It's more meaningful to study big data on connected cars

With the development of the Internet, big data is becoming a craze, and the industry's discussion of big data has reached an unprecedented peak. As a product of the mobile Internet, the vehicle interconnection, whether it is the access of vehicles, the choice of service content or the precision of service, can not be separated from the big data.

Each set of data uploaded by the vehicle has location information and time, and it is easy to form huge amounts of data. On the one hand, if big data is characterized as complete and mixed, the big data features associated with car networking are complete and precise. If some data related to the vehicle itself, there is a clear ID, according to the ID can be linked to the corresponding owner information, and the information is accurate.

On the other hand, we can see that the big data features, such as car networking and drivers ' consumption habits and hobbies, are complete and partly accurate. So it makes sense to study big data on the Internet of cars.

Definition and characteristics of big data

Big data, or huge amounts of data, refers to the magnitude of the volume of data involved that cannot be captured, managed, processed, and collated in a reasonable amount of time through the current mainstream software tools to help make business decisions more positive.

We can see from the definition of authority that the characteristics of big data are four points, namely: the volume of data is huge. From the TB level, jump to PB level; data types are numerous. Mentioned blog, video, pictures, geo-location information and so on. , low value density, high commercial value. Take video as an example, the data that may be useful during continuous uninterrupted monitoring is only two seconds. Fast processing speed. 1 Second law.

Big data on the Internet of cars can be extremely predictable. For example, predict traffic jams, real-time traffic information, active security, bus scheduling. Analysis of driving behavior of drivers.

The core of big data is forecasting, which is very useful in the telematics industry, for example, when it comes to forecasting traffic flows, big data is needed. For traffic flow, at present our simulation system attaches great importance to the traffic flow, congestion, and the big data age, no longer care about causality, and pay attention to relevance, that is, do not analyze the cause of congestion, but indeed a certain period of time a road congestion. It is also possible to analyze the interest of bikers based on the big data of the Internet of vehicles.

Application of big data in commercial vehicle field

Big data in the field of commercial vehicles has been a considerable number of applications, such as the transport sector in the field of operational scheduling management, taxis in the field of floating vehicle data, logistics industry of large logistics.

How to solve the three problems faced by public transport enterprises: the minimum capacity, the shortest running distance of the vehicle, the minimum operating time of the driver? How to analyze the distribution of passenger flow at each time period and each site? How to realize the security intelligence of operation and the intellectualization of operation scheduling? The above problems are prevalent in the public transport industry,

Through the big data of the Internet, we can solve these problems that the public transport industry faces. According to each time period, the traffic size of each site, line equipped with the number of operating vehicles, the line with the driver, line length, the speed of the vehicle and other big data, can determine a line each time period of the number of vehicles and the departure interval, so as to solve the minimum capacity, the shortest running distance of the vehicle, the least three problems of the

According to passenger traffic, holidays, climate, solar terms, natural disasters, roads, vehicle conditions, historical data, ticketing methods, residential community construction and other criteria to establish a planning model, so that the fastest speed of these factors affecting the business plan to reflect. For example, increase the line, increase the vehicle, increase the driver, effectively set up the bus operation plan. At the same time, accurate management of operational scheduling can be automated scheduling through big data, to optimize the driving operation plan, and quickly adjust and optimize the running line.

Since the emergence of rookie network companies, the concept of large logistics has finally been mentioned by the industry. What is big Logistics? Refers to the enterprise's own logistics system (composed of teams, warehouses, personnel, etc.), and the third-party logistics enterprise distribution information and resources to share, so as to fully utilize all aspects of resources, reduce total logistics expenditure, reduce operating costs.

At present the logistics industry with the expansion of the business, the number of vehicles is increasing, and many models. Many enterprises still use hand-made vehicle management, heavy workload, statistical analysis of the vehicle operating data is difficult, the statistical results are quite lagging, not conducive to the company's decision-making management, while the vehicle in the course of the process is not carried out throughout the monitoring, the Division of personnel violations can not be timely warning, Nor is it possible to respond promptly to requests for assistance from the staff.

On the other hand, in China's current logistics transport mode, whether it is self-logistics, joint logistics or third-party logistics, hidden costs occupy a very important position, these hidden costs in the logistics and transportation process mainly includes the following aspects: Return or departure deadhead: empty cars without cargo drive, these are unreasonable transport way.

How to improve the logistics enterprises in the management of the status quo, to achieve the owner of "high service quality, strict punctuality rate, minimal damage rate, low logistics costs" requirements?

How to solve the logistics industry operation information feedback lag, high operating costs, freight vehicle height, driver cheating to the goods and vehicles security brought great hidden trouble?

How to provide reliable logistics service for users quickly and efficiently?

How to maximize the use of capacity resources to improve the overall operational efficiency?

These are the current logistics industry imminent problem.

To the above problems, the vehicle networking technology can solve the problem of the car owners imminent, through the transparent transport process management, reasonable dispatching of vehicles, according to the vehicle's big data, the traffic flow of vehicles to predict the smooth situation, planning a safe and smooth driving route, reduce the traffic caused by the waiting time in transit.

Through the large data of the vehicle operation, can quickly analyze the same route of fuel consumption, accidents, many sections of the early warning, accurate analysis of vehicle travel, improve the level of enterprise information, at any time to understand the operation of the cargo information and cargo destination of the entire process, to ensure that the transport process of transparent management, Make the enterprise's Operation Management intelligent, service punctuality, improve predictability.

At the same time, through the big data of the vehicle operation, can obtain the high speed, the State Road, the provincial road real-time road condition, simultaneously to the driver's driving rule analysis, provides the reference data for the petrol station, the maintenance station, the station location.

On the other hand, the cost of logistics is a large part of warehousing costs. Through the vehicle networking technology, the large amount of data analysis and calculation, after reasonable scheduling, reduce the vehicle deadhead rate, the movement of each truck can be used as a mobile storage space, improve the storage space turnover, so as to help enterprises reduce warehousing costs.

Application of big data in passenger car field

Big data in the passenger car sector is now more mature application of insurance and active security, the future will have a large number of enterprises in the CRM and call center areas to seek more business growth.

In August 2011, Statefarm, the largest automotive insurance company in North America, was married to the car networking service provider Hughes, the first of its type of car-connected business model led by insurance companies on the world stage. As a result, the network of insurance models is hotly discussed by the industry.

State Farm-led business model of the car network has the following features: Bundling with insurance companies, providing premium rates combined with driving safety, and partnering with vehicle networking service provider (TSP) Hughes; service differentiation, avoidance and OnStar such as the front loading plant leading car networking products and navigation products competition.

In the era of big data, the analysis of driving behavior in the cloud, such as driving mileage, daily travel time and so on, as well as the speed of the brakes and the number of rapid acceleration, effectively help the insurance company to fully understand the driver's driving habits and driving behavior, so as to benefit the insurance company to develop quality customers and provide different types of insurance products.

At present, the active safety measures offered by the Internet are mainly tire pressure monitoring, fault warning, collision alarm, airbag pop-up alarm, emergency rescue and so on. But now the active security device is more of a node on the vehicle, and there is no real correlation with big data.

In the big data age, when the car is driving, the platform can monitor the tyre pressure in real-time, and alarm the tire air leakage and low pressure to ensure the driving safety. Tire pressure monitoring direct and indirect two, directly through the sensor to monitor, and indirect monitoring is when the pressure of a tyre decreases, the weight of the vehicle will make the wheel of the rolling radius will be smaller, resulting in faster than other wheels speed.

By comparing the speed difference between the tires to achieve the purpose of monitoring the tire pressure. The indirect tyre alarm system actually relies on calculating the tyre rolling radius to monitor the air pressure. The indirect tire pressure monitoring needs to upload OBD information to the cloud, the cloud through big data to analyze the tire leakage, and real-time remind the driver to ensure safe driving.

For the call center, many enterprises are simply defined as a simple service department, in fact, the TSP call center, not only assume the role of customer service, but also assume the pre-sales role. Call centers can help organizations quickly find and lock down end users with potential spending power. With the right people, the right time, appropriate words of the operation is the successful marketing.

In the era of big data, the business structure of TSP, auto dealer or 4S shop will be transferred, the original customer service department has gradually changed from the former cost center to the profit center.

Big data for call centers include three aspects of usage, customer interest, and lifestyle habits. Through the call center, we can obtain the use of vehicles, the car networking system of customer experience and related to the vehicle itself, which for the host plant market tracking feedback, to promote the relevant departments to improve the quality of the rapid improvement has important significance.

Through the call center, you can master the owner's consumption habits, the scope of the owner's activities, the owner's life habits and the owner's business (booking, booking hotels, ordering meals, ordering flowers), the owner of the consumer psychology. such as the owner in the life of consumption process, in the daily purchase behavior of the psychological activities of the law and Personality psychology. Consumption needs, consumption time and consumption habits, material consumption and spiritual consumption problems, through the analysis of big data, so as to effectively formulate the corresponding marketing strategies and marketing words.

Thinking about Big Data

The age of big data affects our thinking. Prior to our understanding of the journey process, the traditional concept only focused on providing customers with navigation and entertainment functions, and did not carry out in-depth analysis of the process. The process, respectively, is to go before, on the road, after parking. For this process, we can extend the service content of many car networking, and each stage is inseparable from the acquaintance society, each stage will produce big data, big data can extend many value-added services.

The accuracy of the service content if the service provider is simply relying on the power of the provider, it will have to invest a lot of manpower or capital and it will take a long time, it is obviously not feasible. To solve this problem, the ideal way is through the owner's interaction with the community website, only through this way, can quickly collect the corresponding points of interest. This has to be done with big data analysis.

For customer information, whether it is a depot or a car seller, is regarded as the lifeblood, but what is the truth? The fact is that these customer information is not used at this stage, can extend some value-added services from these customer information? It's hard. Frankly, this information does not bring "customer lifetime value" (Customer Lifetime value), customer lifetime value refers to each purchaser in the future may bring the sum of profits.

Like a product, the customer's contribution to the enterprise's profit can be divided into the import period, the rapid growth period, the maturity period and the recession. Obviously, at this stage of the product form or enterprise informatization level is limited, on the one hand can not complete big data mining, on the other hand, the lack of professional analysis tools, and the era of car networking, gave us unlimited imagination space, so that everything is possible!

It's more meaningful to study big data on connected cars