features are activated to obtain the first impact degree data.

[0018] In some embodiments, determining the first candidate hidden state based on the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the original claim splicing features includes:

[0019] Obtaining second impact degree data of the initial claim hidden state on the first candidate hidden state according to the original claim splicing feature;

[0020] determining a baseline hidden state based on the second impact level data and the initial claim hidden state;

[0021] The first candidate hidden state is determined according to the vehicle insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the baseline hidden state.

[0022] In some embodiments, determining the first candidate hidden state based on the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the baseline hidden state includes:

[0023] Performing feature splicing on the vehicle insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the baseline hidden state to obtain an initial splicing feature;

[0024] Performing linear mapping on the initial splicing features to obtain target splicing features;

[0025] Activate the target splicing feature to obtain the first candidate hidden state.

[0026] In some embodiments, determining the target claim hidden state based on the first impact data, the reference hidden state, and the first candidate hidden state includes:

[0027] determining a second candidate hidden state based on the first influence degree data and the first candidate hidden state;

[0028] Feature fusion is performed on the reference hidden state and the second candidate hidden state to obtain the target claim hidden state.

[0029] In some embodiments, determining a second candidate hidden state based on the first influence degree data and the first candidate hidden state includes:

[0030] Obtaining third impact degree data of the first candidate hidden state on the target claim hidden state according to the first impact degree data;

[0031] The first candidate hidden state is state-weighted according to the third influence degree data to obtain the second candidate hidden state.

[0032] To achieve the above-mentioned objectives, a second aspect of an embodiment of the present application provides a vehicle insurance claims settlement device, the device comprising:

[0033] an acquisition module, configured to acquire automobile insurance claim data of a target vehicle; the automobile insurance claim data including an automobile insurance policy and a driving record, the driving record including a first driving period, a first historical driving behavior, a first historical location, and a first historical driving environment of the target vehicle during the first driving period, a second driving period, a second historical driving behavior, a second historical location, and a second historical driving environment of the target vehicle during the second driving period; the second driving period being subsequent to the first driving period;

[0034] an initial feature extraction module, configured to extract claim features from the vehicle insurance policy, the first historical driving behavior, the first historical location, and the first historical driving environment to obtain an initial claim hidden state;

[0035] a target feature extraction module, configured to extract claim features from the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the initial claim hidden state to obtain a target claim hidden state;

[0036] A determination module is used to determine the claim category of the target vehicle according to the target claim hidden state; the claim category is used to indicate whether the claim is approved or rejected.

[0037] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the method described in the first aspect when executing the computer program.

[0038] To achieve the above-mentioned purpose, the fourth aspect of the embodiments of the present application proposes a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method described in the first aspect.

[0039] The motor vehicle insurance claims settlement method, motor vehicle insurance claims settlement device, electronic device, and computer-readable storage medium proposed in the embodiments of the present application obtain motor vehicle insurance claims settlement data for a target vehicle. The motor vehicle insurance claims settlement data includes a motor vehicle insurance policy and driving record. The motor vehicle insurance policy is the basis for motor vehicle insurance claims settlement, and the driving record is a significant factor influencing the settlement of motor vehicle insurance claims. The method then determines whether to settle a claim for the target vehicle based on the motor vehicle insurance policy and driving record. Claims feature extraction is performed on the motor vehicle insurance policy, the target vehicle's first historical driving behavior, first historical location, and first historical driving environment during a first driving period, to extract feature information from the motor vehicle insurance claims data that is useful for settlement of motor vehicle insurance claims, achieving effective feature extraction and obtaining an initial hidden claim state. Driving data such as historical driving behavior, historical location, and historical driving environment during different driving periods in the driving record are inherently interconnected, and driving data from multiple driving periods jointly determines the outcome of the motor vehicle insurance claim. To capture the long-term dependencies between driving data from different driving periods in the driving record, claims feature extraction is performed on the motor vehicle insurance policy, the target vehicle's second historical driving behavior, second historical location, second historical driving environment during a second driving period, and the initial hidden claim state, to obtain a target hidden claim state. The claim category of the target vehicle is determined based on the target claim hidden status. The claim category is used to indicate whether the claim is approved or rejected. This allows claims to be automatically processed for the target vehicle based on the target claim hidden status without going through a series of complex operational processes, thereby improving the efficiency of auto insurance claims. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 This is a flow chart of the automobile insurance claims method provided in an embodiment of the present application;

[0041] Figure 2yes Figure 1 Flowchart of step S130 in FIG.

[0042] Figure 3 yes Figure 2 Flowchart of step S220 in FIG.

[0043] Figure 4 yes Figure 2 Flowchart of step S240 in FIG.

[0044] Figure 5 yes Figure 4 Flowchart of step S430 in FIG.

[0045] Figure 6 yes Figure 2 Flowchart of step S250 in FIG.

[0046] Figure 7 yes Figure 6 Flowchart of step S610 in FIG.

[0047] Figure 8 This is a schematic diagram of the structure of the vehicle insurance claims settlement device provided in an embodiment of the present application;

[0048] Figure 9 This is a schematic diagram of the hardware structure of the electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0050] It should be noted that although the device schematics illustrate functional module divisions and the flowcharts illustrate logical sequences, in certain circumstances, the steps shown or described may be performed in a sequence that differs from the module divisions in the device or the sequence in the flowcharts. The terms "first," "second," and so on, in the specification, claims, and drawings, are used to distinguish similar items and are not necessarily used to describe a specific sequence or precedence.

[0051] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this application pertains. The terms used herein are for the purpose of describing the embodiments of this application only and are not intended to limit this application.

[0052] When a vehicle is involved in a traffic accident and involves an auto insurance claim, it goes through multiple stages, including accident reporting, on-site investigation, claim document submission, vehicle assessment, and claim approval. These stages are complex and time-consuming, resulting in low claims processing efficiency. Therefore, improving the efficiency of auto insurance claims processing has become a pressing issue.

[0053] Based on this, the embodiments of the present application provide a car insurance claims method, a car insurance claims device, an electronic device and a computer-readable storage medium, aiming to improve the efficiency of car insurance claims.

[0054] The car insurance claims settlement method, car insurance claims settlement device, electronic device and computer-readable storage medium provided in the embodiments of the present application are specifically illustrated through the following embodiments. First, the car insurance claims settlement method in the embodiments of the present application is described.

[0055] The auto insurance claims settlement method provided in the embodiment of the present application relates to the field of financial technology. The auto insurance claims settlement method provided in the embodiment of the present application can be applied in a terminal, can be applied in a server side, or can be software running in a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a laptop computer, a desktop computer, etc.; the server side can be configured as an independent physical server, or as a server cluster or distributed system composed of multiple physical servers, or as a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms; the software can be an application that implements the auto insurance claims settlement method, etc., but is not limited to the above forms.

[0056] The present application can be used in many general or special computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and the like. The present application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, and the like that perform specific tasks or implement specific abstract data types. The present application can also be practiced in distributed computing environments in which tasks are performed by remote processing devices connected via a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media, including storage devices.

[0057] It should be noted that in each specific embodiment of the present application, when it comes to the need to perform relevant processing based on data related to the user's identity or characteristics, such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first, and the collection, use, and processing of such data will comply with relevant laws, regulations, and standards. In addition, when the embodiment of the present application needs to obtain the user's sensitive personal information, the user's separate permission or consent will be obtained through a pop-up window or by jumping to a confirmation page. After clearly obtaining the user's separate permission or consent, the necessary user-related data for the normal operation of the embodiment of the present application will be obtained.

[0058] Figure 1 This is an optional flowchart of the car insurance claim settlement method provided in the embodiment of the present application. Figure 1 The method may include but is not limited to steps S110 to S140.

[0059] Step S110, obtaining the automobile insurance claim data of the target vehicle; the automobile insurance claim data includes the automobile insurance policy and the driving record, the driving record including a first driving period, a first historical driving behavior, a first historical location, and a first historical driving environment of the target vehicle during the first driving period, a second driving period, a second historical driving behavior, a second historical location, and a second historical driving environment of the target vehicle during the second driving period; the second driving period is subsequent to the first driving period;

[0060] Step S120 , extracting claim features from the vehicle insurance policy, the first historical driving behavior, the first historical location, and the first historical driving environment to obtain an initial hidden claim state;

[0061] Step S130 , extracting claim features from the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the initial claim hidden state to obtain a target claim hidden state;

[0062] Step S140, determining the claim category of the target vehicle according to the target claim hidden state; the claim category is used to indicate whether the claim is approved or rejected.

[0063] Steps S110 to S140 shown in the embodiment of the present application capture the long-term dependencies between historical driving behaviors, historical locations, and historical driving environments during different driving periods in the driving records to achieve automated auto insurance claims without going through a series of complex operational processes, thereby improving the efficiency of auto insurance claims.

[0064] In step S110 of some embodiments, the target vehicle's auto insurance claim data is obtained. The target vehicle is the vehicle for which the auto insurance claim is pending, and the auto insurance claim data is the materials required to execute the auto insurance claim process. The auto insurance claim data includes the auto insurance policy and driving records. The auto insurance policy includes information such as the insurance type, insured amount, insurance period, and insured vehicle information. When purchasing auto insurance, the insurance company will provide the auto insurance policy, which can be viewed and downloaded through the insurance company's online service platform. The driving record is used to record the target vehicle's driving conditions during operation. The driving record includes a first driving period, a first historical driving behavior, a first historical location, and a first historical driving environment of the target vehicle during the first driving period, a second driving period, and a second historical driving behavior, a second historical location, and a second historical driving environment of the target vehicle during the second driving period. The first driving period and the second driving period are the driving periods of the target vehicle before the current traffic accident, and the second driving period occurs after the first driving period. The first historical driving behavior, the first historical location, and the first historical driving environment represent the target vehicle's driving behavior, location, and driving environment during the first driving period, respectively. The second historical driving behavior, second historical location, and second historical driving environment are the target vehicle's driving behavior, location, and driving environment during the second driving period, respectively. Vehicles are typically equipped with a dashcam and a location navigation system, which can be used to capture driving behavior and driving environment. Driving behavior includes speed, acceleration, braking frequency, steering wheel angle, brake pedal status, etc. The driving environment includes road conditions (such as waterlogging, bumps), weather conditions (such as fog, rain, snow), and traffic conditions (such as congestion). The vehicle's location can be captured through the location navigation system.

[0065] In step S120 of some embodiments, to implement automated auto insurance claims, an initialization feature vector is obtained. The auto insurance policy, the first historical driving behavior, the first historical location, the first historical driving environment, and the initialization feature vector are input into a feature extraction model to extract claim features, thereby obtaining an initial claim hidden state. The feature extraction model may be a gated recurrent unit (GRU). The initial claim hidden state represents the feature information of the target vehicle during the first driving period learned by the feature extraction model during the feature extraction process. A zero vector may be used as the initialization feature vector, or multiple random values ​​may be drawn from a preset data distribution to construct the initialization feature vector. The preset data distribution may be a Gaussian distribution, a uniform distribution, or the like. Multiple random values ​​may be drawn from a Gaussian distribution with a mean of 0 and a given standard deviation, such as 0.01 or 0.1. Alternatively, multiple random values ​​may be drawn from a uniform distribution with a data range of [-a, a], where a is a small positive number, such as 0.1.

[0066] See also Figure 2In some embodiments, step S130 may include but is not limited to steps S210 to S250:

[0067] Step S210 , performing feature splicing on the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the initial claim hidden state to obtain an original claim splicing feature;

[0068] Step S220, obtaining first impact degree data of the initial claim hidden state on the target claim hidden state based on the original claim splicing feature;

[0069] Step S230, determining a reference hidden state based on the first impact degree data and the initial claim hidden state;

[0070] Step S240 , determining a first candidate hidden state based on the vehicle insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the original claim splicing features;

[0071] Step S250: Determine a target claim hidden state based on the first impact degree data, the reference hidden state, and the first candidate hidden state.

[0072] In step S210 of some embodiments, driving data such as driving behavior, location, and driving environment during different driving periods in the driving record form a time series. The driving data changes dynamically with different driving periods, making the time series temporally dependent. The inherent correlation between driving data during driving periods with long intervals makes the time series long-term dependent. To capture the temporal and long-term dependencies in the time series, claim features are extracted for the auto insurance policy, the second historical driving behavior, second historical location, second historical driving environment of the target vehicle during the second driving period, and the initial claim hidden state. Specifically, feature splicing is performed on the auto insurance policy, the second historical driving behavior, second historical location, second historical driving environment, and the initial claim hidden state, fusing different feature information to provide a more comprehensive and richer feature representation, thereby obtaining the original claim splicing features.

[0073] During the auto insurance claims process, the initial claim hidden state only focuses on the driving characteristics of the target vehicle in the first driving period, ignoring the driving characteristics of the target vehicle in the second driving period and the dependencies between different driving periods. As a result, the initial claim hidden state only contains local features, while the original claim splicing features take into account the dynamic data characteristics of the entire vehicle driving process, providing a more comprehensive feature representation, thereby improving the accuracy of auto insurance claims prediction.

[0074] In step S220 of some embodiments, in order to solve the problems of gradient vanishing and difficulty in modeling long-term dependencies in traditional recurrent neural networks (RNNs), long short-term memory networks (LSTMs) are usually used to extract long-term dependencies in time series. The long short-term memory network introduces an input gate, a forget gate, an output gate, and an additional unit state, which makes the network have a large number of parameters and requires more computing resources and time for claim feature extraction. It takes more than 20 seconds to predict the claim category of a vehicle, resulting in low efficiency in auto insurance claims and reducing the owner's claims experience. In addition, when dealing with long-term dependency problems, the long short-term memory network tends to pay too much attention to the last input data, reducing the accuracy of auto insurance claims. For example, the driving record includes the first driving data of the target vehicle in the first driving period, the second driving data in the second driving period, and the third driving data in the third driving period. The second driving period is after the first driving period, and the third driving period is after the second driving period. The hidden states of the claim extracted based on the first driving data and the second driving data both indicate that the claim category is rejected, and the hidden state of the claim extracted based on the third driving data indicates that the claim category is agreed. LSTM will ignore the first two rejected claims and only focus on the last claim, resulting in low accuracy of auto insurance claims.

[0075] In order to improve the efficiency and accuracy of auto insurance claims, the embodiment of the present application optimizes the time series through a gating mechanism to control the transmission of information, so as to more effectively capture long-term and short-term dependencies and predict more diverse time dynamic features. The gating mechanism includes an update gate and a reset gate, which retain important historical hidden state information. For example, in three claims, more attention will be paid to the rejections of the first two claims. The update gate is used to capture long-term dependencies in the sequence, and the reset gate is used to capture short-term dependencies in the sequence. The gating mechanism merges the input gate and the forget gate in the LSTM model into an update gate, which simplifies the structure of the LSTM model and makes the convergence speed of the gating mechanism faster than the LSTM model. Under the same data and training time, the gating mechanism reaches the optimal performance faster to achieve real-time auto insurance claims.

[0076] The update gate determines the percentage of the initial claim hidden state retained in the target claim hidden state. The original claim concatenation features are fed into the update gate to calculate the first impact of the initial claim hidden state on the target claim hidden state. This first impact is the output parameter of the update gate, indicating the percentage of the initial claim hidden state in the target claim hidden state. The first impact is a value between [0, 1].

[0077] In step S230 of some embodiments, the first impact degree data and the initial claim hidden state are multiplied to obtain a reference hidden state.

[0078] In step S240 of some embodiments, to capture complex patterns and dependencies in the driving record, candidate hidden states are calculated based on the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the original claim splicing features to obtain a first candidate hidden state. The first candidate hidden state is a potential new state adjusted by the reset gate, not a true hidden state. It is used to generate the true hidden state, namely the target claim hidden state, obtained by combining the initial claim hidden state with the current input data.

[0079] In step S250 of some embodiments, the embodiments of the present application do not introduce additional unit states like the LSTM model, but directly construct a linear dependency relationship based on the first impact degree data, the reference hidden state and the first candidate hidden state to obtain the target claim hidden state, thereby avoiding additional calculations and improving the efficiency of auto insurance claims.

[0080] Through the above steps S210 to S250, it is possible to capture the long-term and short-term dependencies in the time series, determine the time-varying interactions between driving behavior, location, and driving environment in the auto insurance claim data, and the impact of these factors on the auto insurance claim, and obtain the target claim hidden state.

[0081] See also Figure 3 In some embodiments, step S220 may include but is not limited to steps S310 to S320:

[0082] Step S310 , performing linear mapping on the original claim splicing features to obtain candidate claim splicing features;

[0083] Step S320: Activate the candidate claim splicing features to obtain first impact degree data.

[0084] In step S310 of some embodiments, the update gate includes a weight matrix, which includes weights of the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the initial claim hidden state. The weight matrix is ​​multiplied by the original claim splicing feature to obtain the candidate claim splicing feature.

[0085] In step S320 of some embodiments, the candidate claim splicing features are activated using a sigmoid activation function to obtain first impact data. The calculation formula for the first impact data is as follows:

[0086] z t =σ(W z ·[h t-1,x t ]),

[0087] Among them, z t represents the first influence degree data; σ represents the sigmoid activation function; W z represents the weight matrix; represents the matrix multiplication operation; h t-1 Indicates the initial claim hidden state; x t represents the current input data, including the car insurance policy, the second historical driving behavior, the second historical location, and the second historical driving environment. t-1 represents the first driving period, and t represents the second driving period.

[0088] The update gate determines how much of the target claim hidden state comes from the initial claim hidden state (old information) and how much comes from the current input data x t (New Information) is used to weigh new information against old information, thereby determining what to retain and what to update. When the first impact level data is close to 1, the initial claim hidden state tends to be retained rather than updated. When the first impact level data is close to 0, the initial claim hidden state is not retained, and the hidden state is updated based on the current input data. For example, a first impact level data of 0.82 indicates that the initial claim hidden state is important information and will not be replaced based on the current input car insurance claim data, ensuring the accuracy of car insurance claims.

[0089] Through the above steps S310 to S320, the first impact degree data can be determined, so as to control the transmission of the initial claim hidden state based on the first impact degree data and complete the modeling of the long-term dependency relationship.

[0090] See also Figure 4 In some embodiments, step S240 may include but is not limited to steps S410 to S430:

[0091] Step S410, obtaining second impact degree data of the initial claim hidden state on the first candidate hidden state based on the original claim splicing feature;

[0092] Step S420, determining a baseline hidden state based on the second impact degree data and the initial claim hidden state;

[0093] Step S430 : determining a first candidate hidden state based on the vehicle insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the baseline hidden state.

[0094] In step S410 of some embodiments, a reset gate controls the influence of the initial claim hidden state on the first candidate hidden state. The original claim concatenation feature is input into the reset gate, and a second influence degree data of the initial claim hidden state on the first candidate hidden state is calculated. The second influence degree data is an output parameter of the reset gate, indicating the proportion of the initial claim hidden state in the first candidate hidden state. The second influence degree data is a value between [0, 1].

[0095] Specifically, the weight matrix of the reset gate is obtained, multiplied by the original claim concatenation feature, and activated using the sigmoid activation function to obtain the second impact data. The weight matrix includes the weights of each feature element in the original claim concatenation feature, such as the weights of the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the initial claim hidden state. The calculation formula for the second impact data is as follows:

[0096] r t =σ(W r ·[h t-1 ,x t ]),

[0097] Among them, r t represents the second degree of influence data; σ represents the sigmoid activation function; W r represents the weight matrix of the reset gate; represents the matrix multiplication operation; h t-1 Indicates the initial claim hidden state; x t represents the current input data, including the car insurance policy, the second historical driving behavior, the second historical location, and the second historical driving environment. t-1 represents the first driving period, and t represents the second driving period.

[0098] In step S420 of some embodiments, the second impact degree data is multiplied by the initial claim hidden state to obtain a baseline hidden state. t , the initial claim hidden state is represented as h t-1 , then the base hidden state is represented as r t ·h t-1 ,· represents the matrix multiplication operation.

[0099] When the second impact value output by the reset gate is close to 0, the initial claim hidden state has little influence on the first candidate hidden state, tending to ignore previous historical information and rely primarily on the current input data. When the second impact value output by the reset gate is close to 1, the initial claim hidden state has a greater influence on the first candidate hidden state, retaining more historical information. For example, if the second impact value is 0.79, the initial claim hidden state will retain more historical information.

[0100] In step S430 of some embodiments, a nonlinear transformation is performed on the vehicle insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the reference hidden state to obtain a first candidate hidden state.

[0101] In the above steps S410 to S430, the influence of the initial claim hidden state on the first candidate hidden state is controlled by the second influence degree data, so as to determine whether to retain the initial claim hidden state, thereby more effectively handling the long-term dependency problem and determining the intrinsic correlation between driving data in different driving periods, so as to accurately assess the driver's accident risk under different driving conditions and obtain more accurate auto insurance claim prediction results.

[0102] See also Figure 5 In some embodiments, step S430 may include but is not limited to steps S510 to S530:

[0103] Step S510, performing feature splicing on the vehicle insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the baseline hidden state to obtain an initial spliced ​​feature;

[0104] Step S520, performing linear mapping on the initial splicing features to obtain target splicing features;

[0105] Step S530: Activate the target splicing feature to obtain a first candidate hidden state.

[0106] In step S510 of some embodiments, the car insurance policy, the second historical driving behavior, the second historical location, and the second historical driving environment are the current input data, which are represented by x t , the base hidden state is denoted as r t ·h t-1 , concatenate the baseline hidden state and the current input data to obtain the initial concatenated feature [r t ·h t-1 ,x t ].

[0107] In step S520 of some embodiments, a weight matrix for calculating the first candidate hidden state is obtained, and the weight matrix is ​​multiplied by the initial splicing feature to obtain the target splicing feature.

[0108] In step S530 of some embodiments, the target splicing feature is activated by the hyperbolic tangent function tanh to obtain a first candidate hidden state. The calculation formula of the first candidate hidden state is as follows:

[0109] h′ t =tanh(W h ·[r t ·h t-1 ,xt ]),

[0110] Where h′ t represents the first candidate hidden state; tanh represents the hyperbolic tangent function; W h represents the weight matrix; r t Indicates the second impact level data; h t-1 Indicates the initial claim hidden state; x t Indicates the current input data; Indicates matrix multiplication operation.

[0111] Through the above steps S510 to S530, the hidden state can be continuously updated over time to obtain a potential new state, namely the first candidate hidden state, to reflect the driver's latest driving risk status. This real-time update mechanism can adapt to dynamically changing driving records, making the prediction results of car insurance claims more accurate.

[0112] See also Figure 6 In some embodiments, step S250 may include but is not limited to steps S610 to S620:

[0113] Step S610, determining a second candidate hidden state based on the first influence degree data and the first candidate hidden state;

[0114] Step S620: Perform feature fusion on the reference hidden state and the second candidate hidden state to obtain the target claim hidden state.

[0115] In step S610 of some embodiments, a linear transformation is performed on the first candidate hidden state according to the first influence degree data to obtain a second candidate hidden state.

[0116] In step S620 of some embodiments, the reference hidden state and the second candidate hidden state are feature-aligned and added to obtain the target claim hidden state.

[0117] By combining the reference hidden state and the second candidate hidden state, steps S610 to S620 can retain important historical information when updating the initial claim hidden state, while introducing new information to accurately reflect the driving risk conditions at different driving times.

[0118] See also Figure 7 In some embodiments, step S610 may include but is not limited to steps S710 to S720:

[0119] Step S710, obtaining third impact degree data of the first candidate hidden state on the target claim hidden state based on the first impact degree data;

[0120] Step S720 : Perform state weighting on the first candidate hidden state according to the third influence degree data to obtain a second candidate hidden state.

[0121] In step S710 of some embodiments, the given value is 1, and the first impact data is subtracted from the given value to obtain the third impact data. The third impact data is used to indicate the proportion of the first candidate hidden state in the target claim hidden state. The third impact data is a value between [0, 1], and its specific value is related to the first impact data. The sum of the first impact data and the third impact data is 1.

[0122] In step S720 of some embodiments, the third influence degree data is multiplied by the first candidate hidden state to obtain a second candidate hidden state.

[0123] Through the above steps S710 to S720, new status information can be obtained, and the initial claim hidden status can be updated using the new status information, so that the claim hidden status changes over time to accurately assess the driving risk conditions under different driving periods, thereby ensuring the accuracy of the car insurance claims process.

[0124] In step S140 of some embodiments, a classification model is used to identify the claim category of the target vehicle using the target hidden state. The claim category indicates whether the claim is approved or rejected. The classification model may be a multilayer perceptron, a decision tree, a support vector machine, or the like. If the claim category is approved, the target hidden state may be input into a regression model to predict the claim amount of the target vehicle. The regression model may be a linear regression model, a random forest regression model, or the like.

[0125] The vehicle insurance claims settlement method of an embodiment of the present application includes: obtaining vehicle insurance claims data for a target vehicle, the vehicle insurance policy, and the target vehicle's driving behavior, location, and driving environment during multiple driving periods, the multiple driving periods being arranged sequentially from earliest to latest. An initialization feature vector is obtained according to the order of the driving periods, and a hidden claim state for the first driving period is determined based on the initialization feature vector, the vehicle insurance policy, and the target vehicle's driving behavior, location, and driving environment during the first driving period. Starting from the second driving period, the vehicle insurance policy, the target vehicle's driving behavior, location, and driving environment during the current driving period are used as current input data. The hidden claim state for the previous driving period is concatenated with the current input data to obtain an original concatenated claim feature. Feature mapping is performed on the original concatenated claim feature using an update gate to obtain first impact data of the hidden claim state for the previous driving period on the hidden claim state for the current driving period. Feature mapping is performed on the original concatenated claim feature using a reset gate to obtain second impact data of the hidden claim state for the previous driving period on candidate hidden states for the current driving period. Multiply the second impact degree data by the claim hidden state of the previous driving period to obtain a baseline hidden state. Perform feature splicing on the baseline hidden state and the current input data to obtain an initial splicing feature. Perform linear mapping on the initial splicing feature to obtain a target splicing feature. Activate the target splicing feature using a hyperbolic tangent function to obtain a candidate hidden state for the current driving period. Multiply the first impact degree data by the claim hidden state of the previous driving period, multiply the preset third impact degree data by the candidate hidden state of the current driving period, and add the two multiplication results to obtain the claim hidden state for the current driving period. The sum of the first impact degree data and the third impact degree data is 1. Repeat the above steps until the claim hidden state of the last driving period is obtained. Determine the claim category of the target vehicle based on the claim hidden state of the last driving period. The claim category is used to indicate whether the claim is approved or rejected.

[0126] The embodiment of the present application simplifies the structure of the LSTM model through the update gate and the reset gate, thereby simplifying the process of updating the hidden state of the claim, avoiding the consumption of a large amount of computing resources and costs, and improving the efficiency of motor vehicle insurance claims. In addition, the update gate and the reset gate process the long-term and short-term dependencies of time series data by controlling the transmission of information, thereby enhancing the flexibility of updating the hidden state of the claim. Driving behavior, position and driving environment will be continuously updated with the driving period, and there is an intrinsic correlation between the driving behavior, position and driving environment of different driving periods. The hidden state of the claim of the current driving period is determined by combining the current input data with the hidden state of the claim of the previous driving period to establish a time dependency relationship between the hidden states of the claim of different driving periods, thereby more accurately predicting the risk of accidents under different driving conditions and ensuring the accuracy of the prediction results of motor vehicle insurance claims.

[0127] See also Figure 8 The present application also provides a vehicle insurance claims settlement device that can implement the above vehicle insurance claims settlement method. The device includes:

[0128] Acquisition module 810 is configured to acquire insurance claim data of a target vehicle; the insurance claim data includes an insurance policy and a driving record, wherein the driving record includes a first driving period, a first historical driving behavior, a first historical location, and a first historical driving environment of the target vehicle during the first driving period, a second driving period, a second historical driving behavior, a second historical location, and a second historical driving environment of the target vehicle during the second driving period; the second driving period is subsequent to the first driving period;

[0129] An initial feature extraction module 820 is configured to extract claim features from the vehicle insurance policy, the first historical driving behavior, the first historical location, and the first historical driving environment to obtain an initial claim hidden state;

[0130] A target feature extraction module 830 is configured to extract claim features from the vehicle insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the initial claim hidden state to obtain a target claim hidden state;

[0131] The determination module 840 is used to determine the claim category of the target vehicle according to the target claim hidden state; the claim category is used to indicate whether the claim is approved or rejected.

[0132] The specific implementation of the vehicle insurance claim settlement device is basically the same as the specific embodiment of the above-mentioned vehicle insurance claim settlement method, and will not be repeated here.

[0133] The present application also provides an electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the above-described vehicle insurance claims settlement method when executing the computer program. The electronic device can be any smart terminal, such as a tablet computer or an in-vehicle computer.

[0134] See also Figure 9 , Figure 9 The hardware structure of an electronic device according to another embodiment is shown. The electronic device includes:

[0135] The processor 910 can be implemented using a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0136] The memory 920 can be implemented in the form of a read-only memory (ROM), a static storage device, a dynamic storage device, or a random access memory (RAM). The memory 920 can store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 920 and is called by the processor 910 to execute the vehicle insurance claims settlement method of the embodiments of this application.

[0137] Input / output interface 930, used to implement information input and output;

[0138] Communication interface 940, used to implement communication interaction between this device and other devices, which can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WiFi, Bluetooth, etc.);

[0139] bus 950 , which transmits information between various components of the device (e.g., processor 910 , memory 920 , input / output interface 930 , and communication interface 940 );

[0140] The processor 910 , the memory 920 , the input / output interface 930 , and the communication interface 940 are connected to each other in communication within the device via a bus 950 .

[0141] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned car insurance claim settlement method.

[0142] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely arranged relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0143] The car insurance claims method, car insurance claims device, electronic device and computer storage medium provided in the embodiments of the present application capture the long-term dependencies between historical driving behaviors, historical locations and historical driving environments during different driving periods in driving records to achieve automated car insurance claims without going through a series of complex operational processes, thereby improving the efficiency of car insurance claims.

[0144] The embodiments described in the embodiments of this application are intended to more clearly illustrate the technical solutions of the embodiments of this application and do not constitute a limitation on the technical solutions provided by the embodiments of this application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0145] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0146] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, i.e., they may be located in one place or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of this embodiment.

[0147] Those skilled in the art will appreciate that all or some of the steps in the methods, systems, and functional modules / units in the devices disclosed above may be implemented as software, firmware, hardware, or appropriate combinations thereof.

[0148] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0149] It should be understood that in this application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0150] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the above-mentioned units is only 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. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0151] The units described above 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 according to actual needs to achieve the purpose of the solution of this embodiment.

[0152] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or 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.

[0153] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes multiple instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: various media that can store programs, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0154] The preferred embodiments of the present invention are described above with reference to the accompanying drawings, but are not intended to limit the scope of the present invention. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and essence of the present invention should be within the scope of the present invention.

Claims

1. A method for settling automobile insurance claims, characterized in that: The method comprises: Obtaining automobile insurance claim data for a target vehicle; the automobile insurance claim data includes an automobile insurance policy and a driving record, the driving record including a first driving period, a first historical driving behavior, a first historical location, and a first historical driving environment of the target vehicle during the first driving period, a second driving period, and a second historical driving behavior, a second historical location, and a second historical driving environment of the target vehicle during the second driving period; the first driving period and the second driving period being the driving periods of the target vehicle before the current traffic accident, and the second driving period being after the first driving period; extracting claim features from the auto insurance policy, the first historical driving behavior, the first historical location, and the first historical driving environment through a gated recurrent unit to obtain an initial claim hidden state; extracting claim features from the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the initial claim hidden state through the gated loop unit to obtain a target claim hidden state; Determining a claim category of the target vehicle according to the target claim hidden state; the claim category is used to indicate whether to approve or reject the claim; The gated loop unit extracts claim features from the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the initial claim hidden state to obtain a target claim hidden state, including: Performing feature splicing on the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the initial claim hidden state to obtain an original claim splicing feature; Obtaining first impact degree data of the initial claim hidden state on the target claim hidden state according to the original claim splicing feature; determining a reference hidden state according to the first impact degree data and the initial claim hidden state; determining a first candidate hidden state based on the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the original claim splicing feature; The target claim hidden state is determined according to the first impact degree data, the reference hidden state and the first candidate hidden state.

2. The method according to claim 1, characterized in that The obtaining, according to the original claim splicing feature, first impact degree data of the initial claim hidden state on the target claim hidden state includes: Performing linear mapping on the original claim splicing features to obtain candidate claim splicing features; The candidate claim splicing features are activated to obtain the first impact degree data.

3. The method according to claim 1, characterized in that The determining of the first candidate hidden state according to the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the original claim splicing feature includes: Obtaining second impact degree data of the initial claim hidden state on the first candidate hidden state according to the original claim splicing feature; determining a baseline hidden state based on the second impact level data and the initial claim hidden state; The first candidate hidden state is determined according to the vehicle insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the baseline hidden state.

4. The method according to claim 3, characterized in that The determining the first candidate hidden state according to the vehicle insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the baseline hidden state includes: Performing feature splicing on the vehicle insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the baseline hidden state to obtain an initial splicing feature; Performing linear mapping on the initial splicing features to obtain target splicing features; Activate the target splicing feature to obtain the first candidate hidden state.

5. The method according to claim 1, wherein The determining the target claim hidden state according to the first impact degree data, the reference hidden state, and the first candidate hidden state includes: determining a second candidate hidden state based on the first influence degree data and the first candidate hidden state; Feature fusion is performed on the reference hidden state and the second candidate hidden state to obtain the target claim hidden state.

6. The method according to claim 5, characterized in that The determining a second candidate hidden state according to the first influence degree data and the first candidate hidden state includes: Obtaining third impact degree data of the first candidate hidden state on the target claim hidden state according to the first impact degree data; The first candidate hidden state is state-weighted according to the third influence degree data to obtain the second candidate hidden state.

7. A car insurance claims device, characterized in that: The device comprises: an acquisition module, configured to acquire insurance claim data of a target vehicle; the insurance claim data including an insurance policy and a driving record, the driving record including a first driving period, a first historical driving behavior, a first historical location, and a first historical driving environment of the target vehicle during the first driving period, a second driving period, and a second historical driving behavior, a second historical location, and a second historical driving environment of the target vehicle during the second driving period; the first driving period and the second driving period being the driving periods of the target vehicle before the traffic accident, and the second driving period being after the first driving period; an initial feature extraction module, configured to extract claim features from the vehicle insurance policy, the first historical driving behavior, the first historical location, and the first historical driving environment through a gated recurrent unit to obtain an initial claim hidden state; a target feature extraction module, configured to extract claim features from the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the initial claim hidden state through the gated loop unit to obtain a target claim hidden state; a determination module, configured to determine a claim category of the target vehicle according to the target claim hidden state; the claim category is used to indicate whether the claim is approved or rejected; The device is also used for: Performing feature splicing on the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the initial claim hidden state to obtain an original claim splicing feature; Obtaining first impact degree data of the initial claim hidden state on the target claim hidden state according to the original claim splicing feature; determining a reference hidden state according to the first impact degree data and the initial claim hidden state; determining a first candidate hidden state based on the auto insurance policy, the second historical driving behavior, the second historical location, the second historical driving environment, and the original claim splicing feature; The target claim hidden state is determined according to the first impact degree data, the reference hidden state and the first candidate hidden state.

8. An electronic device, characterized in that: The electronic device includes a memory and a processor, the memory stores a computer program, and the processor implements the method according to any one of claims 1 to 6 when executing the computer program.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 6 is implemented.

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