Sensor fault identification method and storage medium
A technology for sensor faults and identification methods, which is applied to instruments, program control, test/monitoring control systems, etc., and can solve problems such as inapplicability of the preset value range of the sensor, too simple sensor fault judgment in practical application scenarios, and misjudgment of the sensor. , to prevent misjudgment, eliminate data anomalies, and improve applicability
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Embodiment 1
[0054] In order to solve the above-mentioned technical problems existing in the prior art, an embodiment of the present invention provides a sensor fault identification method.
[0055] figure 1 It is a flowchart of a sensor fault identification method according to an embodiment of the present invention. refer to figure 1 The sensor fault identification method provided in this embodiment specifically includes the following steps:
[0056]S110, using an autonomous learning method to group the historical data collected by the sensor according to the temporal characteristics and / or regional characteristics of the historical data collected by the sensor, and determine the temporal characteristics and / or regional characteristics of the historical data according to the grouped historical data Corresponding value range of historical data, and store historical data, time characteristic and / or geographical characteristic of historical data and historical data value range correspondin...
Embodiment 2
[0074] In order to solve the above-mentioned technical problems in the prior art, the embodiment of the present invention provides a sensor fault identification method based on the first embodiment, wherein the sensor fault identification method in the embodiment of the present invention further improves step S180 in the first embodiment.
[0075] figure 2 It is a flow chart of the sensor fault identification method in Embodiment 2 of the present invention. see figure 2 , the sensor fault identification method of the present embodiment includes the following steps:
[0076] S210, using an autonomous learning method to group the historical data collected by the sensor according to the temporal characteristics and / or regional characteristics of the historical data collected by the sensor, and determine the temporal characteristics and / or regional characteristics of the historical data according to the grouped historical data Corresponding value range of historical data, and ...
Embodiment 3
[0096] This embodiment describes the situation where the method in Embodiment 2 is applied to temperature sensor fault identification.
[0097] The temperature sensor fault identification method of this embodiment includes the following steps:
[0098] S310, the temperature sensor collects temperature data in real time, and adopts the self-learning method to generate the law of temperature data changing with time and / or region, specifically including the following three situations:
[0099] First, the law of temperature data changing with regions can be as follows: the highest temperature of the temperature sensor in the first installation site in a year is 35 °C, and the lowest temperature is 5 °C; the temperature sensor in the second installation site in a year The highest temperature is 42°C and the lowest is 15°C.
[0100] Second, the law of temperature data changing over time can be as follows: the highest temperature of the temperature sensor in the first quarter of the...
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