CN116151460A - Optimization method and device for intelligent vehicle product, server and storage medium - Google Patents
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Abstract
The application relates to an optimization method, device, server and storage medium of an intelligent product of a vehicle, wherein the method comprises the following steps: objective behavior data of a user in the use process of the intelligent product functions of the vehicle are collected; subjective scoring data of the user on the functional satisfaction degree of the intelligent vehicle product is obtained, and a behavior training data set is generated by the objective behavior data and the subjective scoring data; training a training model by using the training data set, constructing a behavior and evaluation relation model, and generating optimization information of any vehicle intelligent product based on satisfaction degree scores output by the behavior and evaluation relation model. According to the method and the device for optimizing the vehicle intelligent product, the objective behaviors and subjective scores of the user in the functional use process of the vehicle intelligent product can be combined, and the behavior and evaluation relation model is built, so that the subjective scores of the user can be supported by the objective behaviors, satisfaction scores with accuracy and reference are obtained, and the vehicle intelligent product is optimized in a more targeted mode.
Description
Technical Field
The present disclosure relates to the field of data processing technologies, and in particular, to a method and apparatus for optimizing an intelligent product of a vehicle, a server, and a storage medium.
Background
The product satisfaction degree can be used for evaluating the degree of the product or service meeting or exceeding the user's expectations, and the product satisfaction degree can be used for evaluating the current quality and market competitiveness of the product through the index, and providing guiding basis for product improvement.
In the related art, the method for obtaining the satisfaction degree of the product mainly comprises subjective scores of users, and comprises the following steps:
1. and setting up a complaint and suggestion system, and acquiring the opinion of the user by collecting the direct feedback of the user, so as to know the satisfaction degree and reason of the user. The method has the advantage that the user's ideas can be more intuitively and comprehensively understood from the active feedback of the user. The method has the defects that the active feedback of the user is difficult to quantify into a satisfaction degree value, the overall satisfaction degree cannot be known from the feedback of a small number of users, timeliness is lost in the process of waiting for the feedback of the user, and the subjectivity of the feedback of the user is large.
2. The satisfaction scale surveys, actively inquiring the satisfaction of the product to the user through telephone, letters, web questionnaires and the like, listing factors possibly influencing the satisfaction of the user, and obtaining the satisfaction score of the user through the forms of sorting, scoring and the like. The method has the advantages that timeliness can be guaranteed by actively initiating investigation, quantitative numerical values can be obtained as the result, and the overall satisfaction condition of the user can be known on the basis of a certain sample size through a statistical method. The method has the defects that the satisfaction degree is subjective evaluation, the detail information which causes dissatisfaction of users is lacking, and the product optimization guidance is weak.
In addition, a method for judging positive and negative emotion and measuring satisfaction through user public opinion data on a network, a method for inviting experience evaluation officers to conduct evaluation and the like are also provided, but the methods are only based on subjective feelings of people and lack of verification of objective data, so that the authenticity of subjective scores is difficult to verify, the method is difficult to be used for product optimization, and the method needs to be improved.
Disclosure of Invention
The application provides an optimization method, device, server and storage medium of an intelligent product of a vehicle, and aims to solve the technical problems that in the related technology, a method for obtaining the satisfaction degree of the product is mainly based on subjective scores of users and verification of objective data is lacking, so that the method is difficult to apply to product optimization.
An embodiment of a first aspect of the present application provides a method for optimizing a vehicle intelligent product, applied to a model building stage, wherein the method includes the following steps: objective behavior data of a user in the use process of the intelligent product functions of the vehicle are collected; subjective scoring data of the user on the functional satisfaction degree of the vehicle intelligent product is obtained, and a behavior training data set is generated by the objective behavior data and the subjective scoring data; and training a training model by using the training data set, and constructing a behavior and evaluation relation model so as to generate optimization information of any vehicle intelligent product based on satisfaction degree scores output by the behavior and evaluation relation model.
According to the technical means, the method and the device can combine objective behavior data of the user in the use process of the intelligent product function of the vehicle and subjective score data of the satisfaction degree of the user on the product function to generate the behavior and evaluation relation model, so that the subjective score of the user can be supported by the objective behavior, the satisfaction degree result is more accurate, and the intelligent product is convenient to optimize.
Optionally, in one embodiment of the present application, the generating the behavior training data set from the objective behavior data and the subjective scoring data includes: based on a preset time window, taking the time of satisfaction degree scoring of each user as a starting point, and generating behavior set data from objective behavior data in a preset duration; and generating the behavior training data set according to the satisfaction degree score of each user and the behavior set data.
According to the technical means, the behavior training data set can be generated, and the subsequent model construction is facilitated.
Optionally, in one embodiment of the present application, before generating the behavior set data, the method further includes: pushing satisfaction survey questionnaire scales to each user; and obtaining the satisfaction degree score of each user according to the satisfaction degree scoring data, time and the user identification subjected to the one-way desensitization treatment of the satisfaction degree investigation questionnaire table.
According to the technical means, the embodiment of the application can perform single-item desensitization on the user identification so as to protect the privacy information of the user.
Optionally, in an embodiment of the present application, the training a training model using the training data set, constructing a behavior and evaluation relationship model includes: obtaining at least one characteristic value according to the training data set; training the behavioral and rating relationship model according to the correlation between the user score and the at least one feature value.
According to the technical means, the embodiment of the application can say that the training data set carries out feature transformation so as to train the behavior and evaluation relation model.
An embodiment of a second aspect of the present application provides a method for optimizing an intelligent product of a vehicle, applied to a model building stage, wherein the method includes the following steps: acquiring objective behavior data of a current user; inputting the objective behavior data into a pre-constructed behavior and evaluation relation model, and outputting the satisfaction degree score of the current user, wherein the behavior and evaluation relation model is constructed by the objective behavior data of the user in the use process of the intelligent product function of the vehicle and the satisfaction degree score of the user; and generating optimization information of the corresponding intelligent vehicle product according to the satisfaction degree score.
According to the technical means, the method and the device can give objective behavior data to the user, and obtain satisfaction degree of the user on functions of the intelligent product, so that optimization information of the vehicle intelligent product is generated to conduct optimization guidance, and the vehicle intelligent product can meet use experience of the user more.
An embodiment of a third aspect of the present application provides an optimizing apparatus for a vehicle intelligent product, applied to a model building stage, where the apparatus includes: the acquisition module is used for acquiring objective behavior data of a user in the use process of the intelligent product function of the vehicle; the generation module is used for acquiring subjective score data of the user on the functional satisfaction degree of the vehicle intelligent product and generating a behavior training data set according to the objective behavior data and the subjective score data; and the modeling module is used for training the training model by utilizing the training data set, constructing a behavior and evaluation relation model, and generating optimization information of any vehicle intelligent product based on satisfaction degree scores output by the behavior and evaluation relation model.
Optionally, in one embodiment of the present application, the generating module includes: the first generation unit is used for generating behavior set data from objective behavior data in a preset duration by taking the time of satisfaction degree scoring of each user as a starting point based on a preset time window; and the second generation unit is used for generating the behavior training data set according to the satisfaction degree score of each user and the behavior set data.
Optionally, in one embodiment of the present application, further includes: the pushing module is used for pushing the satisfaction degree investigation questionnaire list to each user; and the processing module is used for obtaining the satisfaction degree score of each user according to the satisfaction degree scoring data, time and the user identification subjected to the one-way desensitization treatment of the satisfaction degree investigation questionnaire table.
Optionally, in one embodiment of the present application, the modeling module includes: the computing unit is used for obtaining at least one characteristic value according to the training data set; and the training unit is used for training the behavior and evaluation relation model according to the correlation between the user scores and the at least one characteristic value.
An embodiment of a fourth aspect of the present application provides an optimizing apparatus for a vehicle intelligent product, applied to a product application stage, where the apparatus includes: the acquisition module is used for acquiring objective behavior data of the current user; the output module is used for inputting the objective behavior data into a pre-constructed behavior and evaluation relation model and outputting the satisfaction degree score of the current user, wherein the behavior and evaluation relation model is constructed by the objective behavior data of the user in the use process of the intelligent product function of the vehicle and the satisfaction degree score of the user; and the optimization module is used for generating optimization information of the corresponding intelligent vehicle product according to the satisfaction degree score.
An embodiment of a fifth aspect of the present application provides a server, including: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the program to realize the optimization method of the intelligent vehicle product according to the embodiment.
A sixth aspect of the present application provides a computer readable storage medium storing a computer program which when executed by a processor implements a method of optimizing a vehicle intelligent product as above.
The beneficial effects of the embodiment of the application are that:
(1) According to the method and the device for the subjective score, the subjective score data and the objective behavior data of the user can be combined, and the relation between the objective behavior and the subjective score is established, so that a behavior and evaluation relation model is established, and the subjective score of the user can be supported by the objective behavior;
(2) According to the method and the device for evaluating the satisfaction degree of the intelligent vehicle product, the satisfaction degree of the user on the intelligent vehicle product can be obtained according to the objective behavior of the user based on the behavior and evaluation relation model, so that the intelligent vehicle product can be optimized more pertinently;
(3) According to the embodiment of the application, when the user information is collected, the identity of the user can be subjected to single-item desensitization, so that the privacy information of the user is protected.
Additional aspects and advantages of the application 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 application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a flow chart of a method for optimizing a vehicle intelligent product according to an embodiment of the present application;
FIG. 2 is a flow chart of a method for optimizing a vehicle intelligent product according to one embodiment of the present application;
FIG. 3 is a schematic structural view of an optimizing device for intelligent vehicle products according to an embodiment of the present application;
FIG. 4 is a flow chart of a method for optimizing a vehicle intelligent product according to an embodiment of the present application;
FIG. 5 is a schematic structural view of an optimizing device for intelligent vehicle products according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of a server according to an embodiment of the present application.
The system comprises a 10-vehicle intelligent product optimizing device, a 101-acquisition module, a 102-generation module and a 103-modeling module; 20-optimizing device of vehicle intelligent products, 201-acquiring module, 202-output module, 203-optimizing module.
Detailed Description
Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary and intended for the purpose of explaining the present application and are not to be construed as limiting the present application.
The following describes an optimization method, an optimization device, a server and a storage medium of a vehicle intelligent product according to an embodiment of the application with reference to the accompanying drawings. In the method, subjective scoring data of the vehicle intelligent product function satisfaction degree of a user can be combined with objective behavior data of the user in the use process of the vehicle intelligent product function to generate a behavior training data set, a behavior and evaluation relation model is further constructed, satisfaction degree scores are output, optimization information of any vehicle intelligent product is generated based on the satisfaction degree scores, subjective scores of the user can be supported by objective behaviors, accuracy and referential satisfaction degree scores are obtained, and targeted optimization is carried out on the vehicle intelligent product. Therefore, the technical problems that in the related technology, the method for obtaining the satisfaction degree of the product is mainly based on subjective scores of users and verification of objective data is lacking, so that the method is difficult to apply to product optimization are solved.
Specifically, fig. 1 is a schematic flow chart of an optimization method of an intelligent vehicle product according to an embodiment of the present application.
As shown in fig. 1, the optimization method of the vehicle intelligent product is applied to a model construction stage, wherein the method comprises the following steps:
in step S101, objective behavior data of a user during use of the vehicle intelligent product function is collected.
In the actual execution process, the embodiment of the application can acquire the authorization information of the user, such as inquiring and collecting anonymous function use data from the user for product optimization, and after confirming that the user is authorized, acquiring objective behavior data of the user in the use process of the intelligent product function of the vehicle, such as operation, result and time data of the user, and after acquiring the data, performing single desensitization on the identification of the user through tools such as a Hash function, so as to prevent privacy information of the user from being revealed.
In step S102, subjective scoring data of the user' S satisfaction with the vehicle intelligent product function is obtained, and a behavior training data set is generated from the objective behavior data and the subjective scoring data.
As a possible implementation manner, the embodiment of the application may design a satisfaction degree investigation volume table in advance for the intelligent product function of the vehicle, obtain subjective score data of the user on the satisfaction degree of the intelligent product function of the vehicle after obtaining the user authorization, and generate a behavior training data set based on the objective behavior data and the subjective score data, so as to facilitate subsequent model training.
The preset satisfaction degree investigation volume table can be set correspondingly by a person skilled in the art according to different functions of the intelligent product of the actual vehicle, and the satisfaction degree investigation volume table is not particularly limited herein.
Optionally, in one embodiment of the present application, generating the behavioral training data set from the objective behavioral data and the subjective scoring data includes: based on a preset time window, taking the time of satisfaction degree scoring of each user as a starting point, and generating behavior set data from objective behavior data in a preset duration; and generating a behavior training data set according to the satisfaction degree score and the behavior set data of each user.
In some embodiments, the time window T may be preset according to product characteristics, so that n complete function usage records are guaranteed to be on average in one preset time window T, the time of user satisfaction degree scoring is taken as a starting point, and user usage operations in the range from time point T0-T to T0 are taken as behavior set data.
Further, the embodiment of the application can generate a behavior training data set according to the satisfaction degree score and the behavior set data of each user.
Optionally, in one embodiment of the present application, before generating the behavior aggregate data, the method further includes: pushing satisfaction survey questionnaire scales to each user; and obtaining the satisfaction degree score of each user according to the satisfaction degree scoring data, time and the user identification subjected to the one-way desensitization treatment of the satisfaction degree investigation questionnaire scale.
In the actual execution process, the embodiment of the application can push the pre-designed satisfaction degree investigation questionnaire table to each user before generating the behavior collection data, and obtain the satisfaction degree score of each user according to satisfaction degree scoring data and time of the satisfaction degree investigation questionnaire table and the user identification after the one-way desensitization treatment, wherein the one-way desensitization treatment on the user identification can be realized through tools such as a Hash function, so that the privacy information of the user is prevented from being revealed.
In step S103, training the training model by using the training data set, and constructing a behavior and evaluation relationship model to generate optimization information of any vehicle intelligent product based on the satisfaction degree score output by the behavior and evaluation relationship model.
As a possible implementation manner, the embodiment of the application can train the training model by using the obtained training data set to construct the behavior and evaluation relation model, so that the optimization information of any vehicle intelligent product is generated based on the satisfaction degree score output by the behavior and evaluation relation model, the product optimization is conveniently realized aiming at the optimization information, and the use experience of a user is improved.
Optionally, in one embodiment of the present application, training the training model with the training data set, constructing the behavioral and evaluation relationship model includes: obtaining at least one characteristic value according to the training data set; and training a behavior and evaluation relation model according to the correlation between the user scores and the at least one characteristic value.
Specifically, in the embodiment of the present application, the satisfaction score of the user may be set as the dependent variable y, the behavior data set is converted into a plurality of feature values, such as the number of times of a specific behavior, the number of types of different behaviors, the sequential arrangement of different behaviors, and the like, and the independent variables x1, x2, x3, x4 are obtained after the numerical processing is performed, so as to find the correlation between the dependent variable and the independent variable, where the method for determining the correlation includes, but is not limited to, calculating the pearson correlation coefficient between the dependent variable and the independent variable, and the like.
The working principle of the optimization method of the vehicle intelligent product according to the embodiment of the present application will be described with reference to fig. 2.
As shown in fig. 2, an embodiment of the present application may include the following steps:
step S201: and (5) objective behavior data acquisition. According to the method and the device for collecting the anonymous function usage data, namely the objective behavior data, can be collected from the user, and after the user agrees, operation, result and time data of the collected user and the user identification after the one-way desensitization treatment are uploaded to the cloud for storage, wherein the one-way desensitization operation can be achieved through tools such as a Hash function.
Step S202: subjective satisfaction investigation. According to the method and the device for grading the product, the satisfaction degree investigation questionnaire table can be designed in advance, the product can be graded by a user, the questionnaire is pushed to the user, the satisfaction degree of the user is graded after the user agrees, such as satisfaction degree grading data and time of the user and user identification after one-way desensitization treatment are uploaded to a cloud for storage, and grading can be carried out for the whole product, each sub-function or each operation link.
Step S203: and (5) data processing. According to the embodiment of the application, a time window T can be preset according to the characteristics of a product, n times of complete function use records are guaranteed to be on average in one preset time window T, the time of scoring the satisfaction degree of a user is taken as a starting point, and user use operation in the range from the time point T0-T to the time point T0 is taken as behavior set data.
Step S204: and (5) constructing a model. According to the embodiment of the application, the satisfaction degree score of the user can be set as the dependent variable y, the behavior data set is converted into a plurality of characteristic values, such as the number of specific behaviors, the number of types of different behaviors, the sequence arrangement of different behaviors and the like, and independent variables x1, x2, x3, x4 and the like are obtained after the numerical processing is carried out, so that the correlation between the dependent variable and the independent variable is found, wherein the method for judging the correlation comprises, but is not limited to, calculating the pearson correlation coefficient and the like between the dependent variable and the independent variable.
Step S205: and (3) an application stage. The embodiment of the application can obtain the overall function use satisfaction degree of the user group based on the constructed model.
According to the optimization method for the vehicle intelligent product, provided by the embodiment of the application, subjective scoring data of the user on the functional satisfaction degree of the vehicle intelligent product can be combined with objective behavior data of the user in the functional use process of the vehicle intelligent product to generate a behavior training data set, further, a behavior and evaluation relation model is constructed, and a satisfaction degree score is output, so that optimization information of any vehicle intelligent product is generated based on the satisfaction degree score, the subjective score of the user can be supported by the objective behavior, and the satisfaction degree score with accuracy and reference can be obtained, so that the vehicle intelligent product is optimized more specifically. Therefore, the technical problems that in the related technology, the method for obtaining the satisfaction degree of the product is mainly based on subjective scores of users and verification of objective data is lacking, so that the method is difficult to apply to product optimization are solved.
Next, an optimizing device for intelligent products of vehicles according to an embodiment of the present application will be described with reference to the accompanying drawings.
FIG. 3 is a block schematic diagram of an optimization apparatus for vehicle intelligent products according to an embodiment of the present application.
As shown in fig. 3, the optimizing apparatus 10 of the vehicle intelligent product includes: an acquisition module 101, a generation module 102 and a modeling module 103.
Specifically, the collection module 101 is configured to collect objective behavior data of a user during a use process of the vehicle intelligent product function.
The generating module 102 is configured to obtain subjective score data of the user's satisfaction degree of the vehicle intelligent product function, and generate a behavior training data set according to the objective behavior data and the subjective score data.
The modeling module 103 is configured to train the training model with the training data set, and construct a behavior and evaluation relationship model to generate optimization information of any vehicle intelligent product based on the satisfaction score output by the behavior and evaluation relationship model.
Optionally, in one embodiment of the present application, the generating module 102 includes: a first generation unit and a second generation unit.
The first generation unit is used for generating behavior set data from objective behavior data in a preset duration by taking the time of satisfaction degree scoring of each user as a starting point based on a preset time window.
And the second generation unit is used for generating a behavior training data set according to the satisfaction degree score and the behavior set data of each user.
Optionally, in one embodiment of the present application, the optimizing device 10 of the vehicle intelligent product further includes: the pushing module and the processing module.
The pushing module is used for pushing the satisfaction degree questionnaire list to each user.
And the processing module is used for obtaining the satisfaction degree score of each user according to the satisfaction degree scoring data, time and the user identification after the one-way desensitization treatment of the satisfaction degree investigation questionnaire table.
Optionally, in one embodiment of the present application, the modeling module 103 includes: a calculation unit and a training unit.
The computing unit is used for obtaining at least one characteristic value according to the training data set.
And the training unit is used for training a behavior and evaluation relation model according to the correlation between the user scores and the at least one characteristic value.
It should be noted that the foregoing explanation of the embodiment of the method for optimizing a vehicle intelligent product is also applicable to the device for optimizing a vehicle intelligent product of this embodiment, and will not be repeated here.
According to the optimization device for the vehicle intelligent product, provided by the embodiment of the application, subjective scoring data of the user on the functional satisfaction degree of the vehicle intelligent product can be combined with objective behavior data of the user in the functional use process of the vehicle intelligent product to generate a behavior training data set, further, a behavior and evaluation relation model is constructed, and a satisfaction degree score is output, so that optimization information of any vehicle intelligent product is generated based on the satisfaction degree score, the subjective score of the user can be supported by the objective behavior, and the satisfaction degree score with accuracy and reference can be obtained, so that the vehicle intelligent product is optimized more specifically. Therefore, the technical problems that in the related technology, the method for obtaining the satisfaction degree of the product is mainly based on subjective scores of users and verification of objective data is lacking, so that the method is difficult to apply to product optimization are solved.
The above embodiments describe the model building phase, and the following describes embodiments of the product application phase.
As shown in fig. 4, the optimization method of the intelligent vehicle product is applied to the application stage of the product, wherein the method comprises the following steps:
in step S401, objective behavior data of the current user is acquired.
In the actual execution process, the embodiment of the application can acquire the objective behavior data of the current user based on the authorization information of the user.
In step S402, objective behavior data is input into a pre-constructed behavior and evaluation relationship model, and a satisfaction degree score of the current user is output, wherein the behavior and evaluation relationship model is constructed by objective behavior data of the user in the use process of the vehicle intelligent product function and the satisfaction degree score of the user.
As one possible implementation manner, the embodiments of the present application may use a pre-constructed behavior and rating relationship model to convert the objective behavior of the current user into a satisfaction score of the user.
In step S403, optimization information of the corresponding vehicle intelligent product is generated according to the satisfaction score.
Further, according to the embodiment of the application, the optimization direction of the vehicle intelligent product can be determined according to the satisfaction degree score, and the corresponding optimization information of the vehicle intelligent product is generated so as to optimize the vehicle intelligent product more specifically, so that the subsequent use experience of a user is improved.
Specifically, as shown in fig. 2, in the application stage, according to the found characteristic relationship between the user satisfaction degree score and the objective behavior, the embodiment of the application can calculate the overall user satisfaction degree after identifying the behavior characteristics of the full number of users by using the constructed behavior and evaluation relationship model. Taking the pearson correlation coefficient as an example for calculation, the pearson correlation coefficient p >0.8 is obtained by calculating the known product satisfaction degree score j and the product function failure times k, and is highly positively correlated, if the product function failure times of users in the satisfaction degree j1 group are [ k1, k2] within the range from the time point T0-T to the time point T0, the duty ratio of the product function failure times of the users falling in [ k1, k2] between the current time tnow and the time tnow-T is calculated as q1=20%, and the overall satisfaction degree of the users can be obtained by weighting calculation according to the satisfaction degree jn and the corresponding user duty ratio qn.
According to the optimization method for the vehicle intelligent product, provided by the embodiment of the application, subjective scoring data of the user on the functional satisfaction degree of the vehicle intelligent product can be combined with objective behavior data of the user in the functional use process of the vehicle intelligent product to generate a behavior training data set, further, a behavior and evaluation relation model is constructed, and a satisfaction degree score is output, so that optimization information of any vehicle intelligent product is generated based on the satisfaction degree score, the subjective score of the user can be supported by the objective behavior, and the satisfaction degree score with accuracy and reference can be obtained, so that the vehicle intelligent product is optimized more specifically. Therefore, the technical problems that in the related technology, the method for obtaining the satisfaction degree of the product is mainly based on subjective scores of users and verification of objective data is lacking, so that the method is difficult to apply to product optimization are solved.
Next, an optimizing device for intelligent products of vehicles according to an embodiment of the present application will be described with reference to the accompanying drawings.
FIG. 5 is a block schematic diagram of an optimization device for vehicle intelligent products according to an embodiment of the present application.
As shown in fig. 5, the optimizing device 20 of the intelligent product of the vehicle is applied to the application stage of the product, wherein the device comprises: an acquisition module 201, an output module 202 and an optimization module 203.
Specifically, the obtaining module 201 is configured to obtain objective behavior data of a current user.
The output module 202 is configured to input objective behavior data into a pre-constructed behavior and evaluation relationship model, and output a satisfaction degree score of the current user, where the behavior and evaluation relationship model is constructed by objective behavior data of the user in a use process of the vehicle intelligent product function and the satisfaction degree score of the user.
And the optimizing module 203 is used for generating optimizing information of the corresponding intelligent vehicle product according to the satisfaction degree score.
It should be noted that the foregoing explanation of the embodiment of the method for optimizing a vehicle intelligent product is also applicable to the device for optimizing a vehicle intelligent product of this embodiment, and will not be repeated here.
According to the optimization device for the vehicle intelligent product, provided by the embodiment of the application, subjective scoring data of the user on the functional satisfaction degree of the vehicle intelligent product can be combined with objective behavior data of the user in the functional use process of the vehicle intelligent product to generate a behavior training data set, further, a behavior and evaluation relation model is constructed, and a satisfaction degree score is output, so that optimization information of any vehicle intelligent product is generated based on the satisfaction degree score, the subjective score of the user can be supported by the objective behavior, and the satisfaction degree score with accuracy and reference can be obtained, so that the vehicle intelligent product is optimized more specifically. Therefore, the technical problems that in the related technology, the method for obtaining the satisfaction degree of the product is mainly based on subjective scores of users and verification of objective data is lacking, so that the method is difficult to apply to product optimization are solved.
Fig. 6 is a schematic structural diagram of a server according to an embodiment of the present application. The server may include:
a memory 601, a processor 602, and a computer program stored on the memory 601 and executable on the processor 602.
The processor 602 implements the optimization method of the vehicle intelligent product provided in the above embodiment when executing the program.
Further, the server further includes:
a communication interface 603 for communication between the memory 601 and the processor 602.
A memory 601 for storing a computer program executable on the processor 602.
The memory 601 may comprise a high-speed RAM memory or may further comprise a non-volatile memory (non-volatile memory), such as at least one disk memory.
If the memory 601, the processor 602, and the communication interface 603 are implemented independently, the communication interface 603, the memory 601, and the processor 602 may be connected to each other through a bus and perform communication with each other. The bus may be an industry standard architecture (Industry Standard Architecture, abbreviated ISA) bus, an external device interconnect (Peripheral Component, abbreviated PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, abbreviated EISA) bus, among others. The buses may be divided into address buses, data buses, control buses, etc. For ease of illustration, only one thick line is shown in fig. 6, but not only one bus or one type of bus.
Alternatively, in a specific implementation, if the memory 601, the processor 602, and the communication interface 603 are integrated on a chip, the memory 601, the processor 602, and the communication interface 603 may perform communication with each other through internal interfaces.
The processor 602 may be a central processing unit (Central Processing Unit, abbreviated as CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, abbreviated as ASIC), or one or more integrated circuits configured to implement embodiments of the present application.
The present embodiment also provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method of optimizing a vehicle intelligent product as described above.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or N embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include at least one such feature. In the description of the present application, the meaning of "N" is at least two, such as two, three, etc., unless explicitly defined otherwise.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and additional implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order from that shown or discussed, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the embodiments of the present application.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or N wires, a portable computer cartridge (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the N steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. As with the other embodiments, if implemented in hardware, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. Although embodiments of the present application have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the application, and that variations, modifications, alternatives, and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the application.
Claims (10)
1. A method for optimizing a vehicle intelligent product, applied to a model building stage, wherein the method comprises the steps of:
objective behavior data of a user in the use process of the intelligent product functions of the vehicle are collected;
subjective scoring data of the user on the functional satisfaction degree of the vehicle intelligent product is obtained, and a behavior training data set is generated by the objective behavior data and the subjective scoring data; and
and training a training model by using the training data set, and constructing a behavior and evaluation relation model so as to generate optimization information of any vehicle intelligent product based on satisfaction degree scores output by the behavior and evaluation relation model.
2. The method of claim 1, wherein the generating a behavioral training data set from the objective behavioral data and the subjective scoring data comprises:
based on a preset time window, taking the time of satisfaction degree scoring of each user as a starting point, and generating behavior set data from objective behavior data in a preset duration;
and generating the behavior training data set according to the satisfaction degree score of each user and the behavior set data.
3. The method of claim 2, further comprising, prior to generating the behavior collection data:
pushing satisfaction survey questionnaire scales to each user;
and obtaining the satisfaction degree score of each user according to the satisfaction degree scoring data, time and the user identification subjected to the one-way desensitization treatment of the satisfaction degree investigation questionnaire table.
4. The method of claim 1, wherein training a training model using the training data set to construct a behavioral and evaluation relationship model comprises:
obtaining at least one characteristic value according to the training data set;
training the behavioral and rating relationship model according to the correlation between the user score and the at least one feature value.
5. A method for optimizing a vehicle intelligent product, characterized by being applied to a product application stage, wherein the method comprises the steps of:
acquiring objective behavior data of a current user;
inputting the objective behavior data into a pre-constructed behavior and evaluation relation model, and outputting the satisfaction degree score of the current user, wherein the behavior and evaluation relation model is constructed by the objective behavior data of the user in the use process of the intelligent product function of the vehicle and the satisfaction degree score of the user; and
and generating optimization information of the corresponding intelligent vehicle product according to the satisfaction degree score.
6. An optimization device for a vehicle intelligent product, characterized by being applied to a model construction stage, wherein the device comprises:
the acquisition module is used for acquiring objective behavior data of a user in the use process of the intelligent product function of the vehicle;
the generation module is used for acquiring subjective score data of the user on the functional satisfaction degree of the vehicle intelligent product and generating a behavior training data set according to the objective behavior data and the subjective score data; and
the modeling module is used for training the training model by utilizing the training data set, constructing a behavior and evaluation relation model, and generating optimization information of any vehicle intelligent product based on satisfaction degree scores output by the behavior and evaluation relation model.
7. The apparatus of claim 6, wherein the generating module comprises:
the first generation unit is used for generating behavior set data from objective behavior data in a preset duration by taking the time of satisfaction degree scoring of each user as a starting point based on a preset time window;
and the second generation unit is used for generating the behavior training data set according to the satisfaction degree score of each user and the behavior set data.
8. An optimization device for intelligent products of a vehicle, characterized by being applied to a product application phase, wherein the device comprises:
the acquisition module is used for acquiring objective behavior data of the current user;
the output module is used for inputting the objective behavior data into a pre-constructed behavior and evaluation relation model and outputting the satisfaction degree score of the current user, wherein the behavior and evaluation relation model is constructed by the objective behavior data of the user in the use process of the intelligent product function of the vehicle and the satisfaction degree score of the user; and
and the optimization module is used for generating optimization information of the corresponding intelligent vehicle product according to the satisfaction degree score.
9. A server, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the processor executing the program to implement the method of optimizing a vehicle intelligent product according to any one of claims 1-4 or 5.
10. A computer-readable storage medium, on which a computer program is stored, characterized in that the program is executed by a processor for implementing a method for optimizing a vehicle intelligent product according to any one of claims 1-4 or 5.
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