Business recommendation method and device, equipment and storage medium thereof

Abstract

The embodiment of the invention belongs to the technical field of machine learning, is applied to a vehicle insurance renewal recommendation scene, and relates to a business recommendation method, device and equipment and a storage medium thereof. Carrying out model training; inputting the multi-dimensional business index data of the target vehicle in the latest insurance renewal period into the trained model, and predicting the insurance renewal data of the target vehicle in the next insurance renewal period; and sending a service recommendation message to the owner terminal of the target vehicle according to the predicted insurance renewal data. According to the method, model training is carried out in combination with the multi-dimensional business index data, and in a subsequent practical application scene, the predicted insurance renewal data can be intelligently output and recommended to the vehicle owner terminal only by inputting the multi-dimensional business index data in the latest insurance renewal period of the vehicle into the trained model, so that business recommendation automation is realized, and the user experience is improved. And meanwhile, due to a multi-dimensional training and recommendation evaluation mode, the business recommendation reasonability is improved.

Description

Technical Field

[0001] The present application relates to the field of machine learning technology, and is applied to a scenario of auto insurance renewal recommendation, and in particular to a business recommendation method, device, equipment and storage medium thereof. Background Art

[0002] At present, given the booming automobile market, market competition is becoming increasingly fierce. As a key link for insurance companies to maintain customer relationships and ensure stable returns, the efficiency and accuracy of the recommendation system are of great significance to insurance companies.

[0003] The current renewal business has the following problems: it mainly relies on the basic information and historical purchase data of customers, and fails to comprehensively collect other important factors that may affect customers' renewal decisions; the current system mostly uses a simple recommendation algorithm based on historical purchase records, and the recommended renewal prices often fail to satisfy car owners; therefore, the existing auto insurance renewal business cannot intelligently and comprehensively conduct business recommendation evaluations from multiple dimensions, and the recommendation results are not reasonable, resulting in the problem of excessively high recommended prices. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a business recommendation method, device, equipment and storage medium thereof to solve the problem that the existing motor insurance renewal business cannot intelligently and comprehensively evaluate business recommendations from multiple dimensions, and the recommendation results are not reasonable, resulting in excessively high recommended prices.

[0005] In order to solve the above technical problems, the embodiment of the present application provides a service recommendation method, which adopts the following technical solution:

[0006] A service recommendation method comprises the following steps:

[0007] Obtain multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle, wherein the multi-dimensional business indicator data includes vehicle model, as well as claims data, vehicle age, vehicle driving times, vehicle driving violations, vehicle loan times, and vehicle accident times;

[0008] Obtain historical multi-dimensional business indicator data and historical renewal data for batch vehicles;

[0009] Inputting the historical multi-dimensional business indicator data of the batch of vehicles and the historical renewal data into the business recommendation evaluation model to be trained, performing model training, obtaining the business comprehensive evaluation relationship between the T-th renewal data and the T-1-th claim data, the vehicle service life, the number of vehicle driving violations, the number of vehicle loan driving violations, and the number of vehicle accidents of different models of vehicles, and obtaining a trained business recommendation evaluation model, wherein T is a positive integer greater than 1;

[0010] Inputting the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, predicting the renewal data of the target vehicle in the next renewal cycle according to the comprehensive business evaluation relationship, and outputting the predicted renewal data;

[0011] A service recommendation message is sent to the owner terminal of the target vehicle according to the predicted renewal data.

[0012] Furthermore, after executing the step of obtaining the historical multi-dimensional business indicator data and historical renewal data of batch vehicles, the method further includes:

[0013] According to different vehicle models, clustering processing is performed on the historical multi-dimensional business indicator data of the batch of vehicles to obtain the historical multi-dimensional business indicator data corresponding to all vehicle models;

[0014] According to different vehicle models, the historical insurance renewal data of the batch of vehicles are clustered to obtain the historical insurance renewal data corresponding to all vehicle models.

[0015] Further, the step of inputting the historical multi-dimensional business indicator data of the batch of vehicles and the historical renewal data into the business recommendation evaluation model to be trained, performing model training, and obtaining the business comprehensive evaluation relationship between the T-th renewal data and the T-1-th claim data, the vehicle service life, the number of vehicle driving violations, the number of vehicle loan driving violations, and the number of vehicle accidents of different models, specifically includes:

[0016] Constructing implicit business indicator data based on the number of times the vehicle is driven, the number of illegal and irregular driving of the vehicle, the number of times the vehicle is loaned out for driving, and the number of vehicle accidents;

[0017] Based on the historical multi-dimensional business indicator data, the historical renewal data and the implicit business indicator data, the business recommendation evaluation model to be trained is trained to obtain the renewal impact weights corresponding to all the business indicator data respectively;

[0018] The renewal impact weights corresponding to all the business indicator data are set as the business comprehensive evaluation relationship.

[0019] Furthermore, the step of constructing implicit business indicator data based on the number of vehicle driving times, the number of vehicle driving violations, the number of vehicle loan driving times and the number of vehicle accidents specifically includes:

[0020] According to the number of times the vehicle is driven and the number of illegal and irregular driving of the vehicle, a proportion of the number of times the vehicle is not driven in a standardized manner is calculated as the first implicit business indicator data;

[0021] Calculate the vehicle loan driving ratio based on the vehicle driving times and the vehicle loan driving times as the second implicit business indicator data;

[0022] According to the number of times the vehicle is driven and the number of vehicle accidents, the proportion of the number of times the vehicle is driven and accidents occurs is calculated as the third implicit business indicator data.

[0023] Furthermore, the step of training the business recommendation evaluation model to be trained based on the historical multi-dimensional business indicator data, the historical renewal data and the implicit business indicator data to obtain the renewal impact weights corresponding to all business indicator data respectively specifically includes:

[0024] Identify the implicit business indicator data corresponding to each piece of multi-dimensional business indicator data to obtain a first identification result;

[0025] Identify the vehicle model corresponding to each piece of multi-dimensional business indicator data to obtain a second identification result;

[0026] Arrange the multi-dimensional business indicator data and implicit business indicator data corresponding to vehicles of the same vehicle model according to the first recognition result and the second recognition result to obtain an arrangement result;

[0027] Extracting all the renewal data of vehicles of the same vehicle model according to the sorting results;

[0028] According to the correspondence between the renewal data and each multi-dimensional business indicator data, the T-th renewal data is used as the comprehensive value, and the T-1-th multi-dimensional business indicator data and implicit business indicator data are used as parameter values ​​to perform function fitting to obtain a fitting function, where T is a positive integer greater than 1;

[0029] Based on the fitting function, the renewal impact weights corresponding to the claims data, vehicle years of use, vehicle driving times, vehicle driving violations, vehicle loan times, vehicle accident times and implicit business indicator data of different vehicle models are identified when renewing insurance;

[0030] When renewing insurance for vehicles of different vehicle models, the renewal impact weights corresponding to the claims data, vehicle usage years, vehicle driving times, vehicle driving violations, vehicle loan times, vehicle accidents and implicit business indicator data are deployed as renewal calculation knowledge into the business recommendation evaluation model to be trained.

[0031] Furthermore, after executing the step of extracting all the renewal data included in the vehicles of the same vehicle model according to the sorting result, the method further includes:

[0032] Filter out the lowest renewal data corresponding to all vehicle models from all renewal data included in vehicles of the same vehicle model;

[0033] Identify whether the multi-dimensional business indicator data of the target vehicle in the most recent renewal period only includes the vehicle model;

[0034] If the multi-dimensional business indicator data of the target vehicle in the most recent renewal period only includes the vehicle model, determining whether the target vehicle is a new vehicle;

[0035] If the target vehicle is a new vehicle, the vehicle model of the target vehicle is identified, and the minimum renewal data corresponding to the vehicle model is output as the predicted renewal data;

[0036] If the target vehicle is not a new vehicle, the vehicle model of the target vehicle is identified, and the renewal data is calculated as the predicted renewal data output according to the minimum renewal data corresponding to the vehicle model and the preset increase ratio.

[0037] Furthermore, the step of inputting the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, predicting the renewal data of the target vehicle in the next renewal cycle according to the comprehensive business evaluation relationship, and outputting the predicted renewal data specifically includes:

[0038] Identify the vehicle model of the target vehicle based on the multi-dimensional business indicator data of the target vehicle in the most recent renewal period;

[0039] Obtaining insurance renewal calculation knowledge corresponding to the vehicle model of the target vehicle;

[0040] The predicted renewal data is obtained by using the fitting function to perform calculations based on the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle and the renewal calculation knowledge corresponding to the vehicle model of the target vehicle.

[0041] In order to solve the above technical problems, the embodiment of the present application also provides a service recommendation device, which adopts the following technical solution:

[0042] A business recommendation device, comprising:

[0043] A business evaluation data acquisition module is used to acquire multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle, wherein the multi-dimensional business indicator data includes vehicle model, as well as claims data, vehicle usage years, vehicle driving times, vehicle driving violations, vehicle loan driving times, and vehicle accident times;

[0044] Model training data acquisition module, used to obtain historical multi-dimensional business indicator data and historical renewal data of batch vehicles;

[0045] A model training module is used to input the historical multi-dimensional business indicator data of the batch of vehicles and the historical renewal data into the business recommendation evaluation model to be trained, perform model training, obtain the business comprehensive evaluation relationship between the T-th renewal data and the T-1-th claim data, the vehicle service life, the number of vehicle driving violations, the number of vehicle loan driving violations, and the number of vehicle accidents of different models of vehicles, and obtain the trained business recommendation evaluation model, wherein T is a positive integer greater than 1;

[0046] A model-based business evaluation module is used to input the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, predict the renewal data of the target vehicle in the next renewal cycle according to the comprehensive business evaluation relationship, and output the predicted renewal data;

[0047] The service recommendation message sending module is used to send a service recommendation message to the owner terminal of the target vehicle according to the predicted renewal data.

[0048] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0049] A computer device includes a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the above-mentioned business recommendation method when executing the computer-readable instructions.

[0050] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0051] A computer-readable storage medium stores computer-readable instructions, which, when executed by a processor, implement the steps of the business recommendation method described above.

[0052] Compared with the prior art, the embodiments of the present application have the following beneficial effects:

[0053] The business recommendation method described in the embodiment of the present application obtains historical multi-dimensional business indicator data and historical renewal data of batch vehicles; performs model training to obtain a trained business recommendation evaluation model; inputs the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, predicts the renewal data of the target vehicle in the next renewal cycle based on the comprehensive business evaluation relationship, and outputs the predicted renewal data; sends a business recommendation message to the owner terminal of the target vehicle based on the predicted renewal data. The business recommendation method described in the present application trains the business recommendation evaluation model in combination with multi-dimensional business indicator data. In subsequent actual application scenarios, it is only necessary to input the multi-dimensional business indicator data of the vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, and the predicted renewal data can be intelligently output and recommended to the owner terminal, thereby realizing the automation of business recommendation. At the same time, the multi-dimensional training and recommendation evaluation methods improve the rationality of business recommendations. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0055] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0056] Figure 2 is a flow chart of an embodiment of a business recommendation method according to the present application;

[0057] Figure 3 It is a flowchart of a specific embodiment of the steps of performing data clustering processing in the business recommendation method described in the present application;

[0058] Figure 4 yes Figure 2 A flowchart of a specific embodiment of step 203 is shown;

[0059] Figure 5 yes Figure 4 A flowchart of a specific embodiment of step 401 is shown;

[0060] Figure 6 yes Figure 4 A flowchart of a specific embodiment of step 402 is shown;

[0061] Figure 7 It is a flowchart of a specific embodiment of the renewal data acquisition step under unconventional circumstances in the business recommendation method described in this application;

[0062] Figure 8 yes Figure 2 A flowchart of a specific embodiment of step 204 is shown;

[0063] Fig. 9 is a structural diagram of an embodiment of a business recommendation device according to the present application;

[0064] Fig.10 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0065] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0066] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0067] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0068] like Figure 1 As shown, the system architecture 100 may include a terminal device 101, a network 102 and a server 103. The terminal device 101 may be a laptop 1011, a tablet computer 1012 or a mobile phone 1013. The network 102 is used to provide a medium for a communication link between the terminal device 101 and the server 103. The network 102 may include various connection types, such as wired, wireless communication links or optical fiber cables.

[0069] The user can use the terminal device 101 to interact with the server 103 through the network 102 to receive or send messages, etc. Various communication client applications can be installed on the terminal device 101, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0070] The terminal device 101 can be any electronic device with a display screen and supporting web browsing. In addition to a laptop computer 1011, a tablet computer 1012 or a mobile phone 1013, the terminal device 101 can also be an e-book reader, an MP3 player (Moving Picture Experts Group Audio Layer III), an MP4 (Moving Picture Experts Group Audio Layer IV), a laptop computer, a desktop computer, etc.

[0071] The server 103 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal device 101 .

[0072] It should be noted that the service recommendation method provided in the embodiment of the present application is generally executed by a terminal device, and accordingly, the service recommendation device is generally arranged in the terminal device.

[0073] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0074] Continue to refer Figure 2 , shows a flow chart of an embodiment of a service recommendation method according to the present application. The service recommendation method comprises the following steps:

[0075] Step 201, obtaining multi-dimensional business indicator data of the target vehicle in the most recent renewal period, wherein the multi-dimensional business indicator data includes vehicle model, and claim data, vehicle usage years, vehicle driving times, vehicle driving violations, vehicle loan driving times, and vehicle accident times;

[0076] Specifically, in the vehicle renewal recommendation, multi-dimensional evaluation data such as the number of times the vehicle is driven, the number of vehicle driving violations, the number of times the vehicle is loaned out, and the number of vehicle accidents are introduced. It is no longer just simple basic customer information and historical purchase data, making the business recommendation evaluation results more reasonable.

[0077] Step 202, obtaining historical multi-dimensional business indicator data and historical renewal data of batch vehicles;

[0078] Step 203, inputting the historical multi-dimensional business indicator data of the batch of vehicles and the historical renewal data into the business recommendation evaluation model to be trained, performing model training, obtaining the business comprehensive evaluation relationship between the T-th renewal data and the T-1-th claim data, the vehicle service life, the number of vehicle driving violations, the number of vehicle loan driving violations, and the number of vehicle accidents of different models of vehicles, and obtaining a trained business recommendation evaluation model, wherein T is a positive integer greater than 1;

[0079] Specifically, the business recommendation evaluation model is trained through the historical multi-dimensional business indicator data and historical renewal data of batches of vehicles to obtain a trained business recommendation evaluation model. The machine learning method is used to enable the model to learn the comprehensive business evaluation relationship between the T-th renewal data and the T-1-th claim data, the number of years of vehicle use, the number of vehicle driving violations, the number of vehicle loan driving violations, and the number of vehicle accidents. This is convenient for predicting the next renewal data based on the multi-dimensional business indicator data within this renewal cycle, which is more intelligent and the renewal data prediction results are more reasonable.

[0080] Step 204, inputting the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, predicting the renewal data of the target vehicle in the next renewal cycle according to the comprehensive business evaluation relationship, and outputting the predicted renewal data;

[0081] Step 205: Send a service recommendation message to the owner terminal of the target vehicle according to the predicted renewal data.

[0082] In this embodiment, by acquiring historical multi-dimensional business indicator data and historical renewal data of batch vehicles; performing model training to obtain a trained business recommendation evaluation model; inputting the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, predicting the renewal data of the target vehicle in the next renewal cycle based on the comprehensive business evaluation relationship, and outputting the predicted renewal data; sending a business recommendation message to the owner terminal of the target vehicle based on the predicted renewal data. The business recommendation method described in this application trains the business recommendation evaluation model in combination with multi-dimensional business indicator data. In subsequent actual application scenarios, it is only necessary to input the multi-dimensional business indicator data of the vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, and the predicted renewal data can be intelligently output and recommended to the owner terminal, thus realizing the automation of business recommendation. At the same time, the multi-dimensional training and recommendation evaluation methods improve the rationality of business recommendations.

[0083] Continue to refer Figure 3In some optional implementations, after executing step 202, a step of performing data clustering processing is also included. Figure 3 This is a flowchart of a specific embodiment of the steps of performing data clustering processing in the business recommendation method described in the present application, including the following steps:

[0084] Step 301, clustering the historical multi-dimensional business indicator data of the batch of vehicles according to different vehicle models to obtain the historical multi-dimensional business indicator data corresponding to all vehicle models;

[0085] Step 302 , clustering the historical insurance renewal data of the batch of vehicles according to different vehicle models to obtain the historical insurance renewal data corresponding to all vehicle models.

[0086] The data is clustered according to the different vehicle models, so that in subsequent computer processing, the corresponding relationship between different vehicle models and related multi-dimensional business indicator data can be quickly identified, thereby improving the computer's data recognition and processing efficiency.

[0087] Continue to refer Figure 4 , in some optional implementations, Figure 4 yes Figure 2 The flowchart of a specific embodiment of step 203 shown includes the following steps:

[0088] Step 401, constructing implicit business indicator data according to the number of vehicle driving times, the number of vehicle driving violations, the number of vehicle loan driving times and the number of vehicle accidents;

[0089] By constructing implicit business indicator data based on the number of vehicle driving times, vehicle driving violations, vehicle loan times and vehicle accident times, more implicit business indicator data can be constructed using the business indicator data of basic dimensions, thereby improving the rationality and accuracy of business recommendation evaluation results.

[0090] Step 402: training the to-be-trained business recommendation evaluation model based on the historical multi-dimensional business indicator data, the historical renewal data, and the implicit business indicator data to obtain renewal impact weights corresponding to all business indicator data;

[0091] Step 403, setting the renewal impact weights corresponding to all the business indicator data as the business comprehensive evaluation relationship.

[0092] By training the business recommendation evaluation model to be trained based on the historical multi-dimensional business indicator data, the historical renewal data and the implicit business indicator data, the renewal impact weights corresponding to all the business indicator data are obtained, and the renewal impact weights corresponding to all the business indicator data are set as the business comprehensive evaluation relationship, so as to facilitate the prediction of the next renewal data according to the multi-dimensional business indicator data in this renewal cycle, which is more intelligent and the renewal data prediction result is more reasonable.

[0093] Continue to refer Figure 5 , in some optional implementations, Figure 5 yes Figure 4 The flowchart of a specific embodiment of step 401 shown includes the following steps:

[0094] Step 501, calculating the proportion of the vehicle's non-standard driving times according to the vehicle's driving times and the vehicle's driving violations times as the first implicit business indicator data;

[0095] Step 502, calculating the vehicle loan driving ratio according to the vehicle driving times and the vehicle loan driving times as the second implicit business indicator data;

[0096] Step 503, based on the vehicle driving times and the vehicle accident times, calculate the ratio of vehicle driving accident times as the third implicit business indicator data.

[0097] By constructing implicit business indicator data based on the number of vehicle driving times, vehicle driving violations, vehicle loan times and vehicle accident times, more implicit business indicator data can be constructed by utilizing the business indicator data of basic dimensions, thereby improving the rationality and accuracy of business recommendation evaluation results.

[0098] Continue to refer Figure 6 , in some optional implementations, Figure 6 yes Figure 4 The flowchart of a specific embodiment of step 402 shown includes the following steps:

[0099] Step 601, identifying the implicit business indicator data corresponding to each piece of multi-dimensional business indicator data, and obtaining a first identification result;

[0100] Step 602, identifying the vehicle model corresponding to each piece of multi-dimensional business indicator data, and obtaining a second identification result;

[0101] Step 603: sorting the multi-dimensional business indicator data and implicit business indicator data corresponding to vehicles of the same vehicle model according to the first recognition result and the second recognition result to obtain a sorting result;

[0102] Step 604, extracting all the renewal data of vehicles of the same vehicle model according to the sorting result;

[0103] Step 605, according to the correspondence between the renewal data and each piece of multi-dimensional business indicator data, the T-th renewal data is used as the comprehensive value, and the T-1-th multi-dimensional business indicator data and the implicit business indicator data are used as parameter values ​​to perform function fitting to obtain a fitting function, wherein T is a positive integer greater than 1;

[0104] Step 606, based on the fitting function, identifying the renewal impact weights corresponding to the claims data, vehicle years of use, vehicle driving times, vehicle driving violations, vehicle loan times, vehicle accident times, and implicit business indicator data of different vehicle models when renewing insurance;

[0105] Step 607, when renewing insurance for different vehicle models, the insurance renewal impact weights corresponding to the claim data, vehicle usage years, vehicle driving times, vehicle driving violations, vehicle loan driving times, vehicle accident times, and implicit business indicator data are deployed as renewal calculation knowledge into the business recommendation evaluation model to be trained.

[0106] By training the business recommendation evaluation model to be trained based on the historical multi-dimensional business indicator data, the historical renewal data and the implicit business indicator data, the renewal impact weights corresponding to all the business indicator data are obtained, and the renewal impact weights corresponding to all the business indicator data are set as the business comprehensive evaluation relationship, so as to facilitate the prediction of the next renewal data according to the multi-dimensional business indicator data in this renewal cycle, which is more intelligent and the renewal data prediction result is more reasonable.

[0107] Continue to refer Figure 7 In some optional implementations, after executing step 604, a renewal data acquisition step is also included in an unconventional situation. Figure 7 This is a flowchart of a specific embodiment of the renewal data acquisition step under unconventional circumstances in the business recommendation method described in this application, including the following steps:

[0108] Step 701, filtering out the lowest renewal data corresponding to all vehicle models from all renewal data included in vehicles of the same vehicle model;

[0109] Step 702, identifying whether the multi-dimensional business indicator data of the target vehicle in the most recent renewal period only includes the vehicle model;

[0110] Step 703: if the multi-dimensional business indicator data of the target vehicle in the most recent renewal period only includes the vehicle model, determine whether the target vehicle is a new vehicle;

[0111] Step 704: if the target vehicle is a new vehicle, the vehicle model of the target vehicle is identified, and the minimum renewal data corresponding to the vehicle model is output as the predicted renewal data;

[0112] Step 705, if the target vehicle is not a new vehicle, the vehicle model of the target vehicle is identified, and the renewal data is calculated as the predicted renewal data output according to the minimum renewal data corresponding to the vehicle model and the preset increase ratio.

[0113] Specifically, if it is identified that the multi-dimensional business indicator data of the target vehicle in the most recent renewal period does not only include the vehicle model, step 204 is executed.

[0114] By identifying whether the multi-dimensional business indicator data of the target vehicle in the most recent renewal period only includes the vehicle model, when the target vehicle is a new vehicle or a vehicle that has not been renewed in the most recent renewal period, renewal data recommendation evaluation can be performed separately, thereby ensuring the applicability and rationality of the model.

[0115] Continue to refer Figure 8 , in some optional implementations, Figure 8 yes Figure 2 The flowchart of a specific embodiment of step 204 shown includes the following steps:

[0116] Step 801, identifying the vehicle model of the target vehicle according to the multi-dimensional business indicator data of the target vehicle in the most recent renewal period;

[0117] Step 802, obtaining insurance renewal calculation knowledge corresponding to the vehicle model of the target vehicle;

[0118] Step 803, based on the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle and the renewal calculation knowledge corresponding to the vehicle model of the target vehicle, the fitting function is used to perform calculation to obtain the predicted renewal data.

[0119] This application obtains historical multi-dimensional business indicator data and historical renewal data of batch vehicles; performs model training to obtain a trained business recommendation evaluation model; inputs the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, predicts the renewal data of the target vehicle in the next renewal cycle based on the comprehensive business evaluation relationship, and outputs the predicted renewal data; sends a business recommendation message to the owner terminal of the target vehicle based on the predicted renewal data. The business recommendation method described in this application trains the business recommendation evaluation model in combination with multi-dimensional business indicator data. In subsequent actual application scenarios, it is only necessary to input the multi-dimensional business indicator data of the vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, and the predicted renewal data can be intelligently output and recommended to the owner terminal, thus realizing the automation of business recommendation. At the same time, the multi-dimensional training and recommendation evaluation methods improve the rationality of business recommendations.

[0120] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0121] AI basic technologies generally include sensors, dedicated AI chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. AI software technologies mainly include computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0122] In the embodiment of the present application, historical multi-dimensional business indicator data and historical renewal data of batch vehicles are obtained; model training is performed to obtain a trained business recommendation evaluation model; the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle is input into the trained business recommendation evaluation model, and the renewal data of the target vehicle in the next renewal cycle is predicted based on the comprehensive business evaluation relationship, and the predicted renewal data is output; a business recommendation message is sent to the owner terminal of the target vehicle based on the predicted renewal data. The business recommendation method described in the present application trains the business recommendation evaluation model in combination with multi-dimensional business indicator data. In subsequent actual application scenarios, it is only necessary to input the multi-dimensional business indicator data of the vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, and the predicted renewal data can be intelligently output and recommended to the owner terminal, thereby realizing the automation of business recommendation. At the same time, the multi-dimensional training and recommendation evaluation methods improve the rationality of business recommendations.

[0123] Further references Fig. 9 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a business recommendation device, and the device embodiment is Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0124] like Fig. 9 As shown, the service recommendation device 900 described in this embodiment includes: a service evaluation data acquisition module 901, a model training data acquisition module 902, a model training module 903, a model service evaluation module 904 and a service recommendation message sending module 905. Among them:

[0125] The business evaluation data acquisition module 901 is used to acquire multi-dimensional business indicator data of the target vehicle in the most recent renewal period, wherein the multi-dimensional business indicator data includes vehicle model, as well as claim data, vehicle usage years, vehicle driving times, vehicle driving violations, vehicle loan driving times, and vehicle accident times;

[0126] Model training data acquisition module 902, used to obtain historical multi-dimensional business indicator data and historical renewal data of batch vehicles;

[0127] The model training module 903 is used to input the historical multi-dimensional business indicator data of the batch of vehicles and the historical renewal data into the business recommendation evaluation model to be trained, perform model training, obtain the business comprehensive evaluation relationship between the T-th renewal data and the T-1-th claim data, the vehicle service life, the number of vehicle driving violations, the number of vehicle loan driving violations, and the number of vehicle accidents of different models of vehicles, and obtain the trained business recommendation evaluation model, where T is a positive integer greater than 1;

[0128] The model-based business evaluation module 904 is used to input the multi-dimensional business indicator data of the target vehicle in the most recent renewal period into the trained business recommendation evaluation model, predict the renewal data of the target vehicle in the next renewal period according to the comprehensive business evaluation relationship, and output the predicted renewal data;

[0129] The service recommendation message sending module 905 is used to send a service recommendation message to the owner terminal of the target vehicle according to the predicted renewal data.

[0130] This application obtains historical multi-dimensional business indicator data and historical renewal data of batch vehicles; performs model training to obtain a trained business recommendation evaluation model; inputs the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, predicts the renewal data of the target vehicle in the next renewal cycle based on the comprehensive business evaluation relationship, and outputs the predicted renewal data; sends a business recommendation message to the owner terminal of the target vehicle based on the predicted renewal data. The business recommendation method described in this application trains the business recommendation evaluation model in combination with multi-dimensional business indicator data. In subsequent actual application scenarios, it is only necessary to input the multi-dimensional business indicator data of the vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, and the predicted renewal data can be intelligently output and recommended to the owner terminal, thus realizing the automation of business recommendation. At the same time, the multi-dimensional training and recommendation evaluation methods improve the rationality of business recommendations.

[0131] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0132] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0133] To solve the above technical problems, the present application also provides a computer device. Fig.10 , Fig.10 This is a basic structural block diagram of the computer device in this embodiment.

[0134] The computer device 10 includes a memory 10a, a processor 10b, and a network interface 10c which are interconnected and communicated via a system bus. It should be noted that: Fig.10Only a computer device 10 having a component memory 10a, a processor 10b, and a network interface 10c is shown, but it should be understood that it is not required to implement all the components shown, and more or fewer components may be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to a microprocessor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital processor (DSP), an embedded device, etc.

[0135] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0136] The memory 10a includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, disk, optical disk, etc. In some embodiments, the memory 10a can be an internal storage unit of the computer device 10, such as a hard disk or memory of the computer device 10. In other embodiments, the memory 10a can also be an external storage device of the computer device 10, such as a plug-in hard disk equipped on the computer device 10, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. Of course, the memory 10a can also include both the internal storage unit of the computer device 10 and its external storage device. In this embodiment, the memory 10a is generally used to store the operating system and various application software installed on the computer device 10, such as a computer-readable instruction of a business recommendation method, etc. In addition, the memory 10a can also be used to temporarily store various types of data that have been output or are to be output.

[0137] The processor 10b may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip in some embodiments. The processor 10b is generally used to control the overall operation of the computer device 10. In this embodiment, the processor 10b is used to run the computer-readable instructions stored in the memory 10a or process data, such as computer-readable instructions for running the business recommendation method.

[0138] The network interface 10c may include a wireless network interface or a wired network interface. The network interface 10c is generally used to establish a communication connection between the computer device 10 and other electronic devices.

[0139] The computer device proposed in this embodiment belongs to the field of machine learning technology and is applied to the scenario of auto insurance renewal recommendation. This application obtains historical multi-dimensional business indicator data and historical renewal data of batch vehicles; performs model training to obtain a trained business recommendation evaluation model; inputs the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, predicts the renewal data of the target vehicle in the next renewal cycle based on the comprehensive business evaluation relationship, and outputs the predicted renewal data; sends a business recommendation message to the owner terminal of the target vehicle based on the predicted renewal data. The business recommendation method described in this application combines multi-dimensional business indicator data to train the business recommendation evaluation model. In the subsequent actual application scenario, it only needs to input the multi-dimensional business indicator data of the vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, and the predicted renewal data can be intelligently output and recommended to the owner terminal, realizing the automation of business recommendation. At the same time, the multi-dimensional training and recommendation evaluation methods improve the rationality of business recommendations.

[0140] The present application also provides another implementation, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by a processor to enable the processor to perform the steps of the business recommendation method as described above.

[0141] The computer-readable storage medium proposed in this embodiment belongs to the field of machine learning technology and is applied to the scenario of auto insurance renewal recommendation. This application obtains historical multi-dimensional business indicator data and historical renewal data of batch vehicles; performs model training to obtain a trained business recommendation evaluation model; inputs the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, predicts the renewal data of the target vehicle in the next renewal cycle based on the comprehensive business evaluation relationship, and outputs the predicted renewal data; sends a business recommendation message to the owner terminal of the target vehicle based on the predicted renewal data. The business recommendation method described in this application combines multi-dimensional business indicator data to train the business recommendation evaluation model. In the subsequent actual application scenario, it only needs to input the multi-dimensional business indicator data of the vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, and the predicted renewal data can be intelligently output and recommended to the owner terminal, realizing the automation of business recommendation. At the same time, the multi-dimensional training and recommendation evaluation methods improve the rationality of business recommendations.

[0142] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0143] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A business recommendation method, characterized in that: The steps include: Obtain multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle, wherein the multi-dimensional business indicator data includes vehicle model, as well as claims data, vehicle age, vehicle driving times, vehicle driving violations, vehicle loan times, and vehicle accident times; Obtain historical multi-dimensional business indicator data and historical renewal data for batch vehicles; Inputting the historical multi-dimensional business indicator data of the batch of vehicles and the historical renewal data into the business recommendation evaluation model to be trained, performing model training, obtaining the business comprehensive evaluation relationship between the T-th renewal data and the T-1-th claim data, the vehicle service life, the number of vehicle driving violations, the number of vehicle loan driving violations, and the number of vehicle accidents of different models of vehicles, and obtaining a trained business recommendation evaluation model, wherein T is a positive integer greater than 1; Inputting the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, predicting the renewal data of the target vehicle in the next renewal cycle according to the comprehensive business evaluation relationship, and outputting the predicted renewal data; A service recommendation message is sent to the owner terminal of the target vehicle according to the predicted renewal data.

2. The service recommendation method according to claim 1, characterized in that: After executing the step of acquiring historical multi-dimensional business indicator data and historical renewal data of batch vehicles, the method further includes: According to different vehicle models, clustering processing is performed on the historical multi-dimensional business indicator data of the batch of vehicles to obtain the historical multi-dimensional business indicator data corresponding to all vehicle models; According to different vehicle models, the historical insurance renewal data of the batch of vehicles are clustered to obtain the historical insurance renewal data corresponding to all vehicle models.

3. The service recommendation method according to claim 2, characterized in that: The step of inputting the historical multi-dimensional business indicator data of the batch of vehicles and the historical renewal data into the business recommendation evaluation model to be trained, performing model training, and obtaining the business comprehensive evaluation relationship between the T-th renewal data and the T-1-th claim data, the vehicle service life, the vehicle driving times, the vehicle driving violations, the vehicle loan driving times, and the vehicle accident times of different models of vehicles specifically includes: Constructing implicit business indicator data based on the number of times the vehicle is driven, the number of illegal and irregular driving of the vehicle, the number of times the vehicle is loaned out for driving, and the number of vehicle accidents; Based on the historical multi-dimensional business indicator data, the historical renewal data and the implicit business indicator data, the business recommendation evaluation model to be trained is trained to obtain the renewal impact weights corresponding to all the business indicator data respectively; The renewal impact weights corresponding to all the business indicator data are set as the business comprehensive evaluation relationship.

4. The service recommendation method according to claim 3, characterized in that: The step of constructing implicit business indicator data according to the number of vehicle driving times, the number of vehicle driving violations, the number of vehicle loan driving times and the number of vehicle accidents specifically includes: According to the number of times the vehicle is driven and the number of illegal and irregular driving of the vehicle, a proportion of the number of times the vehicle is not driven in a standardized manner is calculated as the first implicit business indicator data; Calculate the vehicle loan driving ratio according to the vehicle driving times and the vehicle loan driving times as the second implicit business indicator data; According to the number of times the vehicle is driven and the number of vehicle accidents, the proportion of the number of vehicle driving accidents is calculated as the third implicit business indicator data.

5. The service recommendation method according to claim 3, characterized in that: The step of training the business recommendation evaluation model to be trained based on the historical multi-dimensional business indicator data, the historical renewal data and the implicit business indicator data to obtain the renewal impact weights corresponding to all business indicator data specifically includes: Identify the implicit business indicator data corresponding to each piece of multi-dimensional business indicator data to obtain a first identification result; Identify the vehicle model corresponding to each piece of multi-dimensional business indicator data to obtain a second identification result; Arrange the multi-dimensional business indicator data and implicit business indicator data corresponding to vehicles of the same vehicle model according to the first recognition result and the second recognition result to obtain an arrangement result; Extracting all the renewal data of vehicles of the same vehicle model according to the sorting results; According to the correspondence between the renewal data and each multi-dimensional business indicator data, the T-th renewal data is used as the comprehensive value, and the T-1-th multi-dimensional business indicator data and implicit business indicator data are used as parameter values ​​to perform function fitting to obtain a fitting function, where T is a positive integer greater than 1; Based on the fitting function, the renewal impact weights corresponding to the claims data, vehicle years of use, vehicle driving times, vehicle driving violations, vehicle loan times, vehicle accident times and implicit business indicator data of different vehicle models are identified when renewing insurance; When renewing insurance for vehicles of different vehicle models, the renewal impact weights corresponding to the claims data, vehicle usage years, vehicle driving times, vehicle driving violations, vehicle loan times, vehicle accidents and implicit business indicator data are deployed as renewal calculation knowledge into the business recommendation evaluation model to be trained.

6. The service recommendation method according to claim 5, characterized in that: After executing the step of extracting all the renewal data included in the vehicles of the same vehicle model according to the sorting result, the method further includes: Filter out the lowest renewal data corresponding to all vehicle models from all renewal data included in vehicles of the same vehicle model; Identify whether the multi-dimensional business indicator data of the target vehicle in the most recent renewal period only includes the vehicle model; If the multi-dimensional business indicator data of the target vehicle in the most recent renewal period only includes the vehicle model, determining whether the target vehicle is a new vehicle; If the target vehicle is a new vehicle, the vehicle model of the target vehicle is identified, and the minimum renewal data corresponding to the vehicle model is output as the predicted renewal data; If the target vehicle is not a new vehicle, the vehicle model of the target vehicle is identified, and the renewal data is calculated as the predicted renewal data output according to the minimum renewal data corresponding to the vehicle model and the preset increase ratio.

7. The service recommendation method according to claim 5, characterized in that: The step of inputting the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, predicting the renewal data of the target vehicle in the next renewal cycle according to the comprehensive business evaluation relationship, and outputting the predicted renewal data specifically includes: Identify the vehicle model of the target vehicle based on the multi-dimensional business indicator data of the target vehicle in the most recent renewal period; Obtaining insurance renewal calculation knowledge corresponding to the vehicle model of the target vehicle; The predicted renewal data is obtained by using the fitting function to perform calculations based on the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle and the renewal calculation knowledge corresponding to the vehicle model of the target vehicle.

8. A business recommendation device, characterized in that: include: A business evaluation data acquisition module is used to acquire multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle, wherein the multi-dimensional business indicator data includes vehicle model, as well as claims data, vehicle usage years, vehicle driving times, vehicle driving violations, vehicle loan driving times, and vehicle accident times; Model training data acquisition module, used to obtain historical multi-dimensional business indicator data and historical renewal data of batch vehicles; A model training module is used to input the historical multi-dimensional business indicator data of the batch of vehicles and the historical renewal data into the business recommendation evaluation model to be trained, perform model training, obtain the business comprehensive evaluation relationship between the T-th renewal data and the T-1-th claim data, the vehicle service life, the number of vehicle driving violations, the number of vehicle loan driving violations, and the number of vehicle accidents of different models of vehicles, and obtain the trained business recommendation evaluation model, wherein T is a positive integer greater than 1; A model-based business evaluation module is used to input the multi-dimensional business indicator data of the target vehicle in the most recent renewal cycle into the trained business recommendation evaluation model, predict the renewal data of the target vehicle in the next renewal cycle according to the comprehensive business evaluation relationship, and output the predicted renewal data; The service recommendation message sending module is used to send a service recommendation message to the owner terminal of the target vehicle according to the predicted renewal data.

9. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the service recommendation method according to any one of claims 1 to 7 when executing the computer-readable instructions.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the service recommendation method according to any one of claims 1 to 7 are implemented.

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