32 results about "Chemical risk" patented technology

The invention discloses a coal mine safety information intelligent monitoring system applying an industrial internet, and relates to the technical field of intelligent monitoring. During operation of the system, multi-source data are collected from equipment and subjected to primary processing, and data from different sources are subjected to space-time matching and association by using a data fusion technology; and obtaining a coal mine safety information early warning index Yjzs after calculation, analyzing the stress change and risk of the underground rock stratum, and simulating and predicting the chemical risk in the mine by processing the hydrogen sulfide diffusion rate and nitrogen oxide concentration gradient data and adopting a computational fluid dynamics (CFD) chemical reaction rate model. And monitoring and evaluating behaviors of coal miners and operation conditions of equipment, comparing a coal mine safety information early warning index Yjzs with a preset warning threshold value M and a danger threshold value N, generating a corresponding strategy scheme according to a comparison result, and dynamically adjusting the threshold value to adapt to different mine conditions and safety standards.

The embodiment of the invention discloses a chemical early warning interception method and device, electronic equipment and a medium. A specific embodiment of the method comprises the following steps: carrying out identity recognition on a target chemical detection terminal set to obtain a detection terminal identity recognition information set; performing state information extraction on the multi-modal target chemical information set to obtain a chemical multi-modal state information set; performing data deduplication on the multi-modal state information set of the chemicals and then performing association identification to obtain a multi-modal association information set; performing chemical risk identification on the de-duplicated multi-modal chemical information set to obtain a to-be-tracked chemical information set; and performing trajectory tracking early warning on the to-be-tracked chemical article information set to obtain a chemical article trajectory early warning information set, and performing early warning interception on the to-be-tracked chemical article information set. According to the embodiment, a data barrier can be broken, data sharing and safety are improved, and the accuracy of accurate interception and risk early warning of target chemicals is improved.

The invention belongs to the technical field of green substitute design for chemical risk prevention and control, and discloses a deep learning method for designing a molecular structure of a chemical green substitute. On the basis of known chemical molecular structures and substitute target attributes, a green substitute molecular structure with synergistically optimized function and hazard attributes is obtained by applying the constructed method. The method is simple, convenient and efficient, resource investment of experimental testing can be saved, and regret replacement is prevented. The construction process of the method comprises the following steps: (1) generating a molecular structure of structural constraint based on a generative model; (2) realizing high-throughput prediction of multi-target attributes based on a decision-making model; and (3) realizing multi-target attribute collaborative optimization based on a Pareto optimization theory. The method established by the invention can effectively explore a wide chemical space, predict the multi-target attributes of the chemicals in a high-throughput manner, generate molecular structures with necessary functions and lower harmfulness, and provide a basic tool for green substitution of harmful chemicals.

The invention relates to the technical field of chemical plant area intelligent evacuation, and discloses an emergency evacuation place intelligent management system and method, and the method comprises the steps: constructing a chemical risk semantic unified representation space through a risk semantic mapping algorithm, and generating a mapping relation between a risk state and semantic expression; applying a feature portrait algorithm to generate an employee feature representation model; on the basis of the chemical risk semantic personalized adaptation model, personalized semantic guidance for employees with different characteristics is generated; generating a multilevel chemical risk semantic model by using a regional hierarchical clustering algorithm; processing real-time position and risk change data through a collective intelligent optimization algorithm, and calculating a dynamic evacuation path planning scheme; differentiated safety management based on employee characteristics, hazard source distribution and semantic understanding is realized, the problem of low efficiency caused by one-step instructions in traditional evacuation management is solved, and the emergency evacuation efficiency and safety of the chemical plant area are improved.

The invention relates to the field of chemical risks, and discloses a method for determining the risk of a refining device, a processor and a storage medium. The method comprises the following steps: acquiring process parameter data of each process point location of the refining device; determining a corresponding first threshold value of upper limit alarm, a second threshold value of lower limit alarm and a first base point coefficient according to the type of the process parameter data; determining a first difference value between the first threshold value and the second threshold value; determining a first calculation base point according to the product of the first difference value and the first base point coefficient; determining a second difference value between the process parameter data and the first calculation base point and a third difference value between the first threshold value and the first calculation base point; determining a process risk index of a process point location corresponding to the process parameter data according to a ratio of the second difference value to the third difference value; and determining the process risk of the refining device according to each process risk index of each process point location. The scientific and accurate risk calculation method for the refining device is formulated, and risk early warning can be carried out on the refining device more accurately.

The invention discloses a chemical engineering change risk management and control method and system based on generative artificial intelligence, and belongs to the technical field of risk management and control. Historical chemical engineering risk influence data in a chemical engineering production process are collected and preprocessed; the method comprises the steps of preprocessing historical chemical engineering risk influence data, training a chemical engineering change risk identification model by adopting the preprocessed historical chemical engineering risk influence data, collecting real-time chemical engineering risk influence data after the chemical engineering change when the chemical engineering change is generated, and identifying the real-time chemical engineering risk influence data by adopting the trained chemical engineering change risk identification model. And finally, the chemical change risk is managed and controlled according to the identified chemical change risk identification result, so that the monitoring efficiency of the chemical change risk can be effectively improved, the monitoring accuracy of the chemical change risk can also be improved, and the safety in the chemical production process is powerfully guaranteed.

The invention belongs to but is not limited to the technical field of natural language processing and risk management, and discloses a PPO-RAG-based chemical risk field management and control measure generation method and system, and the method comprises the step of providing a PPO-RAG-based management and control measure generation method. In order to solve the problem that LLM has knowledge hysteresis and insufficient user preference adaptability in a management and control measure generation task, a PPO-RAG framework is established by adopting a mode of combining RAG and reinforcement learning based on human feedback. The framework aims to improve the timeliness and speciality of LLM generation management and control measures, so that the LLM generation management and control measures can better meet diversified requirements in actual application scenes. A final experiment result shows that the method effectively improves the comprehensive capability of LLM generation management and control measures. According to the embodiment of the invention, high-quality management and control measure recommendation service can be provided for the user aiming at the characteristics of high professional property and high complexity of the management and control measures in the risk field.

A dangerous chemical risk dynamic management and control system comprises a dynamic detection subsystem, an information management subsystem and a risk prediction subsystem. The dynamic detection subsystem comprises a dangerous chemical detection mechanism and an environment detection mechanism, the dangerous chemical detection mechanism is used for detecting the use dynamic state of dangerous chemicals to obtain state information, and the environment detection mechanism is used for detecting the use environment of the dangerous chemicals to obtain environment information; the information management subsystem is used for collecting and managing process information generated in the use process of the dangerous chemicals; the risk prediction subsystem is used for generating a risk prediction warning based on the state information and the environment information, generating a management and control strategy based on the risk prediction warning, and generating a management and control record based on the risk prediction warning and the process information. According to the dangerous chemical risk dynamic management and control system and method provided by the invention, whether a risk accident possibly occurs can be predicted based on dynamic information and environmental information generated in a dangerous chemical use process, and personnel and property safety is ensured.

The invention relates to a chemical risk management method integrating leakage simulation analysis and property evaluation of a vinyl chloride gas holder, and aims to accurately predict and quantify the safety risk and potential economic loss after leakage of the vinyl chloride gas holder by using an advanced simulation technology and an economic evaluation means. According to the method, [Areal Location of Hazard] ALOHA simulation software approved by the industry is adopted, the gas diffusion process after vinyl chloride leakage is simulated in a high-precision mode, and in combination with a [Quantum Geographic Information System] QGIS (Quality Geographic Information System), visual display of data is carried out. On the basis, through comprehensive consideration of key factors such as physical and chemical characteristics, process environment parameters and leakage scale of vinyl chloride, hazard indexes of fire and explosion are scientifically calculated, and then possible economic losses are deduced. By constructing a set of comprehensive and quantitative risk assessment system, powerful support is provided for safety assessment of chemical equipment, and an important basis is provided for a management layer of a chemical enterprise to formulate scientific and effective safety strategies and risk control measures.

The invention belongs to the technical field of natural language processing and risk management, and discloses a GRPO-RAG-based chemical risk field management and control measure generation method and system, and the method comprises the step of providing a GRPO-RAG-based management and control measure generation method. In order to solve the problem that LLM has knowledge hysteresis and insufficient user preference adaptability in a management and control measure generation task, a GRPO-RAG framework is established by adopting a mode of combining RAG and reinforcement learning based on human feedback. The framework aims to improve the timeliness and speciality of LLM generation management and control measures, so that the LLM generation management and control measures can better meet diversified requirements in actual application scenes. A final experiment result shows that the method effectively improves the comprehensive capability of LLM generation management and control measures. According to the embodiment of the invention, high-quality management and control measure recommendation service can be provided for the user aiming at the characteristics of high professional property and high complexity of the management and control measures in the risk field.

The embodiment of the invention provides a dangerous chemical database design method and system, and belongs to the technical field of data processing. The system comprises a system management module, a database management module, a hazardous chemical data module, a scene adaptation module and a dynamic analysis and evaluation module. The system management module comprises a user management sub-module and a role management sub-module; the database management module comprises a hazardous chemical substance data management sub-module and a report data management sub-module; the hazardous chemical substance data management sub-module is used for newly adding, editing and deleting hazardous chemical substance data; the report data management sub-module is used for newly adding, editing and deleting report data; the hazardous chemical data module is used for storing data information of chemicals; the scene adaptation module is used for adapting to different management scenes; the dynamic analysis and evaluation module is used for generating a management scheme. According to the invention, customer groups can be analyzed according to basic data, customer resources can be developed in a targeted manner, and statistical data of chemical risk assessment can be summarized and processed.

The application discloses a kind of port dangerous chemical storage area inspection robot autonomous navigation method and system, it is related to robot autonomous navigation technical field.The energy state data of collection inspection robot, position information and dangerous chemical environment detection data are determined remaining available energy and forecast dangerous area;Based on position information and forecast dangerous area determine candidate navigation target, carry out path planning to each candidate navigation target, obtain first stage navigation path information, and carry out energy consumption analysis to obtain first forecast energy consumption;Further carry out evacuation path analysis to determine corresponding backup safety target and backup evacuation path, and determine emergency reserve energy;Based on remaining available energy, first forecast energy consumption and emergency reserve energy carry out energy security accessibility evaluation, determine energy security accessible target and control inspection robot to carry out autonomous navigation.The application can improve the navigation safety and energy executability of inspection robot in dynamic dangerous chemical risk environment.

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 relates to the technical field of chemical production, in particular to a dangerous chemical safety production risk monitoring and early warning system and method, and the method comprises the steps: obtaining a production plan and a logistics plan from a database; flow paths and participated production links of different chemicals are obtained through production process information; constructing a chemical risk monitoring list, updating the chemical risk monitoring list in real time according to the form change of the chemicals in each process link in the production process flow, and representing the states of the corresponding chemicals in different process links by using arrays; according to the process links in which the chemicals participate, constructing a state representation matrix of the corresponding chemicals; based on the final product information, obtaining a state representation matrix of a plurality of chemicals participating in the production process flow, and constructing a finished product state representation matrix; and analyzing the state representation matrix of the chemicals and the state representation matrix of the finished products to obtain risk prediction values of different process links.

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 present invention discloses a method and system for assessing the risk of hazardous chemicals transportation based on a neural network, which belongs to the technical field of safe transportation assessment of hazardous chemicals, and includes an information collection module, an objective risk assessment module, a subjective risk weighting module, a result determination module, and an optimization solution generation module; the information collection module collects information on hazardous chemicals related to transportation, including types of hazardous chemicals, hazardous chemical transportation volume, transportation methods, and transportation routes, and establishes a database of hazardous chemical transportation companies. The method and system for assessing the risk of hazardous chemicals transportation based on a neural network of the present invention can realize data acquisition, objective risk assessment, subjective risk assessment, subjective and objective risk weighting assessment, result determination, and optimization solution generation, which is helpful to comprehensively assess the relevant hazardous chemicals transportation risks from multiple dimensions, and provide risk optimization suggestions for relevant enterprises, thereby enhancing the accuracy and reliability of hazardous chemicals risk assessment.

The invention discloses a thermal risk assessment method for a fine chemical distillation process, and relates to the technical field of chemical risk assessment. The method comprises the following steps: acquiring a process temperature upper limit Tp of a to-be-evaluated distillation process, distilling to-be-distilled materials involved in the to-be-evaluated distillation process, selecting materials with different concentrations in each distillation stage to carry out a thermal stability screening test, and carrying out combined test research on the materials with high dangerous concentrations to obtain the thermal stability of the materials. The combined test research means comprise differential scanning calorimetry, pressure tracking differential scanning calorimetry, rapid screening calorimetry, adiabatic calorimetry and microcalorimetry, decomposition kinetics analysis is further carried out, the temperature TD24 corresponding to the maximum reaction rate reaching time of 24 h under the adiabatic condition is obtained, and the thermal safety risk of the distillation process is evaluated with TD24 and Tp as evaluation criteria. The risk of thermal runaway in the distillation process can be effectively prevented and controlled.

The invention relates to the technical field of chemical risk management and control, and particularly discloses a common dangerous chemical risk and emergency disposal measure analysis system, which comprises the steps of collecting historical accident data to obtain an accident occurrence value and a casualty number occurrence value, calculating a risk reference coefficient Fx, marking dangerous chemicals as high-risk chemicals and low-risk chemicals, and determining the risk of the high-risk chemicals and the low-risk chemicals according to the risk reference coefficient Fx. The method comprises the following steps: marking dangerous chemicals as high-risk chemicals and low-risk chemicals based on a single marking result of the dangerous chemicals, calculating to obtain a maintenance deviation coefficient Wh based on the high-risk chemicals, comparing the maintenance deviation coefficient Wh with a maintenance deviation coefficient threshold value Whz, judging to generate a standard signal and a substandard signal, adjusting the maintenance duration according to the substandard signal, and judging whether the maintenance duration reaches the standard or not according to the substandard signal. The system considers multi-dimensional data, accurately divides risk levels, realizes dynamic monitoring and fine management, can effectively deal with dangerous chemical risks, and improves the emergency disposal efficiency and the chemical management level.

The present application relates to a risk management method, platform, device and computer-readable medium for chemicals. The method includes: creating a corresponding risk monitoring task for each hazardous chemical control object; in the case where a risk event is determined to have occurred in the hazardous chemical control object through the risk monitoring task, determining the risk level of the risk item associated with the hazardous chemical control object based on the risk event; and executing control measures of the same level according to the risk level of each risk item. The present application creates different risk monitoring tasks for different hazardous chemicals, thereby performing targeted risk monitoring on different hazardous chemicals, and designs a risk classification control process, that is, in-depth, comprehensive and targeted risk identification of different hazardous chemicals, and also improves the efficiency and accuracy of hazardous chemical risk management, ensures the safety of the workplace and the environment, and solves the technical problems of insufficient targeted risk management and incomplete risk identification of hazardous chemicals.

The invention provides a gridding dangerous chemical knowledge base establishment method, medium and system, and belongs to the technical field of knowledge base establishment methods, and the method comprises the steps: firstly collecting data, and screening high-quality data through an acceptance degree index; then constructing an accuracy evaluation model to automatically mark various types of knowledge and forming a knowledge correlation degree matrix to quantify correlation strength; establishing a gridding knowledge mapping structure, dividing knowledge grid units, calculating gridding centrality and determining knowledge point weights; automatically constructing a minimum spanning tree to process knowledge conflicts, and processing defect knowledge by applying a matrix completion algorithm; a chemical risk association network is constructed, and dangerous chemical grid indexes are calculated to quantify danger levels; then designing a knowledge reasoning mechanism and processing super-long knowledge by adopting a grid knowledge compression function; and finally, implementing an adaptive optimization algorithm to dynamically adjust the grid structure, and forming a grid dangerous chemical knowledge base which reduces manual dependence and enhances relevance expression.

An oil and gas pipeline comprehensive risk assessment model construction method based on a Vague fuzzy set and a matter-element extension theory belongs to the field of oil and gas chemical risk assessment, and comprises the following steps: step 1, establishing an oil and gas pipeline risk index assessment system; 2, representing an expert evaluation language by using a Vague fuzzy set, calculating a second-level risk index weight, and establishing a multi-risk factor simultaneous occurrence probability prediction model; step 3, calculating a third-level risk index weight; step 4, calculating a first-level risk index security level; and 5, verifying the accuracy of the oil and gas pipeline comprehensive risk assessment model construction method based on the Vague fuzzy set and the matter element extension theory and the strong pertinence to risk factors. Experimental results show that the method can be used for scientifically, accurately and efficiently evaluating the safety level of the risk index, and is more suitable for the field process.

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.

The present application provides a kind of based on multi-sensor monitoring's dangerous chemical goods shelf anti-collapse regulation method, system and medium, belong to the field of stacking safety monitoring.The method comprises: obtaining first point cloud and second point cloud and carrying out weighted registration, obtains fusion point cloud;Determine the inclination rate of change based on fusion point cloud;According to the inclination rate of change and the material characteristics of dangerous chemical, estimate the risk occurrence time and risk occurrence position of dangerous chemical risk;And in response to risk occurrence time less than time threshold, determine the main swing characteristics of liquid in dangerous chemical stacking according to fusion point cloud;Based on main swing characteristics, generate time-varying driving signal;Based on time-varying driving signal, drive actuator to apply pulse force with the frequency, phase and amplitude indicated by time-varying driving signal to the shelf.The present application can improve the efficiency and accuracy of stacking monitoring, while the effectiveness and safety of emergency disposal are enhanced for emergency risk, and intelligent grading active regulation is realized.

The invention discloses a dangerous chemical industry risk assessment method and system, belongs to the technical field of data processing, and aims to solve the technical problem of how to improve the accuracy and efficiency of dangerous chemical risk assessment. Comprising the following steps that an evaluation index system is constructed, specifically, according to the definition of risks, multi-dimensional evaluation indexes are selected to design and construct an industry safety production comprehensive risk evaluation system, and the evaluation system follows the SMART principle; risk grading model construction: constructing a risk grading model, wherein the risk grading model is used for evaluating the real score of the index and calculating the risk value of the industry; and risk grading evaluation: sorting the industries according to the risk values of the industries based on a predefined risk grading rule.

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 present invention relates to the technical field of hazardous chemicals safety prevention, and discloses a hazardous chemicals safety prevention risk warning method and system, the method comprising: constructing a hazardous chemicals risk assessment index system, and collecting and preprocessing risk assessment index data corresponding to the hazardous chemicals risk assessment index; constructing a hazardous chemicals leakage warning model, and performing hazardous chemicals leakage risk warning on the preprocessed risk assessment index data. The present invention extracts the storage risk degree of hazardous chemicals themselves and the safety factor of the hazardous chemicals storage environment, calculates the neighbor weight information of the neighbor local data according to the linear coefficient between the neighbor local data, performs dimensionality reduction processing on the gas concentration sequence data while ensuring that the neighbor weight information remains unchanged, performs hazardous chemicals leakage risk warning on the data after dimensionality reduction processing, obtains hazardous chemicals leakage warning results that characterize the hazardous chemicals leakage concentration and the hazard to the storage environment, and realizes hazardous chemicals safety prevention risk warning.

The invention discloses an early warning method and system for dangerous chemical substance risk operation behaviors, and relates to the field of dangerous chemical substance risk control. The method is applied to a risk early warning system, and the method comprises the following steps: obtaining various risk index data of hazardous chemicals in a production management process, the various risk index data including video data, sensor monitoring data and an operation log; performing semantic recognition on the multiple risk index data to obtain multiple risk operation events; calculating a risk equivalent value corresponding to each of the plurality of risk operation events and a risk increment value cooperatively caused by the plurality of risk operation events; determining a comprehensive risk value according to the risk equivalent values and the risk increment values corresponding to the plurality of risk operation events; and when the comprehensive risk value is greater than or equal to a risk threshold value, triggering risk early warning. By implementing the technical scheme provided by the invention, the problem that a traditional early warning mode cannot effectively quantify the cumulative effect and the synergistic effect of risk operation is solved.

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.