CN115564475A - Automobile repair shop rating method, device, equipment and storage medium - Google Patents
Automobile repair shop rating method, device, equipment and storage medium Download PDFInfo
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- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
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- G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
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Abstract
The application provides a rating method, a rating device, rating equipment and a computer readable storage medium of an automobile repair shop, wherein the method comprises the steps of obtaining first, second and third characteristic data of a target network point of the automobile repair shop; vector coding is carried out on the first characteristic data to obtain a first vector, a second vector and a third vector; classifying the first vector, determining a class group of the first vector, and determining a first weight value according to the class group; determining an evaluation odds rate according to the first weight value, the second vector and the third vector; and determining a composite cost rate according to the evaluation odds, the discount rate and the expense rate determined by the second characteristic data, the maintenance cost rate determined by the third characteristic data, and the rating of the target network point according to the composite cost rate. The method and the device determine the rating of the target network point by determining various data of the target network point, so that the associated company can make an operation strategy for the target network point based on the rating, and the operation cost of the associated company for the target network point is reduced.
Description
Technical Field
The present application relates to the field of intelligent rating technologies, and in particular, to a rating method, device, and apparatus for an automobile repair shop, and a computer-readable storage medium.
Background
The rating of the automobile repair factory can be used as an important index for monitoring the operation cost and scale condition of the automobile insurance network points, plays an important role in automobile insurance operation, and can make a differential operation strategy based on the rating of the automobile repair factory to determine an operation target.
However, no corresponding rating rule is formulated for the automobile repair shop at present, so that no perfect management method and cost control are provided for each automobile repair shop, and the operating cost of a company associated with the automobile repair shop is high.
Disclosure of Invention
The present application mainly aims to provide a rating method, device, equipment and computer readable storage medium for an auto repair shop, which aim to determine the rating corresponding to each website of the auto repair shop by automatically analyzing the operation data of each website of the auto repair shop, so as to make an operation strategy according to the rating and reduce the operation cost of companies associated with the auto repair shop.
In a first aspect, the present application provides a method for rating an automobile repair shop, comprising the steps of:
acquiring first characteristic data, second characteristic data and third characteristic data of a target network point of an automobile repair plant, wherein the first characteristic data comprises the number of insured vehicles, the overdue odds rate and the estimated odds rate;
vector coding is carried out on the first characteristic data to obtain a first vector of the number of the underwriting vehicles, a second vector of the expired odds and a third vector of the estimated odds;
classifying the first vector, determining a class family of the first vector, and determining a first weight value according to the class family of the first vector;
determining the estimated odds based on the first weight value, the second vector, and the third vector;
determining the discount rate and the expense rate of the target website according to the second characteristic data, and determining the maintenance cost rate of the target website according to the third characteristic data;
and determining the composite cost rate of the target website according to the evaluation odds and rates, the discount rate, the expense rate and the maintenance cost rate, and determining the rating of the target website according to the composite cost rate.
In a second aspect, the present application further provides a rating device for an automobile repair shop, the rating device for an automobile repair shop including:
the system comprises a characteristic data acquisition module, a characteristic data processing module and a characteristic data processing module, wherein the characteristic data acquisition module is used for acquiring first characteristic data, second characteristic data and third characteristic data of a target network point of an automobile repair plant, and the first characteristic data comprises the number of insured vehicles, the full-term odds and the estimated odds;
the vector coding module is used for carrying out vector coding on the first characteristic data to obtain a first vector of the number of the insured vehicles, a second vector of the expired odds ratio and a third vector of the estimated odds ratio;
the weight calculation module is used for classifying the first vector, determining the class family of the first vector and determining a first weight value according to the class family of the first vector;
an estimated odds calculation module, configured to determine the estimated odds according to the first weight value, the second vector, and the third vector;
the discount rate and maintenance cost rate calculation module is used for determining the discount rate and the expense rate of the target network point according to the second characteristic data and determining the maintenance cost rate of the target network point according to the third characteristic data;
and the rating determination module is used for determining the composite cost rate of the target network point according to the evaluation odds and ends rate, the discount rate, the expense rate and the maintenance cost rate, and determining the rating of the target network point according to the composite cost rate.
In a third aspect, the present application further provides a computer device comprising a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, implements the steps of the rating method for an auto repair shop as described above.
In a fourth aspect, the present application further provides a computer readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the rating method for an auto repair shop as described above.
The method comprises the steps of obtaining first characteristic data, second characteristic data and third characteristic data of a target network point of the automobile repair factory, wherein the first characteristic data comprises the number of insured vehicles, the overdue odds rate and the estimated odds rate; vector coding is carried out on the first characteristic data to obtain a first vector of the number of the underwriting vehicles, a second vector of the expired odds and a third vector of the estimated odds; classifying the first vector, determining a class family of the first vector, and determining a first weight value according to the class family of the first vector; determining the estimated odds based on the first weight value, the second vector, and the third vector; determining the discount rate and the expense rate of the target network point according to the second characteristic data, and determining the maintenance cost rate of the target network point according to the third characteristic data; and determining the composite cost rate of the target network point according to the evaluation odds and ends rate, the discount rate, the expense rate and the maintenance cost rate, and determining the rating of the target network point according to the composite cost rate. The method and the system determine the rating of the target network point of the automobile repair shop by determining various data of the target network point of the automobile repair shop, so that the company associated with the automobile repair shop can make an operation strategy for the target network point of the automobile repair shop based on the rating, and the operation cost of the company associated with the automobile repair shop for the automobile repair shop is reduced.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating a rating method of an auto repair shop according to an embodiment of the present disclosure;
FIG. 2 is a diagram illustrating a scenario of a rating method for an auto repair shop according to an embodiment of the present disclosure;
FIG. 3 is a schematic block diagram of a rating apparatus of an auto repair shop according to an embodiment of the present application;
fig. 4 is a block diagram schematically illustrating a structure of a computer device according to an embodiment of the present disclosure.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The flow diagrams depicted in the figures are merely illustrative and do not necessarily include all of the elements and operations/steps, nor do they necessarily have to be performed in the order depicted. For example, some operations/steps may be decomposed, combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
The embodiment of the application provides a rating method and device for an automobile repair shop, computer equipment and a computer readable storage medium. Referring to fig. 2, fig. 2 is a usage scenario diagram of a rating method of an auto repair shop according to an embodiment of the present application, where the rating method of the auto repair shop is applicable to a terminal device, and the terminal device may be an electronic device such as a tablet computer, a laptop computer, and a desktop computer. The present invention may also be applied to a server, which may be an independent server, or a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, a cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the application to the server is taken as an example for explanation.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments described below and the features of the embodiments can be combined with each other without conflict.
Referring to fig. 1, fig. 1 is a flowchart illustrating a rating method for an auto repair shop according to an embodiment of the present disclosure.
As shown in fig. 1, the rating method of the automobile repair shop includes steps S101 to S104.
Step S101, obtaining first characteristic data, second characteristic data and third characteristic data of a target network point of an automobile repair plant, wherein the first characteristic data comprises the number of vehicles to be insured, the overdue odds rate and the estimated odds rate.
For example, in a city, a plurality of network points exist in a same type or brand of automobile repair shop, and each network point is arranged at a different position in the city, so that the operation condition and the cost of the target network point of the automobile repair shop can be analyzed by acquiring feature data corresponding to the target network point of the automobile repair shop, thereby realizing the rating of the target network point.
For example, by obtaining first characteristic data, second characteristic data and third characteristic data of a target site, it can be understood that the first characteristic data is used for determining an estimated odds ratio of the target site, the second characteristic data is used for determining a discount rate and a cost rate of the target site, and the third characteristic data is used for determining a maintenance cost rate of the target site, so that the rating of the target site can be determined through the estimated odds ratio, the discount rate, the cost rate and the maintenance cost rate.
Illustratively, the first characteristic data comprises the number of underwriting vehicles, the expired odds and the estimated odds, wherein the number of underwriting vehicles is used for indicating the number of vehicles underwriting at the target website; the full-term pay rate is used for indicating the vehicle insurance policy with the policy starting time in a preset statistical period, the sum of accumulated settled claims generated by insurance accidents occurring on the deadline statistical day and pending claims on the deadline statistical day, and the ratio of the policy starting time to the premium of which the statistical deadline is full; the estimated odds are determined by the pure loss amount of all the insurance policies of the target network points and the insurance fee after calculation according to the indemness good treatment coefficient.
In some embodiments, the obtaining the estimated odds comprises: acquiring fourth characteristic data, wherein the fourth characteristic data comprises the loss amount of each policy and the premium of each policy after calculation according to the indemness good treatment coefficient (NCD); summing the loss amount for each policy, and summing the premium for each policy, and after determining the sum of the loss amount for the policy and the sum of the premium, determining an estimated odds based on the sum of the loss amount and the sum of the premium.
It will be appreciated that summing the loss amounts of the individual policies in the target network point, and likewise, summing the premiums of the individual policies in the target network point, results in the sum of the loss amounts of all the policies in the target network point and the sum of the premiums of all the policies, so that the estimated odds can be determined based on the sum of the loss amounts and the sum of the premiums.
For example, the premium calculated according to the indemness benefit coefficient may be determined by querying data, and determining the indemness benefit coefficient according to data corresponding to the vehicle, such as a brand of the vehicle, a number of times of making an insurance sale of the vehicle, a total amount of the insurance sale, and the like, so as to multiply the base premium by the indemness benefit coefficient to obtain the premium calculated according to the indemness benefit coefficient.
In some embodiments, determining the estimated odds based on the sum of the loss amounts and the sum of the premiums comprises: acquiring historical odds corresponding to each automobile brand in the insurance policy, the cooperation age of the target network points and the number of first type of vehicle insurance policies; the estimated odds are determined based on the sum of the lost amounts, the sum of the premiums, the historical odds, the years of the cooperation, and the amount of the first type of vehicle warranty.
For example, historical odds and data of the number of first-type vehicle insurance policies corresponding to each brand of vehicles in the insurance policy in a preset time period of the target website may be obtained to determine the estimated odds, for example, the preset time period is a time period of the target website in the last year.
For example, the first type of vehicle is used to indicate vehicles with a selling price not higher than the preset first selling price, that is, the number of first type of vehicle insurance policy in the target site indicates the number of insurance policies corresponding to vehicles with a selling price not higher than the preset first selling price.
It can be understood that the historical odds may be determined by the estimated odds in the preset time period of the target website, or may be determined by the estimated odds in the preset time periods of other websites, which is not limited herein.
Illustratively, the cooperation age of the target network point is used to indicate the cooperation age duration of the target network point and the associated company of the auto repair shop, so that the estimated payout rate of the target network point can be determined according to the historical payout rate, the number of the first type of vehicle insurance policies, the cooperation age, the sum of the loss amount and the sum of the insurance fees corresponding to each brand of the vehicle of the target network point.
In some embodiments, said determining said estimated odds based on said sum of lost amounts, said sum of premiums, said historical odds, said years of cooperation, and said first type of vehicle warranty amount comprises: calculating the ratio of the sum of the lost amount to the sum of the premium to obtain a target ratio; determining a second weight value corresponding to the historical odds, a third weight value of the cooperation age and a fourth weight value corresponding to the first type of vehicle insurance policy quantity according to the weight values corresponding to the various families based on a weight determination model; and determining the estimated odds according to the target ratio, the second weighted value, the third weighted value and the fourth weighted value.
For example, the weight values corresponding to the historical odds, the years of cooperation, and the number of first-type vehicles in the deposit can be determined by determining a target ratio of the sum of the loss amounts and the sum of the premiums, and based on the weight determination model.
In a specific implementation process, based on the weight determination model, a fourth vector of historical odds, a fifth vector of the cooperation age and a sixth vector of the first type of vehicle insurance policy number are determined, the fourth vector is classified and operated, a class group determined by the fourth vector is determined, similarly, the fifth vector and the sixth vector are classified and operated, a class group corresponding to the fifth vector and the sixth vector is determined, and a weight value corresponding to each vector is determined according to a weight value corresponding to each class group.
For example, the weight value corresponding to each category may be determined by the historical odds and the number of the first type of vehicle warranties, the years of cooperation, and the number of the plurality of network points. For example, historical odds and the number of the cooperation years of the multiple network points and the number of first-type vehicle insurance policies are obtained, clustering calculation is performed on the historical odds and the number of the first-type vehicle insurance policies corresponding to the multiple network points, multiple families are determined, the center point of each family is determined, the target value section of each family is determined according to the center point of each family, and therefore whether the vector to be classified can be classified into the family can be determined according to the target value section of the family.
For example, the class corresponding to the historical odds ratio includes class a and class B, and the target value interval of the class a is 10-20; the target value interval of the class B is 35-65; if the modulus of the fourth vector corresponding to the historical odds of the target website is 17, the fourth vector can be classified into the class group a, so that the second weight value corresponding to the fourth vector is determined according to the weight value corresponding to the class group a. It is understood that the fifth vector and the sixth vector may refer to the processing procedure of the fourth vector, and determine the family corresponding to the fifth vector and the family corresponding to the sixth vector, so as to determine the third weight value of the fifth vector according to the family corresponding to the fifth vector and determine the fourth weight value of the sixth vector according to the family corresponding to the sixth vector.
For example, after the second weight value, the third weight value and the fourth weight value are determined, the estimated payoff rate may be determined according to the target ratio, the second weight value, the third weight value and the fourth weight value, for example, the target ratio, the second weight value, the third weight value and the fourth weight value are calculated through a preset operation rule to obtain the estimated payoff rate, and optionally, the preset operation rule is a multiplication operation.
And S102, carrying out vector coding on the first characteristic data to obtain a first vector of the number of the insured vehicles, a second vector of the expired odds ratio and a third vector of the estimated odds ratio.
For example, after acquiring the number of insured vehicles and the full-term odds of the target website and determining the estimated odds, vector coding may be performed on the first feature data, so as to determine a first vector of the number of insured vehicles, a second vector of the full-term odds and a third vector of the estimated odds.
Illustratively, the above process may be implemented by a vector coding model or network determination.
Step S103, classifying the first vector, determining a class group of the first vector, and determining a first weight value according to the class group of the first vector.
For example, after the first vector is obtained, the first vector is classified to determine a class corresponding to the first vector, so that the first weight value can be determined according to the class of the first vector.
For example, the classification process for the first vector may be implemented by calculating a euclidean distance, thereby determining a class family for the first vector.
For example, a plurality of families may be determined according to the number of underwriting vehicles corresponding to a plurality of network points, for example, the number of underwriting vehicles corresponding to the plurality of network points is subjected to clustering calculation, for example, K-means clustering, and a corresponding weight value is given to each family, after a first vector is obtained, the family of the first vector is determined according to a calculation result of an euclidean distance between the first vector and each family, so that the first weight value can be determined according to the weight value corresponding to the family.
And step S104, determining the evaluation odds according to the first weight value, the second vector and the third vector.
For example, the first weight value, the second vector and the third vector may be calculated by using a preset operation rule, and the estimated odds are determined, for example, the first weight value, the second vector and the third vector are multiplied, and a vector decoding process is performed on a multiplication result, so as to obtain an estimated odds.
Step S105, determining the discount rate and the expense rate of the target network point according to the second characteristic data, and determining the maintenance cost rate of the target network point according to the third characteristic data.
For example, the discount rate and the fee rate of the target website can be determined according to the second characteristic data acquired at the target website.
In some embodiments, the second characteristic data includes a charge amount of the policy, a premium calculated from the indemnity claim free factor, and a payout amount, and the determining of the discount rate and the payout rate of the target site based on the second characteristic data includes: based on the preset calculation rule, determining the discount rate according to the charge amount of the policy and the premium, and determining the rate of charge according to the expenditure amount and the charge amount of the policy.
It will be appreciated that the predetermined calculation rule may be a ratio calculation, calculating a ratio of the amount charged by the policy to the premium, and taking the ratio as the discount rate; and calculating the ratio of the expenditure amount to the collection amount of the policy, and taking the ratio as the charge rate so as to determine the discount rate and the charge rate of the target network point.
In some embodiments, the third characterization data includes a yield ratio, a maintenance amount ratio, and a maintenance premium rate; determining the maintenance cost rate of the target network point according to the third characteristic data, comprising the following steps: determining the sum of the ratio of the production guarantee ratio and the maintenance amount; and determining the maintenance cost rate according to the sum of the ratio of the yield-guarantee ratio and the maintenance amount and the maintenance premium amount.
Illustratively, a yield-to-guarantee ratio of the target network point can be obtained, wherein the yield-to-guarantee ratio is used for indicating a ratio of a total amount of the insurance policy insurance proposal issued by the target network point in a preset time period to a real premium of the insurance policy started by the target network point in the preset time period; the maintenance sum proportion is used for indicating the total sum of the solutions issued by the target network points in the preset time period and the total sum of all the solutions issued by the target network points in the preset time period; the maintenance premium rate may include a maintenance man-hour premium rate and a maintenance accessory premium rate, optionally, the maintenance man-hour premium rate may be determined by an effective man-hour loss amount, an effective accessory loss amount, a man-hour actual loss amount, and a man-hour average loss amount, and the maintenance accessory premium rate may be determined by an effective man-hour loss amount, an effective accessory loss amount, an accessory actual loss amount, and an accessory average loss amount, so that the maintenance premium rate may be determined according to the maintenance man-hour premium rate and the maintenance accessory premium rate, and the maintenance cost rate may be determined according to the production guarantee ratio, the sum of the maintenance amount ratio, and the maintenance premium rate.
Illustratively, the sum of the yield ratio and the maintenance amount ratio is determined, and the sum of the yield ratio and the maintenance amount ratio is multiplied or added with the maintenance cost rate to obtain the maintenance cost rate.
And S106, determining the composite cost rate of the target network point according to the evaluation odds and ends rate, the discount rate, the expense rate and the maintenance cost rate, and determining the rating of the target network point according to the composite cost rate.
For example, after determining the estimated odds, discount rate, cost rate and maintenance cost rate, a composite cost rate of the target site may be determined according to the estimated odds, discount rate, cost rate and maintenance cost rate, and a rating of the target site may be determined according to the composite cost rate.
In some embodiments, determining a composite cost rate for the target site based on the assessed odds, discount rate, rates, and the repair cost rate comprises: determining a ratio of the assessed odds and the discount rate; and summing the ratio with the expense rate and the maintenance cost rate to obtain the composite cost rate.
For example, the composite cost rate of the target site may be obtained by determining a ratio of the estimated odds to the discount rate, and summing the ratio with the rate of charges and the rate of maintenance costs.
It can be understood that, after determining the composite cost rate of the target website, the rating of the target website can be determined according to the composite cost rate of the target website, for example, by performing vector calculation on the composite cost rate, determining a target vector corresponding to the composite cost rate, performing classification processing on the target vector, and determining a family corresponding to the target vector so as to determine the rating corresponding to the target vector, which may be written as the above classification processing, and therefore, the description is not repeated here.
In the method for rating an automobile repair shop provided in the above embodiment, the first feature data, the second feature data, and the third feature data of the target website of the automobile repair shop are obtained, where the first feature data includes the number of insured vehicles, the full-term odds ratio, and the estimated odds ratio; vector coding is carried out on the first characteristic data to obtain a first vector of the number of the underwriting vehicles, a second vector of the expired odds and a third vector of the estimated odds; classifying the first vector, determining a class family of the first vector, and determining a first weight value according to the class family of the first vector; determining the estimated odds based on the first weight value, the second vector, and the third vector; determining the discount rate and the expense rate of the target network point according to the second characteristic data, and determining the maintenance cost rate of the target network point according to the third characteristic data; and determining the composite cost rate of the target website according to the evaluation odds and rates, the discount rate, the expense rate and the maintenance cost rate, and determining the rating of the target website according to the composite cost rate. Various data of the target network point of the automobile repair factory can be determined to determine the grade of the target network point of the automobile repair factory, so that the company associated with the automobile repair factory can make an operation strategy for the target network point of the automobile repair factory based on the grade, and the operation cost of the company associated with the automobile repair factory for the automobile repair factory is reduced.
Referring to fig. 3, fig. 3 is a schematic diagram of a rating apparatus of an auto repair shop according to an embodiment of the present application, where the rating apparatus of the auto repair shop may be configured in a server or a terminal for executing the aforementioned rating method of the auto repair shop.
As shown in fig. 3, the rating apparatus of a garage includes: the feature data acquisition module 110, the vector encoding module 120, the weight calculation module 130, the estimated odds calculation module 140, the discount rate and maintenance cost rate calculation module 150, and the rating determination module 160.
The characteristic data obtaining module 110 is configured to obtain first characteristic data, second characteristic data, and third characteristic data of a target website of an automobile repair shop, where the first characteristic data includes the number of insured vehicles, the expired odds and the estimated odds.
The vector coding module 120 is configured to perform vector coding on the first feature data to obtain a first vector of the number of insured vehicles, a second vector of the expired odds and a third vector of the estimated odds.
The weight calculation module 130 is configured to classify the first vector, determine a family of the first vector, and determine a first weight value according to the family of the first vector.
An estimated odds calculation module 140, configured to determine the estimated odds according to the first weight value, the second vector, and the third vector.
A discount rate and maintenance cost rate calculating module 150, configured to determine a discount rate and a cost rate of the target website according to the second feature data, and determine a maintenance cost rate of the target website according to the third feature data.
A rating determining module 160, configured to determine a composite cost rate of the target website according to the estimated payout rate, the discount rate, the expense rate, and the maintenance cost rate, and determine a rating of the target website according to the composite cost rate.
Illustratively, the characteristic data acquiring module 110 includes a fourth characteristic data acquiring sub-module, an amount summing sub-module, and an estimated odds calculating sub-module;
and the fourth characteristic data acquisition submodule is used for acquiring fourth characteristic data, wherein the fourth characteristic data comprises the loss amount of each policy and the premium calculated by each policy according to the indemness good treatment coefficient.
And the sum submodule is used for summing the loss sum of each policy to obtain the sum of the loss sums, and summing the premium of each policy to obtain the sum of the premiums.
And the estimated odds calculation submodule is used for determining the estimated odds according to the sum of the loss amount and the premium.
Illustratively, the estimated odds calculation sub-module is further configured to obtain historical odds corresponding to each automobile brand in the policy, the cooperation years of the target network points, and the number of the first type of vehicle policies; and determining the estimated odds based on the sum of the lost amounts, the sum of the premiums, the historical odds, the years of cooperation, and the first type of vehicle warranty amount.
Illustratively, the estimated odds calculation submodule is further configured to calculate a ratio of the sum of the lost amount to the sum of the premium to obtain a target ratio; based on a weight determination model, performing classification operation on the historical odds, the cooperation years and the first type of vehicle insurance policy quantity, and determining respective corresponding families of the historical odds, the cooperation years and the first type of vehicle insurance policy quantity; determining a second weight value corresponding to the historical odds and the rates according to weight values corresponding to all the families, a third weight value corresponding to the cooperation age and a fourth weight value corresponding to the number of the first type of vehicle insurance policies; and determining the pre-estimated odds according to the target ratio, the second weight value, the third weight value and the fourth weight value.
Illustratively, the discount rate and maintenance cost rate calculating module 150 is further configured to determine the discount rate according to the charging amount of the policy and the premium based on a preset calculation rule, and determine the rate according to the expenditure amount and the charging amount of the policy.
Illustratively, the discount rate and maintenance cost rate calculation module 150 is further configured to determine a sum of the ratio of the yield-to-guarantee ratio and the maintenance amount; and determining the maintenance cost rate according to the sum of the ratio of the production to the guarantee ratio and the maintenance amount and the maintenance premium rate.
Illustratively, the rating determination module 160 is further configured to determine a ratio of the assessed odds and the discount rate; and summing the ratio with the rate of charge and the rate of maintenance cost to obtain the composite cost rate.
Referring to fig. 4, fig. 4 is a schematic block diagram of a structure of a computer device according to an embodiment of the present disclosure. The computer device may be a server or a terminal.
As shown in fig. 4, the computer device includes a processor, a memory, and a network interface connected by a system bus, wherein the memory may include a storage medium and an internal memory.
The storage medium may store an operating system and a computer program. The computer program includes program instructions that, when executed, cause a processor to perform any of the automotive repair shop rating methods.
The processor is used for providing calculation and control capability and supporting the operation of the whole computer equipment.
The internal memory provides an environment for the execution of a computer program in a storage medium, which computer program, when executed by a processor, causes the processor to perform any of the automotive repair shop rating methods.
The network interface is used for network communication, such as sending assigned tasks and the like. Those skilled in the art will appreciate that the architecture shown in fig. 4 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
It should be understood that the Processor may be a Central Processing Unit (CPU), and the Processor may be other general purpose processors, digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components, etc. Wherein a general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
Wherein, in one embodiment, the processor is configured to execute a computer program stored in the memory to implement the steps of:
acquiring first characteristic data, second characteristic data and third characteristic data of a target network point of an automobile repair plant, wherein the first characteristic data comprises the number of insured vehicles, the overdue odds rate and the estimated odds rate;
vector coding is carried out on the first characteristic data to obtain a first vector of the number of the underwriting vehicles, a second vector of the expired odds and a third vector of the estimated odds;
classifying the first vector, determining a class family of the first vector, and determining a first weight value according to the class family of the first vector;
determining the estimated odds based on the first weight value, the second vector, and the third vector;
determining the discount rate and the expense rate of the target network point according to the second characteristic data, and determining the maintenance cost rate of the target network point according to the third characteristic data;
and determining the composite cost rate of the target network point according to the evaluation odds and ends rate, the discount rate, the expense rate and the maintenance cost rate, and determining the rating of the target network point according to the composite cost rate.
In one embodiment, the processor, in implementing obtaining the estimated odds, is configured to implement:
acquiring fourth characteristic data, wherein the fourth characteristic data comprises the loss amount of each policy and the premium of each policy after calculation according to the indemness good treatment coefficient;
summing said loss amounts for each policy to obtain a sum of loss amounts, and summing said premiums for each policy to obtain a sum of premiums;
and determining the estimated odds ratio according to the sum of the loss amount and the sum of the premium.
In one embodiment, the processor, in effecting determining the estimated odds based on the sum of the loss amounts and the sum of the premiums, is operative to effect:
acquiring historical odds rate corresponding to each automobile brand in the insurance policy, cooperation years of target network points and the number of first type of vehicle insurance policies;
determining the estimated odds based on the sum of lost amounts, the sum of premiums, the historical odds, the years of cooperation, and the first type of vehicle warranty amount.
In one embodiment, the processor, in effecting determining the estimated odds based on the sum of lost amounts, the sum of premiums, the historical odds, the years of cooperation, and the first type of vehicle warranty amount, is operative to effect:
calculating the ratio of the sum of the lost amount to the sum of the premium to obtain a target ratio;
based on a weight determination model, performing classification operation on the historical odds, the cooperation years and the first type of vehicle insurance policy quantity, and determining respective corresponding families of the historical odds, the cooperation years and the first type of vehicle insurance policy quantity; determining a second weight value corresponding to the historical claims rate according to weight values corresponding to all the groups, a third weight value corresponding to the cooperation age and a fourth weight value corresponding to the number of the first type of vehicle insurance policies;
and determining the pre-estimated odds according to the target ratio, the second weight value, the third weight value and the fourth weight value.
In one embodiment, the processor, in implementing determining the discount rate and the cost rate of the target mesh point according to the second characteristic data, is configured to implement:
and determining the discount rate according to the charge amount of the policy and the premium based on a preset calculation rule, and determining the rate according to the expenditure amount and the charge amount of the policy.
In one embodiment, the processor, in implementing determining the cost to repair rate for the target site from the third feature data, is configured to implement:
determining the sum of the ratio of the production to the guarantee ratio and the maintenance amount;
and determining the maintenance cost rate according to the sum of the production guarantee ratio and the maintenance sum and the maintenance price premium rate.
In one embodiment, the processor, in implementing determining the composite cost rate for the target site from the assessed odds ratio, discount rate, rates, and the repair cost rate, is configured to implement:
determining a ratio of the assessed odds and the discount rate;
and summing the ratio with the expense rate and the maintenance cost rate to obtain the composite cost rate.
It should be noted that, as will be clearly understood by those skilled in the art, for convenience and brevity of description, the specific working process for describing the rating of the automobile repair shop may refer to the corresponding process in the foregoing embodiment of the rating control method of the automobile repair shop, and details are not repeated herein.
Embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, where the computer program includes program instructions, and when the program instructions are executed, the method implemented may refer to the various embodiments of the rating method for an auto repair shop of the present application.
The computer-readable storage medium may be an internal storage unit of the computer device described in the foregoing embodiment, for example, a hard disk or a memory of the computer device. The computer readable storage medium may also be an external storage device of the computer device, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the computer device.
It is to be understood that the terminology used in the description of the present application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this specification and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items. It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or system that comprises the element.
The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments. While the invention has been described with reference to specific embodiments, the scope of the invention is not limited thereto, and those skilled in the art can easily conceive various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. A method of rating an automotive repair shop, comprising:
acquiring first characteristic data, second characteristic data and third characteristic data of a target network point of an automobile repair plant, wherein the first characteristic data comprises the number of insured vehicles, the overdue odds rate and the estimated odds rate;
vector coding is carried out on the first characteristic data to obtain a first vector of the number of the underwriting vehicles, a second vector of the expired odds and a third vector of the estimated odds;
classifying the first vector, determining a class family of the first vector, and determining a first weight value according to the class family of the first vector;
determining the estimated odds according to the first weight value, the second vector and the third vector;
determining the discount rate and the expense rate of the target network point according to the second characteristic data, and determining the maintenance cost rate of the target network point according to the third characteristic data;
and determining the composite cost rate of the target website according to the evaluation odds and rates, the discount rate, the expense rate and the maintenance cost rate, and determining the rating of the target website according to the composite cost rate.
2. The method for rating an auto repair shop of claim 1, wherein said obtaining the estimated odds comprises:
acquiring fourth characteristic data, wherein the fourth characteristic data comprises the loss amount of each policy and the premium of each policy after calculation according to the indemness good treatment coefficient;
summing said loss amounts for each policy to obtain a sum of loss amounts, and summing said premiums for each policy to obtain a sum of premiums;
and determining the estimated odds ratio according to the sum of the loss amount and the sum of the premium.
3. A method for rating an auto repair shop as claimed in claim 2, wherein said determining said estimated odds based on said sum of said loss amount and said sum of said premium comprises:
acquiring historical odds corresponding to each automobile brand in the insurance policy, the cooperation age of the target network points and the number of first type of vehicle insurance policies;
determining the estimated odds based on the sum of lost amounts, the sum of premiums, the historical odds, the years of cooperation, and the first type of vehicle policy amount.
4. A method for rating an auto repair shop as claimed in claim 3 wherein said determining said estimated odds based on said sum of lost amounts, said sum of premiums, said historical odds, said years of cooperation and said first type of vehicle warranty amount comprises:
calculating the ratio of the sum of the lost amount to the sum of the premium to obtain a target ratio;
based on a weight determination model, performing classification operation on the historical odds, the cooperation years and the first type of vehicle insurance policy quantity, and determining respective corresponding families of the historical odds, the cooperation years and the first type of vehicle insurance policy quantity; determining a second weight value corresponding to the historical claims rate according to weight values corresponding to all the groups, a third weight value corresponding to the cooperation age and a fourth weight value corresponding to the number of the first type of vehicle insurance policies;
and determining the pre-estimated odds according to the target ratio, the second weighted value, the third weighted value and the fourth weighted value.
5. A method for rating an auto repair shop according to any one of claims 1 to 4, wherein the second characteristic data includes a charge amount of a policy, a premium calculated from a dividenless merit coefficient, and a payout amount, and the determining of the discount rate and the fee rate of the target site based on the second characteristic data includes:
and determining the discount rate according to the charge amount of the policy and the premium based on a preset calculation rule, and determining the rate according to the expenditure amount and the charge amount of the policy.
6. A method for rating an auto repair shop according to any one of claims 1 to 4, wherein the third characteristic data comprises a yield ratio, a maintenance amount ratio and a maintenance premium rate; the determining the maintenance cost rate of the target network point according to the third feature data includes:
determining the sum of the ratio of the production to the guarantee ratio and the maintenance amount;
and determining the maintenance cost rate according to the sum of the ratio of the yield to the guarantee ratio and the maintenance amount and the maintenance premium rate.
7. The method for rating an auto repair shop according to any one of claims 1 to 4, wherein the determining a composite cost rate of the target site according to the assessed odds ratio, discount rate, cost rate and the repair cost rate comprises:
determining a ratio of the assessed odds and the discount rate;
and summing the ratio with the expense rate and the maintenance cost rate to obtain the composite cost rate.
8. A vehicle repair shop rating device, comprising:
the system comprises a characteristic data acquisition module, a characteristic data processing module and a characteristic data processing module, wherein the characteristic data acquisition module is used for acquiring first characteristic data, second characteristic data and third characteristic data of a target network point of an automobile repair plant, and the first characteristic data comprises the number of insured vehicles, the full-term odds and the estimated odds;
the vector coding module is used for carrying out vector coding on the first characteristic data to obtain a first vector of the number of the insurance vehicles, a second vector of the full-term odds and a third vector of the estimated odds;
the weight calculation module is used for classifying the first vector, determining the class family of the first vector and determining a first weight value according to the class family of the first vector;
an estimated odds rate calculation module, configured to determine the estimated odds rate according to the first weight value, the second vector, and the third vector;
the discount rate and maintenance cost rate calculation module is used for determining the discount rate and the expense rate of the target network point according to the second characteristic data and determining the maintenance cost rate of the target network point according to the third characteristic data;
and the rating determination module is used for determining the composite cost rate of the target network point according to the evaluation odds and ends rate, the discount rate, the expense rate and the maintenance cost rate, and determining the rating of the target network point according to the composite cost rate.
9. A computer arrangement, characterized in that the computer arrangement comprises a processor, a memory, and a computer program stored on the memory and executable by the processor, wherein the computer program, when executed by the processor, carries out the steps of the method of rating a garage according to any of claims 1 to 7.
10. A computer-readable storage medium, characterized in that a computer program is stored on the computer-readable storage medium, wherein the computer program, when being executed by a processor, carries out the steps of the method for rating a vehicle repair shop according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211164066.5A CN115564475A (en) | 2022-09-23 | 2022-09-23 | Automobile repair shop rating method, device, equipment and storage medium |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202211164066.5A CN115564475A (en) | 2022-09-23 | 2022-09-23 | Automobile repair shop rating method, device, equipment and storage medium |
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| CN115564475A true CN115564475A (en) | 2023-01-03 |
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| CN202211164066.5A Pending CN115564475A (en) | 2022-09-23 | 2022-09-23 | Automobile repair shop rating method, device, equipment and storage medium |
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| Country | Link |
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| CN (1) | CN115564475A (en) |
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