CN113313155A

CN113313155A - Data processing method and related device

Info

Publication number: CN113313155A
Application number: CN202110553993.5A
Authority: CN
Inventors: 钟子宏
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2021-05-20
Filing date: 2021-05-20
Publication date: 2021-08-27

Abstract

The embodiment of the application discloses a data processing method and a related device, which can be applied to a cloud server, at least relates to machine learning in artificial intelligence, and determines the state probability of a target account in an (i + 1) th period according to user characteristic data of a vehicle in the ith period, namely whether the user behavior of the target account in the ith period through an ETC system is qualified or not influences the state probability. Because the state probability represents the possibility that the target account is added into the ETC blacklist in the (i + 1) th period, aiming at discount levels for identifying different discount rates, the discount distribution weight of the target account in the (i + 1) th period is obtained based on the state probability, and the comprehensive discount rate of the target account in the (i + 1) th period is determined according to the discount distribution weight and the discount level, so that the discount rate of the user corresponding to the ETC in the next period is determined, and the discount rate can be displayed through electronic map application in terminal equipment such as a vehicle-mounted computer. Therefore, the discount rate system of the ETC system is more reasonable, and traffic jam is reduced.

Description

Data processing method and related device

Technical Field

The present application relates to the field of data processing technologies, and in particular, to a data processing method and a related apparatus.

Background

Electronic Toll Collection (ETC) system utilizes computer networking technology and bank to carry out backstage settlement processing through the special short distance communication of going on between the on-vehicle Electronic tags of installing on the vehicle windshield and the microwave antenna on Toll station ETC lane to it just can accept highway or bridge expense need not to park when reaching the vehicle and passing through highway or bridge Toll station, compares in artifical Toll lane, and the ETC lane is more convenient quick.

If the user who uses the ETC system is improper to operate, if the bank card in-balance is insufficient and results in the ETC system to deduct money failure etc., this user may be added into the ETC blacklist, and whether the user can not be in the meaning of being added into the ETC blacklist for the ETC lane appears blocking up, reduces other user's trip and experiences.

Disclosure of Invention

In order to solve the technical problem, the application provides a data processing method and a related device, which are used for encouraging a user to civilized travel, reducing the times of congestion of an ETC lane and improving the travel experience of the user.

The embodiment of the application discloses the following technical scheme:

in one aspect, the present application provides a data processing method, including:

acquiring user characteristic data of a vehicle in an ith period, wherein the user characteristic data is used for identifying user behaviors generated by a target account of the vehicle through an ETC (electronic toll collection) system;

determining the state probability of the target account in the (i + 1) th cycle according to the user characteristic data, wherein the state probability is used for identifying the probability that the target account is added into an ETC blacklist in the (i + 1) th cycle, and the ETC blacklist is used for identifying the account which is not allowed to use the ETC system;

obtaining discount distribution weights of the target account in the (i + 1) th cycle based on the state probability, wherein the discount distribution weights are used for identifying the association degrees of the target account with different discount levels respectively under the state probability;

determining a comprehensive discount rate of the target account in the (i + 1) th period according to the discount distribution weight and the discount level, and determining the comprehensive discount rate as a discount rate of the target account corresponding to the ETC system in the (i + 1) th period.

In another aspect, the present application provides a data processing method, including:

in the (i + 1) th period, starting an electronic map application based on the map account;

if the map account number is determined to have an associated target account number in an ETC system, displaying discount information corresponding to a comprehensive discount rate of the target account number in the (i + 1) th period, wherein the comprehensive discount rate is determined according to a discount level of the ETC system and a state probability of the target account number in the (i + 1) th period, the state probability is used for identifying the probability that the target account number is added into an ETC blacklist in the (i + 1) th period, and the state probability is determined based on a user behavior of the target account number generated by the ETC system in the (i) th period.

In another aspect, the present application provides a data processing apparatus, the apparatus comprising: the system comprises an acquisition unit, a state probability determination unit, a discount distribution weight acquisition unit and a comprehensive discount rate determination unit;

the obtaining unit is used for obtaining user characteristic data of a vehicle in an ith period, and the user characteristic data is used for identifying user behaviors of a target account of the vehicle generated by an ETC (electronic toll collection) system;

the state probability determining unit is used for determining the state probability of the target account in the (i + 1) th cycle according to the user characteristic data, wherein the state probability is used for identifying the probability that the target account is added into an ETC blacklist in the (i + 1) th cycle, and the ETC blacklist is used for identifying the account which is not allowed to use the ETC system;

the discount distribution weight obtaining unit is configured to obtain discount distribution weights of the target account in the (i + 1) th cycle based on the state probability, where the discount distribution weights are used to identify degrees of association between the target account and different discount levels respectively under the state probability;

the comprehensive discount rate determining unit is used for determining the comprehensive discount rate of the target account in the (i + 1) th period according to the discount distribution weight and the discount level, and determining the comprehensive discount rate as the discount rate of the target account corresponding to the ETC system in the (i + 1) th period.

In another aspect, the present application provides a data processing apparatus, the apparatus comprising: a starting unit and a display unit;

the starting unit is used for starting the electronic map application based on the map account in the (i + 1) th cycle;

the display unit is used for displaying discount information corresponding to a comprehensive discount rate of the target account in the (i + 1) th period corresponding to the ETC system if it is determined that the map account has an associated target account in the ETC system, wherein the comprehensive discount rate is determined according to a discount level of the ETC system and a state probability of the target account in the (i + 1) th period, the state probability is used for identifying the probability that the target account is added into an ETC blacklist in the (i + 1) th period, and the state probability is determined based on user behaviors of the target account generated by the ETC system in the ith period.

In another aspect, the present application provides a computer device comprising a processor and a memory:

the memory is used for storing program codes and transmitting the program codes to the processor;

the processor is configured to perform the method of the above aspect according to instructions in the program code.

In another aspect, the present application provides a computer-readable storage medium for storing a computer program for executing the method of the above aspect.

In another aspect, embodiments of the present application provide a computer program product or a computer program, which includes computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the method of the above aspect.

According to the technical scheme, the user behavior of the target account of the vehicle generated by the ETC system is clarified according to the user characteristic data of the vehicle in the ith period, the state probability of the target account in the (i + 1) th period is determined based on the user characteristic data, therefore, based on the continuity of the use habits of the user, whether the user behavior of the target account in the ith period through the ETC system is qualified or not directly influences the state probability, as the state probability represents the possibility that the target account is added into the ETC blacklist in the (i + 1) th period, aiming at the discount levels for identifying different discount rates, the discount distribution weight of the target account in the (i + 1) th period is obtained based on the state probability, the discount distribution weight identifies the association degree of the target account corresponding to different discount levels respectively, and the comprehensive discount rate of the target account in the (i + 1) th period is determined according to the discount distribution weight and the discount level, thereby determining the discount rate of the ETC corresponding to the user in the next period. Therefore, when the user uses the ETC system through the target account, the user is no longer based on a fixed discount rate, the individual user behaviors made by different users based on the ETC system in the period can directly influence the discount rate of the account used in the next period in the ETC system, the user behaviors with poor implementation can be taken as the cost through the poorly distributed discount rate, so that the user can standardize the user behaviors of using the ETC system for avoiding the cost, the better discount rate cannot be used due to the fact that the user is added into an ETC black list is avoided, the corresponding discount rate is distributed based on the individual user behaviors, the rationalization of the ETC system discount rate system is facilitated, and the benign development of the ETC system is facilitated. Furthermore, the account number stock in the ETC blacklist can be effectively reduced, and the times of congestion of the ETC lane can be reduced.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.

Fig. 1 is a schematic view of an application scenario of a data processing method according to an embodiment of the present application;

fig. 2 is a flowchart of a data processing method according to an embodiment of the present application;

fig. 3 is a schematic diagram illustrating an electronic map application displaying discount information according to an embodiment of the present application;

fig. 4 is a schematic diagram of an application scenario embodiment of a data processing method provided in an embodiment of the present application;

fig. 5 is a schematic diagram of an application scenario embodiment of a data processing method provided in an embodiment of the present application;

fig. 6 is a schematic diagram of a data processing apparatus according to an embodiment of the present application;

fig. 7 is a schematic diagram of a data processing apparatus according to an embodiment of the present application;

fig. 8 is a schematic structural diagram of a server according to an embodiment of the present application;

fig. 9 is a schematic structural diagram of a terminal device according to an embodiment of the present application.

Detailed Description

Embodiments of the present application are described below with reference to the accompanying drawings.

After the user is relieved from the ETC blacklist, the user can enjoy the same treatment with other users who are not added to the ETC blacklist, for example, the treatment in the aspects of the discount rate of the ETC and the like cannot be influenced by whether the user is added to the ETC blacklist once, so that the user cannot be aware of whether the user is possibly added to the ETC blacklist, and the ETC lane often loses the convenient and quick significance of the ETC lane due to the fact that the user in the ETC blacklist is jammed.

Based on this, the embodiment of the application provides a data processing method and a related device, which allocate a corresponding ETC discount rate based on a user personalized behavior, and are beneficial to rationalization of a discount rate system of an ETC system, so that a user can standardize own behavior, and the times of congestion of an ETC lane is further reduced. The data processing method provided by the application can be applied to data processing equipment with data processing capacity, such as terminal equipment and servers. The terminal device may be a smart phone, a desktop computer, a notebook computer, a tablet computer, a vehicle-mounted computer, a smart watch, and the like, but is not limited thereto; the server may be an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, or a cloud server providing cloud computing services. The terminal device and the server may be directly or indirectly connected through wired or wireless communication, and the application is not limited herein.

The data processing method provided by the embodiment of the application can be realized based on a cloud computing technology, wherein cloud computing (cloud computing) refers to a delivery and use mode of an IT infrastructure, and refers to acquiring required resources in an on-demand and easily-extensible manner through a network; the generalized cloud computing refers to a delivery and use mode of a service, and refers to obtaining a required service in an on-demand and easily-extensible manner through a network. Such services may be IT and software, internet related, or other services. Cloud Computing is a product of development and fusion of traditional computers and Network Technologies, such as Grid Computing (Grid Computing), Distributed Computing (Distributed Computing), Parallel Computing (Parallel Computing), Utility Computing (Utility Computing), Network Storage (Network Storage Technologies), Virtualization (Virtualization), Load balancing (Load Balance), and the like.

With the development of diversification of internet, real-time data stream and connecting equipment and the promotion of demands of search service, social network, mobile commerce, open collaboration and the like, cloud computing is rapidly developed. Different from the prior parallel distributed computing, the generation of cloud computing can promote the revolutionary change of the whole internet mode and the enterprise management mode in concept.

In the data processing method provided by the embodiment of the application, the comprehensive discount rate which accords with the personalized behavior of the user in the (i + 1) th period can be determined through the user behavior generated by the ETC system in the ith period based on the target account of the vehicle through the cloud server.

The data processing method provided by the embodiment of the application can also be realized based on Artificial Intelligence (AI), which is a theory, method, technology and application system for simulating, extending and expanding human Intelligence by using a digital computer or a machine controlled by the digital computer, sensing the environment, acquiring knowledge and obtaining the best result by using the knowledge. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the essence of intelligence and produce a new intelligent machine that can react in a manner similar to human intelligence. Artificial intelligence is the research of the design principle and the realization method of various intelligent machines, so that the machines have the functions of perception, reasoning and decision making.

The artificial intelligence technology is a comprehensive subject and relates to the field of extensive technology, namely the technology of a hardware level and the technology of a software level. The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

In the embodiment of the present application, the artificial intelligence techniques mainly involved include the above-mentioned machine learning/deep learning directions.

The aforementioned data processing apparatus may also be provided with machine learning capabilities. Machine learning is a multi-field cross discipline, and relates to a plurality of disciplines such as probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and the like. The special research on how a computer simulates or realizes the learning behavior of human beings so as to acquire new knowledge or skills and reorganize the existing knowledge structure to continuously improve the performance of the computer. Machine learning is the core of artificial intelligence, is the fundamental approach for computers to have intelligence, and is applied to all fields of artificial intelligence. Machine learning and deep learning typically include techniques such as artificial neural networks.

In the data processing method provided by the embodiment of the application, the adopted artificial intelligence model mainly relates to the application of machine Learning, and the state probability of the target account in the (i + 1) th period is determined through a Deep Learning (Deep Learning) model and the like according to the user characteristic data.

In order to facilitate understanding of the technical solution of the present application, a data processing method provided in the embodiments of the present application is described below with reference to an actual application scenario and a server as a data processing device.

Referring to fig. 1, the figure is a schematic view of an application scenario of a data processing method according to an embodiment of the present application. In the Application scenario shown in fig. 1, including the terminal device 100 and the server 200, an Application (APP) corresponding to an ETC system may be installed in the terminal device 100, which is hereinafter referred to as an ETC APP. The user can log in ETC APP based on the target account number, and user behaviors such as clicking, recharging, consuming and the like are generated in the ETC system. The target account may be an ETC account, and one vehicle is bound to one ETC account.

The server 200 obtains, from the terminal device 100, user characteristic data of the vehicle in the ith period, where the user characteristic data is used to identify user behaviors of the target account of the vehicle generated by the ETC system, such as n user behaviors corresponding to the ETC account, for example, click-type data, recharge-type data, consumption-type data, comment-type data, account-type data, and vehicle toll fee.

The user characteristic data may represent a usage habit of the user using the ETC system, and the usage habit has continuity, so the server 200 may determine a state probability of the target account in the (i + 1) th cycle according to the user characteristic data, where the state probability is used to identify a probability that the target account is added to the ETC blacklist in the (i + 1) th cycle. That is, the user behavior generated by the ETC system in the ith cycle based on the account number directly affects the state probability in the (i + 1) th cycle. For example, if a user does not perform well in the ith period based on an account number, so that the account number is frequently added to the ETC blacklist, the state probability of the account number in the (i + 1) th period is higher, that is, the probability of the account number being added to the ETC blacklist is higher.

In the related art, the ETC system has a plurality of discount levels, and different discount levels correspond to different discount rates, for example, the discount levels are nine folds, eight folds and seven folds respectively, and the discount rates are 10%, 20% and 30% respectively. However, the discount rate is fixed, and no matter whether the account is added to the ETC blacklist, the discount rate of the account is not affected, so that the user does not intend to be added to the ETC blacklist or not.

In order to enable the user to standardize the user behavior of using the ETC system, the server 200 determines a corresponding discount rate based on the user behavior, that is, obtains a discount distribution weight of the target account in the (i + 1) th cycle based on the state probability, determines a comprehensive discount rate of the target account in the (i + 1) th cycle according to the discount distribution weight and the discount level, and determines the discount rate of the target account corresponding to the ETC in the (i + 1) th cycle.

The discount distribution weight is used for identifying the association degree of the target account with different discount grades under the state probability. For example, in the application scenario shown in fig. 1, since the user behavior of the user in the ith period is relatively good, and the state probability of the target account in the (i + 1) th period is 20%, the target account can correspond to a relatively high discount rate in the (i + 1) th period, the discount distribution weights obtained based on the state probabilities are respectively 4.8%, 19%, and 76.2%, that is, the probability that the target account obtains a ninth discount in the (i + 1) th period is 4.8%, the probability that the target account obtains an eighth discount is 19%, the probability that the target account obtains a seventh discount is 76.2%, and further the comprehensive discount rate of the target account in the (i + 1) th period is 27.14%, so that the user can obtain a relatively large discount fee.

Therefore, when the user uses the ETC system through the target account, the user is no longer based on a fixed discount rate, personalized user behaviors made by different users based on the ETC system in the period directly influence the discount rate of the account used in the next period, and the user behaviors which are not well implemented may take the discount rate which is not well distributed as a cost, so that the user can standardize the user behaviors using the ETC system for avoiding the cost, and the condition that the user cannot obtain a better discount rate due to the fact that the user is added into an ETC black list is avoided. The method has the advantages that the corresponding discount rate is distributed based on the user personalized behavior, so that the rationalization of an ETC system discount rate system is facilitated, and the benign development of the ETC system is facilitated. Furthermore, the account number stock in the ETC blacklist can be effectively reduced, and the times of congestion of the ETC lane can be reduced.

The data processing method provided by the embodiment of the present application is described below with reference to the accompanying drawings, where a cloud server is used as a data processing device.

Referring to fig. 2, the figure is a flowchart of a data processing method according to an embodiment of the present application. As shown in fig. 2, the data processing method includes the steps of:

s201: and acquiring user characteristic data of the vehicle in the ith period.

In practical application, a user registers an account number for a vehicle in an ETC system, the vehicle corresponds to the account number one by one, the user can use equipment such as terminal equipment, the ETC system is used based on the account number, and user characteristic data for identifying user behaviors are generated. The user characteristic data can be click data, recharge data, consumption data, comment data, account data, vehicle toll fee and the like, and the use habit of the user when the ETC system is used can be analyzed through the user characteristic data.

The ETC system may accumulate user characteristic data for different users based on different accounts. As a possible implementation manner, the cloud server may acquire and store the user feature data from the ETC system through a network. Or preprocessing the acquired user characteristic data according to a database format, storing the user characteristic data into a database of the cloud server, and even generating user characteristic data sets corresponding to different users based on different accounts.

The time length of the period is not particularly limited in the embodiments of the present application, for example, a week time is one period, a month time is one period, a year time is one period, and the like.

S202: and determining the state probability of the target account in the (i + 1) th period according to the user characteristic data.

Because the use habits of the users generally have continuity, the state probability of the users in the (i + 1) th period can be predicted according to the user characteristic data of the users in the (i) th period, the state probability is used for identifying the possibility that the target account is added into the ETC blacklist in the (i + 1) th period, and vehicles corresponding to the account in the ETC blacklist are not allowed to automatically pass through the ETC lane.

That is, the behavior of the user generated by the ETC system in the ith cycle directly affects the state probability in the (i + 1) th cycle. For example, a user often charges his account number in the ith period, so that the account has sufficient balance to realize that each fee deduction operation of the ETC system is successful, and the use habit of the user is good, so that the probability that the user has good use habit is high in the (i + 1) th period, the possibility that the account number is added into the ETC blacklist is low, and the corresponding state probability is low.

As a possible implementation manner, the state probability of the target account in the (i + 1) th period may be determined through a classification model according to the user feature data. The training mode of the classification model is described later with reference to S2021-S2022, which is not described herein again.

Therefore, based on the user characteristic data of the ith period, the use habit of the target account for using the ETC system is excavated through the trained classification model, and the state probability 1-P of the target account in the (i + 1) th period is predicted₀Wherein P is₀Can be represented as P₀＝P(Y_i+1＝0|X_t) I.e. the probability that the number of times that the target account is added to the ETC blacklist in the (i + 1) th period is zero. Further, the state probabilities corresponding to different vehicles can be stored in the cloud server based on the account numbers.

S203: and obtaining the discount distribution weight of the target account in the (i + 1) th period based on the state probability.

The ETC system carries out indiscriminate basic discount on ETC vehicles in the traffic local area, and in order to encourage users to use the ETC system, in the related technology, different discount grades can exist in the discount, and the different discount grades correspond to different discount rates, for example, discount rates corresponding to nine-fold discount, eight-fold discount and seven-fold discount grades are 10%, 20% and 30% respectively. However, the discount rate is fixed, and no matter whether the account number is added to the ETC blacklist, the discount rate finally obtained by the user in the ETC system is not affected, so that the user cannot be interested in whether the account number is added to the ETC blacklist, and the benign development of the ETC system is not facilitated.

Based on this, in order to enable a user to standardize the user behavior of using the ETC system, the discount rate is determined based on the user behavior, that is, the discount distribution weight of the target account in the (i + 1) th cycle is obtained through the predicted state probability in the (i + 1) th cycle, so that the comprehensive discount rate e corresponding to the target account is dynamically adjusted based on the user behavior, that is, the discount rate finally obtained by the user in the ETC system is not fixed, if the user wants to obtain more comprehensive discount rates, the user behavior of the user in the ETC system needs to be standardized, and the greater comprehensive discount rate e is obtained through the better user behavior.

The discount distribution weight is used for identifying the association degree of the target account with different discount levels respectively under the state probability, if the state probability is higher, it indicates that the user behavior corresponding to the target account is poor in performance in the ith period, the association degree of the user behavior with the lower discount level is higher in the (i + 1) th period, the association degree with the higher discount level is lower, and the like. Three discount levels are described below as an example.

The three discount levels are respectively theta₁、θ₂、θ₃Wherein 0 is not more than theta₁＜θ₂＜θ₃≦ 1, the corresponding discount distribution sequence may be expressed as θ ═ θ₁，θ₂，θ₃]The discount level may be obtained according to a discount standard of the ETC system for different road segments, which is not specifically limited in this application. Correspondingly, the discounted distribution weight can be π₁、π₂、π₃I.e. probability of state of target account number 1-P₀Next, a discount level θ is obtained₁Has a probability of pi₁Obtaining a discount rating theta₂Has a probability of pi₂Obtaining a discount rating theta₃Has a probability of pi₃. If state probability 1-P₀Larger, then pi₁>π₂>π₃。

The method of obtaining the weight of the discount distribution according to the state probability is not particularly limited in the present application, and the following description will be given by taking one method as an example, see S2031 to S2033.

S2031: and determining a grade transfer parameter between the discount grades based on the state probability identification of the account according to the upgrading and downgrading relation between the discount grades.

Multiple levels of discount measure typically having a size relationship, e.g.The discount distribution sequence is [ theta ]₁，θ₂，θ₃]Middle theta₁Indicating the lowest discount level (e.g. nine folds), theta₂Indicating a discount level in the middle (e.g. eight folds), theta₃Indicating the highest discount rating (e.g., seven-fold). The ascending and descending relationship between discount levels may be from discount level θ₁Upgrade to discount level θ₂From the discount level θ₂Upgrade to discount level θ₃The degradation relationship is the same, and is not described herein again.

Determining grade transfer parameters P between discount grades based on state probability identification of account numbers according to the grading relation between discount grades, wherein the mathematical expression form of the grade transfer parameters P can be a matrix, and each element P in the matrix_mnRepresenting the probability of transitioning from the mth discount level to the nth discount level, m may be equal to n. If the three discount grades theta are continued₁、θ₂、θ₃For example, the level transition parameter P may be expressed as

The level transfer parameter P shows a discount level transfer rule: if the target account number of the vehicle is not added into the ETC blacklist in the ith period, increasing a discount level or staying at the highest discount level theta for the target account number₃If the target account number of the vehicle is added to the ETC blacklist in the ith period, the target account number is reduced by a discount level or stays at the lowest discount level theta₁The discount level transition cannot jump promotion or jump demotion, and the discount level transition rule may be expressed as the following equation:

wherein, P₀Representing the probability that the target account number is not added to the ETC blacklist in the (i + 1) th period, 1-P₀＝P(Y_i+1≥1|X_t) Indicating that the target account number is added to the ETC blacklist once or multiple times in the (i + 1) th periodThe implications of the rate, discount level transition rules are as follows:

θ₁→θ₁：1-P₀when at the lowest discount level theta₁If the target account is added to the ETC blacklist, the discount level theta is set₁Does not rise and stays at the lowest discount level theta₁Has a probability of 1-P₀；

θ₁→θ₂：P₀When at the lowest discount level theta₁If the target account is not added to the ETC blacklist, the discount level theta is set₁Raising one level to discount level theta₂Has a probability of P₀；

θ₁→θ₃: 0 represents when at the lowest discount level θ₁Time, discount level θ since discount level cannot jump₁Cannot be raised directly to the discount level theta₃Thus the probability is 0;

θ₂→θ₁：1-P₀when at the discount level theta₂If the target account is added to the ETC blacklist, the discount level theta is set₂Down one level to a discount level theta₁Has a probability of 1-P₀；

θ₂→θ₂: 0 indicates when at the discount level θ₂While staying at the discount level theta₂The probability of (2) is 0;

θ₂→θ₃：P₀when at the discount level theta₂If the target account is not added to the ETC blacklist, the discount level theta is set₂Raising one level to discount level theta₃Has a probability of P₀；

θ₃→θ₁: 0 indicates when at the highest discount level θ₃Time, discount level θ since discount level cannot jump₃Cannot be directly lowered to discount level theta₁Thus the probability is 0;

θ₃→θ₂：1-P₀when at the highest discount level theta₃If the target account is addedInto ETC blacklist, discount level θ₃Down one level to a discount level theta₂Has a probability of 1-P₀；

θ₃→θ₃：P₀When at the highest discount level theta₃If the target account is not added to the ETC blacklist, the discount level theta is set₃Does not rise and stays at the highest discount level theta₃Has a probability of P₀。

S2032: a discount distribution sequence is determined based on the rank transition parameter.

The discount distribution sequence is used for identifying the association relationship between the state probability of the account and the discount grades respectively, a steady-state equation can be constructed, and the steady-state equation can be expressed as pi · P ═ pi, wherein pi represents the discount distribution sequence, and P represents the grade transition parameter.

If the three discount grades theta are continued₁、θ₂、θ₃For example, the discount distribution sequence pi obtained according to the steady state equation may be expressed as pi ═ pi [ [ pi ] ]₁,π₂,π₃]。

Wherein the content of the first and second substances,

therefore, the constructed discount distribution sequence can be stored in the cloud server in advance, so that the discount distribution weight of the target account in the (i + 1) th period can be calculated based on the discount distribution sequence after the state probability is obtained. Further, the discount distribution weight of the target account in the (i + 1) th period can be stored in a charging system database of the cloud server, so that the discount distribution weight is directly called in the (i + 1) th period to calculate the discount fee of the target account.

S2033: and obtaining the discount distribution weight of the target account in the (i + 1) th period based on the state probability and the discount distribution sequence.

Probability of state 1-P to be obtained₀Substituting into discounted distribution sequence pi ═ pi₁,π₂,π₃]To obtain a discounted distribution weight pi₁、π₂、π₃。

S204: and determining the comprehensive discount rate of the target account in the (i + 1) th period according to the discount distribution weight and the discount grade, and determining the comprehensive discount rate as the discount rate of the target account corresponding to the ETC system in the (i + 1) th period.

Therefore, when the user uses the ETC system through the target account, the discount rate of the (i + 1) th cycle different user in the ETC system is determined through the personalized user behaviors of the (i) th cycle different users based on the ETC system instead of the fixed discount rate, the personalized behavior of the user can influence the discount rate of the account in the ETC system, in order to obtain a higher comprehensive discount rate, the user can standardize the user behavior of the user, the number of times of adding the user into the ETC blacklist is reduced as much as possible, and the number of the accounts in the ETC blacklist is reduced. Meanwhile, the comprehensive discount rates of the account numbers of the users in different periods can be dynamically adjusted based on the user characteristic data in different periods, so that the users can not only guarantee the user behaviors in each period, but also the effect of encouraging the users to travel civilized is achieved.

Further, the comprehensive discount rate corresponding to each account can be stored in a charging system database of the cloud server, so that the comprehensive discount rate is directly called in the (i + 1) th period to calculate the discount fee of the target account.

The determination method of the comprehensive discount rate is not specifically limited in the embodiments of the present application, and the following description will be given by taking three discount levels and corresponding discount distribution weights as examples.

The first method comprises the following steps: and taking the target discount grade with the discount distribution weight meeting the preset condition in the discount grades as the comprehensive discount rate of the target account in the (i + 1) th period.

And determining a target discount distribution weight meeting a threshold condition from the discount distribution weights, and taking the discount grade corresponding to the target discount distribution weight as the target discount grade. For example, distribute the discount by weight π₁、π₂、π₃Medium maximum discounted distribution weight pi₃Corresponding discount grade theta₃As a target discount level, a target discount level θ₃The corresponding discount rate is the targetAnd the comprehensive discount rate e of the account in the (i + 1) th period.

And the second method comprises the following steps: and obtaining the comprehensive discount rate of the target account in the (i + 1) th period based on the influence degree of the discount distribution weight on the discount grade.

Discounted distribution weight pi₁、π₂、π₃Can be used as the corresponding discount grade theta₁、θ₂、θ₃Based on the desired formula

And obtaining the comprehensive discount rate e of the target account in the (i + 1) th period.

According to the technical scheme, the user behavior of the target account of the vehicle generated by the ETC system is clarified according to the user characteristic data of the vehicle in the ith period, the state probability of the target account in the (i + 1) th period is determined based on the user characteristic data, therefore, based on the continuity of the use habits of the user, whether the user behavior of the target account in the ith period through the ETC system is qualified or not directly influences the state probability, as the state probability represents the possibility that the target account is added into the ETC blacklist in the (i + 1) th period, aiming at the discount levels for identifying different discount rates, the discount distribution weight of the target account in the (i + 1) th period is obtained based on the state probability, the discount distribution weight identifies the association degree of the target account corresponding to different discount levels respectively, and the comprehensive discount rate of the target account in the (i + 1) th period is determined according to the discount distribution weight and the discount level, and determining the discount rate of the ETC corresponding to the user in the next period. Therefore, when the user uses the ETC system through the target account, the user is no longer based on a fixed discount rate, the individual user behaviors made by different users based on the ETC system in the period can directly influence the discount rate of the account used in the next period in the ETC system, the user behaviors with poor implementation can be taken as the cost through the poorly distributed discount rate, so that the user can standardize the user behaviors of using the ETC system for avoiding the cost, the better discount rate cannot be used due to the fact that the user is added into an ETC black list is avoided, the corresponding discount rate is distributed based on the individual user behaviors, the rationalization of the ETC system discount rate system is facilitated, and the benign development of the ETC system is facilitated. Furthermore, the account number stock in the ETC blacklist can be effectively reduced, and the times of congestion of the ETC lane can be reduced.

As a possible implementation manner, the state probability of the target account in the (i + 1) th cycle may be determined by a classification model according to the user feature data, and a training manner of the classification model is described below, referring to S2021-S2022.

S2021: user sample data is acquired on a periodic basis.

The user sample data comprises sample user behaviors generated by an account number of the vehicle through an ETC system in one period, and a label of the user sample data is used for identifying whether the account number of the vehicle is added to an ETC blacklist in the next period.

For example, in a cloud server, a database technology is adopted to divide stored user feature data into t periods of user feature data in a time sequence. The database technology may be SQL (Structured Query Language), Hive (a data warehouse tool), and SPARK (a computing engine).

In the t periods of user characteristic data, whether the account number of the vehicle in the j +1 th period is added into the ETC blacklist or not can be used as a label of the j period of user characteristic data. For example, if the account number of the vehicle is added to the ETC blacklist in the j +1 th period, the tag of the user characteristic data in the j th period may be represented by 1; if the account number of the vehicle is not added to the ETC blacklist in the j +1 th period, the tag of the user characteristic data in the j th period may be represented by 0. The user characteristic data with the label can be used as user sample data, the jth period user sample data is used for identifying sample user behaviors generated by an account of a vehicle through an ETC system in the jth period, and j < t.

S2022: and training the initial classification model according to the user sample data to obtain a classification model.

The classification model is not limited in the embodiment of the application, and can be a two-classification model or a multi-classification model, for example, the initial classification model is trained and tested through machine learning algorithms such as a supervised two-classification algorithm, so as to obtain the classification model.

As a possible implementation mode, user sample data can be divided into training user sample data and testing user sample data, an initial classification model is trained through the training of the user sample data, the trained initial classification model is tested through the testing of the user sample data, whether the training of the initial classification model is completed or not is determined through the obtained error, so that the classification model is obtained, and the classification model can be further stored in a cloud server.

Therefore, according to the user characteristic data, the state probability of the target account in the (i + 1) th cycle is determined through the trained classification model.

As a possible implementation manner, the user characteristic data may further include a discount rate of the target account corresponding to the ETC system in the ith period, and the state probability of the (i + 1) th period is determined jointly by the discount rate of the user corresponding to the ETC system in the ith period and the user behavior generated by the user through the ETC system in the ith period, so that the state probability obtained by restricting the discount rate of the user corresponding to the ETC system in the ith period is used to avoid that the account corresponding to the lower discount rate of the ETC system in the ith period jumps by multiple discount levels in the (i + 1) th period.

For example, in the ith period, both user a and user B maintain good user behavior and are not added to the ETC blacklist, but the discount rate of user a is 30% and the discount rate of user B is 10%. If the state probability is determined only according to the user behavior generated by the user through the ETC system in the ith period, the user a and the user B may obtain the same state probability, and thus the user a and the user B may be determined as the same comprehensive discount rate, such as 35%, in the (i + 1) th period. At this time, the discount rate of the account of the user B corresponding to the ETC system is increased by a plurality of discount levels. And if the discount rate of the user A and the user B corresponding to the ETC system in the ith period is taken as a constraint to ensure that the discount grade transfer cannot jump for upgrading or jumping for degrading, and the state probability of the (i + 1) th period is determined according to the discount rate of the user A and the user B corresponding to the ETC system in the ith period and the user behavior generated by the user A and the user B through the ETC system in the ith period, the state probabilities obtained by the user A and the user B are different.

As a possible implementation manner, the user sample data may further include a discount rate of the account of the vehicle corresponding to the ETC system in the ith period, and the classification model is obtained through training in S2021-S2022, so that the state probability of the target account in the (i + 1) th period is determined through the trained classification model according to the user feature data including the discount rate of the target account corresponding to the ETC system in the ith period, and on the premise that user behaviors of multiple users are relatively similar, the obtained state probabilities are different if the discount rates of the multiple users corresponding to the ETC system in the current period are different.

As a possible implementation manner, the labels of the user sample data may also be 0, 1, 2, and the like, and the probability that the target account is not added to the ETC blacklist in the j +1 th period, the probability of being added to the ETC blacklist for one time, the probability of being added to the ETC blacklist for 2 times, and the like are identified, so that the user behavior is further refined. According to the classification model obtained by training the refined user sample data and the user characteristic data of the ith period, the state probability of the ith +1 period can be predicted, and the state probability is used for identifying the probability that the target account is added into the ETC blacklist once or for multiple times in the ith +1 period, so that discount distribution weights of different degrees are given to different degrees of the target account added into the blacklist subsequently.

For example, in the (i + 1) th period, the probability that the number of times of the target account is added to the ETC blacklist is larger, the corresponding comprehensive discount rate is lower, so that the comprehensive discount rate corresponding to the account with the larger number of times of adding the ETC blacklist is lower, the comprehensive discount rate corresponding to the account with the smaller number of times of adding the ETC blacklist is higher, the number of times of adding the user to the ETC blacklist is differentiated, even if the account of the user is added to the ETC blacklist, the user behavior of the user can be standardized as much as possible, the situation that the comprehensive discount rate is lower and lower due to the fact that the number of times of adding the ETC blacklist is larger and larger is avoided, the rationalization of a discount rate system of the ETC system is facilitated, the account stock in the ETC blacklist is effectively reduced, the number of times of congestion of an ETC lane is reduced, and the civilized travel of the user is encouraged.

As a possible implementation manner, the cloud server may push the comprehensive discount rate of the target account in the (i + 1) th period to the user. For example, the cloud server pushes the comprehensive discount rate to the terminal device, the comprehensive discount rate is displayed through an electronic map application installed in the terminal device, or the comprehensive discount rate is pushed to the user through a client of the ETC system, or the comprehensive discount rate is sent to the user through a social account number, a mobile phone short message and the like. The following description will take an example of displaying the comprehensive discount rate through an electronic map application.

The user can associate the account registered in the ETC system with the map account registered in the electronic map application in advance, so that information intercommunication between the ETC system and the electronic map application is realized. The account or map account registered in the ETC system may be a license plate number, a mobile phone number, a social account number, or the like.

In the (i + 1) th period, if a user opens an electronic map application through the terminal device based on the map account, the terminal device sends the map account to the cloud server. If the cloud server determines that the map account has an associated target account in the ETC system, the comprehensive discount rate of the target account corresponding to the map account corresponding to the ETC system in the (i + 1) th period is obtained from the database, the comprehensive discount rate is sent to the terminal equipment, and the comprehensive discount rate is displayed in the electronic map application, so that the comprehensive discount rate is displayed to a user when the user navigates through the electronic map application, and the user can know the current comprehensive discount rate.

The comprehensive discount rate is determined according to the discount grade of the ETC system and the state probability of the target account in the (i + 1) th period, the state probability is used for identifying the probability that the target account is added into the ETC blacklist in the (i + 1) th period, and the state probability is determined based on the user behavior of the target account generated by the ETC system in the (i) th period. The obtaining manner of the comprehensive discount rate can be referred to the aforementioned S201-S204, and is not described herein again.

As a possible implementation manner, the cloud server may further send discount information corresponding to the comprehensive discount rate to the electronic map application, the electronic map application may display the discount information when the user uses navigation, and the discount condition of the current ETC system can be visually and conveniently shown for the user by displaying the discount information, so that the user standardizes the use habit of the ETC system based on the matching degree of the discount information and the self demand.

The discount information may include at least one of the comprehensive discount rate itself, the actual toll to be paid for the trip, and the reduced discount fee. The discount fee is a deduction fee calculated by combining the standard fee to be paid for the trip and the comprehensive discount rate, for example, the discount fee of 100 yuan for the comprehensive discount rate of 10% (equivalent to nine folds) is 10 yuan. And the actual toll is the cost which is actually required to be paid by the user after the deduction fee is subtracted from the standard fee. It should be noted that, after the cloud server sends the discount information to the electronic map application, the electronic map application may display the received discount information. Referring to fig. 3, the figure is a schematic view of displaying discount information for an electronic map application according to an embodiment of the present application. In the present embodiment, the discount information includes the discount fee and the actual toll fee as an example. If the user inputs a place of departure as a place a and a place of destination as a place B in the electronic map application, a discount fee and an actual toll fee may be displayed as discount information during displaying a navigation route from the place a to the place B. The present embodiment is only an example, and does not play a role of limiting the actual discount information display form and the number of display items.

Wherein the standard fee from the A place to the B place is f, and the discount fee is c₁The toll to be paid is c₂(1-e) × f. For example, if the standard fee from the place A to the place B is 100 yuan, and the determined comprehensive discount rate is 10%, the discount fee is 10 yuan, and the actual toll fee is 90 yuan.

As a possible implementation manner, if the user has a plurality of vehicles, each vehicle corresponds to one account. When a user opens the electronic map application through the terminal device, the cloud server obtains a plurality of account numbers with incidence relations based on the map account numbers, displays account numbers corresponding to a plurality of vehicles respectively through the electronic map application, and then determines one account number as a target account number from the account numbers corresponding to the plurality of vehicles according to a selected operation triggered by the user through the terminal device.

It should be noted that, if the electronic map application is installed in a vehicle-mounted computer of a vehicle, the vehicle-mounted computer carries a vehicle identifier of the vehicle, and the vehicle-mounted computer may determine a target account from accounts of multiple vehicles according to the vehicle identifier, and display the target account on the vehicle-mounted computer.

Therefore, in the (i + 1) th period, if the electronic map application based on the map account is started and the map account is determined to have an associated target account in the ETC system, the user behavior of the target account of the vehicle generated by the ETC system is determined according to the user characteristic data of the vehicle in the (i) th period, and the state probability of the target account in the (i + 1) th period is determined according to the user characteristic data, so that whether the user behavior of the target account passing through the ETC system in the (i) th period is qualified or not directly influences the state probability based on the continuity of the use habits of the user, and the comprehensive discount rate of the target account in the (i + 1) th period is determined according to the discount level of the ETC system and the state probability of the target account in the (i + 1) th period, thereby determining the discount rate of the ETC corresponding to the user in the next period. Therefore, when the user uses the ETC system through the target account, the user is no longer based on a fixed discount rate, the individual user behaviors made by different users based on the ETC system in the period can directly influence the discount rate of the account used in the next period in the ETC system, the user behaviors with poor implementation can be taken as the cost through the poorly distributed discount rate, so that the user can standardize the user behaviors of using the ETC system for avoiding the cost, the better discount rate cannot be used due to the fact that the user is added into an ETC black list is avoided, the corresponding discount rate is distributed based on the individual user behaviors, the rationalization of the ETC system discount rate system is facilitated, and the benign development of the ETC system is facilitated. Furthermore, the account number stock in the ETC blacklist can be effectively reduced, and the times of congestion of the ETC lane can be reduced.

Next, a data processing method provided in an embodiment of the present application will be described with reference to fig. 4 and 5. Referring to fig. 4, the figure is a schematic diagram of an application scenario embodiment of a data processing method provided in the embodiment of the present application.

The user registers in the electronic map application through a mobile phone number (map account number) in advance, and registers in the ETC system through a license plate number (target account number), establishes a mapping relation between the mobile phone number and the license plate number, and associates the target account number with the map account number.

The ETC system collects user characteristic data and stores the user characteristic data into the cloud server, and the cloud server can carry out data preprocessing on the obtained user characteristic data according to a database format. The following describes pushing, by the cloud server, information such as the comprehensive discount rate to the electronic map application with reference to fig. 5.

Referring to fig. 5, the figure is a schematic diagram of an application scenario embodiment of a data processing method provided in the embodiment of the present application.

S1: and a user sample data processing stage.

The user characteristic data is divided into x periods of user characteristic data according to the time sequence, the first x-1 periods of user characteristic data have corresponding labels, so that the first x-1 periods of user characteristic data are used as user sample data, the user sample data are randomly divided into training user sample data and testing user sample data, and the x-th period of user characteristic data are used as user characteristic data for prediction.

S2: and (5) a classification model training stage.

According to the training user sample data and the testing user sample data, model training and testing are carried out by adopting a Linear Regression (LR) model, a trained classification model W is obtained, and the classification model W is stored in a cloud server. See S2021-S2022 for details.

S3: and a state probability prediction phase.

Inputting the characteristic data of the user in the x-th period into a classification model W, and calculating the state probability 1-P of the account of each vehicle in the x +1 th period₀。

S4: and constructing a grade transition parameter phase.

According to the ascending and descending relation among the discount grades, determining a grade transition parameter P among the discount grades based on the state probability identification of the account, which can be specifically referred to as S2031.

S5: the discount distribution sequence stage is determined.

Continuing with the three discount levels as an example, the discount distribution sequence pi ═ pi is determined based on the level transition parameter P₁,π₂,π₃]See, in particular, S2032.

S6: and determining a discount distribution weight stage.

S7: and determining a comprehensive discount rate stage.

And determining the comprehensive discount rate e of the target account in the (i + 1) th cycle according to the second mode, and determining the comprehensive discount rate as the discount rate of the target account corresponding to the ETC system in the (i + 1) th cycle.

S8: and a discount information calculation stage.

If the electronic map application is started based on the map account number in the (x + 1) th period, acquiring the driving route inquired by the user, continuing to refer to fig. 4, and calculating the discount fee c according to the standard fee f and the comprehensive discount rate e corresponding to the driving route acquired from the ETC charging system₁E × f, and a toll c to be paid₂Discount information such as (1-e) × f.

S9: and a discount information pushing stage.

With continued reference to fig. 4, the cloud server pushes the comprehensive discount rate e and discount fee c to the electronic map application₁And a toll c₂And the discount information so that the electronic map application can be shown to the user.

Aiming at the data processing method provided by the embodiment, the embodiment of the application also provides a data processing device.

Referring to fig. 6, this figure is a schematic diagram of a data processing apparatus according to an embodiment of the present application. As shown in fig. 6, the data processing apparatus 600 includes: an acquisition unit 601, a state probability determination unit 602, a discount distribution weight obtaining unit 603, and a comprehensive discount rate determination unit 604.

The obtaining unit 601 is configured to obtain user characteristic data of a vehicle in an ith period, where the user characteristic data is used to identify a user behavior of a target account of the vehicle generated by an Electronic Toll Collection (ETC) system;

the state probability determining unit 602 is configured to determine, according to the user feature data, a state probability of the target account in an (i + 1) th cycle, where the state probability is used to identify a probability that the target account is added to an ETC blacklist in the (i + 1) th cycle, and the ETC blacklist is used to identify an account that is not allowed to use the ETC system;

the discount distribution weight obtaining unit 603 is configured to obtain discount distribution weights of the target account in the (i + 1) th cycle based on the state probability, where the discount distribution weights are used to identify degrees of association between the target account and different discount levels respectively under the state probability;

the comprehensive discount rate determining unit 604 is configured to determine a comprehensive discount rate of the target account in the (i + 1) th cycle according to the discount distribution weight and the discount level, and determine the comprehensive discount rate as a discount rate of the target account corresponding to the ETC system in the (i + 1) th cycle.

As a possible implementation manner, the data processing apparatus 600 further includes a level transition parameter determining unit, configured to:

determining a grade transfer parameter between the discount grades based on the state probability identification of the account according to the grade relation between the discount grades;

determining discount distribution sequences based on the grade transition parameters, wherein the discount distribution sequences are used for identifying the incidence relation between the state probability of an account and the discount grade respectively;

the discount distribution weight obtaining unit 603 is configured to:

and obtaining the discount distribution weight of the target account in the (i + 1) th period based on the state probability and the discount distribution sequence.

As a possible implementation manner, the user characteristic data includes a discount rate of the target account corresponding to the ETC system in the ith period.

As a possible implementation manner, the state probability determining unit 602 is configured to:

according to the user characteristic data, determining the state probability of the target account in the (i + 1) th cycle through a classification model;

the apparatus further comprises a training unit for:

acquiring user sample data based on the period, wherein the user sample data comprises sample user behaviors generated by an account of a vehicle through the ETC system in one period, and a label of the user sample data is used for identifying whether the account of the vehicle is added into the ETC blacklist in the next period;

and training an initial classification model according to the user sample data to obtain the classification model.

As a possible implementation manner, the comprehensive discount rate determining unit 604 is configured to:

taking a target discount grade with discount distribution weight meeting preset conditions in the discount grades as a comprehensive discount rate of the target account in the (i + 1) th period; alternatively, the first and second electrodes may be,

and obtaining the comprehensive discount rate of the target account in the (i + 1) th period based on the influence degree of the discount distribution weight on the discount level.

As a possible implementation manner, the state probability is further used to identify a probability that the target account is added to the ETC blacklist one or more times in the (i + 1) th period.

As a possible implementation manner, the data processing apparatus 600 further includes a sending unit, configured to:

acquiring a map account number associated with the target account number;

and in the (i + 1) th period, if it is determined that the electronic map application is started based on the map account, sending the discount rate of the target account corresponding to the ETC system in the (i + 1) th period to the electronic map application.

Referring to fig. 7, this figure is a schematic diagram of a data processing apparatus according to an embodiment of the present application. As shown in fig. 7, the data processing apparatus 700 includes: a turn-on unit 701 and a display unit 702.

The starting unit 701 is configured to start an electronic map application based on a map account in an (i + 1) th cycle;

the display unit 702 is configured to, if it is determined that the map account has an associated target account in an Electronic Toll Collection (ETC) system, display discount information corresponding to a comprehensive discount rate of the target account in the (i + 1) th cycle, where the comprehensive discount rate is determined according to a discount level of the ETC system and a state probability of the target account in the (i + 1) th cycle, where the state probability is used to identify a probability that the target account is added to an ETC blacklist in the (i + 1) th cycle, and the state probability is determined based on a user behavior generated by the ETC system by the target account in the (i) th cycle.

As a possible implementation manner, the data processing apparatus 700 further includes a target account number determining unit, configured to:

if the map account numbers are determined to have account numbers of a plurality of vehicles in the ETC system, displaying the account numbers of the plurality of vehicles through the electronic map application;

determining the target account number from the account numbers of the plurality of vehicles according to the selected operation.

According to the data processing device provided by the embodiment of the application, the user behavior of the target account of the vehicle generated by the ETC system is clarified according to the user characteristic data of the vehicle in the ith period, the state probability of the target account in the (i + 1) th period is determined based on the user characteristic data, therefore, based on the continuity of the use habits of the user, whether the user behavior of the target account in the ith period through the ETC system is qualified or not directly influences the state probability, as the state probability represents the possibility that the target account is added into the ETC blacklist in the (i + 1) th period, aiming at discount levels for identifying different discount rates, the discount distribution weight of the target account in the (i + 1) th period is obtained based on the state probability, the discount distribution weight identifies the association degree of the target account corresponding to different discount levels respectively, the comprehensive discount rate of the target account in the (i + 1) th period is determined according to the discount distribution weight and the discount level, thereby determining the discount rate of the ETC corresponding to the user in the next period. Therefore, when the user uses the ETC system through the target account, the user is no longer based on a fixed discount rate, the individual user behaviors made by different users based on the ETC system in the period can directly influence the discount rate of the account used in the next period in the ETC system, the user behaviors with poor implementation can be taken as the cost through the poorly distributed discount rate, so that the user can standardize the user behaviors of using the ETC system for avoiding the cost, the better discount rate cannot be used due to the fact that the user is added into an ETC black list is avoided, the corresponding discount rate is distributed based on the individual user behaviors, the rationalization of the ETC system discount rate system is facilitated, and the benign development of the ETC system is facilitated. Furthermore, the account number stock in the ETC blacklist can be effectively reduced, and the times of congestion of the ETC lane can be reduced.

The aforementioned data processing device may be a computer device, which may be a server, and may also be a terminal device, and the computer device provided in the embodiments of the present application will be described below from the perspective of hardware implementation. Fig. 8 is a schematic structural diagram of a server, and fig. 9 is a schematic structural diagram of a terminal device.

Referring to fig. 8, fig. 8 is a schematic diagram of a server 1400 according to an embodiment of the present application, where the server 1400 may have a relatively large difference due to different configurations or performances, and may include one or more Central Processing Units (CPUs) 1422 (e.g., one or more processors) and a memory 1432, one or more storage media 1430 (e.g., one or more mass storage devices) for storing applications 1442 or data 1444. Memory 1432 and storage media 1430, among other things, may be transient or persistent storage. The program stored on storage medium 1430 may include one or more modules (not shown), each of which may include a sequence of instructions operating on a server. Still further, CPU 1422 may be configured to communicate with storage medium 1430 to perform a series of instruction operations on server 1400 from storage medium 1430.

The server 1400 may also include one or more power supplies 1426, one or more wired or wireless network interfaces 1450, one or more input-output interfaces 1458, and/or one or more operating systems 1441, such as Windows Server, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM, etc.

The steps performed by the server in the above embodiments may be based on the server structure shown in fig. 8.

The CPU 1422 is configured to perform the following steps:

Alternatively, the CPU 1422 is further configured to perform the following steps:

Optionally, the CPU 1422 may further execute method steps of any specific implementation manner of the data processing method in the embodiment of the present application.

Referring to fig. 9, fig. 9 is a schematic structural diagram of a terminal device according to an embodiment of the present application. Fig. 9 is a block diagram illustrating a partial structure of a smartphone related to a terminal device provided in an embodiment of the present application, where the smartphone includes: a Radio Frequency (RF) circuit 1510, a memory 1520, an input unit 1530, a display unit 1540, a sensor 1550, an audio circuit 1560, a wireless fidelity (WiFi) module 1570, a processor 1580, and a power supply 1590. Those skilled in the art will appreciate that the smartphone configuration shown in fig. 9 is not limiting and may include more or fewer components than shown, or some components in combination, or a different arrangement of components.

The following specifically describes each component of the smartphone with reference to fig. 9:

the RF circuit 1510 may be configured to receive and transmit signals during information transmission and reception or during a call, and in particular, receive downlink information of a base station and then process the received downlink information to the processor 1580; in addition, the data for designing uplink is transmitted to the base station. In general, RF circuit 1510 includes, but is not limited to, an antenna, at least one Amplifier, a transceiver, a coupler, a Low Noise Amplifier (LNA), a duplexer, and the like. In addition, RF circuit 1510 may also communicate with networks and other devices via wireless communication. The wireless communication may use any communication standard or protocol, including but not limited to Global System for Mobile communication (GSM), General Packet Radio Service (GPRS), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (WCDMA), Long Term Evolution (LTE), email, Short Message Service (SMS), and the like.

The memory 1520 may be used to store software programs and modules, and the processor 1580 implements various functional applications and data processing of the smart phone by operating the software programs and modules stored in the memory 1520. The memory 1520 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the smartphone, and the like. Further, the memory 1520 may include high-speed random access memory and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device.

The input unit 1530 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the smartphone. Specifically, the input unit 1530 may include a touch panel 1531 and other input devices 1532. The touch panel 1531, also referred to as a touch screen, can collect touch operations of a user (e.g., operations of the user on or near the touch panel 1531 using any suitable object or accessory such as a finger or a stylus) and drive corresponding connection devices according to a preset program. Alternatively, the touch panel 1531 may include two parts, a touch detection device and a touch controller. The touch detection device detects the touch direction of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch sensing device, converts the touch information into touch point coordinates, and sends the touch point coordinates to the processor 1580, and can receive and execute commands sent by the processor 1580. In addition, the touch panel 1531 may be implemented by various types such as a resistive type, a capacitive type, an infrared ray, and a surface acoustic wave. The input unit 1530 may include other input devices 1532 in addition to the touch panel 1531. In particular, other input devices 1532 may include, but are not limited to, one or more of a physical keyboard, function keys (such as volume control keys, switch keys, etc.), a trackball, a mouse, a joystick, and the like.

The display unit 1540 may be used to display information input by the user or information provided to the user and various menus of the smartphone. The Display unit 1540 may include a Display panel 1541, and optionally, the Display panel 1541 may be configured in the form of a Liquid Crystal Display (LCD), an Organic Light-Emitting Diode (OLED), or the like. Further, the touch panel 1531 may cover the display panel 1541, and when the touch panel 1531 detects a touch operation on or near the touch panel 1531, the touch operation is transmitted to the processor 1580 to determine the type of the touch event, and then the processor 1580 provides a corresponding visual output on the display panel 1541 according to the type of the touch event. Although in fig. 9, the touch panel 1531 and the display panel 1541 are two separate components to implement the input and output functions of the smartphone, in some embodiments, the touch panel 1531 and the display panel 1541 may be integrated to implement the input and output functions of the smartphone.

The smartphone may also include at least one sensor 1550, such as light sensors, motion sensors, and other sensors. Specifically, the light sensor may include an ambient light sensor that may adjust the brightness of the display panel 1541 according to the brightness of ambient light and a proximity sensor that may turn off the display panel 1541 and/or backlight when the smartphone is moved to the ear. As one of the motion sensors, the accelerometer sensor can detect the magnitude of acceleration in each direction (generally, three axes), can detect the magnitude and direction of gravity when stationary, and can be used for applications (such as horizontal and vertical screen switching, related games, magnetometer attitude calibration) for recognizing the attitude of the smartphone, and related functions (such as pedometer and tapping) for vibration recognition; as for other sensors such as a gyroscope, a barometer, a hygrometer, a thermometer, and an infrared sensor, which can be configured on the smart phone, further description is omitted here.

Audio circuit 1560, speaker 1561, microphone 1562 may provide an audio interface between a user and a smartphone. The audio circuit 1560 may transmit the electrical signal converted from the received audio data to the speaker 1561, and convert the electrical signal into an audio signal by the speaker 1561 and output the audio signal; on the other hand, the microphone 1562 converts collected sound signals into electrical signals, which are received by the audio circuit 1560 and converted into audio data, which are processed by the output processor 1580 and then passed through the RF circuit 1510 for transmission to, for example, another smart phone, or output to the memory 1520 for further processing.

WiFi belongs to short-distance wireless transmission technology, and the smart phone can help a user to receive and send e-mails, browse webpages, access streaming media and the like through a WiFi module 1570, and provides wireless broadband internet access for the user. Although fig. 9 shows WiFi module 1570, it is understood that it does not belong to the essential components of the smartphone and may be omitted entirely as needed within the scope not changing the essence of the invention.

The processor 1580 is a control center of the smartphone, connects various parts of the entire smartphone by using various interfaces and lines, and performs various functions of the smartphone and processes data by operating or executing software programs and/or modules stored in the memory 1520 and calling data stored in the memory 1520, thereby integrally monitoring the smartphone. Optionally, the processor 1580 may include one or more processing units; preferably, the processor 1580 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, and the like, and a modem processor, which mainly handles wireless communications. It is to be appreciated that the modem processor may not be integrated into the processor 1580.

The smartphone also includes a power supply 1590 (e.g., a battery) for powering the various components, which may preferably be logically connected to the processor 1580 via a power management system, so as to manage charging, discharging, and power consumption management functions via the power management system.

Although not shown, the smart phone may further include a camera, a bluetooth module, and the like, which are not described herein.

In an embodiment of the application, the smartphone includes a memory 1520 that can store program code and transmit the program code to the processor.

The processor 1580 included in the smart phone may execute the data processing method provided in the foregoing embodiments according to the instructions in the program code.

The embodiment of the present application further provides a computer-readable storage medium for storing a computer program, where the computer program is used to execute the data processing method provided by the foregoing embodiment.

Embodiments of the present application also provide a computer program product or computer program comprising computer instructions stored in a computer-readable storage medium. The processor of the computer device reads the computer instructions from the computer-readable storage medium, and the processor executes the computer instructions to cause the computer device to perform the data processing method provided in the various alternative implementations of the above aspects.

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 may be at least one of the following media: various media that can store program codes, such as read-only memory (ROM), RAM, magnetic disk, or optical disk.

It should be noted that, in the present specification, all the embodiments are described in a progressive manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the apparatus and system embodiments, since they are substantially similar to the method embodiments, they are described in a relatively simple manner, and reference may be made to some of the descriptions of the method embodiments for related points. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.

The above description is only one specific embodiment of the present application, but the scope of the present application is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present application should be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A method of data processing, the method comprising:

2. The method of claim 1, further comprising:

the obtaining of the discount distribution weight of the target account in the (i + 1) th cycle based on the state probability includes:

3. The method according to claim 1, wherein the user characteristic data includes a discount rate of the target account number for the ETC system during the ith period.

4. The method according to claim 1, wherein the determining the state probability of the target account in the (i + 1) th cycle according to the user characteristic data comprises:

the method further comprises the following steps:

5. The method according to any one of claims 1 to 4, wherein the determining the comprehensive discount rate of the target account in the (i + 1) th cycle according to the discount distribution weight and the discount level comprises:

6. The method according to any one of claims 1-4, wherein the state probability is further used to identify a probability that the target account number is added to an ETC blacklist one or more times at the (i + 1) th cycle.

7. The method of claim 1, further comprising:

acquiring a map account number associated with the target account number;

8. A method of data processing, the method comprising:

9. The method of claim 8, further comprising:

10. A data processing apparatus, characterized in that the apparatus comprises: the system comprises an acquisition unit, a state probability determination unit, a discount distribution weight acquisition unit and a comprehensive discount rate determination unit;

11. The apparatus according to claim 10, wherein the apparatus further comprises a registration transfer parameter determination unit configured to:

the discount distribution weight obtaining unit is configured to:

12. A data processing apparatus, characterized in that the apparatus comprises: a starting unit and a display unit;

13. The apparatus of claim 12, further comprising a target account number determination unit configured to:

14. A computer device, the device comprising a processor and a memory:

the processor is configured to perform the method of any one of claims 1-7, or to perform the method of claim 8 or 9, according to instructions in the program code.

15. A computer-readable storage medium, characterized in that the computer-readable storage medium is used to store a computer program for performing the method of any one of claims 1-7, or for performing the method of claim 8 or 9.