Chemical industrial park dangerous chemical supervision method based on identification analysis

Abstract

The invention discloses a chemical industrial park dangerous chemical supervision method based on identification analysis. According to the invention, by monitoring the operation data of the equipment in real time and predicting potential faults and maintenance requirements, preventive maintenance can be carried out in time, and downtime of the equipment caused by faults is avoided, so that the operation efficiency of the equipment and the overall production capacity of a park are improved. And through predictive maintenance, unnecessary maintenance work can be reduced, and the maintenance cost is reduced. And meanwhile, extra maintenance cost and production loss caused by equipment faults are avoided, and the cost is further saved. An RFID reading device is an important part of safety supervision of a chemical industry park, and the stable operation of the RFID reading device is directly related to the safety of the park. Through the predictive analysis model, the equipment fault can be found and processed in advance, and the safety accident risk caused by the equipment fault is reduced, so that the safety of the park is enhanced. The predictive analysis model can automatically generate a maintenance plan and early warning information, manual intervention is reduced, and the management efficiency is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of hazardous chemical supervision, and specifically relates to a hazardous chemical supervision method in a chemical park based on identification resolution. Background Art

[0002] Hazardous chemicals refer to chemical substances that have dangerous properties such as toxicity, corrosion, explosion, and combustion, and may cause serious harm to human bodies, facilities, and the environment. Hazardous chemical supervision is an important part of safe production, aiming to prevent and control safety accidents caused by hazardous chemicals, and to protect people's lives and property and social stability. The following is a brief introduction to the supervision of hazardous chemicals: A strict supervision system is implemented for hazardous chemicals, mainly including the following aspects: A series of laws and regulations on the management of hazardous chemicals have been formulated, such as the "Regulations on the Safety Management of Hazardous Chemicals", which clarify the management requirements for the safe production, storage, transportation, use, and waste disposal of hazardous chemicals, providing a legal basis for supervision. A licensing system is implemented for the production, operation, and use of hazardous chemicals. Enterprises must obtain the corresponding licenses before they can engage in related activities. This move is aimed at raising the industry entry threshold and ensuring that enterprises have the corresponding safe production conditions. Enterprises need to conduct safety evaluations regularly, identify safety hazards, and formulate rectification measures. The regulatory authorities urge enterprises to fulfill their main responsibilities for safe production through inspections and supervision. A hazardous chemical emergency rescue system has been established, including emergency rescue teams, material reserves, and emergency plans, to deal with possible hazardous chemical accidents. Through the above measures, the supervision of hazardous chemicals has achieved remarkable results, the accident rate has decreased year by year, and it has played an important role in protecting the safety of people’s lives and property and social stability.

[0003] However, traditional chemical park equipment maintenance usually relies on regular maintenance plans, which may lead to unnecessary maintenance or failure to detect potential failures in time. At the same time, traditional equipment fault diagnosis and maintenance often rely on the experience and intuition of operators or maintenance personnel. This method may not be accurate enough and is easily affected by personal experience and knowledge level. Summary of the invention

[0004] The purpose of the present invention is to provide a method for supervising hazardous chemicals in a chemical park based on identification resolution in order to solve the above-mentioned problems.

[0005] The technical solution adopted by the present invention is as follows: a method for supervising hazardous chemicals in a chemical park based on identification resolution, the method comprising the following steps:

[0006] S1: Establish a hazardous chemical database in the chemical park: comprehensively collect and organize the property information of all hazardous chemicals in the park, and create a centrally managed database to facilitate the query, update and sharing of chemical safety data;

[0007] S2: Construction of identification resolution system: Select appropriate identification technology (such as RFID, QR code, etc.) to build an identification resolution system to ensure accurate tracking and effective management of hazardous chemicals;

[0008] S3: Real-time monitoring and early warning: Use identification resolution equipment to monitor the dynamics of hazardous chemicals in real time, set early warning thresholds through data analysis, and issue an early warning immediately once an abnormal situation is detected so that preventive measures can be taken in a timely manner;

[0009] S4: Accident emergency response: Develop a detailed accident response process. Once a hazardous chemical accident occurs, quickly locate the accident point through the identification resolution system, activate the emergency plan, and coordinate resources from all parties for emergency disposal;

[0010] S5: Statistical analysis and decision support: Regularly conduct statistical analysis on monitoring data, assess park safety risks, and provide data-driven decision support to park management departments to optimize safety management and enhance emergency response capabilities.

[0011] In a preferred embodiment, the step S1 specifically includes:

[0012] Collect data: Collect basic information on hazardous chemicals produced, stored, used and operated by enterprises in the park, including chemical name, CAS number, molecular formula, use, hazardous properties, etc.;

[0013] Data collation: collate and classify the collected data to ensure that the data is accurate and complete;

[0014] Data entry: Import the sorted data into the chemical park hazardous chemicals database to achieve unified data management.

[0015] In a preferred embodiment, in step S2, RFID (radio frequency identification) technology is selected to build an identification resolution system, and the construction method process includes:

[0016] S1. Demand Analysis and Planning:

[0017] Investigation phase: Understand the types, quantities, storage conditions and usage frequency of hazardous chemicals in the park.

[0018] Demand determination: Clarify the functions that the system needs to achieve, such as automatic identification, real-time tracking, inventory management, etc.

[0019] Budget planning: Determine the budget for system construction based on demand, including costs for hardware equipment, software systems, and personnel training.

[0020] S2. Technology selection and procurement:

[0021] RFID tag selection: Choose RFID tags suitable for hazardous chemical environments, such as tags that are resistant to high temperatures and chemical corrosion.

[0022] Procurement of reading equipment: According to the layout of the park, purchase the corresponding RFID readers, including fixed and handheld reading devices.

[0023] Software system selection: Choose a software system that can support RFID data parsing and management, or customize it.

[0024] S3. System design and implementation:

[0025] Coding rule formulation: Develop a unique identification code for each hazardous chemical, including production date, batch number, chemical composition and other information.

[0026] Tag deployment: Attach RFID tags to the packaging of hazardous chemicals to ensure the tags are secure and have a good reading distance.

[0027] Reading equipment installation: Install RFID readers at key locations in the park, such as warehouse doors, next to production lines, etc.

[0028] Network cabling: Lay out network lines for reading devices to ensure that data can be transmitted to the central server in real time.

[0029] S4.System Integration and Testing:

[0030] Hardware integration: Integrate the RFID reader with the park’s existing security monitoring system, alarm system, etc.

[0031] Software integration: Integrate RFID data management software with the park’s ERP, MES and other information systems.

[0032] Functional test: Perform system functional test to ensure the normal operation of tag reading, data transmission, alarm system, etc.

[0033] Performance testing: testing system response speed, data accuracy, stability and other performance indicators.

[0034] S5. Training and deployment:

[0035] Personnel training: Provide operation training of RFID system to park managers and operators.

[0036] System deployment: The RFID system was officially put into operation to start real-time monitoring of hazardous chemicals.

[0037] Operation manual writing: Write a detailed system operation manual for user reference.

[0038] S6.Maintenance and Optimization:

[0039] Regular inspection: Regularly check the working status of RFID tags and reading devices to ensure stable operation of the system.

[0040] Data backup: Back up system data regularly to prevent data loss.

[0041] System upgrade: Perform necessary upgrades and optimizations on the system based on actual operating conditions.

[0042] In a preferred embodiment, during the system integration and testing process, a device failure prediction model is established based on a machine learning algorithm. The model will analyze the device operation data and predict potential failures and maintenance requirements. The specific steps include:

[0043] S1. Data collection and preprocessing

[0044] Data collection: Collect equipment operation data through sensors installed on RFID reading devices, such as using temperature sensors to monitor the operating temperature of equipment.

[0045] Data cleaning: A sliding average filter is used to remove random noise from the temperature data to ensure smooth data.

[0046] Data integration: Merge temperature data with other sensor data and BK vibration data to form a unified data set for easy analysis.

[0047] S2. Feature Engineering

[0048] Feature selection: Use correlation analysis to select features that are highly correlated with equipment failures, such as the average temperature of the equipment during operation.

[0049] Feature extraction: Extract features through principal component analysis (PCA) to reduce the data dimension while retaining the most important information.

[0050] Feature scaling: Apply the minimum-maximum normalization method to scale the feature values ​​to the [0,1] interval for easier algorithm processing.

[0051] S3. Model selection and training

[0052] Algorithm selection: Gradient Boosting Machine (GBM) algorithm is selected because it performs well in dealing with complex nonlinear relationships.

[0053] Model training: Use the XGBoost library to implement the GBM algorithm, and input the preprocessed data into the model for training.

[0054] Cross-validation: K-fold cross-validation (e.g. 5-fold) is used to evaluate the generalization ability of the model and ensure the performance of the model on unknown data.

[0055] S4. Model evaluation and optimization

[0056] Performance indicators: Calculate the accuracy of the model to measure the accuracy of the model's prediction of equipment failures.

[0057] Model tuning: Use the GridSearch method to adjust the parameters of the XGBoost model (such as learning rate, number of trees, etc.) to find the optimal parameter combination.

[0058] Error Analysis: Analyze cases where the model made incorrect predictions, identify possible feature missing or data imbalance issues, and make adjustments accordingly.

[0059] S5. Prediction and Verification

[0060] Real-time prediction: Apply the trained XGBoost model to real-time data streams to make real-time predictions of equipment failures.

[0061] Prediction verification: Compare the model's prediction results with the actual fault records and calculate the actual accuracy.

[0062] Feedback loop: Adjust the model based on the validation results, such as by adding new features or adjusting feature weights to improve the model.

[0063] S6. Deployment and Application

[0064] System Integration: Integrate the XGBoost model into the existing monitoring system so that it can automatically receive sensor data and output prediction results.

[0065] User Interface: Develop a web-based user interface to display the model's prediction results, including failure probability and recommended maintenance time.

[0066] Maintenance plan: Based on the failure probability predicted by the model, a preventive maintenance plan is developed to conduct equipment inspection and maintenance in advance.

[0067] Through the above detailed steps, a predictive analysis model based on the XGBoost algorithm can be constructed for fault prediction of RFID reading equipment in chemical parks, thereby improving the safety supervision efficiency of the park.

[0068] In a preferred embodiment, in step S1, during the data collection phase, the park management department needs to formulate a detailed collection list and cooperate with the enterprise security management department to ensure the comprehensiveness and accuracy of the data. When sorting data, use automated data cleaning tools, combined with manual review, to standardize the data and eliminate ambiguity and errors. Before data is entered into the database, database managers need to perform multiple data mapping and testing to ensure the integrity and adaptability of the data model. During the warehousing process, batch import and real-time monitoring are used to ensure the efficiency and correctness of data import. After completion, the database is optimized for performance, including index optimization, query optimization, etc., to increase data retrieval speed.

[0069] In a preferred embodiment, in step S3, strict data collection standards are formulated in terms of real-time data collection to ensure the authenticity and validity of the data. In the data transmission link, a multi-channel data transmission mechanism is established and a redundant design is adopted to prevent data loss.

[0070] In a preferred embodiment, in step S3, in the early warning analysis, a warning model based on machine learning is developed, and the warning threshold and model parameters are continuously optimized through historical data analysis. During early warning processing, a hierarchical response mechanism is established, and different processing procedures and measures are formulated according to the warning level to ensure rapid and effective response to potential risks.

[0071] In a preferred embodiment, in step S4, in terms of accident information reporting, a mobile accident reporting application is developed to simplify the reporting process and improve reporting efficiency. In the accident location link, GIS technology is combined to achieve accurate positioning and visual display of the accident site. When the emergency plan is activated, an intelligent plan matching system is used to automatically recommend and activate the corresponding plan according to the type and severity of the accident. In terms of resource scheduling, an emergency resource dynamic management platform is established to achieve rapid query, deployment and tracking of resources.

[0072] In a preferred embodiment, in step S5, in the statistical analysis phase, big data analysis technology is used to deeply mine the massive data to discover potential safety hazards and patterns. In the safety risk assessment, a combination of quantitative and qualitative methods is used to construct a comprehensive risk assessment model to provide a scientific basis for park safety management.

[0073] In a preferred embodiment, in step S5, in terms of decision support, a decision support system is established to provide management with a variety of decision-making plans and expected results through data analysis and simulation exercises to help formulate the best safety management strategy. At the same time, a decision-making effect tracking mechanism is established to continuously adjust and optimize the decision-making process through regular decision-making effect evaluation to improve the scientificity and effectiveness of decision-making.

[0074] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are:

[0075] 1. In the present invention, by real-time monitoring of the operation data of the equipment and predicting potential failures and maintenance needs, preventive maintenance can be carried out in time to avoid downtime caused by equipment failures, thereby improving the operation efficiency of the equipment and the overall production capacity of the park. Through predictive maintenance, unnecessary maintenance work can be reduced and maintenance costs can be reduced. At the same time, additional maintenance costs and production losses caused by equipment failures can be avoided, further saving costs. RFID reading equipment is an important part of the safety supervision of chemical parks, and its stable operation is directly related to the safety of the park. Through the predictive analysis model, equipment failures can be discovered and handled in advance, reducing the risk of safety accidents caused by equipment failures, thereby enhancing the safety of the park. The predictive analysis model can automatically generate maintenance plans and early warning information, reduce manual intervention, and improve management efficiency. At the same time, through data-driven decision support, the park management department can formulate safety management measures more scientifically and further improve management efficiency. Maintenance plans and resource scheduling based on predictive analysis models can achieve the optimal configuration of rescue resources inside and outside the park, improve resource utilization efficiency, and ensure that accidents are effectively handled. Through the predictive analysis model, the park management department can obtain more accurate data analysis results, provide a scientific basis for safety management decisions, and thus enhance data-driven decision support. In summary, building a predictive analysis model based on the XGBoost algorithm for fault prediction of RFID reading equipment in chemical parks has significant beneficial effects on the overall approach, which can improve equipment operation efficiency, reduce maintenance costs, enhance park safety, improve management efficiency, optimize resource allocation, and enhance data-driven decision support.

[0076] 2. In the present invention, data is centrally managed: by establishing a hazardous chemical database for chemical parks, centralized management and unified storage of all hazardous chemical information in the park are achieved, the efficiency of data query, update and sharing is improved, and basic data support is provided for the safety management of the park. The use of RFID technology makes the tracking and management of hazardous chemicals more accurate and efficient. Through real-time monitoring and early warning, potential safety risks can be discovered and dealt with in a timely manner. Once a hazardous chemical accident occurs, the system can quickly locate the accident point, initiate an emergency plan, coordinate resources from all parties for emergency disposal, and effectively improve the speed and efficiency of emergency response to the accident.

[0077] 3. In the present invention, through statistical analysis of monitoring data and safety risk assessment, the park management department can obtain data-driven decision support, thereby optimizing safety management measures and improving the overall safety level of the park. By establishing a machine learning-based equipment failure prediction model, potential failures and maintenance needs can be predicted, predictive maintenance can be achieved, equipment failure rates can be reduced, and the operating efficiency and safety of equipment can be improved. Through the decision-making effect tracking mechanism, the decision-making process can be continuously adjusted and optimized to ensure the scientificity and effectiveness of the safety management strategy, thereby continuously improving the safety management level of the park. DETAILED DESCRIPTION

[0078] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0079] Example:

[0080] A method for monitoring hazardous chemicals in chemical parks based on identification resolution includes the following steps:

[0081] S1: Establish a hazardous chemical database in the chemical park: comprehensively collect and organize the property information of all hazardous chemicals in the park, and create a centrally managed database to facilitate the query, update and sharing of chemical safety data;

[0082] S2: Construction of identification resolution system: Select appropriate identification technology (such as RFID, QR code, etc.) to build an identification resolution system to ensure accurate tracking and effective management of hazardous chemicals;

[0083] S3: Real-time monitoring and early warning: Use identification resolution equipment to monitor the dynamics of hazardous chemicals in real time, set early warning thresholds through data analysis, and issue an early warning immediately once an abnormal situation is detected so that preventive measures can be taken in a timely manner;

[0084] S4: Accident emergency response: Develop a detailed accident response process. Once a hazardous chemical accident occurs, quickly locate the accident point through the identification resolution system, activate the emergency plan, and coordinate resources from all parties for emergency disposal;

[0085] S5: Statistical analysis and decision support: Regularly conduct statistical analysis on monitoring data, assess park safety risks, and provide data-driven decision support to park management departments to optimize safety management and enhance emergency response capabilities.

[0086] Step S1 specifically includes:

[0087] Collect data: Collect basic information on hazardous chemicals produced, stored, used and operated by enterprises in the park, including chemical name, CAS number, molecular formula, use, hazardous properties, etc.;

[0088] Data collation: collate and classify the collected data to ensure that the data is accurate and complete;

[0089] Data entry: Import the sorted data into the chemical park hazardous chemicals database to achieve unified data management.

[0090] In step S2, RFID (radio frequency identification) technology is selected to build an identification resolution system, and the construction method process includes:

[0091] S1. Demand Analysis and Planning:

[0092] Investigation phase: Understand the types, quantities, storage conditions and usage frequency of hazardous chemicals in the park.

[0093] Demand determination: Clarify the functions that the system needs to achieve, such as automatic identification, real-time tracking, inventory management, etc.

[0094] Budget planning: Determine the budget for system construction based on demand, including costs for hardware equipment, software systems, and personnel training.

[0095] S2. Technology selection and procurement:

[0096] RFID tag selection: Choose RFID tags suitable for hazardous chemical environments, such as tags that are resistant to high temperatures and chemical corrosion.

[0097] Procurement of reading equipment: According to the layout of the park, purchase the corresponding RFID readers, including fixed and handheld reading devices.

[0098] Software system selection: Choose a software system that can support RFID data parsing and management, or customize it.

[0099] S3. System design and implementation:

[0100] Coding rule formulation: Develop a unique identification code for each hazardous chemical, including production date, batch number, chemical composition and other information.

[0101] Tag deployment: Attach RFID tags to the packaging of hazardous chemicals to ensure the tags are secure and have a good reading distance.

[0102] Reading equipment installation: Install RFID readers at key locations in the park, such as warehouse doors, next to production lines, etc.

[0103] Network cabling: Lay out network lines for reading devices to ensure that data can be transmitted to the central server in real time.

[0104] S4.System Integration and Testing:

[0105] Hardware integration: Integrate the RFID reader with the park’s existing security monitoring system, alarm system, etc.

[0106] Software integration: Integrate RFID data management software with the park’s ERP, MES and other information systems.

[0107] Functional test: Perform system functional test to ensure the normal operation of tag reading, data transmission, alarm system, etc.

[0108] Performance testing: testing system response speed, data accuracy, stability and other performance indicators.

[0109] S5. Training and deployment:

[0110] Personnel training: Provide operation training of RFID system to park managers and operators.

[0111] System deployment: The RFID system was officially put into operation to start real-time monitoring of hazardous chemicals.

[0112] Operation manual writing: Write a detailed system operation manual for user reference.

[0113] S6.Maintenance and Optimization:

[0114] Regular inspection: Regularly check the working status of RFID tags and reading devices to ensure stable operation of the system.

[0115] Data backup: Back up system data regularly to prevent data loss.

[0116] System upgrade: Perform necessary upgrades and optimizations on the system based on actual operating conditions.

[0117] During the system integration and testing process, a machine learning algorithm is used to establish a device failure prediction model. The model will analyze the device operation data and predict potential failures and maintenance needs. The specific steps include:

[0118] S1. Data collection and preprocessing

[0119] Data collection: Collect equipment operation data through sensors installed on RFID reading devices, such as using temperature sensors to monitor the operating temperature of equipment.

[0120] Data cleaning: A sliding average filter is used to remove random noise from the temperature data to ensure smooth data.

[0121] Data integration: Merge temperature data with other sensor data and BK vibration data to form a unified data set for easy analysis.

[0122] S2. Feature Engineering

[0123] Feature selection: Use correlation analysis to select features that are highly correlated with equipment failures, such as the average temperature of the equipment during operation.

[0124] Feature extraction: Extract features through principal component analysis (PCA) to reduce the data dimension while retaining the most important information.

[0125] Feature scaling: Apply the minimum-maximum normalization method to scale the feature values ​​to the [0,1] interval for easier algorithm processing.

[0126] S3. Model selection and training

[0127] Algorithm selection: Gradient Boosting Machine (GBM) algorithm is selected because it performs well in dealing with complex nonlinear relationships.

[0128] Model training: Use the XGBoost library to implement the GBM algorithm, and input the preprocessed data into the model for training.

[0129] Cross-validation: K-fold cross-validation (e.g. 5-fold) is used to evaluate the generalization ability of the model and ensure the performance of the model on unknown data.

[0130] S4. Model evaluation and optimization

[0131] Performance indicators: Calculate the accuracy of the model to measure the accuracy of the model's prediction of equipment failures.

[0132] Model tuning: Use the GridSearch method to adjust the parameters of the XGBoost model (such as learning rate, number of trees, etc.) to find the optimal parameter combination.

[0133] Error Analysis: Analyze cases where the model made incorrect predictions, identify possible feature missing or data imbalance issues, and make adjustments accordingly.

[0134] S5. Prediction and Verification

[0135] Real-time prediction: Apply the trained XGBoost model to real-time data streams to make real-time predictions of equipment failures.

[0136] Prediction verification: Compare the model's prediction results with the actual fault records and calculate the actual accuracy.

[0137] Feedback loop: Adjust the model based on the validation results, such as by adding new features or adjusting feature weights to improve the model.

[0138] S6. Deployment and Application

[0139] System Integration: Integrate the XGBoost model into the existing monitoring system so that it can automatically receive sensor data and output prediction results.

[0140] User Interface: Develop a web-based user interface to display the model's prediction results, including failure probability and recommended maintenance time.

[0141] Maintenance plan: Based on the failure probability predicted by the model, a preventive maintenance plan is developed to conduct equipment inspection and maintenance in advance.

[0142] Through the above detailed steps, a predictive analysis model based on the XGBoost algorithm can be constructed for the fault prediction of RFID reading equipment in chemical parks, thereby improving the safety supervision efficiency of the park.

[0143] In step S1, during the data collection phase, the park management department needs to develop a detailed collection list and cooperate with the enterprise security management department to ensure the comprehensiveness and accuracy of the data. When sorting the data, use automated data cleaning tools combined with manual review to standardize the data and eliminate ambiguity and errors. Before the data is entered into the database, the database management personnel need to perform multiple data mapping and testing to ensure the integrity and adaptability of the data model. During the warehousing process, batch import and real-time monitoring are used to ensure the efficiency and correctness of data import. After completion, the database performance is optimized, including index optimization, query optimization, etc., to increase data retrieval speed

[0144] In step S3, in terms of real-time data collection, strict data collection standards are formulated to ensure the authenticity and validity of the data. In the data transmission link, a multi-channel data transmission mechanism is established and a redundant design is adopted to prevent data loss.

[0145] In step S3, in the early warning analysis, a warning model based on machine learning is developed, and the warning threshold and model parameters are continuously optimized through historical data analysis. When processing the early warning, a hierarchical response mechanism is established, and different processing procedures and measures are formulated according to the warning level to ensure rapid and effective response to potential risks.

[0146] In step S4, in terms of accident information reporting, a mobile accident reporting application is developed to simplify the reporting process and improve reporting efficiency. In the accident location link, GIS technology is combined to achieve accurate positioning and visual display of the accident site. When the emergency plan is activated, an intelligent plan matching system is used to automatically recommend and activate the corresponding plan according to the type and severity of the accident. In terms of resource scheduling, an emergency resource dynamic management platform is established to achieve rapid query, allocation and tracking of resources.

[0147] In step S5, the statistical analysis phase uses big data analysis technology to conduct in-depth mining of massive data to discover potential safety hazards and patterns. In the safety risk assessment, a combination of quantitative and qualitative methods is used to build a comprehensive risk assessment model to provide a scientific basis for park safety management.

[0148] In step S5, in terms of decision support, a decision support system is established to provide management with a variety of decision-making options and expected results through data analysis and simulation exercises to help formulate the best safety management strategy. At the same time, a decision-making effect tracking mechanism is established to continuously adjust and optimize the decision-making process through regular decision-making effect evaluation to improve the scientificity and effectiveness of decision-making.

[0149] In the present invention, by real-time monitoring of the operation data of the equipment and predicting potential failures and maintenance needs, preventive maintenance can be carried out in time to avoid downtime caused by equipment failures, thereby improving the operation efficiency of the equipment and the overall production capacity of the park. Through predictive maintenance, unnecessary maintenance work can be reduced and maintenance costs can be reduced. At the same time, additional maintenance costs and production losses caused by equipment failures can be avoided, further saving costs. RFID reading equipment is an important part of the safety supervision of chemical parks, and its stable operation is directly related to the safety of the park. Through the predictive analysis model, equipment failures can be discovered and handled in advance, reducing the risk of safety accidents caused by equipment failures, thereby enhancing the safety of the park. The predictive analysis model can automatically generate maintenance plans and early warning information, reduce manual intervention, and improve management efficiency. At the same time, through data-driven decision support, the park management department can formulate safety management measures more scientifically and further improve management efficiency. Maintenance plans and resource scheduling based on the predictive analysis model can achieve the optimal configuration of rescue resources inside and outside the park, improve resource utilization efficiency, and ensure that accidents are effectively handled. Through the predictive analysis model, the park management department can obtain more accurate data analysis results, provide a scientific basis for safety management decisions, and thus enhance data-driven decision support. In summary, building a predictive analysis model based on the XGBoost algorithm for fault prediction of RFID reading equipment in chemical parks has significant beneficial effects on the overall approach, which can improve equipment operation efficiency, reduce maintenance costs, enhance park safety, improve management efficiency, optimize resource allocation, and enhance data-driven decision support.

[0150] In the present invention, data is centrally managed: by establishing a hazardous chemical database for a chemical park, centralized management and unified storage of all hazardous chemical information in the park are achieved, the efficiency of data query, update and sharing is improved, and basic data support is provided for the safety management of the park. The use of RFID technology makes the tracking and management of hazardous chemicals more accurate and efficient. Through real-time monitoring and early warning, potential safety risks can be discovered and dealt with in a timely manner. Once a hazardous chemical accident occurs, the system can quickly locate the accident point, initiate an emergency plan, coordinate resources from all parties for emergency disposal, and effectively improve the speed and efficiency of emergency response to the accident.

[0151] In the present invention, through statistical analysis of monitoring data and safety risk assessment, the park management department can obtain data-driven decision support, thereby optimizing safety management measures and improving the overall safety level of the park. By establishing a machine learning-based equipment failure prediction model, potential failures and maintenance needs can be predicted, predictive maintenance can be achieved, equipment failure rates can be reduced, and the operating efficiency and safety of equipment can be improved. Through the decision-making effect tracking mechanism, the decision-making process can be continuously adjusted and optimized to ensure the scientificity and effectiveness of the safety management strategy, thereby continuously improving the safety management level of the park.

[0152] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for supervising hazardous chemicals in chemical parks based on identification resolution, characterized by: The supervision method comprises the following steps: S1: Establish a hazardous chemical database in the chemical park: comprehensively collect and organize the property information of all hazardous chemicals in the park, and create a centrally managed database to facilitate the query, update and sharing of chemical safety data; S2: Construction of identification resolution system: Select appropriate identification technology and build an identification resolution system to ensure accurate tracking and effective management of hazardous chemicals; S3: Real-time monitoring and early warning: Use identification resolution equipment to monitor the dynamics of hazardous chemicals in real time, set early warning thresholds through data analysis, and issue an early warning immediately once an abnormal situation is detected so that preventive measures can be taken in time; S4: Accident emergency response: Develop a detailed accident response process. Once a hazardous chemical accident occurs, quickly locate the accident point through the identification resolution system, activate the emergency plan, and coordinate resources from all parties for emergency disposal; S5: Statistical analysis and decision support: Regularly conduct statistical analysis on monitoring data, assess park safety risks, and provide data-driven decision support for park management departments.

2. The method for supervising hazardous chemicals in a chemical park based on identification resolution as claimed in claim 1, characterized in that: The step S1 specifically includes: Collect data: Collect basic information on hazardous chemicals produced, stored, used and operated by enterprises in the park, including chemical name, CAS number, molecular formula, use and hazardous properties; Data collation: collate and classify the collected data to ensure that the data is accurate and complete; Data entry: Import the sorted data into the chemical park hazardous chemicals database to achieve unified data management.

3. The method for supervising hazardous chemicals in a chemical park based on identification resolution as claimed in claim 1, characterized in that: In step S2, RFID technology is selected to build an identification resolution system, and the construction method process includes: S1. Demand Analysis and Planning: Research stage: understand the types, quantities, storage conditions and usage frequency of hazardous chemicals in the park; Demand determination: clarify the functions that the system needs to achieve, including automatic identification, real-time tracking, and inventory management; Budget planning: Determine the budget for system construction based on demand, including hardware equipment, software system and personnel training costs; S2. Technology selection and procurement: RFID tag selection: Choose RFID tags suitable for hazardous chemical environments, including tags that are resistant to high temperatures and chemical corrosion; Procurement of reading equipment: Procurement of corresponding RFID readers, including fixed and handheld reading equipment, based on the layout of the park; Software system selection: Choose a software system that can support RFID data analysis and management, or customize it; S3. System design and implementation: Coding rule formulation: formulate a unique identification code for each hazardous chemical, including production date, batch number, and chemical composition information; Tag deployment: Attach RFID tags to the packaging of hazardous chemicals to ensure the tags are secure and have a good reading distance; Reading equipment installation: RFID readers are installed at key locations in the park, including warehouse doors and next to production lines; Network cabling: Lay out network lines for reading devices to ensure that data can be transmitted to the central server in real time; S4.System Integration and Testing: Hardware integration: Integrate the RFID reader with the park’s existing security monitoring system and alarm system; Software integration: Integrate RFID data management software with the park’s ERP and MES information systems; Functional test: Perform system functional test to ensure the normal operation of tag reading, data transmission and alarm system; Performance testing: testing system response speed, data accuracy, and stability performance indicators; S5. Training and deployment: Personnel training: Provide operation training of RFID system to park managers and operators; System deployment: The RFID system was officially put into operation to start real-time monitoring of hazardous chemicals; Operation manual writing: Write a detailed system operation manual for user reference; S6.Maintenance and Optimization: Regular inspection: Regularly check the working status of RFID tags and reading devices to ensure stable operation of the system; Data backup: regularly back up system data to prevent data loss; System upgrade: Perform necessary upgrades and optimizations on the system based on actual operating conditions.

4. The method for supervising hazardous chemicals in a chemical park based on identification resolution as claimed in claim 3 is characterized by: During the system integration and testing process, a device failure prediction model is established based on a machine learning algorithm; The model will analyze equipment operating data and predict potential failures and maintenance needs. The specific steps include: S1. Data collection and preprocessing: Data collection: Collect equipment operation data through sensors installed on RFID reading devices, and use temperature sensors to monitor the operating temperature of equipment; Data cleaning: Use a sliding average filter to remove random noise in temperature data to ensure data smoothness; Data integration: Merge temperature data with other sensor data and BK vibration data to form a unified data set for easy analysis; S2. Feature Engineering: Feature selection: Use correlation analysis to select features that are highly correlated with equipment failures; Feature extraction: Extract features through principal component analysis to reduce data dimensions while retaining the most important information; Feature scaling: Apply the minimum-maximum normalization method to scale the feature values ​​to the [0,1] interval for easier algorithm processing; S3. Model selection and training: Algorithm selection: Gradient boosting algorithm is selected because it performs well in dealing with complex nonlinear relationships; Model training: Use the XGBoost library to implement the GBM algorithm, and input the preprocessed data into the model for training; Cross-validation: K-fold cross-validation is used to evaluate the generalization ability of the model and ensure the performance of the model on unknown data; S4. Model evaluation and optimization: Performance indicators: Calculate the accuracy of the model to measure the accuracy of the model's prediction of equipment failure; Model tuning: Use the grid search method to adjust the parameters of the XGBoost model and find the optimal parameter combination; Error analysis: Analyze cases where the model makes incorrect predictions, identify possible feature missing or data imbalance issues, and make adjustments accordingly; S5. Prediction and verification: Real-time prediction: Apply the trained XGBoost model to real-time data streams to make real-time predictions of equipment failures. Prediction verification: Compare the model's prediction results with the actual fault records and calculate the actual accuracy; Feedback loop: adjust the model based on the validation results, improve the model by adding new features or adjusting feature weights; S6. Deployment and application: System Integration: Integrate the XGBoost model into the existing monitoring system so that it can automatically receive sensor data and output prediction results; User interface: Develop a web-based user interface to display the model's prediction results, including failure probability and recommended maintenance time; Maintenance plan: Develop a preventive maintenance plan based on the failure probability predicted by the model, and perform equipment inspection and maintenance in advance; Through the above detailed steps, a predictive analysis model based on the XGBoost algorithm is constructed for fault prediction of RFID reading equipment in chemical parks, thereby improving the safety supervision efficiency of the park.

5. The method for supervising hazardous chemicals in a chemical park based on identification resolution as claimed in claim 1, characterized in that: In step S1, during the data collection phase, the park management department needs to formulate a detailed collection list and cooperate with the enterprise security management department to ensure the comprehensiveness and accuracy of the data.

6. The method for supervising hazardous chemicals in a chemical park based on identification resolution as claimed in claim 1, characterized in that: In step S3, strict data collection standards are formulated in real-time data collection to ensure the authenticity and validity of the data; In the data transmission link, a multi-channel data transmission mechanism is established and a redundant design is adopted.

7. The method for supervising hazardous chemicals in a chemical park based on identification resolution as claimed in claim 1, characterized in that: In step S3, in the early warning analysis, an early warning model based on machine learning is developed, and the early warning threshold and model parameters are continuously optimized through historical data analysis; during early warning processing, a hierarchical response mechanism is established, and different processing procedures and measures are formulated according to the early warning level.

8. The method for supervising hazardous chemicals in a chemical park based on identification resolution as claimed in claim 1, characterized in that: In step S4, in terms of accident information reporting, a mobile accident reporting application is developed, and in the accident location link, GIS technology is combined to achieve accurate positioning and visual display of the accident site; when the emergency plan is activated, an intelligent plan matching system is used to automatically recommend and activate the corresponding plan according to the type and severity of the accident.

9. The method for supervising hazardous chemicals in a chemical park based on identification resolution as claimed in claim 1, characterized in that: In step S5, in the statistical analysis phase, big data analysis technology is used to conduct in-depth mining of massive data to discover potential safety hazards and patterns; in the safety risk assessment, a combination of quantitative and qualitative methods is used to construct a comprehensive risk assessment model to provide a scientific basis for park safety management.

10. The method for monitoring hazardous chemicals in a chemical park based on identification resolution according to claim 1, characterized in that: In step S5, in terms of decision support, a decision support system is established to provide management with a variety of decision-making options and expected results through data analysis and simulation exercises to help formulate the optimal safety management strategy; at the same time, a decision-making effect tracking mechanism is established to conduct regular decision-making effect evaluations.