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Based on a large number of data generated by the application of the Internet of things, the data information is processed and processed. The contents and methods of data processing in three-tier IOT structure are different. Upper layer: data communication and data communication; data acquisition and application. The data processing process reflects the purpose and mode of data processing in the Internet of things system. A large number of experiments have proved that the data processing methods and forms used in this paper have practical significance.

In this paper, an energy management strategy (EMS) for hybrid batteries and supercapacitor (SCs) electric vehicles (HBSEV) is considered for extending the batteries lifespan and lifting the power performance of HBSEV. In detail, firstly, vehicle dynamics model and hybrid energy storage source (HESS) models including batteries and SCs are adapted and simplified as fundamental of model prediction control (MPC) approach. Secondly, the EMS of HBSEV is designed for reducing batteries power variation and maintaining SCs SOC in a proper value based on MPC method combining adaptive method. That can enhance batteries lifespan and power performance. Finally, simulation results based on the proposed EMS are given, which show batteries lifespan and power performance for HBSEV are improved obviously compared with a real time average (RTA) strategy. Meanwhile, the SOC of batteries and SCs are maintained in a proper range.

In order to improve fuel economy further, an equivalent consumption minimization strategy (ECMS) is designed for the parallel hybrid electric vehicle (PHEV). Firstly, based on the relations among the battery state of charge (SOC) and the equivalent factor and fuel consumption, the equivalent fuel consumption minimization objective function is constructed. Secondly, a variable equivalent factor over real-time driving cycle is obtained by introducing the Pontryagin's Minimum Principle (PMP) and the penalty function of SOC. Finally, the simulation results show that compared with ECMS based on a PI controller, the ICE efficiency increases by 2% and the fuel consumption to travel 100 km decreases by 4 % with ECMS based on a variable equivalent factor.

Measuring instrument for compensating brake master cylinder is mainly to measure the place, dimension and figure of the compensated hole and at the same time to measure the quality of machining and foundry by image for inner exterior of brake master cylinder. The measuring instrument adopt high differentiate rate image technique of CCD and using high precision drive framework to finish the measure assignment. It has some virtues, such as easy to handle, precision is high, automatous degree is good, and eliminate the influence of subjective complication. It is high-powered exactitude measuring instrument which gathered optics, mechanics, electronics and computer language. The measuring instrument for compensating brake master cylinder is made up of computer data acquire and process system, control system, CCD system, optical grating bit shift sensor and micro-machine drive system. The whole system adopt human-computer interactive measurement, when the cylinder was retracting ,we can install the workpiece to be measure to orientation fixture, and then the cylinder hold out and compaction the workpiece to be measure. To the effect that the orientation surface of workpiece to be measure can close to orientation fixture, then the step motor drive ball leading screw circumgyrate, then drive the endoscopy to move, meanwhile worm gear drive the bar circumgyrate, bases on the result of image CCD, then confirm the place of sampling point, note the information of grating ruler, save the quality information of cylinder inner exterior, by way of the member content of the date-base.

Most outlier detection algorithms are based on lazy learning or imply quadratic complexity. Both characteristics make them unsuitable for big data and stream data applications and preclude their applicability in systems that must operate autonomously. In this paper we propose a new algorithm-called SDO (Sparse Data Observers)-to estimate outlierness based on low density models of data. SDO is an eager learner; therefore, computational costs in application phases are severely reduced. We perform tests with a wide variation of synthetic datasets as well as the main datasets published in the literature for anomaly detection testing. Results show that SDO satisfactorily competes with the best ranked outlier detection alternatives. The good detection performance coupled with a low complexity makes SDO highly flexible and adaptable to stand-alone frameworks that must detect outliers fast with accuracy rates equivalent to lazy learning algorithms.