Probability grammar model-based aluminum processing production safety evaluation system

Abstract

The invention discloses an aluminum processing production safety assessment system based on a probability grammar model, the system comprises a static risk assessment module, a dynamic risk assessment module and an aluminum processing production safety comprehensive assessment module, the static risk assessment module is used for assessing fixed safety elements in the aluminum processing production process; the dynamic risk assessment module is used for assessing safety elements changing in real time in the aluminum machining production process; and the aluminum processing production safety comprehensive evaluation module is used for integrating the evaluation results of the static risk evaluation module and the dynamic risk evaluation module and comprehensively evaluating the overall production safety condition of aluminum processing. According to the invention, the overall production safety condition of aluminum processing can be comprehensively assessed in real time, potential safety hazards in each link can be systematically assessed, the safety of each link is ensured, managers can be helped to formulate safety measures and coping strategies, and the safety and efficiency of the aluminum processing production process can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of production safety assessment, and more specifically to an aluminum processing production safety assessment system based on a probability grammar model. Background Art

[0002] As an important part of the manufacturing industry, the aluminum processing industry has a complex and diverse process, which makes safety management particularly important. In the production process of aluminum profiles, there are multiple processes involved, such as smelting, casting, mold manufacturing, extrusion, oxidation, deep processing, and door and window processing and installation. Each process has its own unique risks and challenges. These processes not only involve the danger of operating mechanical equipment, but also involve high-risk environments such as high temperature and high pressure. This process complexity makes the aluminum processing industry face many unsafe factors, making safety management extremely difficult.

[0003] At present, the common methods to solve the safety of aluminum processing production are mainly from the management perspective and the technical perspective. The most common management method is the factor analysis (FMEA) method. FMEA is a management method that mainly relies on prior analysis and the formulation of preventive measures; however, the traditional FMEA method is mainly based on manual experience and historical data, and lacks the use of real-time data. Although some sensor devices are used for data collection in some cases, these data are usually only used for subsequent analysis. In addition, the existing safety management methods lack a unified data platform and integrated system, making it difficult to achieve centralized management and comprehensive analysis of data. The research on technical safety precautions mainly focuses on the safety problems encountered in the three main processing methods of rolling, extrusion and forging, and a series of safety precautions are proposed. However, these traditional methods still have some problems in practical applications, especially in real-time monitoring and the use of technologies such as the Internet of Things to monitor production safety status. There are obvious defects. The current safety precautions mainly rely on regular inspections and manual monitoring, which cannot comprehensively monitor the safety status in real time. The current safety precautions are also mostly single-point, static management methods, lacking systematic dynamic monitoring capabilities.

[0004] With the increasing application of aluminum alloy materials in production and life, given the high-risk characteristics of the aluminum processing industry, there is an urgent need for a comprehensive safety assessment system that can systematically and real-time evaluate the safety hazards in each process link to ensure the safety of each link and improve the safety and efficiency of the processing process. Summary of the invention

[0005] In view of this, the present invention provides an aluminum processing production safety assessment system based on a probabilistic grammar model that solves at least some of the above-mentioned technical problems. The system can conduct a comprehensive assessment of the overall production safety status of aluminum processing in real time, and can systematically assess the safety hazards in each link, thereby ensuring the safety of each link and improving the safety and efficiency of the aluminum processing production process.

[0006] To achieve the above object, the technical solution adopted by the present invention is:

[0007] The present invention provides an aluminum processing production safety assessment system based on a probabilistic grammar model, the system comprising: a static risk assessment module, a dynamic risk assessment module and an aluminum processing production safety comprehensive assessment module, wherein:

[0008] The static risk assessment module is used to assess the fixed safety factors in the aluminum processing production process;

[0009] The dynamic risk assessment module is used to assess the safety factors that change in real time during the aluminum processing production process;

[0010] The aluminum processing production safety comprehensive assessment module is used to integrate the assessment results of the static risk assessment module and the dynamic risk assessment module, and conduct a comprehensive assessment of the overall production safety status of aluminum processing based on a probabilistic grammar model.

[0011] Furthermore, the fixed safety factors in the aluminum processing production process evaluated by the static risk assessment module include: collecting data on fixed and tilting melting furnaces, chutes, and safety facilities through the factory's equipment records, management documents, monitoring systems, standards and specifications, and safety reports; these data include the setting of mechanical locking devices, the configuration of liquid level sensors, the layout of vents and downpipes, and the safety measures for emergency discharge chutes and winch systems.

[0012] Furthermore, the dynamic risk assessment module evaluates the real-time changing safety factors in the aluminum processing production process, including: the interlocking status data of the tilting melting furnace control system and the casting system collected by various monitoring instruments, the working status data of the liquid level sensor and alarm device at the high-temperature aluminum liquid outlet and the launder interface of the fixed melting furnace, and the interlocking status data of the launder liquid level sensor and the quick shut-off valve and emergency discharge valve on the launder.

[0013] Furthermore, the system performs data processing on the collected data, and the data processing includes: cleaning the data, removing noise, removing outliers and normalizing the data.

[0014] Furthermore, for missing data, the system uses a variety of methods to fill in the missing data, including: mean filling, median filling, mode filling, and model-based prediction filling.

[0015] Furthermore, the system also collects experts' evaluation data on the weights of each data indicator and the evaluation data on the risk level of each scenario based on data processing.

[0016] Furthermore, in the static risk assessment module, the processed data is used for model training and assessment, a static risk assessment model is constructed, and a static risk assessment result is output;

[0017] In the dynamic risk assessment module, the processed data is used for model training and evaluation, a dynamic risk assessment model is constructed, and a dynamic risk assessment result is output.

[0018] Furthermore, in the aluminum processing production safety comprehensive assessment module, based on the probabilistic grammar model combined with the assessment results of the static risk assessment module and the dynamic risk assessment module, the comprehensive safety risk level assessment results of each workshop in the aluminum processing production process are calculated layer by layer.

[0019] Furthermore, the system also includes an automatic warning module, which is used to issue a warning when the comprehensive safety risk level assessment result exceeds a threshold.

[0020] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0021] The present invention provides an aluminum processing production safety assessment system based on a probabilistic grammar model, which can comprehensively assess the overall production safety status of aluminum processing in real time, and can systematically assess the potential safety hazards in each link to ensure the safety of each link, which is conducive to helping managers formulate safety measures and response strategies, and helps to improve the safety and efficiency of the aluminum processing production process.

[0022] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0023] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, a brief introduction will be given below to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0025] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0026] Figure 1 A schematic diagram of the structure of an aluminum processing production safety assessment system based on a probabilistic grammar model provided in an embodiment of the present invention.

[0027] Figure 2 A schematic diagram of the structural division of workshop 1 provided in an embodiment of the present invention.

[0028] Figure 3 A schematic diagram of the structural division of workshop 2 provided in an embodiment of the present invention.

[0029] Figure 4 A schematic diagram of the cause-effect and / or relationship of workshop 1 provided in an embodiment of the present invention.

[0030] Figure 5 A schematic diagram of the cause-effect and / or relationship of workshop 2 provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the exemplary embodiments of the present invention will be described in more detail with reference to the accompanying drawings below, and the technical solution of the present invention will be clearly and completely described. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Although the exemplary embodiments of the present invention are shown in the accompanying drawings, it should be understood that the present invention can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present invention and to fully convey the scope of the present invention to those skilled in the art.

[0032] See also Figure 1 As shown, an embodiment of the present invention provides an aluminum processing production safety assessment system based on a probabilistic grammar model, which mainly includes the following modules: a static risk assessment module, a dynamic risk assessment module and an aluminum processing production safety comprehensive assessment module.

[0033] The main function of the static risk assessment module is to evaluate the relatively stable safety factors in the aluminum processing production process. These factors include whether various safety monitoring equipment is fully configured, whether various equipment is equipped with emergency response devices, etc. This module aims to ensure the completeness of production equipment and safety devices, and provide basic safety protection for the production process.

[0034] The dynamic risk assessment module focuses on evaluating the dynamic changes of various safety factors in the production process. This module mainly monitors the numerical status of various instruments in the production process and evaluates whether the on-site staff complies with the safety operation specifications. These data are monitored and analyzed in real time through the dynamic risk assessment module to timely discover and deal with potential safety hazards.

[0035] The aluminum processing production safety comprehensive assessment module integrates the assessment results of the static risk assessment module and the dynamic risk assessment module. Through the comprehensive analysis of multiple single-point static and dynamic risk assessment data, the module can comprehensively assess the safety status of the entire production environment. Its reasoning process is based on a probabilistic grammar model, which can accurately reflect the possible safety risks in the production process.

[0036] The following is a detailed introduction to the module structure of an aluminum processing production safety assessment system based on a probabilistic grammar model of the present invention:

[0037] 1. Static risk assessment module:

[0038] In the embodiment of the present invention, the static risk assessment module is used to assess the fixed safety factors in the production process, including whether the safety monitoring equipment is installed in place, whether various types of equipment are equipped with emergency response devices, etc. This module mainly focuses on safety factors that do not change much in the production process, and ensures that all safety monitoring equipment is in good operating condition and complies with relevant safety standards through a systematic approach.

[0039] In the embodiment of the present invention, comprehensive data collection is first performed. Data on fixed and tilting smelting furnaces, chutes, safety facilities, etc. are obtained from multiple sources such as factory equipment records, management documents, monitoring systems, standards and specifications, and safety reports. These data include the settings of mechanical locking devices, the configuration of liquid level sensors, the layout of vents and downpipes, and the safety measures of emergency discharge chutes and winches.

[0040] After data collection is completed, strict data processing is carried out to ensure data quality. First, the data is cleaned to remove noise and outliers, and then standardized to convert data from different sources to the same scale for subsequent analysis and processing. Normalization converts the data to the range of [0, 100] to eliminate the impact of different dimensions on data processing. These steps are designed to ensure the consistency and availability of the data and provide a reliable data basis for subsequent risk assessment.

[0041] Furthermore, the system uses a variety of methods to fill in missing data, including mean filling, median filling, mode filling, and model-based prediction filling. Choosing the appropriate method depends on the specific data characteristics and data missing situation. For example, mean filling is suitable for situations where there is less missing data and the data is evenly distributed, while model-based prediction filling uses data from other features to make predictions through a machine learning model to fill in missing values.

[0042] On the basis of the above data processing, it is also preferred to collect expert knowledge through expert survey questionnaires. The questionnaire design covers the relationship between various safety assessment indicators and scenario risk levels, and experts in related fields are invited to fill in. These questionnaires fully consider the professional background and experience of different experts to ensure the scientificity and rationality of the questionnaire design. Through the questionnaire survey, the system collects experts' evaluations of the weights of each indicator and the risk levels of each scenario. These expert knowledge provides the system with valuable experience data, which can further improve the accuracy and reliability of risk assessment.

[0043] Finally, the processed data is used for model training and evaluation to build an assessment model that can accurately predict the safety risks of aluminum processing production. The input of the model includes the normalized values ​​of various indicators and the weight set provided by experts, and the output is the risk level of each scenario. Through repeated training and testing, the algorithm is optimized to ensure the accuracy and robustness of the model.

[0044] 2. Dynamic Risk Assessment Module:

[0045] In the embodiment of the present invention, the dynamic risk assessment module is mainly responsible for evaluating the safety factors that change in real time during the production process. This includes monitoring the numerical status displayed by various monitoring instruments, and evaluating whether the on-site staff strictly abide by the safety regulations. The dynamic risk assessment module can timely capture and feedback various safety hazards that may occur during the production process, ensuring that production activities are carried out in a safe environment.

[0046] In an embodiment of the present invention, the evaluation data of the dynamic risk assessment module mainly comes from the real-time monitoring data of the camera or the working status indication data of the equipment. In actual production, it includes monitoring the interlocking status data of the control system of the tilting smelting furnace and the casting system, the working status data of the liquid level sensor and the alarm device at the high-temperature aluminum liquid outlet and the launder interface position of the fixed smelting furnace, and the interlocking status data of the liquid level sensor of the launder and the quick shut-off valve and the emergency discharge valve on the launder. At the same time, it is also necessary to monitor the ground humidity and water accumulation where the aluminum ingots are stored, the flammable and explosive items at the work site, the position of the emergency water source manual valve, and the interlocking status data of the monitoring and alarm device and the quick shut-off valve and the emergency discharge valve on the launder. It is also necessary to regularly check the status of the wire rope and monitor the working status of the emergency power supply of the winch system. In addition, the system will also monitor the number of people on site in the foundry to ensure that there are no non-production personnel.

[0047] Furthermore, in order to ensure data quality and consistency, the system also cleans, standardizes and normalizes the data monitored in real time. Data cleaning removes noise and outliers, standardization converts data of different dimensions to the same scale, and normalization converts data to the range of [0, 100]. For missing data, the system uses mean filling, median filling, mode filling and model-based prediction filling methods to ensure data integrity and accuracy.

[0048] According to the data change rate and application requirements, the system updates the data regularly or in real time to ensure that it reflects the current equipment status and operating environment. For example, equipment status and environmental monitoring data need to be updated in real time, while wire rope inspection status can be updated regularly. The automated update mechanism ensures the real-time and accuracy of the data, thereby providing reliable dynamic data support.

[0049] In the embodiment of the present invention, as in the above-mentioned static risk assessment module, preferably, a relevant questionnaire can also be designed to obtain the weights of the relevant dynamic indicators and the risk level of the current scenario. On this basis, an assessment model with dynamic indicators as input and risk index as output is constructed, which will not be repeated here.

[0050] 3. Aluminum processing production safety comprehensive assessment module:

[0051] In the embodiment of the present invention, the aluminum processing production safety comprehensive assessment module integrates the assessment results of multiple single-point static risk assessment modules and dynamic risk assessment modules to form a comprehensive assessment of the overall production environment. Through the comprehensive analysis of static and dynamic data, the module can provide a comprehensive and accurate safety assessment result, providing strong support and scientific basis for safety management in the aluminum processing production process.

[0052] In an embodiment of the present invention, the core of the comprehensive assessment module for aluminum processing production safety is to construct a hierarchical relationship between modules and workshops based on the comprehensive assessment data of each production line module in the production workshop, and to reflect the causal relationship between different nodes. Specifically, the module is preferably implemented based on a probabilistic grammar model. The probabilistic grammar model is a forest data structure, and the leaf nodes represent each production line module, including an aluminum molten furnace, a crystallizer cooling system, and an aluminum ingot storage area. Each production line module contains data from a static risk assessment module and a dynamic risk assessment module. The upper level of the leaf node represents the production line node, and the upper level is the workshop node. Each workshop is a tree, and the entire forest area includes multiple workshops.

[0053] Furthermore, the probabilistic grammar model combines the data of the static risk assessment module and the dynamic risk assessment module to obtain the comprehensive assessment data of each production line module. These data include static data (such as equipment status and process flow) and dynamic data (such as production environment factors, operator behavior and real-time monitoring data). The system uses these data as the input of the leaf node and calculates the risk assessment results of each node layer by layer through the hierarchical structure of the probabilistic grammar model graph.

[0054] In the embodiment of the present invention, the system needs to fully consider the physical location and relationship of each production line module. For example, the location of the aluminum smelting furnace has a greater impact on the safety of the entire production line, while the crystallizer cooling system may be affected by the ambient temperature and water supply. By analyzing these physical location relationships, the system can accurately construct a probabilistic grammar model.

[0055] The construction of the probabilistic grammar model not only reflects the hierarchical relationship between modules and workshops, but also describes the mutual influence between modules of different production lines. For example, the increased safety risk of a certain aluminum smelting furnace may affect the safety risk of the crystallizer cooling system and the aluminum ingot storage area. The system uses the probabilistic grammar model to express this mutual influence relationship and provide more accurate risk assessment results.

[0056] The leaf nodes are the comprehensive risk assessment values ​​of each production line module. The production line nodes are in a logical AND relationship, indicating that the safety status of the entire production line needs to consider the status of all modules. The workshop nodes are in a logical OR relationship, indicating that as long as there is a production line in the workshop that is in a risk warning state, the workshop is in a risk warning state. The data structure of the entire forest reflects the physical location relationship between production line modules and workshops.

[0057] The static risk assessment module inputs the normalized static risk factors and the corresponding weights of each factor into the neural network model trained by the corresponding module to obtain the static risk level of each module. The dynamic risk assessment module inputs the normalized dynamic risk factors and the corresponding weights of each factor into the neural network model trained by the corresponding module to obtain the dynamic risk level of each module. The static risk level and dynamic risk level of each module are used as inputs to each leaf node of the probabilistic grammar model of comprehensive evaluation reasoning. The system uses a bottom-up calculation method to calculate the safety risk assessment results of each workshop.

[0058] During the calculation process, when encountering the node, the system calculates according to the full probability formula, that is, considering the joint probability of all subnodes under the node. The full probability formula is used to integrate the risk status of each subnode, so as to accurately reflect the overall risk situation of the node. When encountering the or node, the system takes the maximum value of all subnodes under the node, that is, max processing. In this way, the system can identify the overall risk status of the node when any subnode is in a risky state, thereby effectively evaluating the safety risks in the workshop.

[0059] Furthermore, after the calculation is completed, the system sorts the risk assessment values ​​of all workshops and identifies high-risk workshops and low-risk workshops. The sorting results can intuitively display the risk level of each workshop, which is convenient for managers to make decisions and respond. Preferably, the system also presents the safety risk assessment results of each workshop in the form of charts or maps, such as generating a risk heat map to show the risk distribution of each workshop, or generating a risk trend map to show the changing trend of the risk level. These charts and maps can help managers quickly identify high-risk areas and formulate corresponding safety measures and response strategies.

[0060] In one embodiment, the system also includes an automatic early warning module for issuing an early warning when the comprehensive safety risk level assessment result exceeds a threshold value. The early warning information can be sent to the client in real time to support the staff to respond quickly, thereby timely discovering and eliminating potential safety hazards and ensuring the life safety of operators and the property safety of the enterprise.

[0061] The following takes a factory including two workshops as an example to introduce the specific implementation mode of the present invention in detail:

[0062] In this embodiment, a factory with two workshops is considered. Since the safety impact between the two workshops is not significant, they can be divided into two trees. Figure 2 and Figure 3As shown in the figure, each workshop contains three production lines, and each production line is divided into three modules according to the production process: aluminum molten melting furnace, crystallizer cooling system, and aluminum ingot storage area. Each module contains several video surveillance and equipment working signals, which is the smallest unit of the evaluation system and the smallest node in the causal and OR relationship. The safety factors of the three modules together constitute the safety assessment elements of a production line, so the relationship between the production line and the module is AND. There is a parallel relationship between several production lines, so the constructed causal and OR relationship is as follows Figure 4 and Figure 5 shown.

[0063] 1. Static risk assessment module:

[0064] In this embodiment, the system first obtains data on fixed and tilting smelting furnaces, chutes, safety facilities, etc. from multiple sources such as factory equipment records, management documents, monitoring systems, standards and specifications, and safety reports. These data include the settings of mechanical locking devices, the configuration of liquid level sensors, the layout of vents and downpipes, and the safety measures of emergency discharge chutes and winches. For the smelting furnace part, the present invention considers the configuration of the smelting furnace mechanical locking device and the interlocking configuration of the liquid level sensor of the chute and the quick shut-off valve on the chute. For the crystallizer part, the present invention considers whether the cooling water system of the deep well casting crystallizer is equipped with inlet and outlet water temperature, inlet water pressure, and inlet water volume monitoring and alarm devices; the alarm device of the crystallizer cooling water system is interlocked with the quick shut-off valve on the chute. For the aluminum ingot storage area, the present invention considers whether a baffle is set above the aluminum ingot storage position and the shielding treatment of the open return ditch at the aluminum ingot storage position. After data collection, data quality and consistency are ensured through data cleaning, standardization and normalization. For missing data, mean filling and model-based predictive filling methods are used. The results are shown in Tables 1 to 3:

[0065] Table 1 Melting furnace data

[0066]

[0067]

[0068] Table 2 Crystallizer data

[0069]

[0070] Table 3 Data of aluminum ingot storage area

[0071] Point Baffle configuration (category) Backwater ditch treatment status (level) Workshop 1 Production Line 1 Storage Area 1 high Workshop 1 Production Line 2 Storage Area 1 high Workshop 1 Production Line 3 Storage Area 1 middle Workshop 2 Production Line 1 Storage Area 2 Low Workshop 2 Production Line 2 Storage Area 2 middle Workshop 2 Production Line 2 Storage Area 2 high

[0072] In this embodiment, as a preference, the system of the present invention can design a questionnaire including the relationship between risk assessment indicators and scenario risk levels, invite experts to fill it out, collect experts' evaluations of the weights of each indicator, and form an expert weight set, as shown in Table 4.

[0073] Table 4 Expert weights

[0074] Configuration of mechanical locking device of smelting furnace 0.4 Flow tank liquid level sensor and quick cut-off valve interlock 0.6 Crystallizer detection and alarm device 0.5 Crystallizer alarm device interlocked with quick cut-off valve 0.5 Storage area baffle configuration 0.8 Treatment of backwater ditch 0.2

[0075] Furthermore, in data processing, all static risk factor data are normalized to the range of [0, 100], and the results are shown in Tables 5 to 7:

[0076] Table 5 Data normalization results of smelting furnace

[0077]

[0078] Table 6 Data normalization results of crystallizer

[0079]

[0080] Table 7 Data normalization results of aluminum ingot storage area

[0081]

[0082] Furthermore, these results are input into the neural network model in the module to obtain the static risk level of the corresponding area:

[0083] Table 8 Static risk levels

[0084]

[0085]

[0086] The obtained static risk level is shown in Table 8 above.

[0087] 2. Dynamic risk assessment module:

[0088] In this embodiment, the present invention can obtain the values ​​of various measuring instruments at the production site, the on-site working status, etc. by real-time monitoring of the working status indication of the equipment through the sensing device. For a simple example, for the smelting furnace, the present invention considers the state of the mechanical locking device of the smelting furnace and the liquid level sensor reading on the flow channel. For the crystallizer, the inlet and outlet water temperature difference, water inlet pressure, and water inlet volume are considered here. For the storage area, the humidity and water accumulation area are considered. The acquired data are shown in Tables 9 to 11:

[0089] Table 9 Monitoring data of smelting furnace

[0090]

[0091] Table 10 Monitoring data of crystallizer

[0092]

[0093] Table 11 Monitoring data of aluminum ingot storage area

[0094] Point humidity(%) Water accumulation area (m2) Workshop 1 Production Line 1 Storage Area 75 0.2 Workshop 1 Production Line 2 Storage Area 74 0.1 Workshop 1 Production Line 3 Storage Area 77 0.1 Workshop 2 Production Line 1 Storage Area 80 0.0 Workshop 2 Production Line 2 Storage Area 79 0.2 Workshop 2 Production Line 2 Storage Area 81 0.1

[0095] Similarly, the data is cleaned, standardized and normalized to ensure data quality and consistency. All dynamic risk factor data are normalized to the range of [0, 100], and weighted summation is performed based on real-time updated data to obtain the dynamic risk value of each point. The results are shown in Tables 12 to 14:

[0096] Table 12 Normalization results of monitoring data of smelting furnace

[0097]

[0098] Table 13 Normalization results of crystallizer monitoring data

[0099]

[0100]

[0101] Table 14 Normalization results of monitoring data in the aluminum ingot storage area

[0102] Point humidity(%) Water accumulation area (m2) Workshop 1 Production Line 1 Storage Area 75 0.2 Workshop 1 Production Line 2 Storage Area 74 0.1 Workshop 1 Production Line 3 Storage Area 77 0.1 Workshop 2 Production Line 1 Storage Area 80 0.0 Workshop 2 Production Line 2 Storage Area 79 0.2 Workshop 2 Production Line 2 Storage Area 81 0.1

[0103] Furthermore, these results are input into the neural network model in the module to obtain the risk level of the corresponding area.

[0104] Table 15 Dynamic risk levels

[0105] Block Dynamic risk level Workshop 1 production line 1 melting furnace 2.3 Workshop 1 Production Line 2 Melting Furnace 2.1 Workshop 1 Production Line 3 Melting Furnace 1.9 Workshop 2 Production Line 1 Melting Furnace 1.8 Workshop 2 production line 2 melting furnace 2.7 Workshop 2 Production Line 3 Melting Furnace 3.0 Workshop 1 Production Line 1 Crystallizer 3.4 Workshop 1 Production Line 2 Crystallizer 3.1 Workshop 1 Production Line 3 Crystallizer 3.9 Workshop 2 Production Line 1 Crystallizer 3.6 Workshop 2 production line 2 crystallizer 3.1 Workshop 2 Production Line 3 Crystallizer 3.0 Workshop 1 Production Line 1 Storage Area 4.1 Workshop 1 Production Line 2 Storage Area 4.4 Workshop 1 Production Line 3 Storage Area 4.2 Workshop 2 Production Line 1 Storage Area 5.4 Workshop 2 Production Line 2 Storage Area 5.3 Workshop 2 Production Line 3 Storage Area 5.0

[0106] The obtained dynamic risk level is shown in Table 15 above.

[0107] 3. Aluminum processing production safety comprehensive assessment module:

[0108] In this embodiment, the system uses the static risk value (static risk level) and the dynamic risk value (dynamic risk level) as the input of each leaf node of the probability grammar model. By constructing a forest structure of a causal AND / OR graph, the system calculates the risk assessment results of each node from the bottom up. The results are shown in Table 16:

[0109] Table 16 Risk assessment results

[0110] Block Block risk results Sort by level Workshop 1 production line 1 melting furnace 19.0 5 Workshop 1 Production Line 2 Melting Furnace 18.8 6 Workshop 1 Production Line 3 Melting Furnace 19.3 4 Workshop 2 Production Line 1 Melting Furnace 21.2 2 Workshop 2 production line 2 melting furnace 21.3 1 Workshop 2 production line 2 melting furnace 21.0 3 Workshop 1 19.3 2 Workshop 2 21.3 1

[0111] In this embodiment, based on the above results, it can effectively help managers formulate safety measures and response strategies.

[0112] From the description of the above embodiments, those skilled in the art can know that the present invention provides an aluminum processing production safety assessment system based on a probabilistic grammar model, which mainly includes: a static risk assessment module, a dynamic risk assessment module and an aluminum processing production safety comprehensive assessment module, which can effectively assess the aluminum processing production safety, and provide intuitive assessment results and early warning support, ensure the safety of each link, and help improve the safety and efficiency of the aluminum processing production process; the system of the present invention has the following advantages:

[0113] 1. Extensive data utilization: Ability to utilize multiple data sources to provide comprehensive information support.

[0114] 2. Strong interpretability: The evaluation results are highly interpretable and easy to understand and apply.

[0115] 3. High scalability: The method is highly scalable and can adapt to assessment needs of different scales and complexities.

[0116] 4. Make full use of industry knowledge: Be able to effectively integrate and utilize industry knowledge and experience.

[0117] 5. Gradually expand the assessment method: gradually expand from single-point risk assessment to overall assessment, and provide a more comprehensive aluminum processing production safety assessment.

[0118] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as products such as system software or hardware, and computer programs. Therefore, the present invention may take the form of embodiments combining software and hardware aspects. It should be noted that the word "comprising" does not exclude the presence of components or not listed in the claims. The word "a" or "an" preceding a component does not exclude the presence of a plurality of such components. The present invention may be implemented by means of hardware comprising a number of different components and by means of a suitably programmed computer.

[0119] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0120] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A safety assessment system for aluminum processing production based on a probabilistic grammar model, characterized in that: The system includes: static risk assessment module, dynamic risk assessment module and aluminum processing production safety comprehensive assessment module, among which: The static risk assessment module is used to assess the fixed safety factors in the aluminum processing production process; The dynamic risk assessment module is used to assess the safety factors that change in real time during the aluminum processing production process; The aluminum processing production safety comprehensive assessment module is used to integrate the assessment results of the static risk assessment module and the dynamic risk assessment module, and conduct a comprehensive assessment of the overall production safety status of aluminum processing based on a probabilistic grammar model.

2. The aluminum processing production safety assessment system based on the probabilistic grammar model according to claim 1 is characterized in that: The fixed safety factors in the aluminum processing production process evaluated by the static risk assessment module include: collecting data on fixed and tilting melting furnaces, chutes, and safety facilities through the factory's equipment records, management documents, monitoring systems, standards and specifications, and safety reports; these data include the setting of mechanical locking devices, the configuration of liquid level sensors, the layout of vents and downpipes, and the safety measures for emergency discharge chutes and winch systems.

3. The aluminum processing production safety assessment system based on the probabilistic grammar model according to claim 1 is characterized in that: The dynamic risk assessment module evaluates the real-time changing safety factors in the aluminum processing production process, including: the interlocking status data of the tilting melting furnace control system and the casting system collected by various monitoring instruments, the working status data of the liquid level sensor and alarm device at the high-temperature aluminum liquid outlet and the launder interface of the fixed melting furnace, and the interlocking status data of the launder liquid level sensor and the quick shut-off valve and emergency discharge valve on the launder.

4. The aluminum processing production safety assessment system based on the probabilistic grammar model according to claim 2 or 3 is characterized in that: The system performs data processing on the collected data, and the data processing includes: cleaning the data, removing noise, removing outliers and normalizing the data.

5. The aluminum processing production safety assessment system based on the probabilistic grammar model according to claim 4 is characterized in that: The system uses a variety of methods to fill in missing data, including: mean filling, median filling, mode filling, and model-based prediction filling.

6. The aluminum processing production safety assessment system based on the probabilistic grammar model according to claim 5 is characterized in that: Based on data processing, the system also collects experts' evaluation data on the weights of each data indicator and the risk level of each scenario.

7. The aluminum processing production safety assessment system based on the probabilistic grammar model according to claim 6 is characterized in that: In the static risk assessment module, the processed data is used for model training and assessment, a static risk assessment model is constructed, and a static risk assessment result is output; In the dynamic risk assessment module, the processed data is used for model training and evaluation, a dynamic risk assessment model is constructed, and a dynamic risk assessment result is output.

8. The aluminum processing production safety assessment system based on the probabilistic grammar model according to claim 7 is characterized in that: In the aluminum processing production safety comprehensive assessment module, based on the probability grammar model combined with the assessment results of the static risk assessment module and the dynamic risk assessment module, the comprehensive safety risk level assessment results of each workshop in the aluminum processing production process are calculated layer by layer.

9. The aluminum processing production safety assessment system based on the probabilistic grammar model according to claim 8 is characterized in that: The system also includes an automatic early warning module, which is used to issue an early warning when the comprehensive safety risk level assessment result exceeds a threshold value.

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