Data processing device and method for checking parameter values of the data processing device

A data processing device acquires a first parameter value of a hardware component, and calculates a first prediction value of the first parameter using a prediction algorithm. If a difference of the first prediction value and the first parameter falls within a deviation range, the first parameter value is determined as a real value and is stored. Otherwise, the device acquires a second parameter value of the hardware component that follows the first parameter value, and calculates a second prediction value of the second parameter value. If a difference between the second prediction value and the second parameter value falls with a second deviation range, the first parameter value is determined as a real value and is stored. Otherwise, the first parameter value is determined as a false value and is abandoned.

BACKGROUND

1. Technical Field

The embodiments of the present disclosure relate to data processing technology, and particularly to a data processing device and a method for checking observed values of the data processing device.

2. Description of Related Art

To ensure a data processing device (e.g., a server) runs in a secure environment, the server will use data acquisition devices (e.g., sensors, voltmeters, tachometers) to detect parameter values of hardware components, such as a voltage and a temperature of a central processing unit (CPU), a rotation speed of a motherboard fan, for example, and control the components based on the parameter values. For example, if the voltage of the CPU exceeds a preset threshold value, the server may send an alarm to clients connected to the server, then power off the server. However, the parameter values may have false values due to faults of the data acquisition devices, and the false values may cause the server to perform wrong operations.

DETAILED DESCRIPTION

FIG. 1is a block diagram of one embodiment of function modules of a data processing device1. In one embodiment, the data processing device1includes a parameter value checking system10, a data acquisition device12, a storage device14, and a central processing unit (CPU)16. The data processing device1may further include more hardware components than shown inFIG. 1, such as motherboards, video cards, graphic cards, hard drivers, fans, power supplies, for example.

In one embodiment, the data processing device may be a client computer or a server. The data acquisition device12may be, but not limited to, a temperature sensor, a voltmeter, or a tachometer, which is used to periodically acquire parameter values of hardware components of the data processing device1. The data processing device1may perform corresponding operations based on the parameter values. For example, if a temperature of the CPU40exceeds a preset threshold value, the data processing device1may automatically power off a power supply. However, the parameter values may have false values due to faults of the data acquisition devices, and the false values may cause the server to perform wrong operations. Therefore, the parameter value checking system10is provided to analyze the detected parameter values, recognize and delete false values from the detected parameter values.

FIG. 3is a flowchart of one embodiment of a parameter value checking method. Depending on the embodiment, additional blocks may be added, others removed, and the ordering of the blocks may be changed.

In block S31, the data acquisition device12acquires a first parameter value of a hardware component (e.g., the CPU16) of the data processing device1, and sends the first parameter value to the parameter value checking system10. In this embodiment, the data acquisition device12is a voltmeter used to detect voltage values of the CPU16. The data receiving module110receives the first parameter value sent by the data acquisition device12.

In block S33, the difference calculation module130calculates a first difference between the first prediction value and the first parameter value. For example, the first parameter value detected by the voltage sensor may be 1.4 volts, the first prediction value of the first parameter value may be 1.5 volts, and then the first difference is 1.5 volts-1.4 volts=0.1 volts.

In block S34, the determination module140determines if the first difference falls within a first deviation range of the first prediction value. In this embodiment, the first deviation range is a value range dynamically determined according a preset proportion of the first prediction value. For example, if the preset proportion is 5%, then the first deviation range is determined to be more than or equal to 1.5 volts×(−5%), and less than or equal to 1.5 volts×5%, namely [−0.075 volts, 0.075 volts]. In other embodiment, the first deviation range may be a preset fixed value range, such as [−0.1 volts, 0.1 volts].

In block S34, if the determination module140determines that the first difference falls within the first deviation range, block S39is implemented, the determination module140determines that the first parameter value is a real value, and stores the first parameter value in a corresponding record that is stored in the storage device14. For example, if the first difference is 0.1 volts, and the first deviation range is [−0.1 volts, 0.1 volts], the first parameter value 1.4 volts are stored in a temperature record of the CPU16. If the first difference falls outside the first deviation range, block S35described below is implemented. For example, if the first difference is 0.1 volts, and the first deviation range is [−0.075 volts, 0.075 volts], block S35is implemented.

In block S35, the data receiving module110receives a second parameter value, which is acquired by the data acquisition device (such as the voltmeter mentioned above) and follows the first parameter value. For example, the second parameter value may be 1.52 volts.

In block S36, the data prediction module120calculates a second prediction value of the second parameter value using the prediction algorithm, such as the exponential smoothing method described above. For example, if the smoothing factor α in the formula is 0.5, the first parameter value is 1.4 volts, the first prediction value is 1.5 volts, and then the second prediction value equals 0.5×1.4 volts+(1−0.5)×1.5 volts=1.45 volts.

In block S37, the difference calculation module130calculates a second difference between the second prediction value and the second parameter value. For example, if the second parameter value is 1.52 volts, and the second prediction value is 1.45 volts, then the second difference is 1.45 volts−1.52 volts=−0.07 volts.

In block S38, the determination module140determines if the second difference falls within a second deviation range of the second prediction value. In this embodiment, the second deviation range is a value range dynamically determined according the preset proportion of the second prediction value. As mentioned above, the preset proportion is 5%, then the second deviation range is determined to be more than or equal to 1.45 volts×(−5%), and less than or equal to 1.45 volts×5%, namely [−0.0725 volts, 0.0725 volts]. In other embodiment, the first deviation range may be the preset fixed value range, such as [−0.1 volts, 0.1 volts]. If the second difference falls within the second deviation range, block S39described above is implemented. For example, if the second difference is −0.07 volts, which falls within the second deviation range [−0.0725 volts, 0.0725 volts], block S39is implemented, the first parameter value 1.4 is determined as a real value and is stored into the storage device14. Otherwise, if the second difference falls outside the second deviation range, block S40is implemented, the determination module140determines the first parameter value is a false value and abandons the first parameter value.