Vehicle insurance loss assessment method and device, computer equipment and storage medium

Abstract

The embodiment of the application belongs to the field of artificial intelligence, and relates to a vehicle insurance damage assessment method, which comprises the following steps: acquiring a vehicle insurance damage project data set comprising a vehicle insurance damage project, a project man-hour type, a project part and a repair internal and external part; respectively extracting a vehicle insurance damage project feature, a project man-hour type feature, a project part feature and a repair internal and external part feature; correlating the extracted features to obtain global correlation features and single-item correlation features; performing vehicle insurance damage assessment on the global correlation features to obtain an assessment result; identifying the type to which an error single-item correlation feature belongs to obtain a target project type; and adjusting a vehicle insurance damage project corresponding to an error correlation feature according to the target project type to obtain a target vehicle insurance damage project. The application also provides a vehicle insurance damage assessment device, a computer device and a storage medium. In addition, the application also relates to blockchain technology, and the vehicle insurance project data set can be stored in a blockchain. The application improves the efficiency and accuracy of vehicle insurance damage assessment.

Description

Technical Field

[0001] The present application relates to the field of artificial intelligence, and in particular to a method, apparatus, computer equipment, and storage medium for automobile insurance damage assessment. Background Art

[0002] With the development of Internet technology, traffic accidents occur frequently, involving a large number of vehicle insurance claims scenarios. Vehicle damage assessment is required during the vehicle insurance claims process, and the use of artificial intelligence technology for vehicle insurance damage assessment has been widely used.

[0003] The existing auto insurance loss assessment method is to configure different auto insurance loss assessment plan rules based on business operation experience, and use these rules to filter and identify auto insurance loss assessment plans to select a suitable auto insurance loss assessment plan.

[0004] However, this method is based on manual business experience and requires a lot of manpower, which makes the efficiency of auto insurance loss assessment low; and if the configured rules are too precise and detailed, hundreds of thousands of rules will be needed to cover the complex scenarios of auto insurance, resulting in a large number of repeated schemes in auto insurance loss assessment; if the configured rules are too broad, they will not be able to cover the complex scenarios of auto insurance, making the generated auto insurance loss assessment scheme unreasonable, resulting in a low accuracy rate of auto insurance loss assessment. Summary of the Invention

[0005] The purpose of the embodiments of the present application is to provide a method, apparatus, computer equipment and storage medium for assessing automobile insurance damages, the main purpose of which is to improve the efficiency and accuracy of automobile insurance damage assessment.

[0006] In order to solve the above technical problems, the present invention provides a method for assessing damages in automobile insurance, which adopts the following technical solutions:

[0007] Obtaining a data set of an automobile insurance project to be evaluated, wherein the data set includes automobile insurance loss assessment items, project work time types, project locations, and internal and external repair locations;

[0008] Extracting the vehicle insurance damage assessment item feature corresponding to the vehicle insurance damage assessment item, the project work hour type feature corresponding to the project work hour type, the project part feature corresponding to the project part, and the maintenance internal and external part features corresponding to the maintenance internal and external parts respectively;

[0009] Correlating the vehicle insurance damage assessment item feature, the item labor type feature, the item location feature, and the repair internal and external location features to obtain correlation features, wherein the correlation features include global correlation features and single item correlation features;

[0010] Performing a vehicle insurance damage assessment on the global correlation features to obtain a vehicle insurance damage assessment result;

[0011] If the vehicle insurance damage assessment result contains an erroneous global correlation feature, extracting an erroneous single-item correlation feature corresponding to the erroneous global correlation feature, and identifying the type of the erroneous single-item correlation feature to obtain a target item type;

[0012] The motor vehicle insurance loss assessment item corresponding to the error association feature is adjusted according to the target item type to obtain a target motor vehicle insurance loss assessment item.

[0013] Furthermore, the extracting of the vehicle insurance damage assessment item features corresponding to the vehicle insurance damage assessment items, the project work time type features corresponding to the project work time type, the project part features corresponding to the project parts, and the repair internal and external part features corresponding to the repair internal and external parts respectively includes:

[0014] Sorting and vectorizing the vehicle insurance loss assessment items according to the character sequence of the vehicle insurance loss assessment items to obtain a sorted vehicle insurance loss assessment vector sequence;

[0015] Identify the project maintenance man-hours corresponding to the vehicle insurance damage assessment item, perform a vectorization operation on the project maintenance man-hours, and obtain a project man-hour type vector sequence;

[0016] Identifying the vehicle body parts corresponding to the vehicle insurance loss assessment items, performing vectorization operations on the vehicle body parts, and obtaining a vehicle body part vector sequence;

[0017] Identify the vehicle body parts corresponding to the vehicle insurance loss assessment items, perform vectorization operations on the vehicle body parts, and obtain a vector sequence of the internal and external parts of the repair;

[0018] Position encoding is performed on the sorted auto insurance damage assessment vector sequence, the project labor time type vector sequence, the vehicle body part vector sequence and the repair internal and external part vector sequence respectively to obtain the auto insurance damage assessment item features corresponding to the sorted auto insurance damage assessment vector sequence, the project labor time type features corresponding to the project labor time type vector sequence, the project part features corresponding to the vehicle body part vector sequence and the repair internal and external part features corresponding to the repair internal and external part vector sequence.

[0019] Furthermore, the vehicle insurance damage assessment item feature, the item labor type feature, the item location feature, and the repair internal and external location feature are correlated to obtain correlation features, including:

[0020] Using the attention mechanism to identify the one-to-one correspondence between the features of the vehicle insurance damage assessment item, the features of the item labor type, the features of the item part, and the features of the internal and external parts of the repair;

[0021] Based on the one-to-one correspondence, the vehicle insurance damage assessment item feature, the item labor type feature, the item part feature and the repair internal and external part feature are associated to obtain the associated feature.

[0022] Furthermore, performing a vehicle insurance damage assessment on the global correlation features to obtain a vehicle insurance damage assessment result includes:

[0023] Rationality classification is performed on the vehicle insurance damage assessment item features corresponding to the project work time type features, project location features, and repair internal and external location features in the global correlation features to obtain classification labels;

[0024] The vehicle insurance damage assessment result is obtained according to the classification label.

[0025] Furthermore, the identifying the type of the erroneous single item association feature to obtain the target item type includes:

[0026] Calculate the probability of the item category to which the vehicle insurance damage assessment item feature corresponding to the erroneous single item correlation feature belongs based on the item part feature and the internal and external repair part features in the erroneous single item correlation feature;

[0027] The project category corresponding to the maximum probability is selected from the project category probabilities as the target project type corresponding to the erroneous single item association feature.

[0028] Furthermore, adjusting the motor vehicle insurance loss assessment item corresponding to the error association feature according to the target item type to obtain a target motor vehicle insurance loss assessment item includes:

[0029] Identifying unreasonable automobile insurance loss assessment items corresponding to the erroneous correlation features;

[0030] Obtain a target item type corresponding to the unreasonable auto insurance damage assessment item, and replace the item type of the unreasonable auto insurance damage assessment item according to the target item type to obtain the target auto insurance damage assessment item.

[0031] Furthermore, before obtaining the data set of the auto insurance project to be evaluated, the method further includes:

[0032] Acquire a vehicle damage image, and extract damage image features of the vehicle damage image;

[0033] Identifying the damage type and damage location of the damage image features;

[0034] The vehicle insurance damage assessment items of the vehicle damage image are determined according to the damage type and the damage location.

[0035] In order to solve the above technical problems, the present application also provides a vehicle insurance damage assessment device, which adopts the following technical solutions:

[0036] An acquisition module is used to acquire a data set of a motor vehicle insurance project to be evaluated, wherein the data set includes motor vehicle insurance loss assessment items, project labor type, project location, and internal and external repair locations;

[0037] An extraction module is used to respectively extract the vehicle insurance loss assessment item features corresponding to the vehicle insurance loss assessment items, the project work time type features corresponding to the project work time type, the project part features corresponding to the project part, and the repair internal and external part features corresponding to the repair internal and external parts;

[0038] an association module, configured to associate the vehicle insurance damage assessment item characteristics, the item labor type characteristics, the item location characteristics, and the repair internal and external location characteristics to obtain association characteristics, wherein the association characteristics include global association characteristics and single-item association characteristics;

[0039] An evaluation module, configured to perform a vehicle insurance damage assessment on the global correlation features to obtain a vehicle insurance damage assessment result;

[0040] an identification module configured to, if the vehicle insurance damage assessment result contains an erroneous global correlation feature, extract an erroneous single correlation feature corresponding to the erroneous global correlation feature, identify the type of the erroneous single correlation feature, and obtain a target item type; and

[0041] An adjustment module is used to adjust the motor vehicle insurance loss assessment item corresponding to the error association feature according to the target item type to obtain a target motor vehicle insurance loss assessment item.

[0042] 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:

[0043] a memory storing at least one computer program; and

[0044] The processor executes the computer program stored in the memory to implement the above-mentioned vehicle insurance damage assessment method.

[0045] 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:

[0046] The computer-readable storage medium stores at least one computer program, and the at least one computer program is executed by a processor in an electronic device to implement the above-mentioned vehicle insurance damage assessment method.

[0047] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0048] In an embodiment of the present application, after first obtaining a dataset of automobile insurance items to be evaluated, features corresponding to automobile insurance loss assessment items, project work time types, project locations, and internal and external repair locations are extracted and associated with the extracted features. This allows identification of the correspondence between automobile insurance loss assessment items and corresponding damaged vehicle parts, thereby facilitating subsequent improvement in the accuracy of automobile insurance loss assessment items. Secondly, by performing automobile insurance loss assessment on global correlation features and extracting erroneous individual correlation features corresponding to the erroneous global correlation features when the automobile insurance loss assessment results contain erroneous global correlation features, and identifying the type of the erroneous individual correlation features, two types of tasks are obtained for the target item type. This allows evaluation of the rationality of automobile insurance loss assessment items, thereby facilitating subsequent improvement in the accuracy of automobile insurance loss assessment. Different automobile insurance loss assessment scheme rules can be configured without relying on business operation experience, thereby achieving automatic automobile insurance loss assessment and improving the efficiency of automobile insurance loss assessment. Finally, by adjusting the automobile insurance loss assessment items corresponding to the erroneous correlation features according to the target item type, the target automobile insurance loss assessment item is obtained, thereby achieving automatic automobile insurance loss assessment, reducing the number of unreasonable automobile insurance loss assessment schemes, and improving the accuracy of automobile insurance loss assessment. Therefore, the vehicle insurance damage assessment method, device, computer equipment and storage medium proposed in this application can improve the efficiency and accuracy of vehicle insurance damage assessment. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the solutions in this application, a brief introduction will be given below to the drawings required for use in the description of the embodiments of this application. Obviously, the drawings described below are some embodiments of this application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0050] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0051] Figure 2 A flowchart of an embodiment of a method for assessing damages in automobile insurance according to the present application;

[0052] Figure 3 yes Figure 2 A flowchart of a specific implementation of step S204;

[0053] Figure 4 This is a schematic structural diagram of an embodiment of a vehicle insurance damage assessment device according to the present application;

[0054] Figure 5 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0055] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as commonly understood by those skilled in the art to which this application belongs. The terms used in the specification of the application are for the purpose of describing specific embodiments only and are not intended to limit this application. The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of this application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0056] References herein to "embodiments" mean that a particular feature, structure, or characteristic described in connection with the embodiments may be included in at least one embodiment of the present application. The appearance of this phrase in various places in the specification does not necessarily refer to the same embodiment, nor does it constitute an independent or alternative embodiment that is mutually exclusive of other embodiments. It is understood, both explicitly and implicitly, by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0057] 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.

[0058] like Figure 1 As shown, system architecture 100 may include terminal devices 101, 102, 103, a network 104, and a server 105. Network 104 is a medium for providing communication links between terminal devices 101, 102, 103 and server 105. Network 104 may include various connection types, such as wired or wireless communication links or fiber optic cables.

[0059] Users can use terminal devices 101, 102, and 103 to interact with server 105 via network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, and 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0060] Terminal devices 101, 102, and 103 can be various electronic devices with display screens and support web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture E perts Group Audio Layer III, Moving Picture Experts Compression Standard Audio Layer 3), MP4 (Moving Picture E perts Group Audio Layer IV, Moving Picture Experts Compression Standard Audio Layer 4) players, laptop computers, desktop computers, etc.

[0061] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0062] It should be noted that the vehicle insurance damage assessment method provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the vehicle insurance damage assessment device is generally set in the server / terminal device.

[0063] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is merely illustrative. Any number of terminal devices, networks and servers may be provided as required.

[0064] Continue to refer Figure 2 , shows a flow chart of an embodiment of a method for assessing damages in automobile insurance according to the present application. The method for assessing damages in automobile insurance comprises the following steps:

[0065] S201. Obtain a data set of an automobile insurance item to be evaluated, wherein the data set includes automobile insurance damage assessment items, item labor type, item location, and internal and external repair locations.

[0066] In an embodiment of the present application, the vehicle insurance item data set to be evaluated can be an initial damage assessment plan for a damaged vehicle, which is specifically determined based on an actual vehicle damage scenario. For example, there is a vehicle insurance item data set that is a damage assessment plan for replacing the front bumper of a vehicle, wherein the vehicle insurance item data set includes vehicle insurance damage assessment items, project labor time type, project location, and vehicle information of internal and external repaired parts.

[0067] In the embodiment of the present application, the motor vehicle insurance damage assessment item is a damage assessment repair item for vehicle damage; the project working hour type is determined based on the repair type (including replacement, sheet metal, painting, disassembly and assembly, etc.) corresponding to the damage assessment repair item; the project part is the body part of the vehicle repaired; the internal and external parts of the repair include four major categories of parts: exterior parts of the vehicle repaired, internal electronic components, internal non-electronic components and auxiliary materials.

[0068] For example, there is a car insurance damage assessment item for replacing the vehicle's front bumper, and the corresponding item type is replacement type. The item replacement working hours are 24 hours, the corresponding item part is the vehicle's front bumper, and the corresponding repair internal and external parts are the front bumper outer panel, buffer material and crossbeam, etc.

[0069] In an optional embodiment of the present application, before obtaining the data set of the auto insurance item to be evaluated, the method further includes:

[0070] Acquire a vehicle damage image, and extract damage image features of the vehicle damage image;

[0071] Identifying the damage type and damage location of the damage image features;

[0072] The vehicle insurance damage assessment items of the vehicle damage image are determined according to the damage type and the damage location.

[0073] The vehicle damage picture is a vehicle damage image in a traffic accident; the damage image features can be extracted through a convolutional network.

[0074] In one embodiment of the present application, the damage type can be identified using the Yolo-v3 algorithm, which uses multi-scale features for damage classification. Specifically, the input image can be downsampled by a factor of 32 to expand the receptive field and segment out regions with larger damage locations; the input image can be downsampled by a factor of 16 to segment out regions with medium damage locations; and the input image can be downsampled by a factor of 8 to segment out regions with smaller damage locations. All damage location regions are output through an activation function to determine the damage type corresponding to each region.

[0075] Furthermore, in an embodiment of the present application, the damage location is identified using a Mask-R-CNN network structure. Specifically, the damage location area is used as a candidate area, and the candidate area is subjected to binary classification to obtain the name of the vehicle component to which the damage location belongs, thereby determining the damage location.

[0076] In an embodiment of the present application, the degree of loss of the vehicle damage image is determined by fusing the damage type and the damage location, and the vehicle damage assessment items are determined based on the degree of loss, wherein the degree of vehicle loss includes mild, moderate and severe.

[0077] For example, if the damage to a vehicle does not involve deformation, the degree of vehicle loss is determined to be minor, and the corresponding vehicle damage assessment item is the painting item type; if the degree of vehicle loss is moderate, the corresponding vehicle damage assessment item is painting + sheet metal + disassembly and assembly; if the degree of vehicle loss is severe, the corresponding vehicle damage assessment item is replacement.

[0078] S202, respectively extracting the vehicle insurance damage assessment item features corresponding to the vehicle insurance damage assessment items, the project working hour type features corresponding to the project working hour type, the project part features corresponding to the project part, and the maintenance internal and external part features corresponding to the maintenance internal and external parts.

[0079] In the embodiment of the present application, the vehicle insurance damage assessment item characteristics, item labor type characteristics, item location characteristics, and repair internal and external location characteristics are a sequence of repair plans filtered and reviewed by the damage assessment plan, and each sequence is a specific repair plan.

[0080] In an embodiment of the present application, the encoding layer of a trained auto insurance damage assessment model can be used to encode the auto insurance damage assessment items, item types, item locations, and internal and external repair locations. The auto insurance damage assessment model comprises a neural network model consisting of an encoding layer, an attention mechanism layer, a classification layer, and an activation function. The encoding layer is used to extract features corresponding to the auto insurance damage assessment items, item labor type, item location, and internal and external repair locations; the attention mechanism layer is used to associate auto insurance damage assessment item features, item labor type features, item location features, and internal and external repair location features; the classification layer is used to perform auto insurance damage assessment on the associated features to identify unreasonable damage assessment items within the auto insurance damage assessment items; and the activation function is used to identify the target item type to which the auto insurance damage assessment item features within the associated features belong.

[0081] In one embodiment of the present application, when training a car insurance damage assessment model, the encoding layer is first used to extract the features corresponding to the car insurance damage assessment items, project working time types, project locations, and internal and external parts of the repair, and then the attention mechanism layer is used to associate the car insurance damage assessment item features, project working time type features, project location features, and internal and external parts of the repair to obtain associated features; secondly, the classification layer is used to perform car insurance damage assessment on the associated features to obtain a car insurance damage assessment result; finally, the activation function is used to identify the target project type to which the car insurance damage assessment item features in the associated features belong, and the loss function (such as the Softmax function) is used to calculate the loss values ​​of the two tasks, namely, the car insurance damage assessment result and the target project type, and the parameters of the car insurance damage assessment model are adjusted according to the loss value until the loss value meets the preset threshold, thereby obtaining a trained car insurance damage assessment model.

[0082] The loss function can be:

[0083]

[0084] Among them, task 1 represents the task of performing a car insurance damage assessment on the associated features to obtain a car insurance damage assessment result; f nrepresents the nth associated feature; and 0 in {0,1} represents that the car insurance loss assessment item is unreasonable, and 1 represents that the car insurance loss assessment item is reasonable; task2 represents the task of identifying the target item type to which the car insurance loss assessment item feature in the associated feature belongs; m represents the mth car insurance loss assessment item, and m=n.

[0085] As an embodiment of the present application, respectively extracting the vehicle insurance loss assessment item features corresponding to the vehicle insurance loss assessment items, the project work hour type features corresponding to the project work hour type, the project part features corresponding to the project part, and the internal and external repair part features corresponding to the internal and external repair parts, includes:

[0086] Sorting and vectorizing the vehicle insurance loss assessment items according to the character sequence of the vehicle insurance loss assessment items to obtain a sorted vehicle insurance loss assessment vector sequence;

[0087] Identify the project maintenance man-hours corresponding to the vehicle insurance damage assessment item, perform a vectorization operation on the project maintenance man-hours, and obtain a project man-hour type vector sequence;

[0088] Identifying the vehicle body parts corresponding to the vehicle insurance loss assessment items, performing vectorization operations on the vehicle body parts, and obtaining a vehicle body part vector sequence;

[0089] Identify the vehicle body parts corresponding to the vehicle insurance loss assessment items, perform vectorization operations on the vehicle body parts, and obtain a vector sequence of the internal and external parts of the repair;

[0090] Position encoding is performed on the sorted auto insurance damage assessment vector sequence, the project labor time type vector sequence, the vehicle body part vector sequence and the repair internal and external part vector sequence respectively to obtain the auto insurance damage assessment item features corresponding to the sorted auto insurance damage assessment vector sequence, the project labor time type features corresponding to the project labor time type vector sequence, the project part features corresponding to the vehicle body part vector sequence and the repair internal and external part features corresponding to the repair internal and external part vector sequence.

[0091] In one embodiment of the present application, the insurance damage assessment items are sorted according to their character order, that is, by the repair item name. For example, one repair item is for painting the left front door, and another repair item is for sheet metal work on the left front door. Since both repair items involve the left front door, sorting the insurance damage assessment items can reduce the spatial distance between repair items on the same vehicle part during the vectorization process, thereby improving the accuracy of subsequent insurance damage assessment solutions.

[0092] In an optional embodiment of the present application, before sorting and vectorizing the vehicle insurance loss assessment items according to the character order of the vehicle insurance loss assessment items to obtain a sorted vehicle insurance loss assessment vector sequence, the method further includes: adding historical vehicle insurance loss assessment items and erroneous vehicle insurance loss assessment items to the vehicle insurance loss assessment items to obtain updated vehicle insurance loss assessment items.

[0093] The historical insurance loss items refer to previously deleted repair items that were discovered to have issues during a historical period (e.g., the previous 30 days) during the review of insurance loss items. These incorrect insurance loss items can be added by randomly adding repair items that do not exist in the original insurance loss plan, adding repair items of varying degrees of repair, and adding repair items corresponding to other parts that have a subordinate relationship with the parts corresponding to the original repair items based on the repair item's dependencies.

[0094] In the embodiment of the present application, the sequence of sorted vehicle insurance damage assessment vectors can be expressed as:

[0095] s project =(s CLS ,s1,s2,...,s n )=sort(P CLS ,P1,P2,

[0096] ...,P e ,...,P d ,...,P m )

[0097] Among them, s project Represents the sequence of sorted car insurance loss vectors, s CLS Represents the global car insurance loss vector sequence, s n represents the nth sorted car insurance loss vector sequence, sort represents the sorting function, P CLS Represents the global car insurance loss assessment item, P e Indicates the wrong car insurance loss item, P d Represents the historical automobile insurance loss assessment items, P n Represents the nth car insurance loss assessment item.

[0098] Furthermore, in the embodiment of the present application, the vehicle body part vector sequence can be determined based on the association relationship between 265 vehicle body parts, components, and vehicle insurance loss assessment items, and the vehicle body part vector sequence can be expressed as:

[0099] l postion =(l CLS ,l1,l2,...,l n )

[0100] Among them, l postionRepresents the vector sequence of body parts, l CLS Represents the global body part vector sequence; l n Represents the nth vehicle-injury part vector sequence.

[0101] Furthermore, in the embodiment of the present application, the project work time type vector sequence is determined based on the four major maintenance work time types of replacement, sheet metal, painting, and disassembly corresponding to the project type:

[0102] w hour =(w CLS ,w1,w2,...,w n )

[0103] w hour Represents the project time type vector sequence, w CLS Represents the global project time type vector sequence, w n Represents the nth project time type vector sequence.

[0104] In the embodiment of the present application, the repair internal and external part vector sequence is determined based on the relationship between the four types of parts of the vehicle repair, namely, the exterior parts, internal electronic components, internal non-electronic components, and auxiliary materials, in the vehicle insurance loss assessment project:

[0105] t inner =(t CLS ,t1,t2,...,t n )

[0106] Among them, t inner Represents the vector sequence of internal and external parts of the repair, t CLS Represents the global maintenance internal and external part vector sequence, t n Represents the nth repair internal and external part vector sequence.

[0107] In the embodiment of the present application, the CLS identifier is used to identify the starting position of the sequence. All sequence information, that is, global sequence information, is identified by CLS, which facilitates the subsequent identification of global correlation features.

[0108] In one embodiment of the present application, the vectorization operation is a token embedding operation, and the position encoding can be implemented by the following formula:

[0109]

[0110]

[0111] Wherein, the PE refers to Positional Encoding, the d modelIt represents the length of the position encoding for sorting the auto insurance loss assessment vector sequence, project labor type vector sequence, vehicle body part vector sequence, or repair internal and external part vector sequence. pos represents the position of the sorting auto insurance loss assessment vector sequence, project labor type vector sequence, vehicle body part vector sequence, or repair internal and external part vector sequence. i represents the dimension of the sorting auto insurance loss assessment vector sequence, project labor type vector sequence, vehicle body part vector sequence, or repair internal and external part vector sequence.

[0112] S203, correlating the vehicle insurance damage assessment item features, the item labor type features, the item location features, and the repair internal and external location features to obtain correlation features, wherein the correlation features include global correlation features and single item correlation features.

[0113] In the embodiment of the present application, the associated features refer to the one-to-one correspondence features between the vehicle insurance damage assessment item features, the project working time type features, the project part features, and the internal and external parts features of the repair.

[0114] For example, a car insurance damage assessment item feature is a vehicle front bumper replacement item, and the corresponding project type is a replacement type. The corresponding project working hour type is 24 hours, the corresponding project part feature is the vehicle front bumper, and the corresponding repair internal and external part features are the front bumper outer panel, buffer material, and crossbeam, etc.

[0115] As an embodiment of the present application, the vehicle insurance damage assessment item feature, the item labor type feature, the item location feature, and the repair internal and external location feature are correlated to obtain the correlation feature, including:

[0116] Using the attention mechanism to identify the one-to-one correspondence between the features of the vehicle insurance damage assessment item, the features of the item labor type, the features of the item part, and the features of the internal and external parts of the repair;

[0117] Based on the one-to-one correspondence, the vehicle insurance damage assessment item feature, the item labor type feature, the item part feature and the repair internal and external part feature are associated to obtain the associated feature.

[0118] Among them, the self-attention mechanism (i.e., Self-Attention) can be used to extract the Q (query), K (key), and V (value) values ​​corresponding to the vehicle insurance loss assessment item features, project working time type features, project part features, and internal and external repair part features, and the attention matrix containing the correspondence between the vehicle insurance loss assessment item features, project working time type features, project part features, and internal and external repair part features is determined through Q, K, and V. The associated features with the correspondence between the vehicle insurance loss assessment item features, project working time type features, project part features, and internal and external repair part features in the attention matrix are output through the tan linear function.

[0119] In one embodiment of the present application, the association feature can be expressed as:

[0120] f project =(f CLS ,f1,f2,...,f n )

[0121] Among them, f project represents the associated feature, f CLS represents the global correlation feature, f n Represents the nth single-item association feature.

[0122] In an embodiment of the present application, by associating the vehicle insurance damage assessment item characteristics, the item labor type characteristics, the item location characteristics and the repair internal and external location characteristics based on the one-to-one correspondence, the associated characteristics are obtained, and the vehicle insurance damage assessment items can be matched with the damaged parts of the vehicle, thereby improving the accuracy of subsequent vehicle insurance losses.

[0123] In the embodiment of the present application, the global correlation feature can be CLS Indicates that the single associated feature is represented by (f1,f2,...,f n ), and the global correlation feature is composed of n single correlation features.

[0124] S204: Perform a vehicle insurance damage assessment on the global correlation features to obtain a vehicle insurance damage assessment result.

[0125] In the embodiment of the present application, the vehicle insurance damage assessment results include two results: the vehicle insurance damage assessment items are reasonable and the vehicle insurance damage assessment items are unreasonable.

[0126] In an embodiment of the present application, by performing a car insurance damage assessment on the global correlation features, a car insurance damage assessment result is obtained. When the rules are too broad, a rationality assessment of the car insurance damage assessment items can be achieved, and when the rules are too detailed, repeated car insurance damage assessment items can be screened out to improve the accuracy of subsequent car insurance damage assessments.

[0127] For example, in a car insurance project data set, the damaged parts of the vehicle are the headlights and the front bumper. If the corresponding car insurance damage assessment item is the headlight and front bumper replacement item, then it means that the car insurance damage assessment item is reasonable; if the corresponding car insurance damage assessment item is the replacement item of all the vehicle lights and the front bumper, since the damaged parts are only the headlights and the front bumper, and the car insurance damage assessment item adds the repair item of the undamaged rear lights, then the car insurance damage assessment item is unreasonable.

[0128] As an embodiment of this application, refer to Figure 3As shown, performing a car insurance damage assessment on the associated features to obtain a car insurance damage assessment result includes the following steps S2041-S2042:

[0129] S2041. Rationality classification is performed on the vehicle insurance damage assessment item features corresponding to the project work time type feature, the project location feature, and the internal and external repair location features in the global correlation feature to obtain a classification label;

[0130] S2042. Obtain the vehicle insurance damage assessment result according to the classification label.

[0131] The rationality classification can be achieved by the following formula:

[0132]

[0133] Among them, s ′ Represents the classification label, s represents the global correlation feature, and e represents an infinite non-repeating decimal.

[0134] In one embodiment of the present application, the classification label can be output as a motor vehicle insurance damage assessment result by utilizing an activation function (such as a softmax function); the classification label includes two labels, 0 and 1, wherein, if the classification label is 0, it indicates that the motor vehicle insurance damage assessment result is that the motor vehicle insurance damage assessment item is unreasonable; if the classification label is 1, it indicates that the motor vehicle insurance damage assessment result is that the motor vehicle insurance damage assessment item is reasonable.

[0135] S205. If the vehicle insurance damage assessment result contains an erroneous global correlation feature, extract the erroneous single correlation feature corresponding to the erroneous global correlation feature, identify the type of the erroneous single correlation feature, and obtain the target item type.

[0136] In the embodiment of the present application, the target project type is the repair type corresponding to the damage assessment repair project, including replacement, sheet metal, painting and disassembly and assembly and other repair types.

[0137] For example, a car insurance damage assessment item is a vehicle headlight replacement item, and the corresponding item type is a replacement type.

[0138] In an embodiment of the present application, if there is an erroneous global correlation feature in the vehicle insurance damage assessment result, it is identified that there is an unreasonable vehicle insurance damage assessment item in the vehicle insurance damage assessment result, and there is an association error in the global correlation feature corresponding to the unreasonable vehicle insurance damage assessment item. By extracting the erroneous single-item correlation feature corresponding to the erroneous global correlation feature and identifying the type of the erroneous single-item correlation feature, the target item type is obtained, and the item category to which the vehicle insurance damage assessment item feature of a single dimension belongs can be identified, thereby avoiding the generation of repeated vehicle insurance damage assessment plans and providing a posteriori experience to the vehicle insurance damage assessment evaluation, thereby further improving the accuracy of the vehicle insurance damage assessment plan evaluation.

[0139] As an embodiment of the present application, the identifying the type of the erroneous single item association feature to obtain the target item type includes:

[0140] Calculate the probability of the item category to which the vehicle insurance damage assessment item feature corresponding to the erroneous single item correlation feature belongs based on the item part feature and the internal and external repair part features in the erroneous single item correlation feature;

[0141] The project category corresponding to the maximum probability is selected from the project category probabilities as the target project type corresponding to the erroneous single item association feature.

[0142] Among them, the project part features and the maintenance internal and external part features corresponding to the error single item association feature can be input into the activation function (such as the Sigmoid function) to output the project category probability to which the error single item association feature belongs.

[0143] For example, a car insurance loss assessment item with an erroneous single-item correlation feature is characterized by the replacement of the front bumper. The probability of belonging to the replacement category is 0.88, the probability of belonging to the disassembly category is 0.78, and the probability of belonging to the painting category is 0.7. Then, the replacement category corresponding to the maximum probability is selected from the above three item category probabilities as the target item type for the replacement of the front bumper.

[0144] In an embodiment of the present application, since the replacement project types include painting project types and disassembly and assembly project types, by selecting the project category corresponding to the maximum probability from the project category probabilities as the target project type corresponding to the vehicle insurance damage assessment project characteristics, it is possible to avoid generating repeated vehicle insurance damage assessment plans and improve the accuracy of vehicle insurance damage assessment plan evaluation.

[0145] S206. Adjust the motor vehicle insurance loss assessment item corresponding to the error association feature according to the target item type to obtain a target motor vehicle insurance loss assessment item.

[0146] In an embodiment of the present application, unreasonable automobile insurance loss assessment items are adjusted in item type to generate reasonable automobile insurance loss assessment items.

[0147] In an embodiment of the present application, by adjusting the motor vehicle insurance damage assessment item corresponding to the erroneous association feature according to the target item type, a target motor vehicle insurance damage assessment item is obtained, and unreasonable motor vehicle insurance damage assessment items can be automatically adjusted to improve the efficiency and accuracy of motor vehicle insurance damage assessment.

[0148] As an embodiment of the present application, adjusting the motor vehicle insurance loss assessment item corresponding to the error association feature according to the target item type to obtain the target motor vehicle insurance loss assessment item includes:

[0149] Identifying unreasonable automobile insurance loss assessment items among the automobile insurance loss assessment items corresponding to the erroneous correlation features;

[0150] Obtain a target item type corresponding to the unreasonable auto insurance damage assessment item, and replace the item type of the unreasonable auto insurance damage assessment item according to the target item type to obtain the target auto insurance damage assessment item.

[0151] For example, the car insurance damage assessment item corresponding to an erroneous correlation feature is a car door replacement and repair item, but the car insurance damage assessment item only requires painting and sheet metal repair of the car door. By replacing the item type of the car insurance damage assessment item, the target car insurance damage assessment item is obtained as car door painting and sheet metal repair.

[0152] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0153] In an embodiment of the present application, after first obtaining a dataset of automobile insurance items to be evaluated, features corresponding to automobile insurance loss assessment items, project work time types, project locations, and internal and external repair locations are extracted and associated with the extracted features. This allows identification of the correspondence between automobile insurance loss assessment items and corresponding damaged vehicle parts, thereby facilitating subsequent improvement in the accuracy of automobile insurance loss assessment items. Secondly, by performing automobile insurance loss assessment on global correlation features and extracting erroneous individual correlation features corresponding to the erroneous global correlation features when the automobile insurance loss assessment results contain erroneous global correlation features, and identifying the type of the erroneous individual correlation features, two types of tasks are obtained for the target item type. This allows evaluation of the rationality of automobile insurance loss assessment items, thereby facilitating subsequent improvement in the accuracy of automobile insurance loss assessment. Different automobile insurance loss assessment scheme rules can be configured without relying on business operation experience, thereby achieving automatic automobile insurance loss assessment and improving the efficiency of automobile insurance loss assessment. Finally, by adjusting the automobile insurance loss assessment items corresponding to the erroneous correlation features according to the target item type, the target automobile insurance loss assessment item is obtained, thereby achieving automatic automobile insurance loss assessment, reducing the number of unreasonable automobile insurance loss assessment schemes, and improving the accuracy of automobile insurance loss assessment. Therefore, the vehicle insurance damage assessment method proposed in this application can improve the efficiency and accuracy of vehicle insurance damage assessment.

[0154] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Artificial Intelligence (AI) is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to achieve optimal results.

[0155] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware via computer-readable instructions. The computer-readable instructions can be stored in a computer-readable storage medium, and when the program is executed, it can include the processes in the above-described method embodiments. The aforementioned storage medium can be a non-volatile storage medium such as a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0156] It should be understood that although the steps in the flowcharts of the accompanying drawings are shown in sequence as indicated by the arrows, these steps are not necessarily executed 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 some of the steps in the flowcharts 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 portion of the sub-steps or stages of other steps.

[0157] Further references Figure 4 , as a response to the above Figure 2 The present application provides an embodiment of a vehicle insurance damage assessment device. Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0158] like Figure 4 As shown, the vehicle damage assessment device 400 of this embodiment includes: an acquisition module 401, an extraction module 402, an association module 403, an assessment module 404, an identification module 405, and an adjustment module 406.

[0159] The acquisition module 401 is used to obtain a data set of the automobile insurance project to be evaluated, wherein the data set includes automobile insurance loss assessment items, project work time types, project locations, and internal and external repair locations;

[0160] The extraction module 402 is used to respectively extract the vehicle insurance loss assessment item features corresponding to the vehicle insurance loss assessment items, the project work time type features corresponding to the project work time type, the project part features corresponding to the project part, and the internal and external repair part features corresponding to the internal and external repair parts;

[0161] The association module 403 is used to associate the vehicle insurance damage assessment item characteristics, the item labor type characteristics, the item location characteristics, and the repair internal and external location characteristics to obtain association characteristics, wherein the association characteristics include global association characteristics and single-item association characteristics;

[0162] The evaluation module 404 is used to perform a vehicle insurance damage assessment on the global correlation features to obtain a vehicle insurance damage assessment result;

[0163] The identification module 405 is configured to extract, if there is an erroneous global correlation feature in the vehicle insurance damage assessment result, an erroneous single correlation feature corresponding to the erroneous global correlation feature, and identify the type of the erroneous single correlation feature to obtain a target item type; and

[0164] The adjustment module 406 is used to adjust the motor vehicle insurance loss assessment item corresponding to the error association feature according to the target item type to obtain a target motor vehicle insurance loss assessment item.

[0165] In this embodiment, the functions of each module / unit are as follows:

[0166] A classification submodule, configured to rationally classify the vehicle insurance damage assessment item features corresponding to the project work time type features, project part features, and internal and external repair part features in the global correlation features to obtain classification labels; and

[0167] The evaluation submodule is used to obtain the vehicle insurance damage assessment result according to the classification label.

[0168] In an embodiment of the present application, after first obtaining a dataset of automobile insurance items to be evaluated, features corresponding to automobile insurance loss assessment items, project work time types, project locations, and internal and external repair locations are extracted and associated with the extracted features. This allows identification of the correspondence between automobile insurance loss assessment items and corresponding damaged vehicle parts, thereby facilitating subsequent improvement in the accuracy of automobile insurance loss assessment items. Secondly, by performing automobile insurance loss assessment on global correlation features and extracting erroneous individual correlation features corresponding to the erroneous global correlation features when the automobile insurance loss assessment results contain erroneous global correlation features, and identifying the type of the erroneous individual correlation features, two types of tasks are obtained for the target item type. This allows evaluation of the rationality of automobile insurance loss assessment items, thereby facilitating subsequent improvement in the accuracy of automobile insurance loss assessment. Different automobile insurance loss assessment scheme rules can be configured without relying on business operation experience, thereby achieving automatic automobile insurance loss assessment and improving the efficiency of automobile insurance loss assessment. Finally, by adjusting the automobile insurance loss assessment items corresponding to the erroneous correlation features according to the target item type, the target automobile insurance loss assessment item is obtained, thereby achieving automatic automobile insurance loss assessment, reducing the number of unreasonable automobile insurance loss assessment schemes, and improving the accuracy of automobile insurance loss assessment. Therefore, the vehicle insurance damage assessment device proposed in this application can improve the efficiency and accuracy of vehicle insurance damage assessment.

[0169] The computer device 5 includes a memory 51, a processor 52, and a network interface 53 that are interconnected through a system bus. It should be noted that the figure only shows a computer device 5 with components 51-53, 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 (ASICs), field-programmable gate arrays (FPGAs), digital signal processors (DSPs), embedded devices, etc.

[0170] The computer device may be a desktop computer, notebook computer, PDA, cloud server, etc. The computer device may interact with the user via a keyboard, mouse, remote control, touchpad, or voice control device.

[0171] The memory 51 includes at least one type of readable storage medium, including flash memory, hard disk, multimedia card, card-type memory (e.g., SD or D auto insurance damage assessment memory), 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 storage, magnetic disk, optical disk, etc. In some embodiments, the memory 51 may be an internal storage unit of the computer device 5, such as the hard disk or memory of the computer device 5. In other embodiments, the memory 51 may also be an external storage device of the computer device 5, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash memory card, etc. equipped on the computer device 5. Of course, the memory 51 may also include both the internal storage unit of the computer device 5 and its external storage device. In this embodiment, the memory 51 is generally used to store the operating system and various application software installed on the computer device 5, such as computer-readable instructions for the auto insurance damage assessment method. In addition, the memory 51 can also be used to temporarily store various types of data that have been output or are to be output.

[0172] In some embodiments, the processor 52 can be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 52 is generally used to control the overall operation of the computer device 5. In this embodiment, the processor 52 is used to execute computer-readable instructions or process data stored in the memory 51, such as computer-readable instructions for executing the vehicle insurance damage assessment method.

[0173] The network interface 53 may include a wireless network interface or a wired network interface. The network interface 53 is generally used to establish a communication connection between the computer device 5 and other electronic devices.

[0174] In an embodiment of the present application, after first obtaining a dataset of automobile insurance items to be evaluated, features corresponding to automobile insurance loss assessment items, project work time types, project locations, and internal and external repair locations are extracted and associated with the extracted features. This allows identification of the correspondence between automobile insurance loss assessment items and corresponding damaged vehicle parts, thereby facilitating subsequent improvement in the accuracy of automobile insurance loss assessment items. Secondly, by performing automobile insurance loss assessment on global correlation features and extracting erroneous individual correlation features corresponding to the erroneous global correlation features when the automobile insurance loss assessment results contain erroneous global correlation features, and identifying the type of the erroneous individual correlation features, two types of tasks are obtained for the target item type. This allows evaluation of the rationality of automobile insurance loss assessment items, thereby facilitating subsequent improvement in the accuracy of automobile insurance loss assessment. Different automobile insurance loss assessment scheme rules can be configured without relying on business operation experience, thereby achieving automatic automobile insurance loss assessment and improving the efficiency of automobile insurance loss assessment. Finally, by adjusting the automobile insurance loss assessment items corresponding to the erroneous correlation features according to the target item type, the target automobile insurance loss assessment item is obtained, thereby achieving automatic automobile insurance loss assessment, reducing the number of unreasonable automobile insurance loss assessment schemes, and improving the accuracy of automobile insurance loss assessment. Therefore, the automobile insurance damage assessment computer device proposed in this application can improve the efficiency and accuracy of automobile insurance damage assessment.

[0175] The present application also provides another embodiment, namely, providing a computer-readable storage medium, which 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 above-mentioned vehicle insurance damage assessment method.

[0176] In an embodiment of the present application, after first obtaining a dataset of automobile insurance items to be evaluated, features corresponding to automobile insurance loss assessment items, project work time types, project locations, and internal and external repair locations are extracted and associated with the extracted features. This allows identification of the correspondence between automobile insurance loss assessment items and corresponding damaged vehicle parts, thereby facilitating subsequent improvement in the accuracy of automobile insurance loss assessment items. Secondly, by performing automobile insurance loss assessment on global correlation features and extracting erroneous individual correlation features corresponding to the erroneous global correlation features when the automobile insurance loss assessment results contain erroneous global correlation features, and identifying the type of the erroneous individual correlation features, two types of tasks are obtained for the target item type. This allows evaluation of the rationality of automobile insurance loss assessment items, thereby facilitating subsequent improvement in the accuracy of automobile insurance loss assessment. Different automobile insurance loss assessment scheme rules can be configured without relying on business operation experience, thereby achieving automatic automobile insurance loss assessment and improving the efficiency of automobile insurance loss assessment. Finally, by adjusting the automobile insurance loss assessment items corresponding to the erroneous correlation features according to the target item type, the target automobile insurance loss assessment item is obtained, thereby achieving automatic automobile insurance loss assessment, reducing the number of unreasonable automobile insurance loss assessment schemes, and improving the accuracy of automobile insurance loss assessment. Therefore, the automobile insurance damage assessment storage medium proposed in this application can improve the efficiency and accuracy of automobile insurance damage assessment.

[0177] 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 the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this 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 enabling 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.

[0178] Obviously, the embodiments described above are only some of the embodiments of the present application, rather than all of the 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 has been described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions described in the aforementioned specific embodiments, or to make equivalent replacements for some of the technical features therein. Any equivalent structure made using the contents of the present application specification and the accompanying drawings, directly or indirectly used in other related technical fields, is also within the scope of patent protection of the present application.

Claims

1. A method for assessing damages in automobile insurance, characterized in that: The steps include: Obtaining a data set of an automobile insurance project to be evaluated, wherein the data set includes automobile insurance loss assessment items, project work time types, project locations, and internal and external repair locations; Extracting the vehicle insurance damage assessment item feature corresponding to the vehicle insurance damage assessment item, the project work hour type feature corresponding to the project work hour type, the project part feature corresponding to the project part, and the maintenance internal and external part features corresponding to the maintenance internal and external parts respectively; Correlating the vehicle insurance damage assessment item feature, the item labor type feature, the item location feature, and the repair internal and external location features to obtain correlation features, wherein the correlation features include global correlation features and single item correlation features; Performing a vehicle insurance damage assessment on the global correlation features to obtain a vehicle insurance damage assessment result; If the vehicle insurance damage assessment result contains an erroneous global correlation feature, extracting an erroneous single-item correlation feature corresponding to the erroneous global correlation feature, and identifying the type of the erroneous single-item correlation feature to obtain a target item type; adjusting the motor vehicle insurance loss assessment item corresponding to the erroneous global correlation feature according to the target item type to obtain a target motor vehicle insurance loss assessment item; The extracting of the vehicle insurance damage assessment item features corresponding to the vehicle insurance damage assessment items, the project work time type features corresponding to the project work time type, the project part features corresponding to the project parts, and the repair internal and external part features corresponding to the repair internal and external parts respectively includes: Sorting and vectorizing the vehicle insurance loss assessment items according to the character sequence of the vehicle insurance loss assessment items to obtain a sorted vehicle insurance loss assessment vector sequence; Identify the project maintenance man-hours corresponding to the vehicle insurance damage assessment item, perform a vectorization operation on the project maintenance man-hours, and obtain a project man-hour type vector sequence; Identifying the vehicle body parts corresponding to the vehicle insurance loss assessment items, performing vectorization operations on the vehicle body parts, and obtaining a vehicle body part vector sequence; Identify the vehicle body parts corresponding to the vehicle insurance loss assessment items, perform vectorization operations on the vehicle body parts, and obtain a vector sequence of the internal and external parts of the repair; Position encoding is performed on the sorted auto insurance damage assessment vector sequence, the project work time type vector sequence, the vehicle body part vector sequence, and the repair internal and external part vector sequence, respectively, to obtain auto insurance damage assessment item features corresponding to the sorted auto insurance damage assessment vector sequence, project work time type features corresponding to the project work time type vector sequence, project part features corresponding to the vehicle body part vector sequence, and repair internal and external part features corresponding to the repair internal and external part vector sequence; The associated features obtained by associating the vehicle insurance damage assessment item features, the item labor type features, the item location features, and the repair internal and external location features include: Using the attention mechanism to identify the one-to-one correspondence between the features of the vehicle insurance damage assessment item, the features of the item labor type, the features of the item part, and the features of the internal and external parts of the repair; Based on the one-to-one correspondence, the vehicle insurance damage assessment item feature, the item labor type feature, the item part feature and the repair internal and external part feature are associated to obtain the associated feature.

2. The method for assessing automobile damage according to claim 1, characterized in that: The performing of a vehicle insurance damage assessment on the global correlation feature to obtain a vehicle insurance damage assessment result includes: Rationality classification is performed on the vehicle insurance damage assessment item features corresponding to the project work time type features, project location features, and repair internal and external location features in the global correlation features to obtain classification labels; The vehicle insurance damage assessment result is obtained according to the classification label.

3. The method for assessing damages of automobile insurance according to any one of claims 1 to 2, characterized in that: The identifying the type of the erroneous single item association feature to obtain the target item type includes: Calculate the probability of the item category to which the vehicle insurance damage assessment item feature corresponding to the erroneous single item correlation feature belongs based on the item part feature and the internal and external repair part features in the erroneous single item correlation feature; The project category corresponding to the maximum probability is selected from the project category probabilities as the target project type corresponding to the erroneous single item association feature.

4. The method for assessing damages of automobile insurance according to any one of claims 1 to 2, characterized in that: The adjusting the motor vehicle insurance loss assessment item corresponding to the error correlation feature according to the target item type to obtain the target motor vehicle insurance loss assessment item includes: Identifying unreasonable automobile insurance loss assessment items corresponding to the erroneous correlation features; Obtain a target item type corresponding to the unreasonable auto insurance damage assessment item, and replace the item type of the unreasonable auto insurance damage assessment item according to the target item type to obtain the target auto insurance damage assessment item.

5. The method for assessing vehicle insurance damages according to any one of claims 1 to 2, wherein before obtaining the vehicle insurance item dataset to be assessed, the method further comprises: Acquire a vehicle damage image, and extract damage image features of the vehicle damage image; Identifying the damage type and damage location of the damage image features; The vehicle insurance damage assessment items of the vehicle damage image are determined according to the damage type and the damage location.

6. A vehicle insurance damage assessment device, characterized in that: The device is used to implement the steps of the vehicle insurance damage assessment method according to any one of claims 1 to 5, and the device includes: An acquisition module is used to acquire a data set of a motor vehicle insurance project to be evaluated, wherein the data set includes motor vehicle insurance loss assessment items, project labor type, project location, and internal and external repair locations; An extraction module is used to respectively extract the vehicle insurance loss assessment item features corresponding to the vehicle insurance loss assessment items, the project work time type features corresponding to the project work time type, the project part features corresponding to the project part, and the repair internal and external part features corresponding to the repair internal and external parts; an association module, configured to associate the vehicle insurance damage assessment item characteristics, the item labor type characteristics, the item location characteristics, and the repair internal and external location characteristics to obtain association characteristics, wherein the association characteristics include global association characteristics and single-item association characteristics; An evaluation module, configured to perform a vehicle insurance damage assessment on the global correlation features to obtain a vehicle insurance damage assessment result; an identification module configured to, if the vehicle insurance damage assessment result contains an erroneous global correlation feature, extract an erroneous single correlation feature corresponding to the erroneous global correlation feature, identify the type of the erroneous single correlation feature, and obtain a target item type; and An adjustment module, configured to adjust the motor vehicle insurance loss assessment item corresponding to the erroneous global correlation feature according to the target item type to obtain a target motor vehicle insurance loss assessment item; The extracting of the vehicle insurance damage assessment item features corresponding to the vehicle insurance damage assessment items, the project work time type features corresponding to the project work time type, the project part features corresponding to the project parts, and the repair internal and external part features corresponding to the repair internal and external parts respectively includes: Sorting and vectorizing the vehicle insurance loss assessment items according to the character sequence of the vehicle insurance loss assessment items to obtain a sorted vehicle insurance loss assessment vector sequence; Identify the project maintenance man-hours corresponding to the vehicle insurance damage assessment item, perform a vectorization operation on the project maintenance man-hours, and obtain a project man-hour type vector sequence; Identifying the vehicle body parts corresponding to the vehicle insurance loss assessment items, performing vectorization operations on the vehicle body parts, and obtaining a vehicle body part vector sequence; Identify the vehicle body parts corresponding to the vehicle insurance loss assessment items, perform vectorization operations on the vehicle body parts, and obtain a vector sequence of the internal and external parts of the repair; Position encoding is performed on the sorted auto insurance damage assessment vector sequence, the project work time type vector sequence, the vehicle body part vector sequence, and the repair internal and external part vector sequence, respectively, to obtain auto insurance damage assessment item features corresponding to the sorted auto insurance damage assessment vector sequence, project work time type features corresponding to the project work time type vector sequence, project part features corresponding to the vehicle body part vector sequence, and repair internal and external part features corresponding to the repair internal and external part vector sequence; The associated features obtained by associating the vehicle insurance damage assessment item features, the item labor type features, the item location features, and the repair internal and external location features include: Using the attention mechanism to identify the one-to-one correspondence between the features of the vehicle insurance damage assessment item, the features of the item labor type, the features of the item part, and the features of the internal and external parts of the repair; Based on the one-to-one correspondence, the vehicle insurance damage assessment item feature, the item labor type feature, the item part feature and the repair internal and external part feature are associated to obtain the associated feature.

7. A computer device comprising a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the vehicle insurance damage assessment method according to any one of claims 1 to 5 when executing the computer-readable instructions.

8. 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 vehicle insurance damage assessment method according to any one of claims 1 to 5.

Images