Management server of automobile part remanufacturing-based repair system for evaluating appropriateness of repair claim costs

Abstract

A management server for an automotive parts remanufacturing-based repair system that manages repair requesters and repair shops is disclosed. The management server includes an information management unit configured to manage repair shop information associated with repair shops; an image receiving unit configured to receive an image of a damaged part of a repair case; a damage degree determination unit configured to generate damage characteristic information indicating the degree of damage from the damaged part image; a repair difficulty determination unit configured to generate repair difficulty information indicating the repair difficulty required to repair the damaged part based on the damage characteristic information; and a claim appropriateness determination unit configured to determine the appropriateness of the amount claimed by each repair shop for each repair case based on repair history data and repair difficulty information stored in the information management unit for each repair case.

Description

Technology Field

[0001] The present invention relates to a management server for an automobile parts remanufacturing-based repair system, and more specifically, to a management server capable of receiving a repair request for a specific damaged part of an automobile and transmitting it to a repair shop, as well as determining whether the amount charged by the repair shop for the repair is appropriate. Background Technology

[0002] In the process of repairing a vehicle after an accident, repair shops bill for repair costs from insurance companies or vehicle owners, and insurance companies determine the settlement amount after a review process. However, the conventional structure for claiming and settling repair costs suffers from the problem of a lack of clear standards for calculating appropriate billing amounts based on the actual extent of damage to the accident vehicle and the required difficulty of the repairs. Most cost reviews and reduction decisions rely on the experience of insurance adjusters or fixed-rate standards, and there are limitations in that quantitative factors such as repair difficulty, repair time, and technical skill levels are not systematically reflected.

[0003] As a result, significant discrepancies in billing amounts occur among repair shops despite the same accident type and similar repair difficulty. Consequently, insurance companies must conduct repeated reviews and negotiations to determine if claims are excessive, leading to frequent disputes arising from overcharging or excessive reductions. Furthermore, since there is no guarantee that the settlement amount proposed by insurance companies is always optimal or reasonable, vehicle owners and repair shops currently lack the means to verify the basis for assessing the appropriateness of such settlement amounts. The problem to be solved

[0004] The objective of the present invention, which aims to solve the aforementioned problems, is to provide a management server capable of receiving a repair request for a specific damaged part of a vehicle and transmitting it to a repair shop, as well as determining whether the amount charged by the repair shop for the repair is appropriate. means of solving the problem

[0005] One aspect of the present invention for achieving an objective relates to a management server of an automobile parts remanufacturing-based repair system that manages repair requesters and repair shops, wherein the management server comprises an information management unit configured to manage repair shop information associated with repair shops, an image receiving unit configured to receive an image of a damaged part of a repair case, a damage degree determination unit configured to generate damage characteristic information indicating the degree of damage from the image of the damaged part, a repair difficulty determination unit configured to generate repair difficulty information indicating the repair difficulty required to repair the damaged part based on the damage characteristic information, and a claim appropriateness determination unit configured to determine the appropriateness of the amount claimed by each repair shop for each repair case based on repair history data and repair difficulty information stored in the information management unit for each repair case. Effects of the invention

[0006] According to embodiments of the present invention, not only can a repair request for a specific damaged part of a vehicle be received and forwarded to a repair shop, but a determination can also be made as to whether the amount charged by the repair shop for the repair is appropriate. Brief explanation of the drawing

[0007] FIG. 1 shows an automobile parts remanufacturing-based repair system according to one embodiment of the present invention. FIGS. 2 to 4 illustrate the operation of a management server according to embodiments of the present invention. FIG. 5 shows a management server according to embodiments of the present invention. FIG. 6 shows a damage degree determination unit according to embodiments of the present invention. FIG. 7 shows a repair difficulty determination unit according to embodiments of the present invention. FIG. 8 shows a claim appropriateness determination unit according to embodiments of the present invention. FIG. 9 is a diagram illustrating the hardware configuration of a management server according to embodiments of the present invention. Specific details for implementing the invention

[0008] The present invention is susceptible to various modifications and may have various embodiments; specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the invention to specific embodiments, and it should be understood that the invention includes all modifications, equivalents, and substitutions that fall within the spirit and scope of the invention. Similar reference numerals have been used for similar components in the description of each drawing.

[0009] Terms such as first, second, A, B, etc., may be used to describe various components, but the components should not be limited by said terms. These terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component. The term "and / or" includes a combination of a plurality of related described items or any of a plurality of related described items.

[0010] When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. On the other hand, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between.

[0011] The terms used in this application are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" are intended to specify the presence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0012] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the present invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0013] Hereinafter, preferred embodiments according to the present invention will be described in detail with reference to the attached drawings.

[0014] FIG. 1 illustrates an automobile parts remanufacturing-based repair system according to an embodiment of the present invention. Referring to FIG. 1, the automobile parts remanufacturing-based repair system (hereinafter, the system (10)) includes a management server (100), a client terminal (201) of a repair client, and a repair shop terminal (203) of a repair shop. At this time, the management server (100) and the terminals (201, 203) can exchange data with each other over a wireless network.

[0015] The management server (100) is a server configured to perform overall management and operation of the system (10), and receives a repair request from a requester such as a repair requester or an insurance company, determines the difficulty of the repair based on images of the damaged parts of the requested vehicle and repair-related information, and determines and proposes a repair shop that can most appropriately handle the repair details. In addition, when a repair shop is determined (automatically or by the repair requester), the management server (100) can provide the details of the repair request to the determined repair shop so that the repair can be carried out.

[0016] Furthermore, the management server (100) can manage the repair progress, insurance claim details, and settlement status in real time, and can perform the role of mediating and managing the repair process between the client and the repair company.

[0017] The management server (100) is connected to the repair company terminal (203) and the client terminal (201) via a network (not shown) and may be operated in the form of an application or a web-based service. For example, a repair client may use their client terminal (201) (e.g., using an application provided by the management server (100)) to send a repair request to the management server (100) along with an image of the damaged part taken at the accident site, and the management server (100) may receive this and calculate the repair difficulty.

[0018] The management server (100) can calculate an appropriate repair amount for a repair case using a deep learning-based model that has learned past settlement data, with the calculated repair difficulty, vehicle information, and damaged part information as inputs. In addition, the management server (100) can determine an acceptable range of claims based on statistical analysis of previously settled repair cases and repair shop reliability information, and evaluate the appropriateness of the claims amount by determining whether the actual submitted claims amount falls within this range.

[0019] The management server (100) can provide the evaluation results to the client terminal (201) or the repair shop terminal (203) to visually provide the basis for judging the appropriateness of the billing amount and to provide quantitative criteria that can be referenced during insurance company review or cost negotiation between the repair shop and the client.

[0020] Furthermore, the management server (100) can provide information (e.g., photos or comments) that can be checked for each repair in progress to the client terminal (201).

[0021] The client terminal (201) may be a user terminal such as an insurance company or a car owner. For example, when requesting vehicle repair, the insurance company may directly select a repair shop or select one of the recommended repair shops provided by the system. Alternatively, the management server (100) may be configured to automatically determine a specific shop among the recommended repair shops as the optimal shop and automatically transmit the repair request to the repair shop terminal (203).

[0022] The repair company terminal (203) may be an operating terminal of each repair shop, specialized repair company or repair network, or a terminal owned by a repair technician, and may receive a repair request transmitted by the management server (100) and reply with response information such as whether the repair is possible and the estimated time required. In addition, the repair company terminal (203) uploads work status by repair progress stage, photos by process, parts replacement, and work details in real time, so that the management server (100) can track and manage the progress of the repair process.

[0023] In particular, the repair company terminal (203) can transmit information on the actual billing amount for the repair case to the management server (100), and the management server (100) can determine whether the billing amount is appropriate by comparing it with the standard amount calculated by the standard amount calculation unit (151) and the allowable range determined by the allowable range calculation unit (153) based on this information.

[0024] Information such as the billing amount, work history, and real-time progress provided through the repair shop terminal (203) can be transmitted to the client terminal (201) or the insurance company through the management server (100), thereby establishing a transparent and objective repair / settlement procedure between the vehicle owner, the insurance company, and the repair shop.

[0025] The automobile repair system (10) according to the present invention provides a function to evaluate the appropriateness of a claim amount by comparing an appropriate repair amount and an actual claim amount for a repair case based on an image of the actual damaged part of the accident vehicle and the results of an analysis of the repair difficulty. Accordingly, unreasonable disputes such as overcharging or excessive reduction of costs during the insurance claim process can be reduced, and transparency and reliability in the calculation of repair costs can be ensured. In addition, since objective indicators such as repair difficulty, technical level, and past settlement data are reflected, cost verification based on quantitative data becomes possible, unlike conventional methods that relied on emotional and subjective judgments.

[0026] Furthermore, the system of the present invention is configured to integrally process all procedures—ranging from repair requests and repair progress management to real-time process status sharing, submission of billing amounts, calculation of standard amounts, setting of acceptable ranges, determination of billing appropriateness, and settlement management—on a single platform. Accordingly, stakeholders such as vehicle owners, repair shops, and insurance companies can transparently share the history and cost information of the entire repair process through the platform, thereby enabling the expectation of various benefits, including ensuring repair quality, streamlining cost negotiation procedures, automating insurance claims processing, and reducing the time to repair completion.

[0027] FIGS. 2 to 4 illustrate the operation of a management server according to embodiments of the present invention. Referring to FIGS. 2 to 4, a screen of a web service or application service provided by the management server is shown.

[0028] Referring to FIG. 2, the client terminal (201) may display input items related to the repair request, such as vehicle number, vehicle name, insurance company information, counterparty insurance company information, wheel quantity, and estimated delivery date. Additionally, input fields for the repair shop name and repair shop address are included, and location information required for the repair request can be registered along with an address search function. At the bottom, a photo registration button for uploading images of damage to the accident vehicle may be included. When the client enters the necessary information into the input items, the entered repair request data and images of the damaged parts are transmitted to the management server (100).

[0029] Referring to FIG. 3, a list of multiple repair shops corresponding to the repair request conditions and the recommendation results of the management server (100) is displayed on the client terminal (201). Each repair shop is provided in a list format including information such as the shop name, region, and recent repair throughput, and can be sorted according to the recommendation score calculated by the management server (100) and displayed to the user in a ranking format. Each shop item in the list includes a selection button that allows the client to proceed with a repair request to the corresponding shop, and when the client selects a specific shop, the management server (100) can automatically transmit the repair request data to the corresponding repair shop terminal (203).

[0030] Referring to FIG. 4, the client terminal (201) visually displays the repair progress stage collected by the management server (100) from the repair shop terminal (203). The repair status can be displayed in the form of a circular stage display UI, categorized by the progress stages of the repair process, such as scheduled pickup, arrival at the shop, repair in progress, and repair completed. Each stage is displayed as either active or inactive depending on the current progress status, and additional information such as a detailed description of the stage, carrier information, insurance company review status, and estimated arrival time can be displayed at the bottom of the screen. Through this, the client can check the repair progress in real time, and the management server (100) updates the status changes through information linkage with the repair shop and insurance company and provides them to the client terminal.

[0031] FIG. 5 illustrates a management server according to embodiments of the present invention. Referring to FIG. 5, the management server (100) includes an information management unit (110), an image receiving unit (120), a damage degree determination unit (130), a repair difficulty determination unit (140), and a claim appropriateness determination unit (150).

[0032] The information management unit (110) stores and manages information necessary for the operation of the management server (100). For example, the information management unit (110) may be a database included within the management server (100) or accessible to the management server (100).

[0033] The information management unit (110) stores and manages information regarding basic information, past repair records, past repair cost claims, etc. for multiple repair shops. For example, the information management unit (110) stores repair shop information for each repair shop, including information on the repair part per repair case, vehicle, repair period, repair difficulty, claimed cost, settlement amount, customer rating (trustworthiness), etc.

[0034] The image receiving unit (120) receives an image of the damaged part associated with each repair case. The image receiving unit (120) can transmit the received information to the damage degree judgment unit (130) and the repair difficulty judgment unit (140) so that it can be used as input data for subsequent processing.

[0035] The damage degree determination unit (130) determines the degree of damage by analyzing the damage state of the vehicle from the damaged area image input through the image receiving unit (120). The damage degree determination unit (130) generates damage characteristic information indicating the degree of damage by performing preprocessing on the input image, extraction of damage-related feature information, and determination of the damage state. Through this, it is possible to make a quantitative judgment based on actual images without relying on simple user descriptions or the visual judgment of a mechanic.

[0036] Damage characteristic information determined by the damage degree determination unit (130) can be transmitted to the repair difficulty determination unit (140).

[0037] The repair difficulty determination unit (140) determines the repair difficulty required to repair the damaged part by synthesizing the damage characteristic information transmitted from the damage degree determination unit (130), vehicle information, and damaged part information. The repair difficulty determination unit (140) evaluates the elements necessary for determining the repair difficulty, such as the estimated repair time, technical level, and repair complexity, calculates repair difficulty information indicating the repair difficulty of the accident vehicle, and can provide this to the claim appropriateness determination unit (150).

[0038] The claim appropriateness judgment unit (150) determines the appropriateness of the actual claim amount for a specific repair case by combining the repair difficulty information calculated by the repair difficulty judgment unit (140) and the past repair history data (claim amount, settlement amount, repair skill level, repair time required, etc.) stored in the information management unit (110). The claim appropriateness judgment unit (150) calculates the appropriate repair amount for the repair case using a deep learning-based standard amount calculation model that learns past settlement amounts by inputting repair difficulty and accident vehicle characteristic information, and then determines the reasonableness and appropriateness of the claim amount by comparing whether the actual claim amount falls within the allowable range based on the allowable range determined by considering past claim-settlement deviation and company reliability information.

[0039] Accordingly, according to the management server (100) of the present invention, the calculation of repair difficulty based on the analysis of damaged images of the accident vehicle, the calculation of an appropriate claim amount based on past settlement results, and the calculation of an allowable range considering reliability and statistical deviation by company are performed in an integrated manner. Through this, disputes caused by overcharging or excessive reduction during the insurance claim process can be reduced, and fairness and transparency in the calculation of repair costs can be ensured. Compared to simple estimate comparison or experience-based judgment methods, the present invention provides a quantitative cost verification system based on objective data analysis and can support smooth negotiation and reasonable cost agreement between repair companies, insurance companies, and vehicle owners.

[0040] FIG. 6 illustrates a damage degree determination unit according to embodiments of the present invention. Referring to FIG. 6, the damage degree determination unit (130) includes an image preprocessing unit (131), a damage feature extraction unit (133), and a damage feature vector calculation unit (135).

[0041] The image preprocessing unit (131) performs basic preprocessing operations for damage feature analysis on an image of a damaged part of an accident vehicle input through the image receiving unit (120). The image preprocessing unit (131) performs preprocessing operations such as resolution normalization, brightness and contrast adjustment, noise removal, and pixel value normalization of the input image, thereby enabling stable operations during the CNN-based feature extraction process for the image transmitted to the damage feature extraction unit (133). Additionally, the image preprocessing unit (131) can automatically or according to user settings extract a region of interest if necessary and convert it into image data centered on the area containing the damaged part.

[0042] The damage feature extraction unit (133) takes an image processed by the image preprocessing unit (131) as input and performs multiple stages of convolutional and pooling operations based on a Convolutional Neural Network (CNN) to generate a damage feature map that constitutes visual features meaningful for damage judgment. The damage feature extraction unit (133) according to the present embodiment hierarchically extracts low-level and intermediate-level visual features such as edges, textures, crack shapes, geometric structures of deformation, and paint damage areas from the input image, and can perform pooling operations or attention-based importance reflection operations to emphasize features that contribute to the judgment of the severity of damage. Such a feature extraction process is not intended for simple image classification or object identification, but rather for generating a feature representation that comprehensively reflects the intensity, extent, and pattern of vehicle damage.

[0043] The damage characteristic vector calculation unit (135) receives the damage characteristic map generated by the damage characteristic extraction unit (133) and generates a multidimensional damage characteristic vector that quantitatively represents the damage state. The damage characteristic vector calculation unit (135) performs intensive operations such as flattening or global average pooling on the feature map to generate a damage characteristic vector that concisely represents only the key features significant for determining the degree of damage, and the generated damage characteristic vector is subsequently provided as input to the repair difficulty judgment unit (140). At this time, each component of the damage characteristic vector may represent a damage indicator from various aspects of the corresponding damaged area. For example, the damage characteristic vector may include multiple dimensional elements reflecting the presence or absence of a specific damage pattern, the distribution of the damaged area, and the damage intensity, and each element is used as a quantitative judgment criterion in the process of calculating the repair difficulty.

[0044] That is, the damage degree determination unit (130) of the present invention performs an in-depth characteristic analysis of the damage state based on an actual image of the damaged area, thereby enabling an analysis of the damage degree based on objective and quantitative criteria, unlike the conventional method in which the severity of damage is determined by visual evaluation by a repair expert or qualitative criteria.

[0045] FIG. 7 illustrates a repair difficulty determination unit according to embodiments of the present invention. Referring to FIG. 7, the repair difficulty determination unit (140) includes a repair characteristic vector generation unit (141) and a repair difficulty vector calculation unit (143).

[0046] The repair characteristic vector generation unit (141) combines the damage characteristic vector input from the damage degree determination unit (130) with the vehicle basic information and repair part information stored in the information management unit (110) to generate a repair characteristic vector, which is an input vector for determining the difficulty of repair.

[0047] For example, vehicle information includes the vehicle model, year of manufacture, body material (steel plate / aluminum / composite material, etc.), and vehicle type classification (sedan, SUV, etc.), while repair part information includes the location of damage to the accident vehicle (e.g., front / rear bumpers, door panels, fenders, radiator, wheels, etc.) or type information of the component to be repaired.

[0048] The mathematical characteristic vector generation unit (141) combines these various attribute data into a single input vector form to produce a result, and uses it for the neural network-based prediction of the mathematical difficulty judgment unit (140).

[0049] The mathematical difficulty vector calculation unit (143) receives the mathematical characteristic vector generated by the mathematical characteristic vector generation unit (141) as input, applies a deep learning-based prediction model for determining mathematical difficulty, and calculates the mathematical difficulty vector as output.

[0050] The mathematical difficulty vector calculation unit (143) may include a deep learning model such as a multilayer perceptron (Deep Neural Network, DNN) or a recurrent neural network (RNN), and learns non-linear relationships and weighted correlations between multiple features within an input vector to estimate mathematical difficulty components that are difficult to derive through simple numerical calculations. For example, by using a fully-connected neural network that includes multiple hidden layers to progressively compress and transform the input dimension, a latent representation that highlights key factors determining mathematical difficulty can be generated.

[0051] Additionally, when the input vector includes past similar accident history and repair performance data based on time change, the repair difficulty vector calculation unit (143) may use an RNN-based structure such as a Long Short-Term Memory (LSTM) or a Gated Recurrent Unit (GRU) to effectively model the time series pattern of the input vector. This structure is advantageous for predicting the trend of difficulty change of a specific repair part or the deviation of repair difficulty by vehicle type by reflecting the change in repetitive or cumulative repair results.

[0052] The numerical difficulty vector, which is the output of the numerical difficulty vector calculation unit (143), is a multidimensional vector that quantitatively expresses multiple elements constituting numerical difficulty. For example, the numerical difficulty vector calculation unit (143) calculates a numerical difficulty vector according to the mathematical formula 1 below.

[0053]

[0054] Here, RDV is a repair difficulty vector, RTime is the estimated repair time (e.g., in days), and RLev is the graded repair skill level required for the repair (e.g., the value of any one of the 10 graded skill levels).

[0055] The calculated repair difficulty vector is subsequently transmitted to the claim appropriateness judgment unit (150) and used to calculate the appropriate repair amount for the repair case and to verify the comparison with the actual claim amount. That is, the claim appropriateness judgment unit (150) can use the repair difficulty vector as a key reference value in the process of deriving a reasonable level of repair costs and determining whether the actual claim amount is within an appropriate range.

[0056] Accordingly, the repair difficulty vector calculation unit (143) of the present invention enables quantitative repair difficulty calculation that reflects complex correlations between damage characteristics and vehicle characteristics through a deep learning-based prediction model, thereby providing a repair shop recommendation result that is more accurate and reliable than existing experience-based or simple estimation methods.

[0058] FIG. 8 illustrates a claim appropriateness determination unit according to embodiments of the present invention. Referring to FIG. 8, the claim appropriateness determination unit (150) includes a standard amount calculation unit (151), an allowable range calculation unit (153), and an appropriateness determination unit (155).

[0059] The standard amount calculation unit (151) receives repair characteristic data, such as a repair difficulty vector, vehicle information (vehicle model, year of manufacture, etc.), and damaged part information, calculated by the damage degree determination unit (130) and the repair difficulty determination unit (140), and performs the function of calculating an appropriate repair amount for the current repair case. The standard amount calculation unit (151) includes a deep learning-based cost prediction model, and this prediction model can be implemented as a deep neural network (DNN) or a recurrent neural network (RNN) structure including time series learning.

[0060] The standard amount calculation unit (151) constructs an input vector using a repair difficulty vector, vehicle information (vehicle model, year of manufacture, etc.), and damaged part information. The standard amount calculation unit (155) obtains a standard amount corresponding to the repair case as an output as a result of inputting the input vector into a deep learning-based prediction model.

[0061] The deep learning model used in the present invention is learned from actual repair history data collected from a past repair database stored in a management server (100). For example, the deep learning model used in the standard amount calculation unit (151) can be pre-trained using the relationship between the (actual) settlement amount and an input vector consisting of a repair difficulty vector, vehicle information, and damaged part information of past repair cases as training data. During the learning process, the prediction model learns a non-linear functional relationship between the input vector and the settlement amount, and the model weights can be iteratively updated based on a loss function so that the error between the predicted amount and the actual settlement amount is minimized during the learning process. At this time, the loss function may be in the form of a composite loss function including a mean squared error (MSE), a mean absolute error (MAE), or a constraint term that considers the directionality of the cost deviation.

[0062] After the learning is completed, the deep learning model can calculate a standard amount based on the repair difficulty vector and repair characteristic information input for a new repair request, and this amount can be used as a standard value to determine the appropriateness of the actual billing amount. The standard amount calculation unit (151) can calculate a prediction result in real time for each repair case and transmit it to the allowable range calculation unit (153) and the appropriateness judgment unit (155).

[0063] The allowable range calculation unit (153) calculates an allowable range to determine whether the actual billed amount is included in an allowable range (i.e., appropriate) based on the standard amount calculated by the standard amount calculation unit (151).

[0064] The standard amount calculation unit (151) estimates the standard amount based on the settlement amount received rather than the actual claimed amount for completed repairs. In general, the actual settlement amount is usually a reasonable amount, but there are cases where the claimed amount is adjusted due to external factors unrelated to the characteristics of the repair of the damaged part (such as budget shortage or insurance company settlement), so rather than judging the appropriateness based simply on the standard amount, it is desirable to set an allowable range that may slightly exceed the standard amount.

[0065] The allowable range calculation unit (153) can calculate a basic allowable range by analyzing the deviation between the billed amount and the settlement amount for past repair cases where settlement has been completed by each repair shop, and can adjust the allowable range by referring to the reliability information of each repair shop. Here, reliability can be calculated in advance from, for example, customer evaluation, rework rate, insurance company evaluation indicators, etc.

[0066] The allowable range calculation unit (153) assumes that for repair companies with high reliability, the amount claimed by the repair company is acceptable and reflects the deviation level as is, but for companies with low reliability, it reduces the deviation level and reflects it.

[0067] For example, the allowable range calculation unit (153) calculates the allowable range for each repair case according to the following mathematical formula 2. At this time, the allowable range indicates the extent to which it can be charged in addition to the standard amount.

[0068]

[0069] Here, MG is the allowable range, M is the total number of repair companies used to calculate the allowable range, a is a positive constant, and T i is the reliability of the i-th repair shop, having a value between 0 and 1, and N i is the number of repairs by the i-th repair shop, and c i,j is the billed amount for the j-th repair case of the i-th repair company, and bi,j is the settlement amount for the j-th repair case of the i-th repair company.

[0070] In other words, referring to mathematical formula 2, the allowable range can be seen as being calculated by determining the representative deviation of each repair company from the deviation (normalized deviation) between the billed amount and the settlement amount for past repair cases of each repair company, and then calculating a weighted sum that reflects the reliability of each repair company.

[0071] The appropriateness judgment unit (155) compares the actual billing amount for each repair case provided by the repair company terminal (203) for a specific repair case with the calculation results of the standard amount calculation unit (151) and the allowable range calculation unit (153) to determine whether the billing amount is appropriate. If necessary, the appropriateness judgment unit (155) may generate information such as the scale of the excess or deficit amount, the direction of the deviation, and the difference from the estimated settlement amount when the billing amount exceeds the allowable range, and provide this information to the client terminal (201) or the repair company terminal (203).

[0072] According to the embodiments, the appropriateness judgment unit (155) compares the appropriate amount calculated according to the following mathematical formula 3 with the claimed amount, and if the claimed amount exceeds the appropriate amount based on the comparison result, it can determine that the claimed amount is not appropriate.

[0073]

[0074] (Here, VC is the appropriate amount, and RC is the standard amount)

[0075] Accordingly, the claim appropriateness judgment unit (150) can reduce cost disputes in the insurance claim process and ensure fairness and transparency in the calculation of repair costs by integrally performing damage image-based repair difficulty analysis, past settlement data-based appropriate amount calculation, and reliability adjustment-based allowable range setting.

[0077] FIG. 9 is a diagram illustrating an exemplary hardware configuration of a management server according to embodiments of the present invention. The electronic device (300) of FIG. 9 may represent the management server (100) described with reference to FIG. 5 to FIG. 8.

[0078] Referring to FIG. 9, the electronic device (300) may include at least one processor (310) and a memory (320) that stores instructions that instruct the at least one processor (310) to perform at least one operation.

[0079] The above at least one operation is interpreted to include at least one of the operations of the aforementioned electronic device (300) or the operations of the functional part, and a specific description is omitted to prevent redundant explanation.

[0080] Here, at least one processor (310) may mean a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which methods according to embodiments of the present invention are performed.

[0081] The memory (320) may be composed of at least one of a volatile storage medium and a non-volatile storage medium. For example, the memory (320) may be composed of at least one of a read-only memory (ROM) and a random access memory (RAM).

[0082] Additionally, the electronic device (300) may include a transceiver (330) that performs communication via a wireless network. Additionally, the electronic device (300) may further include an input interface device (340), an output interface device (350), a storage device (360, which may be referred to interchangeably with internal storage), etc. Each component included in the electronic device (300) may be connected by a bus (370) to communicate with one another.

[0083] The methods according to the present invention may be implemented in the form of program instructions that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc., either alone or in combination. The program instructions recorded on the computer-readable medium may be those specifically designed and configured for the present invention, or they may be those known and available to those skilled in the art of computer software.

[0084] Examples of computer-readable media may include hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory. Examples of program instructions may include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc. The aforementioned hardware devices may be configured to operate as at least one software module to perform the operation of the present invention, and vice versa.

[0085] In addition, the above-described method or device may be implemented by combining all or part of its configuration or function, or by implementing it separately.

[0086] Although the present invention has been described above with reference to preferred embodiments, those skilled in the art will understand that various modifications and changes can be made to the invention without departing from the spirit and scope of the invention as described in the following claims.

Claims

Claim 1 In a management server of an automotive parts remanufacturing-based repair system that manages repair requesters and repair shops, the system comprises: an information management unit configured to manage repair shop information associated with repair shops; an image receiving unit configured to receive an image of a damaged part of a repair case; a damage degree determination unit configured to generate damage characteristic information indicating the degree of damage from the damaged part image; and a repair difficulty determination unit configured to generate repair difficulty information indicating the repair difficulty required to repair the damaged part based on the damage characteristic information. and for each repair case, a claim appropriateness determination unit configured to determine the appropriateness of the claim amount for each repair case of a repair company based on repair history data and repair difficulty information stored in the information management unit, wherein the repair company information includes information regarding the repair part, vehicle, repair period, repair difficulty, claim cost, and settlement amount for each repair case for each repair company, and the claim appropriateness determination unit includes a standard amount calculation unit that receives a repair difficulty vector, vehicle information related to the repair, and damaged part information as input for each repair case to construct an input vector, and inputs the input vector into a deep learning-based prediction model to obtain a standard amount for the repair case as an output; and an allowable range calculation unit configured to calculate an allowable range based on the deviation between the claim amount and the settlement amount for past repair cases of repair companies and the reliability of each repair company. and includes an appropriateness judgment unit configured to determine the appropriateness of the claimed amount by comparing the claimed amount for each repair case with a calculated standard amount and an allowable range, wherein the reliability is pre-calculated using at least one of customer evaluation, rework rate, and insurer evaluation indicator, and the allowable range calculation unit calculates the allowable range according to the following mathematical formula 1, [Mathematical Formula 1] (In the above mathematical formula 1, MG is the allowable range, M is the total number of repair companies used in calculating the allowable range, a is a positive constant, and T i is the reliability of the i-th repair shop, having a value between 0 and 1, and N i is the number of repairs by the i-th repair shop, and c i,j is the billed amount for the j-th repair case of the i-th repair company, and b i,j (is the settlement amount for the j-th repair case of the i-th repair company) The above appropriateness judgment unit calculates an appropriate amount according to the following mathematical formula 2, and [Mathematical Formula 2] (In the above mathematical formula 2, VC is the appropriate amount, RC is the standard amount, and MG is the allowable range) A management server that compares the calculated appropriate amount with the billed amount and determines that the billed amount is not appropriate if the billed amount exceeds the appropriate amount. Claim 2 delete Claim 3 delete

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