Intelligent distribution method, device and equipment for accident vehicles and storage medium

Abstract

The invention relates to the technical field of resource allocation, and discloses an accident vehicle intelligent allocation method, device and equipment and a storage medium, and the method comprises the steps: obtaining the attribute information of an accident vehicle and the maintenance capability data of a vehicle insurance repair factory; according to the attribute information and the maintenance capability data, screening out a repair factory supporting the maintenance of the accident vehicle from the vehicle insurance repair factories; obtaining maintenance resource data of a repair factory; according to the maintenance resource data, carrying out weight calculation on the repair factory to obtain the weight of the repair factory; calculating an expert-level balance loss function of the repair plant based on the weight; and generating a distribution scheme between the accident vehicle and the repair plant according to the expert-level balance loss function. The method can be applied to a vehicle insurance claim settlement scene in the field of financial insurance, and can distribute the accident vehicle to a vehicle insurance repair factory more uniformly and reasonably, so as to improve the maintenance efficiency of the accident vehicle and the resource utilization rate of the vehicle insurance repair factory.

Description

Technical Field

[0001] This invention relates to the field of resource allocation technology, and in particular to an intelligent allocation method, device, equipment, and storage medium for accident vehicles. Background Technology

[0002] In the auto insurance claims process within the financial insurance sector, assigning an accident vehicle to a designated repair shop (referred to as an auto insurance repair shop) for repair is a core step. Traditional allocation strategies typically rely on a single priority strategy, such as prioritizing the allocation of accident vehicles to the repair shop with the largest repair value gap or the one closest to the accident vehicle. Alternatively, a simple weighted scoring strategy may be employed, where quantitative indicators such as repair value gap and distance are linearly weighted and summed to obtain a score for the repair shop, thus prioritizing the allocation of the accident vehicle to the repair shop with the highest score.

[0003] However, a single-priority strategy has limitations. Specifically, its over-focus on a single quantitative indicator (such as the repair output gap) may lead to a few auto insurance repair shops monopolizing accident vehicles, causing other auto insurance repair shops to lack repair tasks, thus causing an imbalance in allocation. In addition, a single-priority strategy cannot ensure that every auto insurance repair shop has accident vehicles to repair while also allocating accident vehicle resources to repair shops with larger repair output gaps, resulting in low overall repair efficiency and low utilization of auto insurance repair shop resources (such as capacity and manpower).

[0004] The weighted scoring strategy relies on manually setting weight coefficients, but these coefficients are fixed and cannot reflect the dynamic changes in repair shop capacity in real time. This results in the weighted scoring strategy lacking adaptability, thus affecting the rationality of the allocation.

[0005] Therefore, how to allocate accident vehicles more evenly and reasonably to auto insurance repair shops in order to improve the repair efficiency of accident vehicles and the resource utilization rate of auto insurance repair shops has become an urgent technical problem to be solved. Summary of the Invention

[0006] This invention provides an intelligent allocation method, device, equipment, and storage medium for accident vehicles, aiming to allocate accident vehicles more evenly and rationally to auto insurance repair shops, thereby improving the repair efficiency of accident vehicles and the resource utilization rate of auto insurance repair shops.

[0007] Firstly, an intelligent allocation method for accident vehicles is provided, including: Obtain the attribute information of the accident vehicle and the repair capability data of the car insurance repair shop; Based on the attribute information and the repair capability data, select repair shops that support the repair of the accident vehicle from the list of car insurance repair shops; Obtain the repair shop's maintenance resource data; Based on the maintenance resource data, the weight of the repair shop is calculated. Based on the weights, calculate the expert-level balance loss function of the repair shop; Based on the expert-level balance loss function, an allocation scheme between the accident vehicle and the repair shop is generated.

[0008] Secondly, an intelligent vehicle allocation device for accidents is provided, comprising: The first acquisition module is used to acquire the attribute information of the accident vehicle and the repair capability data of the car insurance repair shop; The filtering module is used to filter out repair shops that support the repair of the accident vehicle from the car insurance repair shops based on the attribute information and the repair capability data. The second acquisition module is used to acquire the repair shop's maintenance resource data; The first calculation module is used to calculate the weight of the repair shop based on the maintenance resource data. The second calculation module is used to calculate the expert-level balance loss function of the repair shop based on the weights. The generation module is used to generate an allocation scheme between the accident vehicle and the repair shop based on the expert-level balance loss function.

[0009] Thirdly, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the above-described intelligent allocation method for accident vehicles.

[0010] Fourthly, a computer-readable storage medium is provided, which stores a computer program that, when executed by a processor, implements the steps of the above-described intelligent allocation method for accident vehicles.

[0011] The aforementioned intelligent allocation method, device, equipment, and storage medium for accident vehicles involves: acquiring the attribute information of the accident vehicles and the repair capacity data of auto insurance repair shops; selecting repair shops that support the repair of accident vehicles based on the attribute information and repair capacity data; acquiring the repair resource data of the repair shops; calculating the weight of the repair shops based on the repair resource data; calculating the expert-level balance loss function of the repair shops based on the weights; and generating an allocation scheme between the accident vehicles and the repair shops based on the expert-level balance loss function. In this way, on the one hand, based on the attribute information of the accident vehicles and the repair capacity data of auto insurance repair shops, repair shops that can support the repair of accident vehicles can be accurately selected from among the auto insurance repair shops, which can greatly avoid ineffective allocation. On the other hand, based on the repair resource data of the selected repair shops, the weight of the repair shops is calculated, realizing a scientific assessment of the importance of the repair shops from the perspective of repair resources. In order to calculate the expert-level balance loss function of the repair shops based on the weight of the repair shops, the expert-level balance loss function is used to dynamically punish the allocation imbalance, thereby realizing a dynamic balance in the allocation of accident vehicles among repair shops. This avoids a few repair shops from over-allocating accident vehicles while ensuring that each repair shop has the opportunity to be allocated accident vehicles. As a result, accident vehicles are allocated more evenly and rationally to auto insurance repair shops, improving the repair efficiency of accident vehicles and the resource utilization rate of auto insurance repair shops. Attached Figure Description

[0012] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments of the present invention will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0013] Figure 1 This is a flowchart illustrating an intelligent allocation method for accident vehicles according to an embodiment of the present invention; Figure 2 yes Figure 1 A schematic diagram of a specific implementation method for step S50; Figure 3 yes Figure 1 A schematic diagram of a specific implementation method for step S60; Figure 4 This is a schematic diagram of the structure of an intelligent vehicle allocation device in an embodiment of the present invention; Figure 5 This is a schematic diagram of the structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0014] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0015] The intelligent allocation method for accident vehicles provided in this invention can be applied to a server. The server can obtain the attribute information of the accident vehicles and the repair capability data of car insurance repair shops; based on the attribute information and repair capability data, it can filter out repair shops that support the repair of accident vehicles from the car insurance repair shops; obtain the repair resource data of the repair shops; calculate the weight of the repair shops based on the repair resource data; calculate the expert-level balance loss function of the repair shops based on the weight; and generate an allocation scheme between the accident vehicles and the repair shops based on the expert-level balance loss function. In this way, on the one hand, based on the attribute information of the accident vehicles and the repair capacity data of the auto insurance repair shops, repair shops that can support the repair of accident vehicles can be accurately selected from among the auto insurance repair shops, which can greatly avoid ineffective allocation; on the other hand, the weight of the repair shops is calculated based on the repair resource data of the selected repair shops, so as to achieve a scientific assessment of the importance of the repair shops from the perspective of repair resources. In order to calculate the expert-level balance loss function of the repair shops based on the weight of the repair shops, the expert-level balance loss function is used to dynamically punish the allocation imbalance, thereby achieving a dynamic balance in the allocation of accident vehicles among repair shops. This avoids a few repair shops from over-allocating accident vehicles while ensuring that each repair shop has the opportunity to be allocated accident vehicles. This achieves a more balanced and reasonable allocation of accident vehicles to auto insurance repair shops, improving the repair efficiency of accident vehicles and the resource utilization rate of auto insurance repair shops. The server can be implemented using a separate server or a server cluster composed of multiple servers. The invention will be described in detail below through specific embodiments.

[0016] Please see Figure 1 As shown, Figure 1 A flowchart illustrating the intelligent allocation method for accident vehicles provided in an embodiment of the present invention includes the following steps: S10: Obtain the attribute information of the accident vehicle and the repair capability data of the car insurance repair shop.

[0017] The intelligent allocation method for accident vehicles provided by this invention can be applied to auto insurance claims scenarios in the financial insurance field. Through an innovative routing balancing mechanism that introduces Expert-Level Balance Loss (LExpBal), accident vehicles are allocated more evenly and rationally to auto insurance repair shops, thereby improving repair efficiency and resource utilization of repair shops, and providing effective technical support for auto insurance claims operations.

[0018] In step S10, the attribute information of the accident vehicle can be obtained, as well as the repair capacity data of the car insurance repair shop in real time, providing accurate and detailed data support for the intelligent allocation of accident vehicles.

[0019] An accident vehicle refers to a vehicle that has been damaged in an accident and is covered by auto insurance, requiring repairs through auto insurance claims. There must be at least two accident vehicles.

[0020] The attribute information of the accident vehicles mainly includes the number, brand and location of the vehicles. This attribute information can be collected from the accident vehicles' insurance claim records or accident scene investigation records, so as to provide valuable basis for selecting repair shops that can repair accident vehicles.

[0021] The repair capacity data for auto insurance repair shops mainly includes repair capacity data and repair resource data. There must be at least two auto insurance repair shops.

[0022] Repair capability data mainly reflects the hard capabilities of auto insurance repair shops to undertake repair tasks, including compatible vehicle brands, repair service radius, repair capacity, and repair limit.

[0023] Among them, compatible vehicle brands refer to the range of vehicle brands that the auto insurance repair shop can provide repair services for; repair service radius refers to the geographical distance threshold that the auto insurance repair shop can undertake and provide repair services for; repair capacity refers to the actual number of vehicles that the auto insurance repair shop can undertake and provide repair services for; and repair limit refers to the maximum number of vehicles that the auto insurance repair shop can undertake and provide repair services for.

[0024] Repair resource data mainly reflects the resource utilization of auto insurance repair shops in actual operation, including target repair output value, repair output value, and historical visit rate.

[0025] Among them, the repair output gap can be calculated based on the target repair output value and the repaired output value (repair output value gap = target repair output value - repaired output value); the historical store visit rate refers to the ratio between the actual number of vehicles that the car insurance repair shop has undertaken and repaired in the past and the number of vehicles allocated, which reflects the reliability of the car insurance repair shop's performance.

[0026] By obtaining repair capacity data from auto insurance repair shops, we can provide an important basis for optimizing the allocation of resources for these shops.

[0027] S20: Based on attribute information and repair capability data, select repair shops from car insurance repair shops that support the repair of accident vehicles.

[0028] For step S20, based on the attribute information of the accident vehicle and the repair capability data of the car insurance repair shops, repair shops that support the repair of accident vehicles are effectively selected from the car insurance repair shops.

[0029] In step S20 of some embodiments, the attribute information includes brand and location. The brand and compatible vehicle brands are compared to exclude car insurance repair shops that are incompatible with the brand; car insurance repair shops whose accident vehicles are not within the repair service radius are excluded based on location; or, car insurance repair shops whose repair capacity has reached the repair limit are excluded; and the remaining car insurance repair shops are selected as repair shops.

[0030] Specifically, the system can compare the brand of the damaged vehicle with the compatible vehicle brands of the repair shops to eliminate those incompatible with the damaged vehicle's brand, thus filtering out repair shops that do not support the repair of damaged vehicles and avoiding brand mismatch. Location can be calculated using latitude and longitude coordinates, comparing the distance between the damaged vehicle and the repair shop's location with the repair shop's service radius to eliminate repair shops whose distance exceeds the service radius, thus reducing geographical costs. Alternatively, the system can compare the repair shop's capacity with its maximum capacity to eliminate repair shops that have reached their maximum capacity, thus filtering out repair shops that cannot handle damaged vehicles and avoiding ineffective allocation. Finally, the remaining repair shops after these eliminations are those that support the repair of damaged vehicles, ensuring the accuracy and rationality of the repair shop selection.

[0031] S30: Obtain repair resource data from the repair shop.

[0032] For step S30, the maintenance resource data of the selected repair shops is extracted from the maintenance capacity data of the candidate repair shops, and used to calculate the weight of the repair shops.

[0033] S40: Based on the maintenance resource data, calculate the weight of the repair shop to obtain the weight of the repair shop.

[0034] For step S40, the repair shops are weighted according to their maintenance resource data to obtain their weights. These weights can measure the repair shops' maintenance needs and historical performance (or contract fulfillment reliability), which helps to ensure a balanced distribution of accident vehicles among repair shops.

[0035] In step S40 of some embodiments, the repair shop's repair resource data includes the repair shop's repair output gap and historical visit rate, and a preset weighting calculation formula can be obtained. ,in, Indicates repair shop The weight, Indicates the weighting coefficient. Indicates repair shop The normalized value of the maintenance output gap Indicates repair shop Historical store visit rate; based on the repair output gap and historical store visit rate, the weight is calculated using a weighting formula.

[0036] To allocate more accident vehicles to auto insurance repair shops with larger repair revenue deficits and avoid resource waste or underutilization at these shops, a weighted calculation formula that prioritizes those with larger revenue deficits is pre-designed. The weighted calculation formula is shown below:

[0037] in, Indicates repair shop (that is, the first) The weight of each repair shop; The weighting coefficient representing the shortfall in repair output value determines the repair shop's... The relative importance of the repair output gap and historical store visit rate in the weighting calculation. The value ranges from 0 to 1 and can be flexibly adjusted according to the auto insurance business strategy. For example, if a certain auto insurance policy supports a strong gap-priority strategy (prioritizing filling the gap in repair output value), the value can be increased. The value; This indicates a repair shop The normalized value of the maintenance output gap Indicates repair shop The maintenance output value gap This indicates the total repair output shortfall across all repair shops; Indicates repair shop Historical store visit rate.

[0038] In this way, the weight of a repair shop can be calculated based on its repair output gap and historical customer visit rate, combined with the weighting formula. Specifically, first, the total repair output gap of all repair shops can be calculated based on the repair shop's repair output gap. Then, based on the repair shop's repair output gap and the total repair output gap of all repair shops, a normalized value of the repair shop's repair output gap can be calculated. Finally, the normalized value of the repair shop's repair output gap and its historical customer visit rate can be substituted into the weighting formula to calculate the weight of the repair shop.

[0039] By calculating the repair shop's weight using its repair output gap and historical visit rate, the importance of the repair shop can be assessed from the perspectives of resource utilization and contract performance reliability, so as to provide an accurate and valuable basis for optimizing the allocation of accident vehicles.

[0040] S50: Calculate the expert-level balance loss function for the repair shop based on the weights.

[0041] For step S50, an innovative routing balancing mechanism is introduced, that is, based on the calculated weight of the repair shop, the LExpBal function of the repair shop is calculated, and the distribution of accident vehicles among the repair shops is dynamically adjusted through the LExpBal function of the repair shops, so as to avoid a few repair shops occupying too many accident vehicles and ensure that all repair shops are allocated a reasonable number of accident vehicles.

[0042] In this context, repair shops are likened to "experts," meaning they are considered "experts" in the field of vehicle repair, implying that they can undertake and repair a certain number of accident vehicles based on their own capabilities.

[0043] The accident vehicles are likened to "Tokens," meaning they are seen as tasks that need to be assigned to "experts."

[0044] The LExpBal function is specifically designed to measure and penalize allocation imbalances. In this embodiment of the invention, the LExpBal function is used to dynamically penalize allocation imbalances. That is, if some repair shops receive too many accident vehicles while other repair shops do not receive enough allocations, the LExpBal function will generate a higher "loss" based on this imbalance, thereby promoting a more balanced allocation decision.

[0045] By calculating the LExpBal function, it is possible to avoid a few repair shops over-allocating accident vehicles, ensuring that every repair shop has the opportunity to take on accident vehicles and achieving the goal of "every shop has a car to repair".

[0046] In some embodiments, please refer to Figure 2 Step S50 may include, but is not limited to, the following steps: Step S51: Obtain the allocation frequency of the repair shop according to the weight; Step S52: Calculate the expert-level balanced loss function based on the weights and allocation frequency.

[0047] In step S50, the allocation frequency (or selection frequency) of repair shops can be calculated according to their weight ratios. Then, based on the weights and allocation frequencies of the repair shops, the LExpBal function is calculated. The formula for the LExpBal function is shown below:

[0048] in, This represents the expected loss to achieve a balanced distribution. This represents the balance factor hyperparameter, which is dynamically adjusted. The value of can control the strength of the distribution balance, that is, in At this point, degenerating into a pure strong gap-first strategy may lead to allocation imbalance. When the value is greater than 0, a balanced allocation is enforced (i.e., a strong balanced strategy is selected) to ensure that all repair shops receive a reasonable number of accident vehicles and to avoid excessive concentration of allocation.

[0049] Indicates the total number of repair shops; Indicates repair shop The allocated frequency.

[0050] S60: Generate an allocation scheme between the accident vehicle and the repair shop based on the expert-level balance loss function.

[0051] Once the LExpBal function is obtained, an allocation scheme between accident vehicles and repair shops can be generated based on the LExpBal function, thereby achieving optimized allocation of accident vehicles.

[0052] In some embodiments, please refer to Figure 3 Step S60 may include, but is not limited to, the following steps: S61: Solve the allocation matrix between the accident vehicle and the repair shop based on the expert-level balance loss function; S62: Optimize the allocation matrix to obtain the allocation scheme.

[0053] In step S60, the allocation matrix between accident vehicles and repair shops can be solved using the LExpBal function. Then, the allocation matrix is ​​optimized to obtain the final allocation scheme. This scheme can ensure that repair shops can handle accident vehicles in a balanced manner while giving priority to repair shops with high output gaps, thereby improving the overall allocation efficiency and thus improving the repair efficiency of accident vehicles and the resource utilization rate of car insurance repair shops.

[0054] In step S61 of some embodiments, the allocation matrix can be solved with the objective of minimizing the expert-level balance loss function.

[0055] In step S61, the goal is to minimize the LExpBal function, so that the LExpBal function balances the allocation frequency of high-weight repair shops and low-weight repair shops during the allocation process, ensuring that the number of accident vehicles allocated to a repair shop does not exceed its repair limit. That is, if the number of accident vehicles allocated to a certain repair shop exceeds its repair limit during the allocation process, a reallocation will be triggered, thereby transferring the excess accident vehicles to other repair shops that have not exceeded their repair limits, thus obtaining the allocation matrix.

[0056] In step S62 of some embodiments, the allocation matrix can be iteratively updated until the expert-level balance loss function is minimized, thus obtaining the allocation scheme.

[0057] In step S62, specifically, if the LExpBal function does not decrease or decreases only slightly (e.g., less than a preset decrease threshold), the allocation matrix can be iteratively updated and the LExpBal function can be recalculated until the LExpBal function is minimized, thus obtaining the final allocation scheme and achieving a balance between the priority of output gap and the fairness of allocation.

[0058] To better understand the above embodiments, the following are some application scenarios: Assuming there are 10 accident vehicles to be assigned, the repair capacity data of the repair shops selected from the accident vehicles is as follows:

[0059] First, set the weighting coefficients. =0.5、 =0.7.

[0060] Then calculate the weight of each repair shop:

[0061] Next, the allocation frequency for each repair shop is calculated according to its weight ratio. That is, based on the weight ratio, we can assume that 5 accidents are allocated to repair shop A, 3 accidents to repair shop B, and 2 accidents to repair shop C. Then, the allocation frequency for each repair shop is:

[0062] Then, the LExpBal function is calculated based on the weights and allocation frequencies of each repair shop:

[0063] Since repair shop A's allocated number of vehicles (5) exceeds its repair limit (3), a reallocation is triggered to minimize the LExpBal function. Assuming the 2 excess accident vehicles from repair shop A are transferred to repair shop B, the allocation matrix between accident vehicles and each repair shop is obtained: (Repair shop A: 3 vehicles, Repair shop B: 5 vehicles). Repair shop C: 2 vehicles.

[0064] Then the allocation frequency for each repair shop will be updated:

[0065] Therefore, the LExpBal function is updated:

[0066] The LExpBal function's reduction did not exceed the reduction threshold of 0.01, indicating a small reduction. Therefore, the allocation matrix was iteratively updated: (Repair shop A: 3 vehicles, Repair shop B: 4 vehicles, Repair shop C: 3 vehicles).

[0067] Then, the allocation frequency for each repair shop is iteratively updated:

[0068] In this way, the LExpBal function is iteratively updated:

[0069] As can be seen, the LExpBal function decreased from 0.0795 to 0.0692, which exceeded the decrease threshold of 0.01. Therefore, the final allocation scheme is (repair shop A: 3 vehicles, repair shop B: 4 vehicles, repair shop C: 3 vehicles), achieving a balance between prioritizing the output gap and ensuring balanced allocation.

[0070] The intelligent allocation method for accident vehicles provided in the above embodiments obtains the attribute information of the accident vehicles and the repair capacity data of auto insurance repair shops; based on the attribute information and repair capacity data, it selects repair shops that support the repair of accident vehicles from the auto insurance repair shops; it obtains the repair resource data of the repair shops; based on the repair resource data, it calculates the weight of the repair shops; based on the weight, it calculates the expert-level balance loss function of the repair shops; and based on the expert-level balance loss function, it generates an allocation scheme between accident vehicles and repair shops. In this way, on the one hand, by accurately selecting repair shops that support the repair of accident vehicles from the auto insurance repair shops based on the attribute information of the accident vehicles and the repair capacity data of the auto insurance repair shops, it can greatly avoid ineffective allocation; on the other hand, by calculating the weight of the repair shops based on the repair resource data of the selected repair shops, it can scientifically assess the importance of the repair shops from the perspective of repair resources, so that the expert-level balance loss function of the repair shops can be calculated based on the weight of the repair shops. This allows for dynamic punishment of allocation imbalances through the expert-level balance loss function, achieving dynamic balance in the allocation of accident vehicles among repair shops, avoiding over-allocation of accident vehicles to a few repair shops while ensuring that each repair shop has the opportunity to be allocated an accident vehicle. This allows for a more balanced and rational allocation of accident vehicles to auto insurance repair shops, improving the repair efficiency of accident vehicles and the resource utilization rate of auto insurance repair shops.

[0071] It should be understood that the sequence number of each step in the above embodiments does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0072] It should be noted that the software tools or components not belonging to our company that appear in the embodiments of this invention are merely illustrative examples and do not represent actual use.

[0073] In one embodiment, an intelligent allocation device for accident vehicles is provided, which corresponds one-to-one with the intelligent allocation method for accident vehicles in the above embodiments. For example... Figure 4 As shown, the intelligent allocation device for the accident vehicle includes a first acquisition module 101, a filtering module 102, a second acquisition module 103, a first calculation module 104, a second calculation module 105, and a generation module 106. Detailed descriptions of each functional module are as follows: The first acquisition module 101 is used to acquire the attribute information of the accident vehicle and the repair capability data of the car insurance repair shop; The filtering module 102 is used to filter out repair shops that support the repair of the accident vehicle from the car insurance repair shops based on the attribute information and the repair capability data. The second acquisition module 103 is used to acquire the maintenance resource data of the repair shop; The first calculation module 104 is used to calculate the weight of the repair shop based on the maintenance resource data. The second calculation module 105 is used to calculate the expert-level balance loss function of the repair shop based on the weights. The generation module 106 is used to generate an allocation scheme between the accident vehicle and the repair shop based on the expert-level balance loss function.

[0074] In one embodiment, the repair resource data includes repair output gap and historical store visit rate. The first calculation module 104 is specifically used for: Obtain the preset weight calculation formula ,in, Indicates repair shop The weight, Indicates the weighting coefficient. Indicates repair shop The normalized value of the maintenance output gap Indicates repair shop Historical store visit rate; The weight is calculated using the weighting formula based on the repair output gap and the historical store visit rate.

[0075] In one embodiment, the second calculation module 105 is specifically used for: Based on the weights, the allocation frequency of the repair shop is obtained; The expert-level balancing loss function is calculated based on the weights and the allocation frequency.

[0076] In one embodiment, the generation module 106 is specifically used for: Based on the expert-level balance loss function, solve for the allocation matrix between the accident vehicle and the repair shop; The allocation matrix is ​​optimized to obtain the allocation scheme.

[0077] In one embodiment, the generation module 106 is further configured to: The allocation matrix is ​​solved with the objective of minimizing the expert-level balance loss function.

[0078] In one embodiment, the generation module 106 is further configured to: The allocation matrix is ​​iteratively updated until the expert-level balancing loss function is minimized, thus obtaining the allocation scheme.

[0079] In one embodiment, the attribute information includes brand and location, and the repair capability data includes compatible vehicle brands, repair service radius, repair capacity, and repair limit. The filtering module 102 is specifically used for: The brand and the compatible vehicle brands are compared to exclude car insurance repair shops that are incompatible with the brand. Based on the location, exclude auto insurance repair shops where the accident vehicle is not within the repair service radius; or... Exclude auto insurance repair shops whose repair capacity has reached the repair limit; The remaining car insurance repair shops will be designated as the repair shops.

[0080] This invention provides an intelligent allocation device for accident vehicles. On one hand, based on the attribute information of the accident vehicles and the repair capacity data of auto insurance repair shops, it accurately selects repair shops that can support the repair of accident vehicles, greatly avoiding ineffective allocation. On the other hand, it calculates the weight of each repair shop based on their repair resource data, enabling a scientific assessment of the importance of each repair shop from a repair resource perspective. Based on these weights, an expert-level balance loss function is calculated for each repair shop, dynamically penalizing allocation imbalances and achieving a dynamic balance in the allocation of accident vehicles among repair shops. This avoids over-allocation of accident vehicles to a few repair shops while ensuring that each repair shop has a chance to receive accident vehicles. This results in a more balanced and reasonable allocation of accident vehicles to auto insurance repair shops, improving repair efficiency and resource utilization of these shops.

[0081] Specific limitations regarding the intelligent allocation device for accident vehicles can be found in the above description of the intelligent allocation method for accident vehicles, and will not be repeated here. Each module in the aforementioned intelligent allocation device for accident vehicles can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0082] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 5 As shown. The computer device includes a processor, memory, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile and / or volatile storage media and internal memory. The non-volatile storage media stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface is used to communicate with external clients via a network connection. When the computer program is executed by the processor, it implements the functions or steps of a server-side intelligent allocation method for accident vehicles.

[0083] In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to perform the following steps: Obtain the attribute information of the accident vehicle and the repair capability data of the car insurance repair shop; Based on the attribute information and the repair capability data, select repair shops that support the repair of the accident vehicle from the list of car insurance repair shops; Obtain the repair shop's maintenance resource data; Based on the maintenance resource data, the weight of the repair shop is calculated. Based on the weights, calculate the expert-level balance loss function of the repair shop; Based on the expert-level balance loss function, an allocation scheme between the accident vehicle and the repair shop is generated.

[0084] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: Obtain the attribute information of the accident vehicle and the repair capability data of the car insurance repair shop; Based on the attribute information and the repair capability data, select repair shops that support the repair of the accident vehicle from the list of car insurance repair shops; Obtain the repair shop's maintenance resource data; Based on the maintenance resource data, the weight of the repair shop is calculated. Based on the weights, calculate the expert-level balance loss function of the repair shop; Based on the expert-level balance loss function, an allocation scheme between the accident vehicle and the repair shop is generated.

[0085] It should be noted that the functions or steps that can be implemented by the computer-readable storage medium or computer device described above can be referred to the relevant descriptions on the server side in the foregoing method embodiments. To avoid repetition, they will not be described one by one here.

[0086] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0087] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is used as an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above.

[0088] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for intelligent allocation of accident vehicles, characterized in that, include: Obtain the attribute information of the accident vehicle and the repair capability data of the car insurance repair shop; Based on the attribute information and the repair capability data, select repair shops that support the repair of the accident vehicle from the list of car insurance repair shops; Obtain the repair shop's maintenance resource data; Based on the maintenance resource data, the weight of the repair shop is calculated. Based on the weights, calculate the expert-level balance loss function of the repair shop; Based on the expert-level balance loss function, an allocation scheme between the accident vehicle and the repair shop is generated.

2. The intelligent allocation method for accident vehicles as described in claim 1, characterized in that, The repair resource data includes repair output gap and historical visit rate. The weighting of the repair shops based on the repair resource data includes: Obtain the preset weight calculation formula ,in, Indicates repair shop The weight, Indicates the weighting coefficient. Indicates repair shop The normalized value of the maintenance output gap Indicates repair shop Historical store visit rate; The weight is calculated using the weighting formula based on the repair output gap and the historical store visit rate.

3. The intelligent allocation method for accident vehicles as described in claim 1, characterized in that, The calculation of the expert-level equilibrium loss function for the repair shop based on the weights includes: Based on the weights, the allocation frequency of the repair shop is obtained; The expert-level balancing loss function is calculated based on the weights and the allocation frequency.

4. The intelligent allocation method for accident vehicles as described in claim 1, characterized in that, The step of generating an allocation scheme between the accident vehicle and the repair shop based on the expert-level balance loss function includes: Based on the expert-level balance loss function, solve for the allocation matrix between the accident vehicle and the repair shop; The allocation matrix is ​​optimized to obtain the allocation scheme.

5. The intelligent allocation method for accident vehicles as described in claim 4, characterized in that, The step of solving the allocation matrix between the accident vehicle and the repair shop based on the expert-level balance loss function includes: The allocation matrix is ​​solved with the objective of minimizing the expert-level balance loss function.

6. The intelligent allocation method for accident vehicles as described in claim 4, characterized in that, The optimization process of the allocation matrix to obtain the allocation scheme includes: The allocation matrix is ​​iteratively updated until the expert-level balancing loss function is minimized, thus obtaining the allocation scheme.

7. The intelligent allocation method for accident vehicles as described in any one of claims 1 to 6, characterized in that, The attribute information includes brand and location, and the repair capability data includes compatible vehicle brands, repair service radius, repair capacity, and repair limit. The step of selecting repair shops that support the repair of the accident vehicle from the list of car insurance repair shops based on the attribute information and the repair capability data includes: The brand and the compatible vehicle brands are compared to exclude car insurance repair shops that are incompatible with the brand. Based on the location, exclude auto insurance repair shops where the accident vehicle is not within the repair service radius; or... Exclude auto insurance repair shops whose repair capacity has reached the repair limit; The remaining car insurance repair shops will be designated as the repair shops.

8. An intelligent distribution device for accident vehicles, characterized in that, include: The first acquisition module is used to acquire the attribute information of the accident vehicle and the repair capability data of the car insurance repair shop; The filtering module is used to filter out repair shops that support the repair of the accident vehicle from the car insurance repair shops based on the attribute information and the repair capability data. The second acquisition module is used to acquire the repair shop's maintenance resource data; The first calculation module is used to calculate the weight of the repair shop based on the maintenance resource data. The second calculation module is used to calculate the expert-level balance loss function of the repair shop based on the weights. The generation module is used to generate an allocation scheme between the accident vehicle and the repair shop based on the expert-level balance loss function.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the intelligent allocation method for accident vehicles as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the intelligent allocation method for accident vehicles as described in any one of claims 1 to 7.

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