- Wind Sensor
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Talking about the key points of future development of temperature and humidity sensors
How big is the temperature and humidity sensor market? The global market scale increased to 195.5 billion U.S. dollars in 2017. It exceeded 200 billion U.S. dollars in 2018.With the advancement of various projects such as new infrastructure, smart cities, and 5G, The global market will maintain a growth rate of about 8% The market size will exceed 300 billion US dollars! ! ! There is an old saying in the circle: Standing at the right vent, pigs can take off! Recall our protagonist The temperature and humidity sensor, an instrument mainly used to monitor environmental temperature and humidity. At present, it has been widely used in the fields of medicine and chemical industry, electronic communication, meteorology, food, storage, agriculture and cultural relic protection. After entering the 21st century, temperature sensors are developing rapidly in the direction of high-tech, such as high-precision, multi-function, bus standardization, high reliability and safety, the development of virtual sensors and network sensors, and the development of single-chip temperature measurement systems. . The future temperature and humidity sensor market has broad prospects, especially in the fields of consumer electronics and the Internet of Things. The temperature and humidity sensor is a composite technology of electronic technology and physical and chemical principles. The hardware factor accounts for only 50% of it. Another important factor is calibration. If you want to ensure that the measured value is accurate, you need to ensure that the calibration value of each test is always within a fixed range, which is very difficult to do. High precision and stable performance have always been the rigid indicators of temperature and humidity sensors. So what are the development priorities of temperature and humidity sensors in the future? 1. Fault detection and prediction using machine intelligence. Any system must detect or predict the possible problems before errors occur and lead to serious consequences. At present, there is no accurately defined model for abnormal state, and abnormal detection technology is still lacking. It is urgent to combine sensor information with knowledge to improve machine intelligence. 2. Under normal conditions, it can perceive the physical parameters of the target with high precision and high sensitivity; however, there has been little progress in the detection of abnormal conditions and misoperations. Therefore, there is an urgent need for fault detection and prediction, which should be developed and applied vigorously. 3. Current sensing technology can accurately sense physical or chemical quantities at a single point, but it is difficult to sense multi-dimensional states. For example, environmental measurement, whose characteristic parameters are widely distributed and have temporal and spatial correlations, are also a type of problem that needs to be solved urgently. Therefore, it is necessary to strengthen the research and development of multi-dimensional state sensing. 4. Remote sensing for target component analysis. The chemical composition analysis is mostly based on the sample material, and sometimes the sampling of the target material is very difficult. For example, to measure the ozone content in the stratosphere, remote sensing is indispensable, and the combination of spectrometry and radar or laser detection technology is a possible way. Analysis without sample components is susceptible to interference from various noises or media between the sensing system and the target component, and the machine intelligence of the sensing system is expected to solve this problem. 5. Sensor intelligence for efficient circulation of resources. Modern manufacturing systems have realized the automated production process from raw materials to products. When the product is no longer used or abandoned, the cyclic process is neither effective nor automated. If the recycling of renewable resources can be carried out effectively and automatically, environmental pollution and energy shortage can be effectively prevented, and the management of life cycle resources can be realized. For an automated effective cycle process, using machine intelligence to distinguish target components or certain definite components is a very important task for intelligent sensor systems.
One increasingly popular managerial tactic to improve problem-solving performance of sensor solution is to increase the connectedness, or what academics call clustering, of the organization
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But we do think that reckoning with supply chains of sensor solution is a really important step. Even super simple switches in material, or sourcing, or shipping, or worker benefits seems like good place to start.
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