Raman spectrum indicates the vibration and rotation information of the substance molecules. Different materials have different characteristic of spectrum from other materials--characteristic peak, which provides fingerprint spectrum for the substance characterization and identification. CNI launched Raman Spectrometer with the features of compact design, high resolution, high sensitivity, simple measurement and operation, can replace the old spectrophotometer and widely used in chemical and biological research, environmental science, jewelry appraisal, forensic appraisal, food/drug safety inspection, geological exploration and many other fields.
A variety of linewidth options at :
257, 360, 405, 473, 514.5, 532, 633, 660,
785, 830, 980, 1064 nm
High sensitivity;
Up to 80% quantum efficiency;
Spectral range 200- 1100nm
Excitation wavelength;
360, 405, 532, 785, 830 nm...
Fast connection, flexuble operation.
Wavelength is optional according on
Customer`s acyual requirements.
Simple measurement and easy operation,
extemal power supply vehicle- mounted charger,
Dustproof, anti- vibration, arti-glare interference...
Build- in narrow line width laser- power
adjustable, high sensitivity spectrometer-
used for weak signal detection
It combines micro system and Raman
system which greatly tacilitates the Raman
micro area detection
The Raman spectrum of the asmples are compared
with database for qualitative analysis. The database
is accessible to the outside world, and has self-built
database function.
Connect the Raman cuvette and
Raman / fluorescence probe, which is able to switch
solid, liquid and other materials flexibly for metting
testing needs.
Custom Raman Spectroscopy Laser,Raman Spectroscopy Laser,Blue Laser Beam,Raman Spectroscopy Laser Power Changchun New Industries Optoelectronics Technology Co., Ltd. , https://www.cnioptics.com
Composition of Raman Spectrometer
Laser Source
Spectrometer
Probe
Raman Spectrometer Solution
Combined Raman Spectrum System
Portable Raman Spectrometer
Desktop Raman Spectrum System
Micro Raman Spectrum Measurement System
Components & Accessories
Raman Software
Raman Cuvette
Sample Raman Spectrum
Agricultural Mechanization Big Data Blue Sea remains to be tapped
[ China Agricultural Machinery Industry News ] According to the news report of the media on April 6, with a shared bicycle travel big data as a reference, the relevant units released the "2017 Quarterly China Major City Cycling Report", using the city riding index As an evaluation index, in terms of bicycle use level, energy saving and emission reduction level, health contribution level, parking facility level, service environment level and social civilization level, Beijing, Shanghai, Guangzhou, Shenzhen, Tianjin, Nanjing, Xi'an, Hangzhou, etc. The development level of shared bicycles in 20 domestic first- and second-tier cities was evaluated.
According to the report, the 18-45-year-old population is the main user of shared bicycle cycling, accounting for nearly 90%, of which the proportion of people aged 30 and below is 55%, and that of 30-45 is about 35%.
It can be seen that users sharing bicycles not only cover young people, but also are widely recognized and used by middle-aged groups. At the same time, in the distribution of male and female users, different cities are divided into two major factions. One of the five cities represented by Xi'an and Guangzhou has become a shared bicycle cycling city for “menâ€. The proportion of male users has reached 55.90%-59.70%, which is higher than that of female users. The five cities represented by Tianjin and Kunming have become “balanced†shared bicycle cycling cities. The ratio of male to female is between 48% and 52%, which can be said to be basically the same.
But on the whole, the proportion of female users can reach about 45%. The ratings of the city's friendship with shared bicycles show that Hangzhou ranks 12.9 points, Nanjing and Xi'an rank second and third with 12.75 and 12.22 respectively, and Beijing ranks only ninth with 9.94 points. In the evaluation of the service environment level, Beijing ranked with a score of 15 points.
The report also quoted experts as saying that the travel mode of “shared bicycle + public transportation†is gradually replacing the travel mode of “car + pedestrian + public transportationâ€, and the fast-developing shared bicycle is improving the travel mode of urban residents in China. It also has a profound impact on the construction of China's new transportation system. The report can be used as a reference for the development of urban slow-moving traffic in China, tracking the development of the shared bicycle industry, and promoting the construction of smart green cities.
The development of shared bicycles was also a large-scale surge in the country at the beginning of this year. The relevant statistics of just a few months were available, and we have engaged in more than 30 years of cross-regional operations and more than ten years of purchase subsidies. The agricultural machinery sharing model has been promoted for decades, but where are the relevant big data achievements?
I remember that a long time ago, when we studied the purchase subsidy work, we mentioned that through the purchase subsidy statistics, we can predict market development, provide market warning, and product warning. Cross-regional work is also carried out through the flow of the work team, the operation of the market guidance, early warning, and guidance after-sales service. For example, in the informationization of agricultural mechanization social services, Lovol Heavy Industries has the intervention of modern Internet technologies such as E-Tech, Agricultural Machinery 360, Agricultural Machinery, and Agricultural Machinery. The high-tech systems such as positioning systems, counting systems, and navigation systems are also It is applied on the combine harvester, and there is a large amount of data collection every year, but there is no analysis at all, and then the data is used to guide the company to develop products, guide the orderly flow of inter-regional operations, judge the saturation of the market, and predict the service price. Going to wait.
For example, if we work across regions, do we also set up several indicators, including market saturation, market friendliness, price index, machine flow map, etc., which are released regularly every year. Another example is the purchase subsidy, which establishes a data model and analyzes the product under the conditions of protecting the privacy of individuals and enterprises. It is extremely valuable for enterprise variety development and market development.
Whoever holds the data will have the right to lead. Smart cities and smart agriculture are no longer new terms. I think agricultural machinery is still flawed in this respect. We must not only develop intelligent agricultural equipment, but also establish an intelligent agricultural machinery social service system. The basis of this intelligent system is data—massive big data. We have accumulated data for decades. The conditions are quite good. If it is only used to calculate the level of agricultural mechanization, count the annual scale of operations, and then calculate the social and economic benefits, the tip of the iceberg is overkill.
The data is blue ocean, the data is productivity, let us pick up the sleeves to dig! (Original title: Hu Wei: Agricultural data is blue ocean, agricultural data is productivity)