7 results about "Chemical hazard" patented technology

The invention relates to the technical field of chemical hazardous substance detection, in particular to a Tb-CeMOF (at) ATP fluorescent probe as well as a preparation method and application thereof. The fluorescent probe is obtained by taking a terbium / cerium bimetal organic framework Tb-CeMOF as a matrix and an aptamer as a recognition element and modifying the Tb-CeMOF framework with the aptamer through synthesis. The detection mechanism is as follows: when mercury does not exist, the aptamer enables the characteristic fluorescence of terbium to be excited. When a target object exists, the aptamer is specifically combined with mercury, and conformational change causes change of an energy transfer path: the characteristic fluorescence of terbium is quenched; characteristic fluorescence of cerium is obviously enhanced, and an'on-off-on 'type ratio fluorescence signal is formed. A standard curve is established by monitoring the linear relation between the ratio change of the fluorescence intensity and the mercury ion concentration, and accurate quantification of the mercury ion residual quantity in the sample is achieved. By means of simple operation and excellent sensitivity and selectivity, a reliable and efficient new strategy is provided for trace detection of mercury ions.

The application discloses a kind of calculation methods for simulating toxic and harmful gas diffusion concentration in three-dimensional virtual training environment, S1, determine toxic gas diffusion effective influencing factor;S2, the physical model corresponding to training environment is constructed, and the effective influencing factor of gas diffusion is derived;S3, according to gas toxic dose and shelter size, sparse grid is divided, and grid density is distinguished;S4, CFD model is constructed, and the concentration of simulated harmful gas diffusion is calculated;S5, concentration value time sequence situation result offline database of a period of time is established, and gas concentration situation result is stored;S6, when training, in virtual environment, quickly display.The method of the present application can consider terrain, building, meteorological conditions and other factors affecting gas concentration diffusion, can measure the gas diffusion concentration value of any point in space in real time, and its diffusion concentration value is realistic and practical, so that the simulation effect of harmful gas diffusion concentration value in virtual reality chemical hazard simulation training environment is more realistic and credible, and the training effect is improved.

This invention relates to the field of hazardous chemical monitoring technology, and more particularly to a method for monitoring the entire lifecycle of hazardous chemicals and predicting safety hazards based on LIMS. The method includes the following steps: Hazardous chemical information is entered into the LIMS by scanning electronic tags, and the operation steps and status are updated in real time when the chemical moves, forming updated data. The system compares the updated data with early warning rules, generates early warning information when an anomaly is triggered, and pushes it to the terminal server. The terminal server performs risk prediction based on the updated data and automatically generates disposal suggestions and graded contingency plans, ultimately forming a record of hazardous chemical hazard handling information, achieving intelligent monitoring and early warning throughout the entire process. This invention, by combining dynamic monitoring throughout the entire lifecycle, intelligent status recognition, and contextualized risk prediction, achieves accurate identification, early prediction, and graded disposal of hazardous chemical hazards, significantly improving the safety and emergency response capabilities of hazardous chemical management.

The invention provides an intelligent research and judgment method for chemical dangerousness, a medium and a display robot, and belongs to the technical field of chemical dangerousness research and judgment. Inputting the attribute vector into a risk assessment model combining a spiral progressive network structure and a dual learning framework, and expanding a receptive field layer by layer through five spiral expansion layers to realize collaborative extraction of local features and a global mode; reversely deducing safety attributes from a prediction result by using a dual task, performing difference verification with original input to form closed-loop optimization, calling historical cases to perform similarity comparison to correct the prediction result when a danger level exceeds a safety threshold, and finally outputting a research and judgment conclusion through multi-dimensional cross verification. The problem that the misjudgment rate of the mixture and the disguise chemicals is high due to the fact that local features and a global risk mode are difficult to cooperatively recognize in the chemical risk study and judgment process is solved.

The present application relates to the technical field of chemical hazard assessment in petrochemical industry, and discloses a method for rapidly determining compatibility of chemicals based on Raman spectrum. The method comprises the following steps: collecting original spectra of n kinds of chemicals, and storing Raman shift and intensity signals in a spectrometer; preprocessing the original spectra; inputting the preprocessed Raman shift and intensity signals into spectrum characteristic signal analysis software to determine characteristic peak positions and corresponding Raman shift values; substituting the Raman shift values corresponding to the characteristic peak positions into a Raman characteristic peak and active group correlation database to obtain active groups; inputting the active group information into reaction group analysis software to obtain reaction group information; inputting the reaction group information into reactivity analysis software; comparing the reaction group information with reaction groups in a reaction group reaction matrix; and feeding back reaction hazard information. The method can rapidly determine the mixing reaction hazard between unknown chemicals, and provides technical guidance for avoiding hazards.

The invention relates to the technical field of concrete material analysis, and discloses a steel slag cement concrete structure safety test method and system. The method comprises the following steps: identifying heavy metals and sulfides in the steel slag by adopting multispectral cooperative detection, and establishing a chemical hazard evaluation reference; formulating a multi-axial dynamic loading scheme according to a chemical standard, and capturing the synergistic stress behavior of the steel slag particles and the cement matrix; designing temperature and humidity acid freezing composite environment parameters based on stress behaviors, and tracking a performance degradation track; steel slag-cement interface microcosmic detection is carried out on key nodes of the degradation track, and an interface damage mechanism is revealed; and carrying out correlation analysis on the chemical reference, the stress behavior, the degradation track and the damage mechanism to determine the structure safety state. The problem that a chemical-mechanical-environment-microcosmic multi-dimensional coupling damage mechanism in the steel slag concrete cannot be accurately identified is solved.

The invention provides a chemical risk prediction method and system based on a large language model. The method comprises the following steps: obtaining a molecular structure text representation of a to-be-tested compound; classifying and inputting the molecular structure text, and forming an input sequence; transform representation is carried out, wherein the sequence is input into a pre-trained chemical field Transform encoder to obtain context representation of each position, a position vector is read from a final layer of the encoder to serve as a global molecular representation z, encoder parameters are not frozen in the training stage, and end-to-end joint optimization is carried out with a downstream module; performing tree model discrimination, including inputting the vector into a gradient boosting tree classifier to output a prediction probability and a category label of a sample; defining an application domain AD and selecting an optimal threshold pair by taking the product of the classification performance in the AD and the sample coverage rate as a target function so as to obtain an application domain boundary; extracting attention weight to generate molecular structure text interpretability; and outputting the prediction label, the prediction probability, the application domain label and the atomic-scale importance degree.