CN111080353A - Product recommendation method, system and readable storage medium based on vehicle data - Google Patents
Product recommendation method, system and readable storage medium based on vehicle data Download PDFInfo
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Abstract
The invention relates to the field of data analysis, and particularly discloses a product recommendation method and system based on vehicle data and a readable storage medium, wherein the method comprises the following steps: acquiring running data information of a vehicle; performing data cleaning according to the running data information of the vehicle to obtain key data information; analyzing according to the key data information to obtain a user portrait of the client; adjusting the grade and cost of the product according to the user portrait of the client, and comparing the grade and cost with the grade and cost of the historical product of the client to obtain a grade and cost difference value; judging whether the difference value between the grade and the cost is smaller than a preset difference value range or not; and if the product grade and the cost information are smaller than the set value, recommending the product grade and the cost information to the client. The invention discloses a method for acquiring data of a vehicle of a client, establishing a client portrait, and comparing and matching historical products according to the portrait of the client, so that the product conversion rate is improved, and the experience of the client can be improved.
Description
Technical Field
The invention relates to the technical field of data analysis, in particular to a product recommendation method and system based on vehicle data and a readable storage medium.
Background
At present, the automobile insurance market is competitive, however, the premium of different crowds is not very different, and the client can not directly select different insurance companies. That is to say, the car insurance is not combined with the big data analysis of the client, and the currently common big data analysis is the internet of things, which basically aims at the control of the equipment and does not perform the targeted big data analysis processing on the data. There is no value in mining this information. Therefore, the combination of the Internet of things and the vehicle insurance can well mine the behavior of the client, and a solid data base is provided for the personalized premium of the insurance industry. Therefore, at present, it is urgently needed to design a system for analyzing data of different crowds, customizing different services and different premium offers for different crowds, and in addition, finding phenomena of standardizing driving and preventing cheat insurance.
Disclosure of Invention
In order to solve at least one technical problem, the invention provides a product recommendation method, a product recommendation system and a readable storage medium based on vehicle data.
The invention provides a product recommendation method based on vehicle data in a first aspect, which comprises the following steps:
acquiring running data information of a vehicle;
performing data cleaning according to the running data information of the vehicle to obtain key data information;
analyzing according to the key data information to obtain a user portrait of the client;
adjusting the grade and cost of the product according to the user portrait of the client, and comparing the grade and cost with the grade and cost of the historical product of the client to obtain a grade and cost difference value;
judging whether the difference value between the grade and the cost is smaller than a preset difference value range or not;
and if the product grade and the cost information are smaller than the set value, recommending the product grade and the cost information to the client.
In the scheme, the method comprises the following steps of,
the analyzing according to the key data information to obtain the user portrait of the client comprises the following steps:
acquiring stored client history record information;
performing matching calculation according to the historical record information of the client and the information of other clients of the system to obtain a matching value;
setting a matching degree value range, and acquiring information of a first client in the matching degree value range to obtain a user portrait of the first client;
and calculating the key data information and the user portrait of the first client according to a preset rule to obtain the user portrait of the client.
In this scheme, the analyzing according to the key data information to obtain the user portrait of the client, further includes:
acquiring a user portrait of a first client according with the client property through cloud computing;
and calculating the key data information and the user portrait of the first client according to a preset rule to obtain the user portrait of the client.
In this scheme, still include:
acquiring environmental information;
generating type information and cost information of corresponding products according to the environment information;
and sending the category information and the expense information of the product to a client.
In this scheme, still include:
judging whether the operation data information exceeds a preset normal operation numerical range or not;
and if the alarm information exceeds the preset alarm value, sending alarm information to the vehicle and/or the client.
In this scheme, still include:
acquiring client insurance information;
obtaining the insurance category information and insurance time information according to the insurance information;
inquiring running data information within a preset time range according to the insurance time information;
analyzing the operation data information and the insurance category information to obtain insurance probability values;
judging whether the probability value of the occurrence is larger than a preset probability threshold value or not;
if so, determining to allow the risk.
The second aspect of the present invention also discloses a product recommendation system based on vehicle data, which is characterized in that the system comprises: the storage comprises a product recommending program based on vehicle data, and the processor executes the product recommending program based on the vehicle data to realize the following steps:
acquiring running data information of a vehicle;
performing data cleaning according to the running data information of the vehicle to obtain key data information;
analyzing according to the key data information to obtain a user portrait of the client;
adjusting the grade and cost of the product according to the user portrait of the client, and comparing the grade and cost with the grade and cost of the historical product of the client to obtain a grade and cost difference value;
judging whether the difference value between the grade and the cost is smaller than a preset difference value range or not;
and if the product grade and the cost information are smaller than the set value, recommending the product grade and the cost information to the client.
In this scheme, the analyzing is performed according to the key data information to obtain a user portrait of the client, specifically:
acquiring stored client history record information;
performing matching calculation according to the historical record information of the client and other client information of the system to obtain a matching value;
setting a matching degree value range, and acquiring information of a first client in the matching degree value range to obtain a user portrait of the first client;
and calculating the key data information and the user portrait of the first client according to a preset rule to obtain the user portrait of the client.
In this scheme, the analyzing according to the key data information to obtain the user portrait of the client, further includes:
acquiring a first user portrait according with the properties of a client through cloud computing;
and calculating the key data information and the first user portrait according to a preset rule to obtain the user portrait of the client.
In this scheme, still include:
acquiring environmental information;
generating type information and cost information of corresponding products according to the environment information;
and sending the category information and the expense information of the product to a client.
In this scheme, still include:
judging whether the operation data exceeds a preset normal operation numerical range or not;
and if the alarm information exceeds the preset alarm value, sending alarm information to the vehicle and/or the client.
In this scheme, still include:
acquiring client insurance information;
obtaining the insurance category information and insurance time information according to the insurance information;
inquiring running data information within a preset time range according to the insurance time information;
analyzing the operation data information and the insurance category information to obtain insurance probability values;
judging whether the probability value of the occurrence is larger than a preset probability threshold value or not;
if so, determining to allow the risk.
The third aspect of the present invention also discloses a computer-readable storage medium, which includes a vehicle data-based product recommendation program, and when the vehicle data-based product recommendation program is executed by a processor, the steps of the vehicle data-based product recommendation method according to any one of the above-mentioned items are implemented.
According to the product recommendation method and system based on the vehicle data and the readable storage medium, the data of the vehicle of the client are collected, the client portrait is established, and historical products are compared and matched according to the portrait of the client, so that the product conversion rate is improved, and the experience of the client is improved. In addition, the invention adds the user portrait similar to the client in the system to be identified by matching the client of the system, so that the data is more accurate, and the product suitable for the client can be better matched. The invention further increases the accuracy of product recommendation by identifying and matching the environmental information.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 illustrates a flow chart of a vehicle data based product recommendation method of the present invention;
FIG. 2 illustrates a flow chart of a method of obtaining a user representation from a history in accordance with the present invention;
FIG. 3 illustrates a flow diagram of a method of obtaining a user representation in accordance with cloud computing in accordance with the present invention;
FIG. 4 illustrates a block diagram of a vehicle data based product recommendation system of the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
FIG. 1 illustrates a flow chart of a vehicle data based product recommendation method of the present invention.
As shown in fig. 1, the present invention discloses a product recommendation method based on vehicle data, comprising:
s102, acquiring running data information of a vehicle;
s104, performing data cleaning according to the running data information of the vehicle to obtain key data information;
s106, analyzing according to the key data information to obtain a user portrait of the client, wherein the key data information of the client is abstracted into a label, the user portrait comprises one or more labels in the age, the city, the vehicle condition and the family income of the client, and the user portrait comprises the label only;
s108, adjusting the product grade and the cost according to the user portrait of the client, comparing the product grade and the cost with the grade and the cost of the historical product of the client to obtain a grade and cost difference value, for example, adjusting the product grade and the cost by a neural network structure through the user portrait, using the user portrait as the input of the neural network structure and the product grade and the cost as the output of the neural network structure, using the user portrait with the determined product grade and cost as a training set, training the neural network structure, when a new user portrait is input into the neural network structure, obtaining the product grade and the cost corresponding to the new user portrait, for example, setting the weight of each label of the user portrait, obtaining the product grade and the cost by combining a calculation formula of the vehicle insurance product, for example, dividing the product grade according to the range of the cost, and ensuring the vehicle damage of the insurance product is real-time vehicle price multiplied by 0.9% + basic insurance fee, real-time car prices and basic insurance costs are set according to car conditions and cities in a user figure, the basic insurance cost of a first-line city is higher than that of a third-line city, the worse the car conditions, the higher the basic insurance cost, the longer the car age, the more the car maintenance times and the like indicate that the car conditions are worse, the lower the real-time car prices are, when the car conditions of the first-line city are better than the set car conditions, the real-time car prices are higher than that of the third-line city, when the car conditions are not better than the set car conditions, the real-time car prices are lower than that of the third-line city, the real-time situation that the consumption level;
s110, judging whether the grade and cost difference value is smaller than a preset difference value range;
and S112, if the product grade and the cost information are smaller than the set value, recommending the product grade and the cost information to the client.
It should be noted that the vehicle is connected through the internet of things, and the vehicle may have a data transmission module, may perform data communication with the background server, and may send the operation data to the background server. The operation data may be operation data of key components of the vehicle, such as an engine, a gearbox, driving data, etc. The data acquisition is carried out through a central computer of the vehicle, and the data transmission work is completed through a data transmission module. The invention can also carry out data acquisition and transmission through a peripheral sensor or a peripheral module, for example, a peripheral module is additionally arranged for data acquisition and data transmission, the invention is not limited by the data acquisition mode of the vehicle, and any technical scheme adopting the invention is within the protection scope of the invention.
Specifically, after the operation data information is obtained, the operation data is screened and cleaned, useless data is removed, useful and valuable data information is reserved, and then key data information is obtained. The more useful the data, the better the client will be matched, i.e. the recommended product is better suited for the client. After the key data information is acquired, data analysis is performed to generate a client user representation. The client user profile can represent the preference, income capability, suitable product grade and the like of the client, and the product suitable for the client can be matched through the client user profile. After the user portrait of the client is obtained, the grade and the cost of the product are adjusted according to the user portrait of the client, and are compared with the grade and the cost of historical products of the client to obtain a grade and cost difference value. The client historical product can be a product purchased by the client before, for example, a car insurance product, an insurance product, and the like purchased by the client. It is necessary to obtain grade and cost information in the product, for example, whether the third party cost in the car insurance is on the order of 50 ten thousand or 100 ten thousand, and also to obtain this cost information. After all history information is obtained, the grade and cost of the product adjusted according to the user portrait of the client side are compared with the history information to obtain a grade and cost difference. For example, the fee paid by the third party in the client history is on the order of 50 ten thousand, corresponding to a premium of 500; the compensation fee of the third party for the car insurance adjustment calculated by the client portrait is in the order of 100 ten thousand, and the premium is 800. The difference in rating is 100-50 to 50 ten thousand and the premium difference is 300. Judging whether the grade and cost difference value is smaller than a preset difference value range or not; and if the product grade and the cost information are smaller than the set value, recommending the product grade and the cost information to the client. For example, if the preset difference range is 60 ten thousand of level differences and the premium difference is 350, and the adjusted car insurance contents are all within the preset difference range, the product level and the fee information are recommended to the client. The preset difference range can be set by a person skilled in the art according to actual needs, or dynamically adjusted by a background manager. Through difference judgment of the historical records, products more suitable for the client can be found, the loss of the client due to price factors is avoided, and the experience feeling of the client and the conversion rate of product recommendation can be increased.
FIG. 2 illustrates a flow chart of a method of obtaining a user representation from a history in accordance with the present invention.
As shown in fig. 2, according to the embodiment of the present invention, the analyzing according to the key data information to obtain the user portrait of the client specifically includes:
s202, obtaining stored client history record information;
s204, performing matching calculation according to the historical record information of the client and other client information of the system to obtain a matching value;
s206, setting a matching value range, acquiring information of a first client in the matching value range, obtaining a user portrait of the first client, namely presetting the matching value range, taking other clients in the matching value range as the first client, and obtaining the user portrait of the first client according to the information of the first client;
and S208, calculating the key data information and the user portrait of the first client according to a preset rule to obtain the user portrait of the client.
The user profile of the client may be characterized in conjunction with other client information already present in the system. However, since the information of other clients is used for depicting, the portrait of the client itself needs to be accurately located, and thus, the stored history information of the client needs to be obtained. Firstly, in a system, obtaining stored client history record information; and performing matching calculation according to the historical record information of the client and other client information of the system to obtain a matching degree value, namely acquiring a client group similar to the client through the matching degree. After the client group is obtained, the user portraits of the client group are counted. And finally, calculating the key data information and the user portrait of the first client according to a preset rule to obtain the user portrait of the client. The first client here may be a single client that meets the requirement of the matching degree of one other client, or may be a client group that is composed of a plurality of other clients that meet the requirement of the matching degree. When the client-side individual is a single client-side individual, only the user portrait of the individual needs to be calculated; if the user is a client group, the user portrait of the group is calculated.
Specifically, the preset rule may be a rule set by a person skilled in the art according to actual needs. For example, the user profile of the client may be represented by a weighted sum, and the user profile of the first client and the user profile of the client may be calculated in a certain ratio. For example, the user profile of the first client is calculated according to an influence factor of 40% and the user profile of the client is calculated according to an influence factor of 60%, the user profile of the client is de-portrayed, and the obtained user profile is used as a final user profile. Through the setting of different rules, the portrait of the client can be better calculated, so that the product adjusted through the portrait is more suitable for the client, and the conversion rate of the recommended product is increased.
FIG. 3 is a flow chart illustrating a method for obtaining a user representation according to cloud computing in accordance with the present invention. As shown in fig. 3, according to the embodiment of the present invention, the analyzing according to the key data information to obtain the user representation of the client further includes:
s302, acquiring a user portrait of a first client according with the client property through cloud computing;
and S304, calculating the key data information and the user portrait of the first client according to a preset rule to obtain the user portrait of the client.
In the present invention, the user representation of the client can be obtained by cloud computing. And collecting the client or the user portrait meeting the requirements in the network by utilizing the cloud computing technology, and using the collected client or user portrait for computing the user portrait of the client. Firstly, acquiring a user portrait of a first client according with the properties of the client through cloud computing; and then, calculating the key data information and the user portrait of the first client according to a preset rule to obtain the user portrait of the client. Specifically, the preset rule may be set by a person skilled in the art according to actual needs, for example, a weighted summation method is adopted, and may also be an optimal calculation rule obtained by a cloud computing technology. Through participation of cloud computing, not only can the data be obtained through operation, but also other similar user figures participate together, so that the user figure of the client is more accurate, and the conversion rate of product recommendation is increased.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring environmental information;
generating type information and cost information of corresponding products according to the environment information;
and sending the category information and the expense information of the product to a client.
It should be noted that the present invention also adjusts the product type information and the cost information according to the change of the environmental information. The environmental information may be environmental information within a preset time period, for example, rainfall change within one year; or environmental status information of a certain area, for example, rainfall change in Guangzhou city. Whether the corresponding vehicle insurance varieties, such as wading insurance, are purchased or not can be judged through the environmental information. Firstly, the rainfall change condition of the Guangzhou area in one year can be obtained, then whether the possibility of excessive rainfall exists or not is analyzed, if the rainfall is more, the probability that the vehicle wades into the water can be judged to be increased, the wading risk can be increased in the product category, and the cost information corresponding to the wading risk can be adjusted. Then, the category information and the cost information of the product are sent to the client, and the client confirms and selects the product. Of course, the present application does not limit the environment information to the above-mentioned several types, and any technical solutions adopting the present invention will fall into the protection scope of the present invention.
According to the embodiment of the invention, the method further comprises the following steps:
judging whether the operation data exceeds a preset normal operation numerical range or not;
and if the alarm information exceeds the preset alarm value, sending alarm information to the vehicle and/or the client.
It should be noted that, the present invention can also determine whether there is a security problem according to the operation data. And determining the abnormality of the vehicle by judging whether the operation data exceeds a preset normal operation numerical range. For example, when the vehicle is set to travel 50 km/h, the rotation speed of the engine is 1000 + 1800 rpm, but the collected operation data is 2000 rpm, and the above range is exceeded, it indicates that the vehicle is abnormal at this time, and alarm information is sent to the vehicle or the client to remind the client that the vehicle is abnormal. The abnormity reminding method can remind the running state of the client vehicle in real time, and reduce the occurrence of accidents.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring client insurance information;
obtaining the insurance category information and insurance time information according to the insurance information;
inquiring running data information within a preset time range according to the insurance time information;
analyzing the operation data information and the insurance category information to obtain insurance probability values;
judging whether the probability value of the occurrence is larger than a preset probability threshold value or not;
if so, determining to allow the risk.
It should be noted that the present invention also internally determines and confirms the presence of the vehicle. The method specifically comprises the following steps: acquiring client insurance information; and obtaining the insurance type information and the insurance time information according to the insurance information, for example, obtaining the information of the accident occurrence time, place, damage part and the like according to the insurance situation reported by the client. After the information is obtained, the running data information within the preset time range is inquired according to the insurance time information, for example, the running data of the vehicle at the time of accident is obtained. Analyzing and judging the operation data information and the insurance category information to obtain insurance probability values; for example, if the client reports that an accident occurs in the time period A and the vehicle engine is damaged and cannot run, the vehicle running data reported in the time period A is obtained, and if the engine is still in a numerical range with stable running, the probability of the accident is proved to be smaller; if the engine is in an abnormal range or has no data, the probability of the accident is proved to be higher, and a probability value of taking danger is calculated according to the situation. Judging whether the probability value of the occurrence is larger than a preset probability threshold value or not; if so, determining to allow the risk. For example, if the preset probability value is set to 80%, and greater than 80%, it is determined that the accident should be a real accident, and the risk is allowed. If the number of the clients is less than the preset number, failure information or other certification information is sent to the vehicle or the clients to remind the clients. The occurrence probability of fraud can be reduced by judging the probability value of occurrence of the risk.
FIG. 4 illustrates a block diagram of a vehicle data based product recommendation system of the present invention.
As shown in fig. 4, the present invention discloses a product recommendation system based on vehicle data, which is characterized in that the system comprises: a memory 41 and a processor 42, wherein the memory includes a vehicle data based product recommendation program, and the processor executes the vehicle data based product recommendation program to implement the following steps:
acquiring running data information of a vehicle;
performing data cleaning according to the running data information of the vehicle to obtain key data information;
analyzing according to the key data information to obtain a user portrait of the client;
adjusting the grade and cost of the product according to the user portrait of the client, and comparing the grade and cost with the grade and cost of the historical product of the client to obtain a grade and cost difference value;
judging whether the difference value between the grade and the cost is smaller than a preset difference value range or not;
and if the product grade and the cost information are smaller than the set value, recommending the product grade and the cost information to the client.
It should be noted that the vehicle is connected through the internet of things, and the vehicle may have a data transmission module, may perform data communication with the background server, and may send the operation data to the background server. The operation data may be operation data of key components of the vehicle, such as an engine, a gearbox, driving data, etc. The data acquisition is carried out through a central computer of the vehicle, and the data transmission work is completed through a data transmission module. The invention can also carry out data acquisition and transmission through a peripheral sensor or a peripheral module, for example, a peripheral module is additionally arranged for data acquisition and data transmission, the invention is not limited by the data acquisition mode of the vehicle, and any technical scheme adopting the invention is within the protection scope of the invention.
Specifically, after the operation data information is obtained, the operation data is screened and cleaned, useless data is removed, useful and valuable data information is reserved, and then key data information is obtained. The more useful the data, the better the client will be matched, i.e. the recommended product is better suited for the client. After the key data information is acquired, data analysis is performed to generate a client user representation. The client user profile can represent the preference, income capability, suitable product grade and the like of the client, and the product suitable for the client can be matched through the client user profile. After the user portrait of the client is obtained, the grade and the cost of the product are adjusted according to the user portrait of the client, and are compared with the grade and the cost of historical products of the client to obtain a grade and cost difference value. The client historical product can be a product purchased by the client before, for example, a car insurance product, an insurance product, and the like purchased by the client. It is necessary to obtain grade and cost information in the product, for example, whether the third party cost in the car insurance is on the order of 50 ten thousand or 100 ten thousand, and also to obtain this cost information. After all history information is obtained, the grade and cost of the product adjusted according to the user portrait of the client side are compared with the history information to obtain a grade and cost difference. For example, the fee paid by the third party in the client history is on the order of 50 ten thousand, corresponding to a premium of 500; the compensation fee of the third party for the car insurance adjustment calculated by the client portrait is in the order of 100 ten thousand, and the premium is 800. The difference in rating is 100-50 to 50 ten thousand and the premium difference is 300. Judging whether the grade and cost difference value is smaller than a preset difference value range or not; and if the product grade and the cost information are smaller than the set value, recommending the product grade and the cost information to the client. For example, if the preset difference range is 60 ten thousand of level differences and the premium difference is 350, and the adjusted car insurance contents are all within the preset difference range, the product level and the fee information are recommended to the client. The preset difference range can be set by a person skilled in the art according to actual needs, or dynamically adjusted by a background manager. Through difference judgment of the historical records, products more suitable for the client can be found, the loss of the client due to price factors is avoided, and the experience feeling of the client and the conversion rate of product recommendation can be increased.
According to the embodiment of the present invention, the analyzing according to the key data information to obtain the user portrait of the client specifically includes:
acquiring stored client history record information;
performing matching calculation according to the historical record information of the client and other client information of the system to obtain a matching value;
acquiring first client information within a preset matching degree value range to obtain a user portrait of a first client;
and calculating the key data information and the user portrait of the first client according to a preset rule to obtain the user portrait of the client.
The user profile of the client may be characterized in conjunction with other client information already present in the system. However, since the information of other clients is used for depicting, the portrait of the client itself needs to be accurately located, and thus, the stored history information of the client needs to be obtained. Firstly, in a system, obtaining stored client history record information; and performing matching calculation according to the historical record information of the client and other client information of the system to obtain a matching degree value, namely acquiring a client group similar to the client through the matching degree. After the client group is obtained, the user portraits of the client group are counted. And finally, calculating the key data information and the user portrait of the first client according to a preset rule to obtain the user portrait of the client. The first client here may be a single client individual or a group of clients. When the client-side individual is a single client-side individual, only the user portrait of the individual needs to be calculated; if the user is a client group, the user portrait of the group is calculated.
Specifically, the preset rule may be a rule set by a person skilled in the art according to actual needs. For example, the user profile of the client may be represented by a weighted sum, and the user profile of the first client and the user profile of the client may be calculated in a certain ratio. For example, the user profile of the first client is calculated according to an influence factor of 40% and the user profile of the client is calculated according to an influence factor of 60%, the user profile of the client is de-portrayed, and the obtained user profile is used as a final user profile. Through the setting of different rules, the portrait of the client can be better calculated, so that the product adjusted through the portrait is more suitable for the client, and the conversion rate of the recommended product is increased.
According to the embodiment of the present invention, the analyzing according to the key data information to obtain the user portrait of the client further includes:
acquiring a first user portrait according with the properties of a client through cloud computing;
and calculating the key data information and the first user portrait according to a preset rule to obtain the user portrait of the client.
In the present invention, the user representation of the client can be obtained by cloud computing. And collecting the client or the user portrait meeting the requirements in the network by utilizing the cloud computing technology, and using the collected client or user portrait for computing the user portrait of the client. Firstly, acquiring a first user portrait conforming to the property of a client through cloud computing; and then, calculating the key data information and the first user portrait according to a preset rule to obtain the user portrait of the client. Specifically, the preset rule may be set by a person skilled in the art according to actual needs, for example, a weighted summation method is adopted, and may also be an optimal calculation rule obtained by a cloud computing technology. Through participation of cloud computing, not only can the data be obtained through operation, but also other similar user figures participate together, so that the user figure of the client is more accurate, and the conversion rate of product recommendation is increased.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring environmental information;
generating type information and cost information of corresponding products according to the environment information;
and sending the category information and the expense information of the product to a client.
It should be noted that the present invention also adjusts the product type information and the cost information according to the change of the environmental information. The environmental information may be environmental information within a preset time period, for example, rainfall change within one year; or environmental status information of a certain area, for example, rainfall change in Guangzhou city. Whether the corresponding vehicle insurance varieties, such as wading insurance, are purchased or not can be judged through the environmental information. Firstly, the rainfall change condition of the Guangzhou area in one year can be obtained, then whether the possibility of excessive rainfall exists or not is analyzed, if the rainfall is more, the probability that the vehicle wades into the water can be judged to be increased, the wading risk can be increased in the product category, and the cost information corresponding to the wading risk can be adjusted. Then, the category information and the cost information of the product are sent to the client, and the client confirms and selects the product. Of course, the present application does not limit the environment information to the above-mentioned several types, and any technical solutions adopting the present invention will fall into the protection scope of the present invention.
According to the embodiment of the invention, the method further comprises the following steps:
judging whether the operation data exceeds a preset normal operation numerical range or not;
and if the alarm information exceeds the preset alarm value, sending alarm information to the vehicle and/or the client.
It should be noted that, the present invention can also determine whether there is a security problem according to the operation data. And determining the abnormality of the vehicle by judging whether the operation data exceeds a preset normal operation numerical range. For example, when the vehicle is set to travel 50 km/h, the rotation speed of the engine is 1000 + 1800 rpm, but the collected operation data is 2000 rpm, and the above range is exceeded, it indicates that the vehicle is abnormal at this time, and alarm information is sent to the vehicle or the client to remind the client that the vehicle is abnormal. The abnormity reminding method can remind the running state of the client vehicle in real time, and reduce the occurrence of accidents.
According to the embodiment of the invention, the method further comprises the following steps:
acquiring client insurance information;
obtaining the insurance category information and insurance time information according to the insurance information;
inquiring running data information within a preset time range according to the insurance time information;
analyzing the operation data information and the insurance category information to obtain insurance probability values;
judging whether the probability value of the occurrence is larger than a preset probability threshold value or not;
if so, determining to allow the risk.
It should be noted that the present invention also internally determines and confirms the presence of the vehicle. The method specifically comprises the following steps: acquiring client insurance information; and obtaining the insurance type information and the insurance time information according to the insurance information, for example, obtaining the information of the accident occurrence time, place, damage part and the like according to the insurance situation reported by the client. After the information is obtained, the running data information within the preset time range is inquired according to the insurance time information, for example, the running data of the vehicle at the time of accident is obtained. Analyzing and judging the operation data information and the insurance category information to obtain insurance probability values; for example, if the client reports that an accident occurs in the time period A and the vehicle engine is damaged and cannot run, the vehicle running data reported in the time period A is obtained, and if the engine is still in a numerical range with stable running, the probability of the accident is proved to be smaller; if the engine is in an abnormal range or has no data, the probability of the accident is proved to be higher, and a probability value of taking danger is calculated according to the situation. Judging whether the probability value of the occurrence is larger than a preset probability threshold value or not; if so, determining to allow the risk. For example, if the preset probability value is set to 80%, and greater than 80%, it is determined that the accident should be a real accident, and the risk is allowed. If the number of the clients is less than the preset number, failure information or other certification information is sent to the vehicle or the clients to remind the clients.
The third aspect of the present invention also discloses a computer-readable storage medium, which includes a vehicle data-based product recommendation program, and when the vehicle data-based product recommendation program is executed by a processor, the steps of the vehicle data-based product recommendation method according to any one of the above-mentioned items are implemented.
According to the product recommendation method and system based on the vehicle data and the readable storage medium, the data of the vehicle of the client are collected, the client portrait is established, and historical products are compared and matched according to the portrait of the client, so that the product conversion rate is improved, and the experience of the client is improved. In addition, the invention adds the user portrait similar to the client in the system to be identified by matching the client of the system, so that the data is more accurate, and the product suitable for the client can be better matched. The invention further increases the accuracy of product recommendation by identifying and matching the environmental information.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. A method for product recommendation based on vehicle data, comprising:
acquiring running data information of a vehicle;
performing data cleaning according to the running data information of the vehicle to obtain key data information;
analyzing according to the key data information to obtain a user portrait of the client;
adjusting the grade and cost of the product according to the user portrait of the client, and comparing the grade and cost with the grade and cost of the historical product of the client to obtain a grade and cost difference value;
judging whether the difference value between the grade and the cost is smaller than a preset difference value range or not;
and if the product grade and the cost information are smaller than the set value, recommending the product grade and the cost information to the client.
2. The vehicle data-based product recommendation method according to claim 1, wherein the analyzing according to the key data information to obtain a user representation of a client comprises:
acquiring stored client history record information;
performing matching calculation according to the historical record information of the client and the information of other clients of the system to obtain a matching value;
setting a matching degree value range, and acquiring information of a first client in the matching degree value range to obtain a user portrait of the first client;
and calculating the key data information and the user portrait of the first client according to a preset rule to obtain the user portrait of the client.
3. The vehicle data-based product recommendation method according to claim 1, wherein the analyzing according to the key data information to obtain a user representation of a client further comprises:
acquiring a user portrait of a first client according with the client property through cloud computing;
and calculating the key data information and the user portrait of the first client according to a preset rule to obtain the user portrait of the client.
4. The vehicle data-based product recommendation method of claim 1, further comprising:
acquiring environmental information;
generating type information and cost information of corresponding products according to the environment information;
and sending the category information and the expense information of the product to a client.
5. The vehicle data-based product recommendation method of claim 1, further comprising:
judging whether the operation data information exceeds a preset normal operation numerical range or not;
and if the alarm information exceeds the preset alarm value, sending alarm information to the vehicle and/or the client.
6. The vehicle data-based product recommendation method of claim 1, further comprising:
acquiring client insurance information;
obtaining the insurance category information and insurance time information according to the insurance information;
inquiring running data information within a preset time range according to the insurance time information;
analyzing the operation data information and the insurance category information to obtain insurance probability values;
judging whether the probability value of the occurrence is larger than a preset probability threshold value or not;
if so, determining to allow the risk.
7. A product recommendation system based on vehicle data, the system comprising: the storage comprises a product recommending program based on vehicle data, and the processor executes the product recommending program based on the vehicle data to realize the following steps:
acquiring running data information of a vehicle;
performing data cleaning according to the running data information of the vehicle to obtain key data information;
analyzing according to the key data information to obtain a user portrait of the client;
adjusting the grade and cost of the product according to the user portrait of the client, and comparing the grade and cost with the grade and cost of the historical product of the client to obtain a grade and cost difference value;
judging whether the difference value between the grade and the cost is smaller than a preset difference value range or not;
and if the product grade and the cost information are smaller than the set value, recommending the product grade and the cost information to the client.
8. The vehicle data-based product recommendation system according to claim 7, wherein the analysis performed according to the key data information obtains a user representation of a client, specifically:
acquiring stored client history record information;
performing matching calculation according to the historical record information of the client and other client information of the system to obtain a matching value;
setting a matching degree value range, and acquiring information of a first client in the matching degree value range to obtain a user portrait of the first client;
and calculating the key data information and the user portrait of the first client according to a preset rule to obtain the user portrait of the client.
9. The vehicle data-based product recommendation system of claim 7, further comprising:
acquiring environmental information;
generating type information and cost information of corresponding products according to the environment information;
and sending the category information and the expense information of the product to a client.
10. A computer-readable storage medium, characterized in that a vehicle data based product recommendation program is included in the computer-readable storage medium, which when executed by a processor implements the steps of the vehicle data based product recommendation method according to any one of claims 1 to 6.
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