802 results about "Chemical process" patented technology

The invention discloses a big data-driven hydrogen fluoride purification risk entropy assessment and multi-parameter monitoring system, which relates to the technical field of chemical process monitoring, comprises a multi-mode heterogeneous sensor network, and realizes high-precision detection and three-dimensional monitoring by using a nanowire FET (Field Effect Transistor) and the like. The space-time tensor decomposition module extracts features and identifies a causal relationship; the risk entropy manifold learning module evaluates the risk; the self-adaptive twin network monitors the abnormity and simulates and disposes the abnormity; and a knowledge graph-reinforcement learning system decision making subsystem and a plurality of subsystems such as an optical monitoring subsystem and a multi-scale modeling subsystem are also provided, so that whole-process risk management and control are realized. The hydrogen fluoride purification monitoring level is greatly improved, the detection sensitivity and the anomaly detection accuracy are remarkably improved, risk early warning is more timely, decision response is accelerated, the production efficiency is improved, energy consumption is reduced, the system has self-repairing and self-power-supply capabilities, data are safe and traceable, reliable operation of the system is guaranteed, and economic losses and potential safety hazards are reduced.

The invention relates to the technical field of process parameter design, and discloses a silver electrolysis process parameter design method and system based on multi-objective optimization. The method comprises the following steps: performing differential time sequence parameter decomposition on operation data of the silver electrolysis process to obtain a sensitive parameter set and a reference parameter set; performing reweighted periodic feature extraction on the sensitive parameter set and the reference parameter set, and establishing a dynamic mathematical model of the silver electrolysis process parameters; based on the dynamic mathematical model of the silver electrolysis process parameters, physical and chemical process numerical simulation is conducted through COMSOLMultiphysics software, and a response curved surface model is obtained; and according to the response curved surface model, a process parameter combination evaluation value sequence is generated, silver electrolysis process parameter multi-objective optimization is carried out based on the process parameter combination evaluation value sequence, and a silver electrolysis process parameter optimization scheme is output. According to the application, multi-physical-field collaborative simulation of electrode reaction, ion transmission, heat conduction and the like is realized, and performance index responses under different process parameters are accurately predicted.

Devices and methods to mitigate the erroneous signal imparted by physical and / or chemical process incident upon analyte-selective electrochemical sensors that are non-analyte-related in origin are disclosed herein. A sensing system featuring at least one of an analyte-selective sensor and at least one of an analyte-invariant sensor.

The invention discloses a dynamic intelligent control method and system for improving the efficiency of a rectifying tower, particularly relates to the technical field of chemical process control, and is used for solving the technical defect that an operation variable is mismatched with a physical state due to the fact that an existing distributed control system cannot recognize a hidden degradation state of equipment. The method comprises the following steps: acquiring temperature gradient distribution of each tower plate of a rectifying tower, thermal load of a reboiler and tower top condensation load parameters in real time, calculating a mass transfer efficiency offset by combining the temperature gradient distribution to generate a scaling trend index, and synchronously calculating a local entropy yield based on the thermal load and the condensation load to generate a thermodynamic irreversibility index. When the double indexes exceed a preset threshold value, it is judged that a recessive degradation state is entered, then a backflow ratio correction coefficient is reversely derived through a dynamic correlation model, and the correction coefficient is input into a distributed control system to dynamically adjust backflow ratio operation parameters; the product purity is maintained, the tower plate scaling process is obviously delayed, and long-term efficient operation of the rectifying tower is guaranteed.

The invention discloses a chemical process fault diagnosis method and system, and relates to the technical field of chemical process fault diagnos.The scheme aims at the diagnosis bottleneck of catalyst inactivation type progressive faults, fine parameter offset is captured in real time through a dynamic reference model, weak signals are accumulated and amplified in combination with an attenuation weighting mechanism, and the fault diagnosis accuracy is improved. The problem that a traditional method is not sensitive to slow drifting, and consequently report omission is caused is solved, and a fault recognition window is remarkably advanced. The dynamic threshold value is updated in real time based on mobile statistics, and raw material fluctuation and sensor noise can be self-adapted; during working condition switching, the model is automatically reset and the threshold value is relaxed, so that false alarm triggered by parameter mutation is avoided, and the stability of production scheduling is guaranteed; the design that fault half-life period weight and moving window length are associated with an inactivation period is introduced, so that the model autonomously adapts to different catalyst characteristics.

The invention discloses a deep resource fluidized mining disturbance effect evaluation model under a multi-field coupling effect, and the method specifically comprises the following steps: constructing a multi-field coupling parameter system: collecting geologic body basic parameters, covering rock mass mechanical parameters, thermal physical parameters, fluid parameters and chemical parameters in deep three-high, high ground stress, high ground temperature and high karst water pressure environments, and establishing a multi-field coupling parameter system; the method comprises the following steps: establishing a dynamic parameter database, realizing real-time updating of parameters along with a mining process, establishing a heat-flow-force-chemical full-coupling mathematical model, introducing the influence of a rock plastic damage correction term quantification temperature on heat conduction efficiency, associating a dynamic mapping relation between mineral reaction and rock physical property parameters, and establishing a dynamic parameter database. Simplification errors of a traditional model on multi-field effects are thoroughly eliminated, chained interaction of temperature, fluid, stress and chemical processes in a deep three-high environment is accurately described, a model prediction result better fits an actual mining working condition, and prediction deviation is greatly reduced.

The invention discloses a biomass-based reinforcing agent for paper and application of the biomass-based reinforcing agent. The reinforcing agent comprises a biomass-based material, the biomass-based material comprises a micron-sized structure and a nano-sized structure distributed on the micron-sized structure. The reinforcing agent prepared by the invention has abundant nanostructures, is beneficial to physical crosslinking such as entanglement and bridging of fibers in paper pulp, and improves the wet strength of paper; the preparation of the reinforcing agent is a simple physical process, and a complex synthesis chemical process for synthesizing a polymer in a traditional organic reinforcing agent is avoided; the reinforcing agent is prepared by a dissolution regeneration method, so that the energy consumption is low, the solvent can be recycled and reused, no chemicals are consumed, and the reinforcing agent is environment-friendly; the reinforcing agent prepared by the invention can achieve a satisfactory wet strength effect at a lower content.

The embodiment of the invention provides a hydrochloric acid PH control device in a germanium oxide production process and a control method thereof, and belongs to the field of chemical process automatic control. The device comprises an acid liquor storage tank used for storing raw liquor hydrochloric acid and connected to an acid mixing tank through a raw acid feeding control valve; the acid mixing tank is used for mixing an acid solution with a diluent and is connected to the pH regulation reaction tank through a mixed acid feeding control valve; the alkali liquor storage tank is connected to the pH regulation reaction tank through an alkali liquor adding control valve; the pH regulation reaction tank is used for receiving the mixed acid liquor and the alkali liquor and regulating the pH value of a reaction system, and is provided with a pH sensor for detecting the pH value in real time; and the control unit is used for dynamically adjusting the opening degree of each valve by adopting an improved fuzzy self-adaptive PID control algorithm according to a detection result of the pH sensor so as to control the pH value to be within a target range. According to the scheme, the problems of overshoot and lag caused by traditional fixed parameter PID adjustment are solved, and therefore the stability of the pH value of the solution and the consistency of the technology in the germanium oxide production process are guaranteed.

The invention belongs to the technical field of chemical process intelligent control, and discloses an iron phosphate synthesis parameter self-adaptive control system based on an intelligent algorithm. The system is composed of a target quality sensing module, a reaction data acquisition and preprocessing module, a sensitive section identification module, a process boundary modeling module, a multi-strategy control model library module, a strategy evaluation and structure evolution module and a control execution and feedback module. According to the method, a multi-module cooperation mechanism including target scoring modeling, dynamic sensitive segment identification and reaction boundary modeling is constructed, so that whole-process online identification and segmented precise control of the crystal form, the particle size and the crystallinity of a product in the iron phosphate synthesis process are realized; compared with a traditional control system depending on experience setting and univariate feedback, the system has the advantages of being higher in state sensing depth, more accurate in crystal form conversion process recognition, finer in reaction stage regulation and control and the like, and the problems of crystal form deviation, particle size drift and inter-batch quality fluctuation are effectively solved.

A simulation system and method for hydrogen production by water electrolysis. The simulation system for hydrogen production by water electrolysis includes: a first simulation unit used for simulating a hydrogen production power system to obtain hydrogen production electrical parameters; a controller unit used for outputting a control instruction to control hydrogen production process parameters in a hydrogen production chemical system; a second simulation unit used for simulating the hydrogen production chemical system according to the hydrogen production electrical parameters and the control instruction so as to obtain a hydrogen production result; and a data interaction unit, the first simulation unit, the controller unit, and the second simulation unit being capable of performing data interaction by means of the data interaction unit. Joint simulation of complete chemical and electrical processes for hydrogen production by water electrolysis can be realized.

The invention relates to the technical field of computer application, and particularly discloses a chemical industry park safety risk assessment method based on an agent model. The method comprises the following steps: constructing a multi-agent collaborative perception framework, and fusing multi-source heterogeneous data of equipment, environment, materials and personnel to form a park panoramic view under a unified space-time reference; establishing an agent behavior model library embedded with the chemical process mechanism and the safety interlocking logic; an intelligent agent decision strategy is optimized through deep reinforcement learning, and multi-target training is carried out by taking a safety index as a reward function; executing dynamic risk deduction and accident chain simulation in a digital twin environment, and quantifying risk probability and consequences in combination with Monte Carlo sampling; and finally, a visual thermodynamic diagram and an interpretable risk assessment report are generated, weak links are identified, and intervention suggestions are provided. According to the technical scheme, dynamic, accurate and prospective evaluation of the safety risk of the chemical industry park can be realized, and the early warning timeliness and the evaluation reliability are remarkably improved.

The invention discloses a micro-fluidic chip, a sensing electrode and a sensor of wearable equipment, belongs to the technical field of electrochemical method testing, and solves the technical problem that an existing wearable sensor cannot improve the sensitivity and accuracy of sweat component detection. The micro-fluidic chip comprises a substrate layer, a channel layer and a chip cover layer which are attached in sequence, the substrate layer comprises a sweat inlet and a cavity, the channel layer comprises a sweat absorbing opening, a micro-channel, a detection area, a capillary pump, a stop valve and a ventilation opening which are communicated in sequence, the sweat inlet is communicated with the sweat absorbing opening, and the cavity is communicated with the detection area. The sensing electrode is of a four-layer structure, and a working electrode located on a working electrode layer is manufactured through a chemical process. In a sensor of the wearable equipment, a sensing electrode is embedded in a cavity of a micro-fluidic chip and is used for being in contact with sweat flowing into the cavity, so that the content of glucose, sodium ions and cortisol in the sweat is detected. The method is suitable for the technical field of wearable sweat detection and can be applied to research and development of wearable detection equipment.

A system for online-coupled atmospheric chemical transport modeling and data assimilation is built using online coupling and secondary parallelization. In the prototype code of a nested air quality forecasting model system, a parallel data assimilation framework routine is introduced. The system includes observation modules, model modules, and assimilation modules. The observation module is responsible for flexible access and preprocessing of various component-type observation data. The model module handles the model integration of initial fields, involving calculations of physical and chemical processes. The assimilation module performs analysis assimilation of model state variables. This system meets the requirements for coordinated assimilation of multiple chemical component variables, simultaneous introduction and flexible combination of various types of observation data, and effective handling of non-linear and non-Gaussian distribution issues in chemical assimilation.

The invention relates to a chemical production anomaly detection method based on a space-time diagram variational encoder. The method comprises the following steps: selecting public chemical normal working condition data D; an anomaly monitoring model ST-GVAE is constructed, a loss function L (.) is constructed, D serves as input, an Adam optimizer is adopted to train ST-GVAE, model parameters are reversely updated through gradient descent, and trained ST-GVAE'is obtained; a to-be-detected target variable a is selected, normal working condition historical data corresponding to a are input into ST-GVAE ', and an anomaly detection score corresponding to a is obtained and serves as an anomaly detection threshold value of the target variable a; inputting the current chemical working condition data of a into ST-GVAE'to obtain an anomaly detection score corresponding to the current state of a, and if the score is greater than a threshold value, triggering an alarm; and then calculating a reconstruction deviation score B of each node in the PID, taking the node corresponding to the B greater than a deviation threshold as an abnormal node, and outputting the abnormal node. By using the method provided by the invention, the variable abnormal condition of the existing chemical process can be accurately detected and positioned.

The invention discloses a chemical process multi-parameter intelligent monitoring method based on deep learning, and the method comprises the following steps: collecting and preprocessing the data of a chemical production process, and obtaining a standardized input sequence; according to the chemical process flow diagram and the equipment topological relation, a standardized process structure matrix is obtained; inputting the standardized input sequence and the standardized process structure matrix into a constraint decoupling echo state network model, and outputting a hidden state sequence; performing multi-scale gating fusion on the hidden state sequence, and outputting a parameter prediction result; calculating a residual error based on a parameter prediction result and an actual observation value, and outputting an anomaly detection result and channel-level attribution information; triggering a self-adaptive correction process to form a monitoring model after self-adaptive correction; and outputting a parameter prediction result, an anomaly detection result and channel-level attribution information based on the monitoring model after adaptive correction. According to the invention, the constraint decoupling echo state network is adopted, and multi-parameter intelligent monitoring of the chemical process is realized.

The invention relates to the technical field of dynamic monitoring, in particular to a chemical production safety risk dynamic monitoring system based on the Internet of Things, and the system comprises a data collection module which receives pressure monitoring data, temperature fluctuation data and pipeline flow velocity data measured by a chemical plant sensor in real time, records the time of each data point, and transmits the data to a server; and sorting and organizing the data according to a time sequence. According to the method, by receiving and sorting the chemical plant sensor data and implementing comprehensive analysis of the multi-scale time window, the capture and analysis precision of key parameter fluctuation is remarkably improved, the scientificity of rapid identification and risk prediction of variable fluctuation in the chemical process is promoted, potential risks can be found in advance, and the risk prediction efficiency is improved. Therefore, production strategies are adjusted in real time, production safety is ensured, pre-judgment of future risks is optimized by analyzing long-term trends and periodic changes, the accuracy of risk assessment and the safety of operation are improved, the probability of accidents is reduced, and the safety of personnel and equipment is protected.

The invention discloses a fuel gas system management method and system based on data analysis, and belongs to the technical field of fuel gas system management.The fuel gas system management method comprises the steps that a wax deposition kinetic model and a deep neural network are combined, and a hybrid prediction model architecture is designed; the wax deposition kinetic model is used for describing physical and chemical processes of wax deposition and providing basic prediction capability; the deep neural network is used for capturing a non-linear relationship and a complex mode in the data and improving the accuracy and generalization ability of prediction; based on an improved particle swarm optimization algorithm (PSO-ADE), a self-adaptive pigging decision engine is designed; initializing the algorithm to generate an initial particle swarm; operating an algorithm, and evaluating and updating the particles according to a target function; through iterative optimization, an optimal pigging strategy is found; and feedback data is collected, and the algorithm is further adjusted and optimized, so that the accuracy and effectiveness of decision making are improved. The pipeline cleaning strategy of the oil pipeline can be optimized.

The invention relates to a quality prediction method based on spatio-temporal dynamic graph convolution and mutual attention selection, and the method comprises the steps: collecting historical observation values of a target variable and a process variable in a chemical process, combining the historical observation values into a one-dimensional vector, carrying out the dimension rising processing of the time dimension of the one-dimensional vector, and carrying out the feature embedding and grouping processing, thereby obtaining a plurality of grouping features; respectively carrying out convolution operation on each grouping feature to obtain a corresponding time feature representation; obtaining a first time-space representation corresponding to the time feature representation based on the adaptive topological structure and a gating mechanism; aggregating each time feature representation through the attention weight to obtain an aggregated feature; carrying out average pooling and convolution operation on the aggregated features in the time dimension and the node feature dimension to obtain global features, time local features and node local features, and then obtaining second space-time representation; and performing residual connection on the second spatio-temporal representation and each first spatio-temporal representation, and outputting a predicted value of the target variable through a full connection layer.

The invention relates to the technical field of automation control, in particular to a high-quality phosphate chemical process automation system which comprises a resource demand prediction module, a concentration prediction adjustment module, a temperature real-time control module, a production data analysis module and a deviation correction module. According to the method, through material consumption prediction and concentration adjustment, in combination with temperature and flow control, accurate adjustment can be carried out in time, optimization of production efficiency and quality is guaranteed, errors caused by manual operation or system deviation in the production process are effectively avoided by analyzing deviation data and correcting deviation exceeding an allowable range, and the production quality is improved. The self-adaptive capacity of production is enhanced, product consistency and production stability are optimized, production parameters of all links can be adjusted in real time, it is ensured that the whole process is efficient and stable, resource use is optimized, energy consumption and material waste are reduced, and the production process better meets the requirements of green and sustainable development.

The invention discloses an internal-acid and external-alkali type organic-inorganic hybrid molecular sieve catalyst as well as a preparation method and application thereof, and belongs to the technical field of heterogeneous catalytic materials and chemical processes. According to the catalyst, a hydrogen type Beta molecular sieve is used as an acidic inner core, polyvinylpyrrolidone (PVP) is used as an organic shell, and a unique inner-acid outer-alkali structure is formed. The preparation method comprises the following steps: hydrothermally synthesizing a hydrogen type Beta molecular sieve with a specific silica-alumina ratio, and then coating PVP through surface adsorption. The catalyst is used for catalyzing the condensation of methylal and trioxymethylene under mild conditions (40-70 DEG C and normal pressure) to prepare polyoxymethylene dimethyl ethers (DMMn). The PVP shell covers acidic sites on the outer surface of the molecular sieve to form an external neutral / weak base and internal strong acid microenvironment, so that the secondary side reaction of the product is effectively inhibited, the trioxymethylene conversion rate of more than 98% and the DMMn selectivity of more than 96% can be obtained in the reaction at 50 DEG C and normal pressure, the product distribution can be regulated and controlled, and the traditional Anderson-Schulz-Flory distribution limitation is broken through. The catalyst disclosed by the invention has the advantages of high activity, high selectivity, mild conditions and easiness in separation, and has a good industrial application prospect.

The invention discloses a chemical process anomaly type identification method based on a multi-dimensional causal association index, and belongs to the technical field of chemical process monitoring and fault diagnosis. The method comprises the following steps: firstly, carrying out offline modeling: acquiring normal working condition data, constructing a monitoring model by utilizing a trend slow feature analysis algorithm, and determining a statistic control limit; during online application, statistics of real-time data are calculated through a model, and an alarm is given and a type identification process is triggered if the statistics exceed a limit. A multi-dimensional causal association index (MCAI) is constructed by analyzing whether a quality variable deviates or not and further performing comprehensive analysis on a root variable causing an anomaly from four dimensions of a position (CPI) of the root variable in a process, an attribute (CAI) of the root variable in a causal network and an influence propagation capability (CPD) of the root variable. The problem that a traditional anomaly detection method cannot distinguish process faults from new working conditions is solved, false alarm can be effectively avoided, and a decision basis is provided for accurate maintenance.

The invention relates to the technical field of chemical process control, in particular to an HCl oxidation fluidized bed reactor control method and device and computer equipment, and the method comprises the steps: obtaining first real-time state information and second real-time state information of an HCl oxidation fluidized bed reactor; obtaining a target temperature of the HCl oxidation fluidized bed reactor according to the first real-time state information and a preset temperature optimization model; according to the target temperature and the second real-time state information, the HCl raw gas temperature set value, the oxygen temperature set value and the recycle gas preheating temperature set value of the HCl oxidation fluidized bed reactor are adjusted. According to the method, key operation parameters, such as the temperature of each part, the gas flow and the preheating temperature, the use number of cooling pipes, the catalyst operation time and the like, of the reactor are collected in real time, and an intelligent temperature optimization model is combined, so that the state of the reactor is dynamically sensed and accurately regulated and controlled, and the automation degree and the response speed of a control system are remarkably improved.

A method, system, and computer program product are described capable of controlling an industrial based chemical process through accessing sensor data, pre-processing accessed sensor data through an automated process, forming an initial prediction model of the chemical process, and automatically determining linearity or non-linearity of the initial prediction model. As a function of quantitatively measured linearity, being non-linear, quasilinear, or linear, automatically train the initial prediction model, then deploy the trained prediction model in a manner that controls the subject chemical process at the industrial plant, including optimizing consumption of a certain resource. Data-driven modeling of non-linear, continuous-like industrial or chemical processes results.

The invention discloses a chemical process knowledge extraction method fusing auxiliary annotation and dependency analysis, and belongs to the technical field of natural language processing. The method comprises the following steps: collecting production materials and operation and maintenance materials of a chemical process as original corpora; processing and annotating partial data in the original corpus, including data set segmentation, keyword acquisition, keyword annotation, data set annotation and data set format conversion to obtain a data set with a tag; inputting the data set with the label and the original data into a knowledge extraction model to perform preliminary knowledge extraction, including BiLSTM-CRF model training, named entity identification by using the trained model, and relation extraction by using a dependency syntax analysis method; the initial results are fused, and a knowledge extraction task is completed; the extracted knowledge is stored in a graph database in a triple format, which is convenient for workers to inquire and learn, so as to reduce the dependence of operation and maintenance work on experts, improve the operation and maintenance efficiency and reduce the personnel training cost.

The invention discloses an HAZOP analysis method and system based on digital delivery, and relates to the technical field of chemical safety risk analysis, and the method comprises the steps: obtaining to-be-analyzed digital delivery data; carrying out digital delivery model integration and dynamic verification by adopting a topological correlation theory and a rule engine method; performing multi-source process data standardization processing and risk element mapping by adopting a statistical learning and semantic matching method; performing model-driven deviation intelligent generation and causal chain analysis by adopting a topological reasoning and analogue simulation method; carrying out cross-professional collaborative optimization and measure closed-loop tracking by adopting a collaborative filtering and closed-loop control method; and carrying out full-life-cycle data linkage and iterative optimization by adopting a feedback learning and knowledge iteration method to generate a standardized report. According to the method and the system, full-process digitization, intelligentization and full-life-cycle management and control of chemical process risk analysis can be realized, and the precision and the efficiency of HAZOP analysis are improved.

The invention provides a chemical process safe and stable operation method and system combined with a DCS, and relates to the technical field of chemical process control, and the method comprises the steps: firstly collecting multi-dimensional operation data of each operation link of a chemical process, and forming a disturbance trigger signal set through multi-feature correlation analysis; constructing an operation element disturbance diffusion dynamic logic map based on the chemical process network; evaluating the response capability of the DCS control node according to the disturbance trigger signal set and the running element disturbance diffusion dynamic logic map, dividing multiple response stages and determining a core operation range; generating a control node composite operation sequence containing a conventional sequence and an emergency sequence; issuing a control node composite operation sequence and performing real-time monitoring to form a running state dynamic tracking record; and on the basis of the running state dynamic tracking record verification correction atlas and logic, a self-adaptive dynamic response mechanism is formed through multi-round iteration, and safe and stable running of the chemical process is guaranteed.

The invention provides a chemical process key parameter component prediction method based on transfer learning and sparse self-attention, and the method comprises the steps: building a transferable sparse self-attention network TA-Informer, and effectively describing the inertia and nonlinear characteristics in a chemical process through employing a sparse self-attention mechanism; meanwhile, adversarial learning is carried out by adopting a minimum-maximum game theory, the robustness of the model and the implicit extraction capability of high-order statistical information are improved, in addition, transfer learning is introduced, sufficient process data in a source domain is fully utilized for model training, knowledge of the source domain pre-training model is utilized, learned features are effectively migrated to a target domain, and the robustness of the model is improved. The problem that modeling is difficult due to the fact that offline test analysis data samples are extremely scarce is effectively solved, and a powerful basis is provided for operation such as follow-up process monitoring, optimization and control.

The following relates generally to systems and methods for using chemical reactions, electrodialysis of cation-containing brines, such as NaCl and biogenic carbon dioxide sources, such as seawater, to create net-zero cation carbonates, such as soda ash, and / or carbon dioxide gas with an acid such as HC1 as byproduct for sales and / or further use.

The present invention discloses a soaking device for leather modification based on chemical process in the field of leather processing technology, comprising a tanning barrel for soaking leather, a lifting assembly for conveying the leather into the tanning barrel being arranged above the tanning barrel, and a plurality of fixing assemblies distributed in a circular shape and at equal intervals being arranged at the bottom of the lifting assembly. The present invention adopts the fixing assembly, clamps the leather at both ends, and stretches the leather from the middle section so that the leather is fixed and stretched and laid. In a vertical manner, the height of the support block gradually rises, so that the animal skin is gradually stretched and extended; in the horizontal direction, the support blocks on both sides are stretched by the rod body to slide toward the two ends of the support rod, so that the animal skin is also gradually stretched and extended in the horizontal direction, so that the leather can fully contact with the tanning agent during the tanning process, thereby improving the tanning treatment effect on the leather.

The invention relates to the technical field of chemical production safety, in particular to a chemical production safety risk intelligent reasoning method and system and a storage medium, and the method comprises the steps: building a multi-level flow model according to a chemical production flow chart, and constructing a fault tree analysis qualitative model for the multi-level flow model through a danger and operability analysis technology; fusing and correcting the multiple groups of evidence sources according to the D-S evidence theory to obtain the initial abnormal probability of each basic event; and establishing a dynamic Bayesian network quantitative model in combination with a logic gate rule, and performing forward reasoning and backward reasoning. According to the invention, forward reasoning and backward reasoning can be carried out on abnormal event nodes in the chemical production process, causal traceability is completed, an abnormal propagation path is explored, and an effective management means is provided for safe production in the chemical process.