Chemical enterprise safety production informatization management system and working method thereof

Abstract

The invention relates to the technical field of informatization management systems, in particular to a chemical enterprise safety production informatization management system and a working method thereof. Comprising a safety early warning monitoring module, a chemical management module, a data analysis prediction maintenance module, a chemical operation reaction module, a safety behavior management module, a carbon emission management module, an energy management energy-saving optimization module and a virtual chemical experiment simulation module. The safety early warning monitoring module is used for real-time monitoring, early warning prediction and virtual reality emergency drilling; by implementing the safety production informatization management system, the safety production level and management efficiency are remarkably improved, pollution monitoring is performed through the environment monitoring system, the emission conditions of waste gas, waste liquid and waste residues are monitored in real time, safety production education and training are performed on staff regularly, the safety consciousness and operation skills of the staff are improved, and the safety production management efficiency is improved. Safety awareness and operation skills of employees are enhanced, and safety production informatization management of chemical enterprises is realized and better served.

Description

Technical Field

[0001] The present invention relates to the technical field of information management systems, and in particular to a chemical enterprise production safety information management system and a working method thereof. Background Art

[0002] Chemical industry refers to the industry that uses chemical methods and chemical reactions to process raw materials into products. Since the 19th century, the chemical industry has developed rapidly on a global scale and has become one of the important pillars of modern industry. The development of chemical industry has been accompanied by the advancement of science and technology, and has undergone a major transformation from traditional alchemy to modern chemical production. Modern chemical industry covers multiple fields such as petrochemicals, basic chemicals, organic chemicals, inorganic chemicals, fine chemicals and new materials, and is widely used in agriculture, medicine, food, energy, materials and other industries. The main production contents of chemical companies include basic chemicals, petrochemical products, agricultural chemicals, fine chemicals, pharmaceutical chemicals and new materials.

[0003] However, chemical companies are often accompanied by many risks in the production process, mainly including the leakage of flammable, explosive and toxic chemicals that may cause fire, explosion and poisoning incidents, the failure of equipment such as reactors, pressure vessels and transmission pipelines may cause safety accidents, human operational errors may lead to accidents, and improper handling of waste gas, waste liquid and waste residue may cause serious pollution to the environment. Chemical companies use a large amount of flammable and explosive substances, and the slightest carelessness may cause fire and explosion, causing immeasurable casualties and property losses. Therefore, it is particularly important to carry out efficient management in the production process of chemical companies. The production management of traditional chemical companies formulates production plans according to market demand and production capacity, regularly maintains and inspects production equipment, formulates and implements production safety rules and regulations, and establishes a quality management system.

[0004] However, in general, the production management methods of traditional chemical companies mainly rely on manual records and reports, information acquisition is delayed, and it is difficult to detect and deal with problems in a timely manner. Production management and safety management are mostly dependent on manual work, which is inefficient and prone to omissions. There is a lack of real-time monitoring and early warning systems, and it is difficult to respond and deal with accidents in a timely manner. Traditional safety hazard inspection methods rely on manual experience, and there are blind spots and omissions. Environmental pollution monitoring methods are backward, making it difficult to detect and control pollution sources in a timely manner, and are weak in energy and carbon emission optimization.

[0005] In summary, it is necessary to propose a chemical enterprise safety production information management system and its working method to solve the above problems. Summary of the invention

[0006] The purpose of the present invention is to provide a chemical enterprise production safety information management system and a working method thereof to solve the problems raised in the above background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A chemical enterprise production safety information management system, including a safety early warning monitoring module, a chemical management module, a data analysis prediction maintenance module, a chemical operation reaction module, a safety behavior management module, a carbon emission management module, an energy management energy-saving optimization module, and a virtual chemical experiment simulation module;

[0009] The safety early warning monitoring module is used for real-time monitoring, early warning prediction and virtual reality emergency drills;

[0010] The chemical management module is used for chemical risk assessment and dynamic safety label generation;

[0011] The data analysis and predictive maintenance module is used for data mining analysis and predictive maintenance optimization;

[0012] The chemical operation reaction module is used for chemical operation optimization and reaction prediction control;

[0013] The safety behavior management module is used for safety culture construction and employee behavior analysis;

[0014] The carbon emission management module is used for environmental monitoring and analysis and carbon emission optimization;

[0015] The energy management energy saving optimization module is used for energy consumption prediction and energy optimization;

[0016] The virtual chemical experiment simulation module is used for virtual chemical experiment design and simulation prediction feedback.

[0017] Preferably, the safety warning monitoring module further includes a real-time monitoring unit, a warning prediction unit and a virtual reality emergency drill unit;

[0018] The real-time monitoring unit uses IoT devices and AI algorithms to monitor key parameters in the chemical process in real time, including gas concentration, pressure, temperature, and equipment status;

[0019] The early warning prediction unit is based on big data analysis and machine learning to predict possible safety risks and accidents and provide real-time warnings and recommended safety measures;

[0020] The virtual reality emergency drill unit is used to conduct virtual reality drills for chemical accidents using VR technology to train employees to deal with complex and dangerous situations.

[0021] Preferably, the chemical management module further comprises a chemical risk assessment unit and a dynamic safety label generation unit;

[0022] The chemical risk assessment unit is used to combine the chemical database and the safety assessment algorithm to assess the risk level of different chemicals and formulate safe use standards and procedures;

[0023] The dynamic safety label generation unit is used to generate dynamic safety labels in real time according to chemical properties and storage conditions to ensure that employees can correctly identify and handle chemicals.

[0024] Preferably, the data analysis and predictive maintenance module further includes a data mining and analysis unit and a predictive maintenance optimization unit;

[0025] The data mining and analysis unit is used to analyze implicit relationships and potential safety hazards in the chemical process using data mining technology and deep learning algorithms;

[0026] The predictive maintenance optimization unit is used to predict equipment failures and optimize preventive maintenance strategies by analyzing equipment sensor data and historical maintenance records.

[0027] Preferably, the chemical operation reaction module further includes a chemical operation optimization unit and a reaction prediction control unit;

[0028] The chemical operation optimization unit is used to combine the automated control system and the AI ​​algorithm to optimize the operating parameters of the chemical process and improve production efficiency and safety;

[0029] The reaction prediction control unit is used to use machine learning technology to predict the results and possible by-products of chemical reactions, and realize real-time reaction control and adjustment.

[0030] Preferably, the safety behavior management module also includes a safety culture construction unit and an employee behavior analysis unit;

[0031] The safety culture construction unit is used to provide personalized safety education and culture training programs, and enhance employees' safety awareness through online games and simulation scenarios;

[0032] The employee behavior analysis unit identifies and provides feedback on unsafe behaviors and promotes the development of safe behaviors by analyzing employee behavior data and real-time monitoring.

[0033] Preferably, the carbon emission management module further includes an environmental monitoring and analysis unit and a carbon emission optimization unit;

[0034] The environmental monitoring and analysis unit is used to monitor environmental parameters of chemical enterprises, including noise, water quality, and air quality, and analyze their impact on employee health and production safety;

[0035] The carbon emission optimization unit utilizes a carbon emission calculation model and an optimization algorithm to achieve real-time monitoring and optimization of carbon emissions during chemical production.

[0036] Preferably, the energy management energy-saving optimization module further includes an energy consumption prediction unit and an energy optimization unit;

[0037] The energy consumption prediction unit is used to analyze energy consumption patterns in chemical production, predict future consumption trends, and make energy-saving recommendations;

[0038] The energy optimization unit is used to combine the control system and the energy management algorithm to optimize the energy utilization efficiency in the chemical process and reduce energy waste.

[0039] Preferably, the virtual chemical experiment simulation module further includes a virtual chemical experiment design unit and a simulation prediction feedback unit;

[0040] The virtual chemical experiment design unit is used to design and simulate experimental schemes of different chemical processes using the virtual chemical experiment platform to reduce experimental risks and costs;

[0041] The simulation prediction feedback unit is used to predict experimental results using advanced simulation technology and optimize production processes and safety measures based on experimental feedback.

[0042] Based on the above system, the present invention also proposes a working method of a chemical enterprise production safety information management system, comprising the following steps:

[0043] S1. System deployment and integration: Determine the system deployment plan and schedule, install and integrate each module into the existing enterprise information system;

[0044] S2. System configuration and initialization: Perform system configuration, including setting monitoring parameters, safety standards and alarm thresholds, and initializing databases and safety management rules;

[0045] S3. Data collection and interface establishment: Deploy IoT devices and sensors to ensure the reliability of real-time data collection, establish interfaces with various devices and systems, and ensure effective data transmission and integration;

[0046] S4. Real-time monitoring and data processing: Set real-time monitoring tasks, monitor key parameters including gas concentration, pressure, and temperature, process and analyze the collected data in real time, identify abnormal situations and generate real-time alarms;

[0047] S5. Safety warning and response: Based on the output of the warning prediction module, implement corresponding safety emergency measures and train employees to deal with emergencies, including emergency drills using virtual reality technology;

[0048] S6. Safety culture construction and behavior analysis: Implement safety culture training programs, including personalized safety education and training, and identify potential safety risks and improvement points through employee behavior analysis;

[0049] S7. Environmental monitoring and carbon emission control: Implement environmental monitoring programs, monitor and analyze environmental parameters including noise, water quality, and air quality, and use carbon emission management modules to optimize carbon emissions to ensure compliance with environmental regulations and corporate goals;

[0050] S8. Energy management and energy-saving optimization: Analyze energy consumption patterns, predict future energy demand trends, and implement energy optimization measures, including improving equipment efficiency and optimizing energy utilization in the production process;

[0051] S9. Virtual Chemical Experiment Design and Simulation: Use the virtual chemical experiment platform to design and simulate experimental plans for chemical processes, and optimize production processes and safety measures through simulation prediction feedback.

[0052] Compared with the prior art, the beneficial effects of the present invention are as follows: the present invention significantly improves the safety production level and management efficiency by implementing a safety production information management system, monitors the equipment status through a real-time monitoring system, collects and analyzes equipment operation data in real time, promptly discovers equipment failures, improves the safety and reliability of equipment, reduces downtime and maintenance costs, and controls the production process through an automated control system: automatically controls chemical reaction conditions to ensure the stability and consistency of the production process, improves production efficiency and product quality, conducts safety hazard investigation through data analysis, uses big data analysis technology to identify and evaluate potential safety hazards, formulates corresponding prevention and control measures, improves the accuracy and timeliness of safety hazard investigation, and reduces the probability of accidents, conducts pollution monitoring through an environmental monitoring system, monitors the emission of waste gas, waste liquid, and waste residue in real time, reduces environmental pollution, and enhances the environmental protection image of the enterprise, conducts employee training through a training management system, and regularly conducts safety production education and training for employees in a combination of online and offline methods, improves employees' safety awareness and operating skills, enhances employees' safety awareness and operating skills, reduces human operating errors, and realizes and better serves the safety production information management of chemical enterprises. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] Figure 1 A topological diagram of a chemical enterprise production safety information management system of the present invention;

[0054] Figure 2 The present invention is a flow chart of the working method of the chemical enterprise production safety information management system. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0056] Example 1

[0057] See also Figure 1 The present invention proposes a chemical enterprise safety production information management system, which consists of a safety early warning monitoring module, a chemical management module, a data analysis prediction maintenance module, a chemical operation reaction module, a safety behavior management module, a carbon emission management module, an energy management energy-saving optimization module, and a virtual chemical experiment simulation module;

[0058] Among them, it should be noted that this system performs real-time monitoring, early warning prediction and virtual reality emergency drills through the safety early warning monitoring module, this system performs chemical risk assessment and dynamic safety label generation through the chemical management module, this system performs data mining analysis and predictive maintenance optimization through the data analysis and predictive maintenance module, this system performs chemical operation optimization and reaction predictive control through the chemical operation reaction module, this system carries out safety culture construction and employee behavior analysis through the safety behavior management module, this system performs environmental monitoring analysis and carbon emission optimization through the carbon emission management module, this system performs energy consumption prediction and energy optimization through the energy management and energy-saving optimization module, and this system performs virtual chemical experiment design and simulation prediction feedback through the virtual chemical experiment simulation module.

[0059] In this embodiment, it should also be noted that the safety warning monitoring module of the chemical enterprise's production safety information management system also includes a real-time monitoring unit, a warning prediction unit and a virtual reality emergency drill unit. The real-time monitoring unit uses Internet of Things devices and AI algorithms to monitor key parameters in the chemical process in real time, including gas concentration, pressure, temperature, and equipment status. The warning prediction unit is based on big data analysis and machine learning to predict possible safety risks and accidents, and provide real-time warnings and recommended safety measures. The virtual reality emergency drill unit is used to use VR technology to conduct virtual reality drills of chemical accidents and train employees to deal with complex and dangerous situations.

[0060] In this embodiment, it should also be noted that the chemical management module of the chemical enterprise's production safety information management system also includes a chemical risk assessment unit and a dynamic safety label generation unit. The chemical risk assessment unit is used to combine the chemical database and the safety assessment algorithm to assess the risk level of different chemicals and formulate safe use standards and procedures. The dynamic safety label generation unit is used to generate dynamic safety labels in real time based on the characteristics of the chemicals and storage conditions to ensure that employees correctly identify and handle chemicals.

[0061] In this embodiment, it should also be noted that the data analysis and predictive maintenance module of the chemical enterprise safety production information management system also includes a data mining analysis unit and a predictive maintenance optimization unit. The data mining analysis unit is used to use data mining technology and deep learning algorithms to analyze implicit relationships and potential safety hazards in the chemical process. The predictive maintenance optimization unit is used to predict equipment failures and optimize preventive maintenance strategies by analyzing equipment sensor data and historical maintenance records.

[0062] In this embodiment, it should also be noted that the chemical operation reaction module of the chemical enterprise safety production information management system also includes a chemical operation optimization unit and a reaction prediction control unit. The chemical operation optimization unit is used to combine the automatic control system and the AI ​​algorithm to optimize the operating parameters of the chemical process and improve production efficiency and safety. The reaction prediction control unit is used to use machine learning technology to predict the results and possible by-products of chemical reactions and realize real-time reaction control and adjustment.

[0063] In this embodiment, it should also be noted that the safety behavior management module of the chemical enterprise's production safety information management system also includes a safety culture construction unit and an employee behavior analysis unit. The safety culture construction unit is used to provide personalized safety education and cultural cultivation programs, and enhance employee safety awareness through online games and simulation scenarios. The employee behavior analysis unit analyzes employee behavior data and real-time monitoring to identify and feedback unsafe behaviors, thereby promoting the development of safe behaviors.

[0064] In this embodiment, it should also be noted that the carbon emission management module of the chemical enterprise's production safety information management system also includes an environmental monitoring and analysis unit and a carbon emission optimization unit. The environmental monitoring and analysis unit is used to monitor the environmental parameters of the chemical enterprise, including noise, water quality, and air quality, and analyze their impact on employee health and production safety. The carbon emission optimization unit uses a carbon emission calculation model and optimization algorithm to achieve real-time monitoring and optimization of carbon emissions in the chemical production process.

[0065] In this embodiment, it should also be noted that the energy management and energy-saving optimization module of the chemical enterprise's production safety information management system also includes an energy consumption prediction unit and an energy optimization unit. The energy consumption prediction unit is used to analyze the energy consumption pattern in chemical production, predict future consumption trends, and put forward energy-saving suggestions. The energy optimization unit is used to combine the control system and the energy management algorithm to optimize the energy utilization efficiency in the chemical process and reduce energy waste.

[0066] In this embodiment, it should also be noted that the virtual chemical experiment simulation module of the chemical enterprise's production safety information management system also includes a virtual chemical experiment design unit and a simulation prediction feedback unit. The virtual chemical experiment design unit is used to use the virtual chemical experiment platform to design and simulate experimental plans for different chemical processes to reduce experimental risks and costs. The simulation prediction feedback unit is used to use advanced simulation technology to predict experimental results and optimize production processes and safety measures based on experimental feedback.

[0067] Example 2

[0068] See also Figure 2 Based on the above chemical enterprise production safety information management system, in actual application, the working method of the chemical enterprise production safety information management system specifically includes the following steps:

[0069] S1. System deployment and integration:

[0070] Determine the system deployment plan and schedule, install and integrate each module into the existing enterprise information system;

[0071] Develop a deployment plan:

[0072] Use Microsoft Project to develop a detailed deployment schedule;

[0073] Define the deployment order and dependencies of each module;

[0074] Allocate resources and personnel and identify key milestones;

[0075] Hardware Installation:

[0076] Deploy the required servers, storage devices, and network infrastructure;

[0077] Configure IoT devices and sensors to ensure they are physically and network connected;

[0078] Software Installation:

[0079] Install the operating system and necessary basic software, including MySQL;

[0080] Deploy software for each module, including monitoring system, analysis system and control system;

[0081] System Integration:

[0082] Use Apache Kafka middleware to implement data transmission and message communication between modules;

[0083] Ensure that the interfaces of each module comply with predefined standards and protocols;

[0084] Tested and validated:

[0085] Perform integration testing to ensure the correctness of data flow and functionality between modules;

[0086] Record test results and make necessary adjustments and corrections;

[0087] S2. System configuration and initialization:

[0088] Perform system configuration, including setting monitoring parameters, safety standards and alarm thresholds, initializing databases and safety management rules;

[0089] Parameter settings:

[0090] Use expert systems to set initial values ​​for key monitoring parameters such as gas concentration, pressure, and temperature;

[0091] Determine the normal operating range and alarm thresholds for each parameter;

[0092] Safety standard configuration:

[0093] Configure safety standards and operating procedures according to enterprise safety regulations and industry standards;

[0094] Use the Drools rule engine to automate the management of security standards;

[0095] Database initialization:

[0096] Create the initial database structure, defining tables, views, and indexes;

[0097] Import initial data, including equipment information, historical data, and chemical information;

[0098] Security management rules initialization:

[0099] Write and deploy security management rule scripts to define the processing logic of security incidents;

[0100] Use the rule engine to test and validate the rules;

[0101] System Verification:

[0102] Perform verification testing of system configuration to ensure that all configuration parameters and rules are correctly effective;

[0103] Record configuration documents to facilitate subsequent maintenance and updates;

[0104] S3. Data collection and interface establishment:

[0105] Deploy IoT devices and sensors to ensure the reliability of real-time data collection, establish interfaces with various devices and systems, and ensure effective data transmission and integration;

[0106] IoT Device Deployment:

[0107] Install and calibrate sensors to ensure they can collect data accurately;

[0108] Connect sensors to data collection gateways to ensure real-time data transmission;

[0109] Data transmission protocol configuration:

[0110] Configure the MQTT data transmission protocol to ensure efficient and reliable data transmission;

[0111] Define data formats and transmission frequencies;

[0112] Interface development:

[0113] Develop system interfaces using RESTful API or SOAP API to ensure data interoperability;

[0114] Integrate SCADA system and interact with control system through API;

[0115] Data Integration:

[0116] Use ETL tool Apache Nifi for data integration;

[0117] Convert the collected data into a unified format and store it in a data warehouse;

[0118] Data Validation:

[0119] Perform data validation testing to ensure data integrity and accuracy;

[0120] Use Tableau to conduct preliminary analysis of data and verify the correctness of data collection and transmission;

[0121] S4. Real-time monitoring and data processing:

[0122] Set up real-time monitoring tasks, monitor key parameters, process and analyze collected data in real time, identify abnormal situations and generate real-time alarms;

[0123] Task setting:

[0124] Use Apache Airflow to set up real-time monitoring tasks;

[0125] Define the frequency and priority of tasks;

[0126] Data collection:

[0127] Configure a real-time data acquisition system to ensure that data can be transmitted to the central database in real time;

[0128] Monitor key parameters including gas concentration, pressure, and temperature;

[0129] Data processing:

[0130] Use Apache Flink stream processing framework to process data in real time;

[0131] Perform data cleaning, filtering, and aggregation operations;

[0132] Anomaly Detection:

[0133] K-means clustering is applied for anomaly detection to identify parameters that are outside the normal range;

[0134] Based on a chemical production data set containing multiple parameters, the abnormal data points are identified. The process is as follows:

[0135] import numpy as np

[0136] import pandas as pd

[0137] from sklearn.cluster import KMeans

[0138] from sklearn.preprocessing import StandardScaler

[0139] #Generate sample data

[0140] np.random.seed(42)

[0141] data = np.random.randn(1000,3) # Assume there are 1000 samples, each with 3 parameters

[0142] data=pd.DataFrame(data,columns=['Parameter1','Parameter2','Parameter3'])

[0143] #Standardize the data

[0144] scaler = StandardScaler()

[0145] data_scaled=scaler.fit_transform(data)

[0146] Select features from the dataset and train a K-means model:

[0147] #Train K-means model

[0148] kmeans=KMeans(n_clusters=3,random_state=42)

[0149] kmeans.fit(data_scaled)

[0150] #Get the distance from each data point to its nearest cluster center

[0151] distances=kmeans.transform(data_scaled).min(axis=1)

[0152] Calculate distance threshold

[0153] Determine the distance threshold of the outlier points, using the quantile of the distance as the threshold, the 95% quantile:

[0154] #Determine the distance threshold

[0155] threshold=np.percentile(distances,95)

[0156] #Mark outliers

[0157] anomalies=distances>threshold

[0158] # Add the results to the data frame

[0159] data['Anomaly'] = anomalies

[0160] The cluster center calculation formula is: (\mu_j=\frac{1}{|C_j|}\sum_{x\inC_j}x) where (\mu_j) is the center of the (j)th cluster, (C_j) is the set of data points belonging to the (j)th cluster, and (x) is the data point;

[0161] The distance calculation formula is: (d(x,\mu_j)=|x-\mu_j|) where (d(x,\mu_j)) is the distance from the data point (x) to the cluster center (\mu_j);

[0162] The threshold determination formula is: (\text{Threshold}=\text{percentile}(d,95)) where (d) is the set of distances from all data points to the nearest cluster center, and (\text{percentile}(d,95)) is the 95% quantile of the distance;

[0163] Generate real-time alarm information based on preset alarm thresholds;

[0164] Alarm processing:

[0165] Transmit alarm information to the control center and notify relevant personnel through PagerDuty;

[0166] Record alarm logs to facilitate subsequent analysis and improvement;

[0167] S5. Security warning and response:

[0168] According to the output of the early warning and prediction module, implement corresponding safety emergency measures and train employees to deal with emergencies, including emergency drills using virtual reality technology;

[0169] Early warning forecast:

[0170] Use Hadoop to analyze historical and real-time data to predict possible security risks;

[0171] Apply random forests for risk prediction and assessment;

[0172] Alert generation:

[0173] Generate early warning information based on the forecast results, including possible risk types and recommended response measures;

[0174] Use alarm management system to send early warning notifications;

[0175] Emergency Response:

[0176] Develop and implement emergency response plans, including evacuation and emergency operations;

[0177] Use Camunda to track emergency response performance;

[0178] Virtual Reality Walkthrough:

[0179] Use the Oculus VR technology platform to conduct virtual reality drills for chemical accidents;

[0180] Train employees to deal with complex and dangerous situations and improve emergency response capabilities;

[0181] Feedback and Improvements:

[0182] Collect feedback from emergency drills and analyze the effectiveness of emergency response;

[0183] Optimize emergency response plans and training programs based on feedback results;

[0184] S6. Safety culture construction and behavior analysis:

[0185] Implement a safety culture training program, including personalized safety education and training, and identify potential safety risks and improvement points through employee behavior analysis;

[0186] Safety Education Program Development:

[0187] Use Moodle to develop a safety education plan covering all types of safety knowledge and operating procedures;

[0188] Customize personalized training content and provide differentiated training based on employees' positions and responsibilities;

[0189] Online training implementation:

[0190] Conduct online training courses, using multimedia courseware and interactive teaching;

[0191] Verify employees’ learning effectiveness through online tests and assessments;

[0192] Behavioral data collection:

[0193] Install behavior monitoring equipment (including cameras and sensors) to collect employee behavior data;

[0194] Use Splunk to process and store behavioral data;

[0195] Behavioral analysis:

[0196] Apply decision trees to identify unsafe behaviors of employees;

[0197] Generate behavior analysis reports and make suggestions for improvement;

[0198] Safety culture construction:

[0199] Enhance employees' safety awareness through safety lectures, competitions and safety culture activities;

[0200] Use online games and simulations to reinforce employees’ understanding and practice of safe operations;

[0201] S7. Environmental monitoring and carbon emission control:

[0202] Implement environmental monitoring programs, monitor and analyze environmental parameters, and use carbon emission management modules to optimize carbon emissions to ensure compliance with environmental regulations and corporate goals;

[0203] Environmental monitoring equipment installation:

[0204] Deploy noise sensors, water quality monitors and environmental monitoring equipment;

[0205] Configure the network connection of monitoring equipment to ensure real-time data transmission;

[0206] Environmental data collection:

[0207] Use AWS IoT to collect environmental parameter data in real time;

[0208] Monitoring includes noise, water quality, and air quality parameters;

[0209] Environmental data analysis:

[0210] Use MATLAB to analyze environmental data;

[0211] Assess the impact of environmental parameters on employee health and production safety;

[0212] Carbon emissions calculation:

[0213] Apply GHG Protocol to calculate carbon emissions from chemical production processes;

[0214] Generate carbon emission reports based on emission data;

[0215] Carbon Emission Optimization:

[0216] Using genetic algorithms to optimize carbon emissions in production processes;

[0217] Specifically, the objective function in the optimization problem is defined to minimize the carbon emissions generated during the production process. The process is as follows:

[0218]

[0219] Initialize the population:

[0220] Randomly generate the initial population:

[0221]

[0222]

[0223] Evaluate the fitness of each individual:

[0224]

[0225] Select individuals with high fitness as parents:

[0226]

[0227] Generate new individuals by crossover:

[0228]

[0229]

[0230] Mutate the new individual:

[0231]

[0232] Replace the old population with new individuals:

[0233] population[0:num_parents,:]=parents

[0234] population[num_parents:,:]=offspring

[0235] # Calculate the new fitness

[0236] fitness=evaluate_population(population)

[0237] Repeat the above steps until the termination condition is met, which is when the maximum number of iterations is reached or the fitness is no longer significantly improved;

[0238]

[0239]

[0240] It should also be noted that the fitness calculation formula is: (f(x)=\sum_{i=1}^n x_i^2);

[0241] Among them, (f(x)) is the objective function, (x_i) is the (i)th parameter;

[0242] Crossover operation: (\text{offspring}[k,:]=\begin{cases}\text{parents}[p1,:crossover_point]\\text{parents}[p2,crossover_point:]\end{cases});

[0243] Among them, (k) is the (k)th offspring, (p1) and (p2) are the indices of the parents;

[0244] Mutation operation: (\text{offspring}[idx]+=\text{mutation_value});

[0245] Among them, (\text{mutation_value}) is the mutation value randomly generated within the parameter range;

[0246] Implement carbon reduction measures, including process improvements and equipment efficiency improvements;

[0247] S8. Energy management and energy saving optimization:

[0248] Analyze energy consumption patterns, predict future energy demand trends, and implement energy optimization measures, including improving equipment efficiency and optimizing energy use in production processes;

[0249] Energy data collection:

[0250] Install smart meters and energy consumption monitoring equipment to collect energy consumption data of each production link in real time;

[0251] Use energy management system to integrate all energy consumption data to ensure the comprehensiveness and accuracy of data collection;

[0252] Energy consumption pattern analysis:

[0253] Use R or Python to perform time series analysis on energy consumption data;

[0254] Identify peak and trough periods of energy consumption and identify key factors affecting energy consumption;

[0255] Energy consumption forecast:

[0256] Apply the ARIMA model to predict future energy demand;

[0257] Combine historical data and production plans to generate energy consumption forecast reports for the next period of time;

[0258] Energy Optimization Strategy Development:

[0259] Use GAMS to develop energy optimization strategies and determine the best energy usage plan;

[0260] Optimization strategies include equipment modification, process optimization, and scheduling optimization;

[0261] Energy saving measures implementation:

[0262] Implement energy-saving renovation projects, including replacing high-efficiency equipment, improving production processes and optimizing production scheduling;

[0263] Monitor the implementation effect of energy-saving measures, verify the energy-saving effect through comparative analysis, and adjust and optimize energy-saving strategies;

[0264] S9. Virtual chemical experiment design and simulation:

[0265] Use the virtual chemical experiment platform to design and simulate the experimental scheme of chemical processes, and optimize the production process and safety measures through simulation prediction feedback;

[0266] Virtual experiment platform construction:

[0267] Use Aspen HYSYS virtual simulation software to build a virtual chemical experiment platform;

[0268] Configure various parameters of the virtual platform, including chemical reaction conditions and equipment parameters;

[0269] Experimental design:

[0270] Design experimental schemes for different chemical processes, including reaction conditions and raw material ratios;

[0271] Use the design of experiments (DOE) method to optimize the experimental plan and ensure the effectiveness and reliability of the experiment;

[0272] Simulation:

[0273] Simulate chemical processes on a virtual experiment platform to predict reaction results and possible by-products;

[0274] Use multiple regression analysis data analysis method to analyze and verify the simulation results;

[0275] Results Feedback:

[0276] Collect simulation results and analyze the feasibility and safety of the experimental scheme;

[0277] Optimize production processes and safety measures based on simulation results to ensure efficient and safe production processes;

[0278] Continuous Optimization:

[0279] Apply simulation results to actual production processes to monitor and evaluate the effectiveness of optimization measures;

[0280] Continuously adjust and optimize experimental plans and simulation models according to actual production conditions to improve production efficiency and safety.

[0281] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A chemical enterprise production safety information management system, characterized in that: It includes safety early warning monitoring module, chemical management module, data analysis prediction maintenance module, chemical operation reaction module, safety behavior management module, carbon emission management module, energy management energy saving optimization module, and virtual chemical experiment simulation module; The safety warning monitoring module is used for real-time monitoring, warning prediction and virtual reality emergency drills; The chemical management module is used for chemical risk assessment and dynamic safety label generation; The data analysis and predictive maintenance module is used for data mining analysis and predictive maintenance optimization; The chemical operation reaction module is used for chemical operation optimization and reaction prediction control; The safety behavior management module is used for safety culture construction and employee behavior analysis; The carbon emission management module is used for environmental monitoring analysis and carbon emission optimization; The energy management energy saving optimization module is used for energy consumption prediction and energy optimization; The virtual chemical experiment simulation module is used for virtual chemical experiment design and simulation prediction feedback.

2. A chemical enterprise production safety information management system according to claim 1, characterized in that: The safety early warning monitoring module also includes a real-time monitoring unit, an early warning prediction unit and a virtual reality emergency drill unit; The real-time monitoring unit uses IoT devices and AI algorithms to monitor key parameters in the chemical process in real time, including gas concentration, pressure, temperature, and equipment status; The early warning prediction unit is based on big data analysis and machine learning to predict possible safety risks and accidents and provide real-time warnings and recommended safety measures; The virtual reality emergency drill unit is used to conduct virtual reality drills for chemical accidents using VR technology to train employees to deal with complex and dangerous situations.

3. A chemical enterprise production safety information management system according to claim 2, characterized in that: The chemical management module also includes a chemical risk assessment unit and a dynamic safety label generation unit; The chemical risk assessment unit is used to combine the chemical database and the safety assessment algorithm to assess the risk level of different chemicals and formulate safe use standards and procedures; The dynamic safety label generation unit is used to generate dynamic safety labels in real time according to chemical properties and storage conditions to ensure that employees can correctly identify and handle chemicals.

4. A chemical enterprise production safety information management system according to claim 3, characterized in that: The data analysis and predictive maintenance module also includes a data mining and analysis unit and a predictive maintenance optimization unit; The data mining and analysis unit is used to analyze implicit relationships and potential safety hazards in the chemical process using data mining technology and deep learning algorithms; The predictive maintenance optimization unit is used to predict equipment failures and optimize preventive maintenance strategies by analyzing equipment sensor data and historical maintenance records.

5. A chemical enterprise production safety information management system according to claim 4, characterized in that: The chemical operation reaction module also includes a chemical operation optimization unit and a reaction prediction control unit; The chemical operation optimization unit is used to combine the automated control system and the AI ​​algorithm to optimize the operating parameters of the chemical process and improve production efficiency and safety; The reaction prediction control unit is used to use machine learning technology to predict the results and possible by-products of chemical reactions, and realize real-time reaction control and adjustment.

6. A chemical enterprise production safety information management system according to claim 5, characterized in that: The safety behavior management module also includes a safety culture construction unit and an employee behavior analysis unit; The safety culture construction unit is used to provide personalized safety education and culture training programs, and enhance employees' safety awareness through online games and simulation scenarios; The employee behavior analysis unit identifies and provides feedback on unsafe behaviors and promotes the development of safe behaviors by analyzing employee behavior data and real-time monitoring.

7. A chemical enterprise production safety information management system according to claim 6, characterized in that: The carbon emission management module also includes an environmental monitoring and analysis unit and a carbon emission optimization unit; The environmental monitoring and analysis unit is used to monitor environmental parameters of chemical enterprises, including noise, water quality, and air quality, and analyze their impact on employee health and production safety; The carbon emission optimization unit utilizes a carbon emission calculation model and an optimization algorithm to achieve real-time monitoring and optimization of carbon emissions during chemical production.

8. A chemical enterprise production safety information management system according to claim 7, characterized in that: The energy management energy-saving optimization module also includes an energy consumption prediction unit and an energy optimization unit; The energy consumption prediction unit is used to analyze energy consumption patterns in chemical production, predict future consumption trends, and make energy-saving recommendations; The energy optimization unit is used to combine the control system and the energy management algorithm to optimize the energy utilization efficiency in the chemical process and reduce energy waste.

9. A chemical enterprise production safety information management system according to claim 8, characterized in that: The virtual chemical experiment simulation module also includes a virtual chemical experiment design unit and a simulation prediction feedback unit; The virtual chemical experiment design unit is used to design and simulate experimental schemes of different chemical processes using the virtual chemical experiment platform to reduce experimental risks and costs; The simulation prediction feedback unit is used to predict experimental results using advanced simulation technology and optimize production processes and safety measures based on experimental feedback.

10. A working method of a chemical enterprise production safety information management system, according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1. System deployment and integration: Determine the system deployment plan and schedule, install and integrate each module into the existing enterprise information system; S2. System configuration and initialization: Perform system configuration, including setting monitoring parameters, safety standards and alarm thresholds, and initializing databases and safety management rules; S3. Data collection and interface establishment: Deploy IoT devices and sensors to ensure the reliability of real-time data collection, establish interfaces with various devices and systems, and ensure effective data transmission and integration; S4. Real-time monitoring and data processing: Set real-time monitoring tasks, monitor key parameters including gas concentration, pressure, and temperature, process and analyze the collected data in real time, identify abnormal situations and generate real-time alarms; S5. Safety warning and response: Based on the output of the warning prediction module, implement corresponding safety emergency measures and train employees to deal with emergencies, including emergency drills using virtual reality technology; S6. Safety culture construction and behavior analysis: Implement safety culture training programs, including personalized safety education and training, and identify potential safety risks and improvement points through employee behavior analysis; S7. Environmental monitoring and carbon emission control: Implement environmental monitoring programs, monitor and analyze environmental parameters including noise, water quality, and air quality, and use carbon emission management modules to optimize carbon emissions to ensure compliance with environmental regulations and corporate goals; S8. Energy management and energy-saving optimization: Analyze energy consumption patterns, predict future energy demand trends, and implement energy optimization measures, including improving equipment efficiency and optimizing energy utilization in the production process; S9. Virtual Chemical Experiment Design and Simulation: Use the virtual chemical experiment platform to design and simulate experimental plans for chemical processes, and optimize production processes and safety measures through simulation prediction feedback.

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