8 results about "Chemical risk" patented technology

The invention relates to a hazardous chemical substance dynamic risk assessment and disposal method and system, and belongs to the field of hazardous chemical substance risk assessment. The method comprises the following steps: acquiring original data to obtain structured data; performing risk density assessment based on the structured data; source sink statistics is carried out based on the structured data, and a risk source item and a risk sink item of each grid are obtained; a risk discretization continuity equation is constructed and solved, and a risk density update value is obtained; updating the risk flow rate based on the risk density value to obtain a risk flow rate update value; based on the risk density update value and the risk flow rate update value, obtaining a risk assessment key index and outputting a risk assessment report; and generating an optimal disposal strategy based on the risk assessment report. The intelligent assessment and disposal method integrates multi-source real-time data, realizes risk field space quantification, can dynamically simulate and predict the evolution of the risk field, and has important practical significance for improving qualitative change of hazardous chemical substance safety management from passive response to active prevention and control.

The invention provides a multi-angle persistent chemical screening method based on a multi-task graph neural network, and belongs to the technical field of high-throughput screening for chemical risk management. The method aims at achieving synchronous, efficient and accurate screening of the related attributes of the durability of the chemicals. The method comprises the following steps: firstly, constructing a multi-angle chemical durability parameter data set; the method comprises the following steps: taking a molecular graph generated by a chemical structure as model input, constructing a model through a graph neural network, and finishing synchronous training by adopting a mean square error and a superposition loss function with positive sample weight cross entropy; evaluating the performance of the model through indexes such as a determination coefficient and an area under a subject working curve; and characterizing a model application domain by adopting structure-activity relationship morphology analysis. The model constructed by the research relates to more persistent parameters, is more comprehensive, larger in data volume and wider in coverage range, has good fitting ability, prediction ability, robustness and reliability, and can provide technical support for environmental risk assessment of persistent chemicals.

The invention provides a chemical risk prediction model based on interpretable machine learning and a construction method thereof, and the method comprises the steps: S1, obtaining chemical data with risk endpoints, S2, generating a high-dimensional feature set containing physicochemical properties and structural topology for a molecular structure through employing a molecular descriptor and a fingerprint calculation platform; s3, performing comparison modeling on each risk endpoint by independently adopting different types of machine learning models, re-training an optimal model on a training set, and reporting a final result in an independent prediction set; s4, the model application range of the optimal model is determined, and an application domain framework of the model is constructed based on conformal prediction, and S5, the importance of global and sample-level key descriptors is output; and finally, batch reasoning and result exporting: outputting a prediction label, a prediction probability, an application domain framework mark, a significance level and an explanatory result one by one for a chemical list corresponding to the target chemical data.

This invention discloses a method for assessing the risk of heavy metal contamination in soil of newly constructed reservoir areas through multi-scenario coupling simulation. The method includes the following steps: selecting soil samples with the highest content of the target heavy metal in the reservoir area for Al content detection and heavy metal speciation analysis; collecting background control samples from areas far from the reservoir area for heavy metal and Al content detection and heavy metal speciation analysis; calculating enrichment factors and geoaccumulation indices to determine pollution sources at sampling points; preparing average and extreme samples; preparing simulated reservoir water; and conducting static leaching experiments, dynamic disturbance experiments, and vertical leaching experiments, covering chemical risks during quiescent periods, physical risks during disturbance periods, and long-term infiltration geological risks. This invention not only couples multiple scenarios and simulates the most unfavorable conditions to accurately identify the maximum instantaneous risk of heavy metal release, but also quantifies and determines pollution sources, possessing source tracing capabilities.

The invention belongs to the technical field of high-throughput screening for chemical risk management, and discloses a method for screening chemicals with 90-day equivalent toxicity based on a stacked integrated model. Calculating molecular characteristics according to simplified molecular linear input specifications of chemicals, and preprocessing and screening the molecular characteristics; according to the model, a support vector machine, extreme gradient lifting and a multi-layer perceptron serve as base classifiers, logistic regression serves as a meta learning device, fusion of multi-model prediction results is achieved through a stacking integration strategy, and the stability and generalization performance of the model are improved. The performance of the model is evaluated through the accuracy rate and the area under the subject working characteristic curve, and the application domain of the model is represented by structure-activity relation morphology analysis according to the QSAR evaluation framework of the economic cooperation and development organization, so that the model is ensured to have reliability and interpretability in screening of chemicals in the application domain. According to the method, the chemicals with 90-day toxicity in the application domain are effectively screened, and a tool is provided for sound management of the chemicals.

The invention belongs to the field of chemical risk assessment and safety management, and particularly relates to a chemical compatibility testing device and method. The device comprises a track part, a capsule testing bin and a controller. The rail part comprises a first rail assembly and a second rail assembly. The capsule testing bin is constructed to contain at least two chemicals in a separated mode, and the two ends of the capsule testing bin are erected on the first track assembly and the second track assembly respectively. And the controller is configured to control the capsule test bin to reciprocate on the track part. In the reciprocating motion process, chemicals in the capsule testing bin can be mixed, and heat absorption and release and / or air pressure change conditions in the mixing process can be monitored and measured, so that the compatibility between the chemicals can be judged. The device can be used for measuring the compatibility of multiphase chemicals including solid chemicals, and has a good application prospect.

The invention discloses a method for detecting early neurodevelopment toxicity of pollutants based on a neural crest specific molecular marker and application, and belongs to the technical field of environmental toxicology and molecular diagnosis. The method comprises the following steps: 1) exposing an early embryo to a to-be-detected sample or pollutant; 2) detecting the change of the specific molecular marker of the neural crest and the peripheral neuron; and 3) evaluating the early neurodevelopmental toxicity according to the marker expression difference. According to the method, the problems of early warning lag and low sensitivity of the traditional neural behavior endpoint (zebra fish 6dpf) are solved, the early neurotoxicity of low-concentration pollutants can be detected within 24 hours of pollutant exposure (zebra fish sox10 and prph), and the method can be used for early toxicity early warning and risk assessment in environmental monitoring and chemical risk assessment.

This invention provides a method, medium, and system for establishing a gridded hazardous chemicals knowledge base, belonging to the technical field of knowledge base establishment methods. First, data is collected and high-quality data is filtered through a recognition index. Then, an accuracy evaluation model is constructed to automatically label various types of knowledge and form a knowledge association matrix to quantify the association strength. Next, a gridded knowledge mapping structure is established, knowledge grid units are divided, and grid centrality is calculated to determine the weight of knowledge points. Subsequently, a minimum spanning tree is automatically constructed to handle knowledge conflicts, and a matrix completion algorithm is applied to handle defective knowledge. Then, a chemical risk association network is constructed, and a hazardous chemicals grid index is calculated to quantify the hazard level. Next, a knowledge reasoning mechanism is designed, and a grid knowledge compression function is used to handle excessively long knowledge. Finally, an adaptive optimization algorithm is implemented to dynamically adjust the grid structure, forming a gridded hazardous chemicals knowledge base that reduces manual dependence and enhances the expression of associations.