Exhaust gas treatment intelligent early warning system based on digital twinning

Abstract

The application relates to the technical field of waste gas treatment, and discloses a waste gas treatment intelligent early warning system based on digital twinning, which comprises a model construction module, a monitoring module, a factor extraction module and the like. The model construction module constructs a dynamic risk model and a three-dimensional concentration field model based on waste gas components and equipment operation state data, and combines simulation optimization parameters; the monitoring module generates a prediction table containing a risk index and a regulation parameter; the factor extraction module identifies risk correlation variables; the evaluation module calculates a risk accumulation index; the adjustment module updates the prediction table accordingly; and the early warning execution module triggers an alarm. The system is also provided with an instruction processing unit to realize conflict detection and priority management. The system improves the risk early warning accuracy and the intelligent regulation level, is suitable for an industrial waste gas treatment scene, and guarantees the treatment efficiency and safety.

Description

Technical Field

[0001] This invention relates to the field of waste gas treatment technology, specifically to an intelligent early warning system for waste gas treatment based on digital twins. Background Technology

[0002] In industrial production, waste gas treatment is a crucial aspect of environmental protection and safe production. With the development of industrial intelligence, traditional waste gas treatment systems have gradually revealed many limitations, making it difficult to meet the demands for accurate monitoring, risk warning, and efficient control under current complex operating conditions.

[0003] Traditional waste gas treatment systems primarily rely on manual experience and fixed thresholds for risk assessment and equipment control. On one hand, manual analysis of historical treatment records makes it difficult to comprehensively and dynamically identify risk-related variables, easily overlooking indirect risk factors and leading to inaccurate risk assessments and a failure to promptly identify potential safety hazards. On the other hand, fixed control parameters cannot be flexibly adjusted according to real-time risk conditions. When waste gas composition, equipment operating status, or environmental conditions change, treatment efficiency may decrease, and untimely control may even lead to safety accidents.

[0004] Furthermore, traditional systems lack intelligent conflict detection and priority management mechanisms in instruction processing. When receiving similar operation instructions consecutively, they cannot automatically determine whether the time interval is reasonable, which may lead to frequent device start-ups and shutdowns or operational conflicts, affecting device lifespan and the stability of the processing flow. For different types of instructions, they also cannot intelligently allocate processing threads according to the operation type, which may cause processing delays or wasted resources.

[0005] In terms of risk model construction, traditional methods are usually based on static models, which cannot integrate multi-source information such as sensor data, equipment logs, and environmental meteorological data in real time, making it difficult to accurately reflect the dynamic risk changes in the waste gas treatment process. For example, waste gas diffusion is significantly affected by environmental meteorological conditions (such as wind speed, wind direction, and temperature). Traditional models cannot incorporate these dynamic factors in real time, resulting in inaccurate predictions of waste gas diffusion boundaries, failure to activate emergency adsorption devices in a timely manner, and difficulty in effectively responding to emergencies.

[0006] With the development of digital twin technology, its application in the field of waste gas treatment has become an important direction for improving the intelligence level of systems. Digital twin technology can map the state of physical systems in real time through virtual models and integrate multi-source data for dynamic analysis and prediction. However, research on combining digital twin technology with intelligent early warning systems for waste gas treatment is still in the exploratory stage. Issues such as how to construct accurate dynamic risk models, intelligently extract risk factors, accurately assess risk accumulation indicators, and achieve intelligent processing of instructions and dynamic optimization of control parameters still require further research and solutions. Summary of the Invention

[0007] The purpose of this invention is to provide an intelligent early warning system for waste gas treatment based on digital twins, so as to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution: an intelligent early warning system for waste gas treatment based on digital twins, the system comprising:

[0009] The model building module is used to receive waste gas treatment instructions and build a dynamic risk model of the waste gas treatment process based on waste gas composition data and the operating status data of waste gas treatment equipment.

[0010] The monitoring module is used to acquire historical waste gas treatment records and generate a risk level prediction table in combination with the dynamic risk model. The risk level prediction table includes a risk index and adsorption device control parameters.

[0011] The factor extraction module is used to analyze the historical waste gas treatment records, identify risk-related variables, and determine indirect and direct risk factors.

[0012] The assessment module is used to calculate the cumulative risk index of the waste gas treatment system based on the indirect risk factors and the direct risk factors.

[0013] The adjustment module is used to set the correction weight of the risk level prediction table according to the risk accumulation index, and update the risk level prediction table based on the correction weight to form an optimized risk level prediction table.

[0014] The early warning execution module is used to trigger alarm control operations of the waste gas treatment equipment based on the optimized risk level prediction table.

[0015] Preferably, the system further includes:

[0016] The instruction comparison unit is used to compare the operation type of the current exhaust gas treatment instruction with that of the previously executed instruction to determine whether the two belong to the same type of operation.

[0017] The timestamp acquisition unit is used to record the initiation time of the current instruction and the execution time of the previous instruction if the operation is determined to be of the same type.

[0018] The interval analysis unit is used to calculate the time interval between two instructions and compare it with a preset interval threshold to determine whether the current instruction can be processed.

[0019] The instruction interception unit is used to prevent instruction execution and generate an operation conflict log if the time interval length is less than the preset interval threshold.

[0020] The priority determination unit is used to allocate an independent processing thread to execute the current exhaust gas treatment instruction if the operation type comparison result does not match.

[0021] Preferably, the factor extraction module includes:

[0022] All risk-related variables are arranged according to the monitoring time sequence to form an analysis sequence. Adjacent variable pairs are randomly selected to obtain the corresponding concentration readings and subsequent concentration readings. The absolute value of the difference between the concentration readings is calculated.

[0023] Determine the concentration peaks and troughs in the full monitoring data, and calculate the overall concentration fluctuation range;

[0024] Calculate the ratio of the absolute value of the difference to the fluctuation amplitude;

[0025] Calculate the difference in risk association between the variable pairs;

[0026] Determine the maximum and minimum correlation among all variables, and calculate the global correlation span;

[0027] Calculate the ratio of the risk correlation difference to the global correlation span;

[0028] The concentration ratio is multiplied by the correlation ratio and then standardized to obtain the real-time risk coefficient.

[0029] Iterate through the remaining variable pairs to generate a set of indirect risk factors, and calculate the mean of all real-time risk coefficients as the direct risk factors.

[0030] Preferably, the evaluation module includes:

[0031] Set upper and lower thresholds for indirect risk factors;

[0032] The set of high-impact factors is selected based on the upper threshold, and the set of low-impact factors is selected based on the lower threshold.

[0033] Calculate the positive deviation of high impact factors from the upper limit threshold, and arrange them in descending order of deviation to form a high-order sorting queue;

[0034] Calculate the negative deviation of the low impact factor from the lower limit threshold, and sort them in ascending order according to the magnitude of the deviation to form a low-order sorting queue;

[0035] The elements of the high-order queue and the low-order queue are paired one by one to form an impact pair group, and the risk accumulation index is calculated based on all impact pairs groups.

[0036] Preferably, the evaluation module further includes:

[0037] Count the total number of groups affected by the statistics, and calculate the squared difference between the highest and lowest elements in each group;

[0038] Identify the minimum and maximum values ​​among all squared differences, and calculate the dispersion parameter of all squared differences;

[0039] The risk accumulation index is calculated based on the minimum value, maximum value, and dispersion parameters.

[0040] Preferably, the adjustment module includes:

[0041] Set primary risk thresholds and advanced risk thresholds;

[0042] Configure the base correction coefficient, intermediate correction coefficient, and enhancement correction coefficient;

[0043] When the risk accumulation index is lower than the primary risk threshold, the basic correction coefficient is activated; when the risk accumulation index is between the primary and advanced risk thresholds, the intermediate correction coefficient is activated; and when the risk accumulation index exceeds the advanced risk threshold, the enhanced correction coefficient is activated.

[0044] Preferably, the adjustment module further includes:

[0045] The target control value is obtained by multiplying the corrected weight by the control parameters of the adsorption device;

[0046] The expected risk index corresponding to the target control value is determined by matching the preset parameter mapping relationship;

[0047] The optimized risk level prediction table is constructed by integrating the target control value and the expected risk index.

[0048] Preferably, the model building module further includes:

[0049] Real-time integration of sensor data, equipment logs, and environmental meteorological data to construct a three-dimensional dynamic concentration field model;

[0050] The waste gas diffusion boundary is calculated based on the three-dimensional concentration field model. When the predicted diffusion range exceeds the safe area, the emergency adsorption device is activated.

[0051] The energy consumption / purification rate matrix under different combinations of operating parameters is generated through Monte Carlo simulation, and the Pareto optimal solution set is generated.

[0052] Preferably, the system divides the emergency response into three levels based on the diffusion boundary breakthrough distance: Level 1 activates the local adsorption device, Level 2 activates the workshop isolation barrier, and Level 3 triggers the reverse pressurization of the whole plant exhaust system.

[0053] Preferably, the instruction comparison unit includes: extracting key operational semantics from unstructured operation instructions using natural language processing technology, storing vectorized features of historical instruction conflict cases and their solutions, and when an instruction conflict is detected, retrieving an appropriate solution from the conflict knowledge base through similarity matching and generating a priority adjustment suggestion.

[0054] Compared with the prior art, the beneficial effects of the present invention are:

[0055] The model building module constructs a three-dimensional dynamic concentration field model and a dynamic risk model based on waste gas composition data, equipment operating status data, and multi-source environmental data, which can reflect the dynamic changes in the waste gas treatment process in real time. Through Monte Carlo simulation, an energy consumption / purification rate matrix and a Pareto optimal solution set are generated, providing a scientific basis for optimizing equipment operating parameters and reducing energy consumption while ensuring purification effects. A three-level emergency response is defined based on the diffusion boundary breakthrough distance, enabling precise, tiered response to waste gas diffusion risks and improving the system's emergency handling capabilities.

[0056] The monitoring module, combined with a dynamic risk model, generates a risk level prediction table containing risk indices and adsorption device control parameters, providing a direct basis for real-time risk warnings and equipment control. The factor extraction module, through in-depth analysis of historical waste gas treatment records, can identify risk-related variables and determine indirect and direct risk factors, comprehensively considering various factors affecting waste gas treatment risk and avoiding the limitations of traditional methods that rely on only a single factor.

[0057] The assessment module calculates risk accumulation indicators based on indirect and direct risk factors. By setting thresholds to filter and rank high- and low-impact factors, and combining this with the dispersion parameter of the squared difference value, it achieves a scientific and quantitative assessment of risk accumulation, making the risk assessment results more accurate and reliable. The adjustment module sets correction weights based on the risk accumulation indicators and dynamically updates the risk level prediction table to form an optimized risk level prediction table. This ensures that the control parameters can be flexibly adjusted according to the real-time risk situation, improving the system's adaptability and processing efficiency.

[0058] In terms of instruction processing, the system utilizes modules such as an instruction comparison unit, a timestamp acquisition unit, and an interval analysis unit to analyze time intervals and detect conflicts in similar operation instructions. This avoids frequent start-ups and shutdowns and conflicts caused by excessively short operation intervals, and the generated operation conflict logs provide a reference for subsequent fault analysis. For different types of instructions, the priority determination unit can allocate independent processing threads to ensure timely and orderly execution of instructions, improving system stability and processing efficiency. The instruction comparison unit also utilizes natural language processing technology and a conflict knowledge base to achieve intelligent parsing of unstructured instructions and rapid retrieval of conflict solutions, further enhancing the system's intelligence and ease of operation. Attached Figure Description

[0059] Figure 1 This is a schematic diagram illustrating the working principle of the intelligent early warning system for waste gas treatment based on digital twins as described in this invention.

[0060] Figure 2 A flowchart for detecting and handling conflicting exhaust gas treatment commands;

[0061] Figure 3 A flowchart for risk correlation variable analysis and risk factor determination;

[0062] Figure 4 This is a flowchart for calculating the risk accumulation index based on indirect risk factors;

[0063] Figure 5 This is a flowchart for calculating and analyzing the squared difference of the risk accumulation indicator. Detailed Implementation

[0064] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0065] Please see Figures 1-5 The present invention relates to an intelligent early warning system for waste gas treatment based on digital twins, the specific implementation steps of which are as follows:

[0066] The model building module receives waste gas treatment instructions and constructs a dynamic risk model of the waste gas treatment process based on waste gas composition data and the operating status data of the waste gas treatment equipment. The waste gas composition data, including parameters such as the concentration, humidity, and temperature of various pollutants, can be collected in real time by gas sensors installed at the inlet of the waste gas treatment equipment. The operating status data of the waste gas treatment equipment is obtained through the equipment's built-in monitoring devices, such as fan speed, adsorption unit operating time, and valve opening.

[0067] The monitoring module acquires historical waste gas treatment records and generates a risk level prediction table based on the dynamic risk model. This table includes a risk index and adsorption device control parameters. Historical waste gas treatment records are stored in the system database, including information such as the composition of the waste gas treated each time, treatment time, equipment parameters used, and final treatment effect. The dynamic risk model learns from and analyzes this historical data to establish a mapping relationship between waste gas composition, equipment operating status, and the risk index, thereby predicting the risk level and corresponding adsorption device control parameters under different conditions.

[0068] The factor extraction module analyzes historical waste gas treatment records to identify risk-related variables and determine indirect and direct risk factors. During the analysis, data mining algorithms are used to analyze various data points in the historical records to identify variables associated with risk occurrence, such as changes in the concentration of specific pollutants in the waste gas and fluctuations in equipment operating parameters. Then, based on the degree of correlation between these variables and the risk, indirect and direct risk factors are distinguished.

[0069] The assessment module calculates the cumulative risk index of the waste gas treatment system based on indirect and direct risk factors. By establishing an assessment model, the impact of each risk factor on the system risk is comprehensively considered, and the indirect and direct risk factors are weighted to obtain a cumulative risk index that reflects the overall risk level of the system.

[0070] The adjustment module sets correction weights for the risk level prediction table based on the risk accumulation index, and updates the risk level prediction table based on these correction weights to form an optimized risk level prediction table. According to the magnitude of the risk accumulation index, the weight coefficients for correcting the risk level prediction table are determined. By adjusting these weight coefficients, the risk level prediction table more accurately reflects the current risk status of the system.

[0071] The early warning execution module triggers alarm control operations on the waste gas treatment equipment based on the optimized risk level prediction table. When the risk index in the optimized risk level prediction table exceeds the preset threshold, the early warning execution module will send an alarm signal to the waste gas treatment equipment and perform corresponding adjustment operations on the equipment according to the preset control strategy, such as adjusting the fan speed and switching the adsorption device, to reduce system risk.

[0072] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.

[0073] Example 1:

[0074] This embodiment relates to the system's instruction processing function, specifically including an instruction comparison unit, a timestamp acquisition unit, an interval analysis unit, an instruction interception unit, and a priority determination unit. The instruction comparison unit extracts key operational semantics from unstructured operation instructions using natural language processing technology. For example, from the instruction "Turn on adsorption device 1, adjust fan speed to 800 rpm," key semantics such as "turn on," "adsorption device," "adjust," and "fan speed" are extracted. Simultaneously, the system stores vectorized features of historical instruction conflict cases and their solutions. These features, obtained through analysis and processing of historical conflict cases, include information such as the instruction's operation type, the involved equipment, and the manifestation of the conflict. When an instruction conflict is detected, if the operation type of the current instruction and the previously executed instruction might cause a conflict in equipment operation, the system retrieves a suitable solution from the conflict knowledge base through similarity matching. For example, if the previous instruction was to turn off adsorption device 1 for maintenance, and the current instruction is to turn on adsorption device 1, the system will detect an operation type conflict and then search for similar conflict cases in the knowledge base. If a corresponding solution is found, such as a prompt to wait for maintenance to be completed before turning on the device, a priority adjustment suggestion is generated, advising the user to postpone executing the current instruction.

[0075] When the timestamp acquisition unit determines that the current exhaust gas treatment command and the previously executed command are of the same type, it records the initiation time of the current command and the execution time of the previous command. For example, if the previous command was to adjust the fan speed to 600 rpm, and the current command is also to adjust the fan speed, they are of the same type of operation. In this case, the initiation time of the current command is recorded as 10:00:00 on May 25, 2025, and the execution time of the previous command is recorded as 9:30:00 on May 25, 2025.

[0076] The interval analysis unit calculates the time interval between two commands, i.e., the time interval between 10:00:00 and 9:30:00 is 30 minutes, and compares it with a preset interval threshold to determine whether the current command can be processed. The preset interval threshold is set according to the equipment's performance and operating requirements; for example, for adjusting the fan speed, the preset interval threshold is 20 minutes. If the time interval is 30 minutes, which is greater than the preset interval threshold of 20 minutes, the current command can be processed; if the time interval is less than the preset interval threshold, such as 15 minutes, the command is intercepted by the command interception unit.

[0077] The instruction interception unit prevents instruction execution and generates an operation conflict log when the time interval is less than a preset threshold. The operation conflict log records information such as the content of the conflicting instruction, the time interval length, and the preset threshold, so as to facilitate subsequent analysis and optimization of system operation.

[0078] When the operation type comparison result does not match, the priority determination unit allocates an independent processing thread to execute the current waste gas treatment instruction. For example, if the previous instruction was to start the adsorption device and the current instruction is to close the valve, the operation types are different. The system allocates an independent processing thread for the current instruction to ensure that it can be executed independently and in a timely manner, avoiding conflicts with other instructions.

[0079] When processing unstructured operation instructions, the instruction comparison unit first performs word segmentation on the instruction text, breaking it down into individual words or phrases. For example, for the instruction "Adjust the temperature of the No. 2 reactor to 85 degrees Celsius", the words obtained after word segmentation are "Adjust", "No. 2", "reactor", "of", "temperature", "to", "85 degrees Celsius", etc. Then, through词性标注 and syntactic analysis, the grammatical role and semantic relationship of each word in the sentence are determined, and key verbs, nouns, and numerals are identified. In this example, "Adjust" is the key verb, "reactor" and "temperature" are the key nouns, and "No. 2" and "85 degrees Celsius" are the key numerals.

[0080] The instruction comparison unit matches the extracted key operation semantics with the pre-set operation type library in the system. The operation type library contains various possible operation types in the waste gas treatment system and their corresponding semantic descriptions, such as "Start the device", "Stop the device", "Adjust the parameters", "Switch the mode", etc. By calculating the semantic similarity, the operation type of the current instruction is determined. For example, "Adjust the temperature" matches the operation type of "Adjust the parameters", and "Start the No. 1 adsorption device" matches the operation type of "Start the device".

[0081] When storing the vectorized features of historical instruction conflict cases and their solutions, the system uses a word vector model to convert text information into numerical vectors. The word vector model can map words into a low-dimensional vector space, making words with similar semantics closer in the vector space. For each historical conflict case, the system extracts key information such as the instruction content, conflict type, and solution, and converts this information into a vector representation. For example, after converting the instructions "Close the No. 1 adsorption device for maintenance" and "Start the No. 1 adsorption device" into vectors, calculate the vector similarity between them. If the similarity exceeds a certain threshold, it is considered that these two instructions may conflict.

[0082] It should be noted that the term "词性标注" in the original text is likely a misspelling. It should probably be "词性标注" (lexical tagging). The above translation assumes this correction for the sake of a more meaningful translation.When a command conflict is detected, the system retrieves a suitable solution from the conflict knowledge base. The conflict knowledge base stores a large number of historical conflict cases and their solutions, each case stored as a vectorized feature. The system calculates the similarity between the current conflict case and cases in the knowledge base, identifies the most similar cases, and extracts their solutions. For example, in a conflict between the current command "turn on adsorption device 1" and the previous command "turn off adsorption device 1 for maintenance," the system retrieves a similar historical case whose solution is "wait for maintenance to be completed before turning on the device." This solution is then used as the suitable solution for the current conflict.

[0083] The timestamp acquisition unit employs a high-precision clock system to ensure time accuracy when recording the initiation and execution times of instructions. The system internally establishes a unified time base, upon which all time recordings are based. When recording the instruction initiation time, the system acquires the current time the instant the instruction is received and stores it as the instruction initiation time. When recording the instruction execution time, the system acquires the current time the instant the instruction begins execution and stores it as the instruction execution time.

[0084] To ensure the accuracy of time recording, the system also performs time synchronization. It periodically synchronizes with an external high-precision time source, such as a Network Time Protocol (NTP) server, to correct deviations in the system's internal clock. Simultaneously, the system records the time and result of each synchronization for time calibration and verification when needed.

[0085] When calculating the time interval between two instructions, the interval analysis unit first converts the recorded time into a unified time format, such as a millisecond-level timestamp. Then, it calculates the difference between the two timestamps to obtain the time interval length. For example, if the execution timestamp of the previous instruction is 1643074200000 milliseconds (corresponding to May 25, 2025, 9:30:00), and the initiation timestamp of the current instruction is 1643076000000 milliseconds (corresponding to May 25, 2025, 10:00:00), then the time interval length is 1643076000000 - 1643074200000 = 1800000 milliseconds, or 30 minutes.

[0086] When comparing preset interval thresholds to determine whether to allow processing the current instruction, the interval analysis unit sets different preset interval thresholds based on different devices and operation types. For devices with high operation frequency requirements, such as valve opening and closing operations, the preset interval threshold may be shorter; while for devices with low operation frequency requirements, such as large fan start-up and shutdown operations, the preset interval threshold may be longer. The setting of preset interval thresholds is based on a comprehensive consideration of factors such as equipment performance parameters, service life, and safety requirements.

[0087] When the command interception unit blocks command execution and generates an operation conflict log, it first sends an interception notification to the command initiator, explaining the reason and basis for the command interception. The interception notification can be sent to the command initiator via system interface, SMS, email, etc., ensuring that the initiator can be informed of the command execution status in a timely manner.

[0088] When generating operation conflict logs, the instruction interception unit records detailed conflict information, including the content of the conflicting instruction, the initiation time, the execution time, the time interval length, the preset interval threshold, and the conflict type. The operation conflict logs are stored in a structured manner in the system database for easy subsequent querying, statistics, and analysis. Simultaneously, the system generates a unique identifier for each operation conflict log to facilitate the tracking and management of conflict events.

[0089] When allocating an independent processing thread to execute the current exhaust gas treatment instruction, the priority determination unit first checks the current system resource usage, including CPU utilization, memory utilization, and thread pool status, to ensure that the system has sufficient resources to execute the new instruction. If system resources are sufficient, the priority determination unit will obtain an idle thread from the thread pool and assign the current instruction to that thread for execution.

[0090] To ensure the independence and security of instruction execution, each processing thread has its own independent memory space and execution context. During instruction execution, the thread calls the corresponding system function modules according to the instructions to complete the control operations of the exhaust gas treatment equipment. Simultaneously, the thread monitors the execution status of the instructions in real time, such as execution progress and whether any exceptions occur, and feeds this information back to the system.

[0091] After the instruction is executed, the processing thread returns the execution result to the system, releases the occupied resources, and returns to the thread pool to wait for the next task assignment. In this way, the priority determination unit ensures that instructions of different operation types can be executed independently and efficiently, avoiding mutual interference and conflicts between instructions.

[0092] Example 2:

[0093] The factor extraction module plays a crucial role in the entire intelligent early warning system for waste gas treatment. Its main function is to accurately identify risk-related variables from historical waste gas treatment records and determine indirect and direct risk factors. This process requires the comprehensive application of various data processing and analysis techniques to ensure that all factors affecting system risk can be captured comprehensively and accurately.

[0094] Before starting the analysis, the factor extraction module arranges all risk-related variables according to the monitoring time sequence, forming an ordered analysis sequence. The monitoring time sequence refers to the order in which risk-related variables are collected and recorded chronologically. For example, the concentration of various pollutants in the exhaust gas and the operating parameters of equipment are collected every 10 minutes, forming a sequence arranged in chronological order. This ordered arrangement helps in subsequent analysis of the temporal relationships and trends among the variables.

[0095] The system randomly selects adjacent variable pairs, obtains the corresponding concentration readings and subsequent concentration readings, and calculates the absolute value of the difference between the concentration readings. For example, in the analysis sequence, if a variable pair at time n and time n+1 is selected, assuming the concentration reading of a certain pollutant at time n is C1 and the concentration reading at time n+1 is C2, then the absolute value of the difference is |C1-C2|. The purpose of this step is to initially detect the magnitude of change between variables, providing basic data for subsequent risk analysis.

[0096] After obtaining a sufficient number of absolute values ​​of the differences between variables, the system determines the concentration peaks and troughs in the full monitoring data and calculates the overall concentration fluctuation range. Full monitoring data refers to the concentration data of all risk-related variables collected within a certain time period. By analyzing this data, the system identifies the maximum value (concentration peak) and the minimum value (concentration trough), and the difference between the two is the overall concentration fluctuation range. This indicator reflects the range of change in exhaust gas concentration throughout the entire monitoring period and is an important basis for assessing system stability.

[0097] The system calculates the ratio of the absolute value of the difference to the fluctuation range, i.e., |C1-C2| / fluctuation range. This ratio reflects the degree to which the concentration changes of adjacent variables are relative to the overall concentration fluctuation. A larger ratio indicates more drastic concentration changes between adjacent time points, potentially posing a greater risk; a smaller ratio indicates relatively stable concentration changes and lower risk. This ratio calculation standardizes the concentration changes of different variable pairs, facilitating subsequent comparisons and analyses.

[0098] In addition to concentration change analysis, the system also calculates the difference in risk association between variable pairs. This difference is calculated based on correlation analysis between variable pairs, using statistical methods to determine the difference in risk association between adjacent variable pairs. Specifically, the system analyzes the synergistic relationships between different variables; for example, whether changes in certain variables lead to changes in other variables, and the extent and direction of such changes. This correlation analysis identifies which variable pairs have strong risk associations, providing a basis for determining indirect and direct risk factors.

[0099] After calculating the difference in risk correlation between all variable pairs, the system determines the maximum and minimum correlation among all variables and calculates the global correlation span, which is the difference between the maximum and minimum correlation. The global correlation span reflects the range of variation in risk correlation between all variable pairs and is an important indicator for assessing the system's risk complexity. A larger global correlation span indicates a greater difference in risk correlation between different variable pairs in the system, with a more dispersed risk distribution; a smaller global correlation span indicates a more concentrated risk correlation between variable pairs in the system, with a relatively even risk distribution.

[0100] The system calculates the ratio of the difference in risk correlation to the span of the overall correlation, i.e., the difference in risk correlation / the span of the overall correlation. This ratio reflects the magnitude of the difference in risk correlation between variable pairs relative to the span of the overall correlation. By calculating this ratio, the difference in risk correlation between different variable pairs can be standardized, facilitating comprehensive comparison and analysis.

[0101] After obtaining the concentration ratio and correlation ratio, the system multiplies these two values ​​and performs standardization to obtain the real-time risk coefficient. Standardization transforms values ​​from different ranges into a unified range for easier comparison and analysis. For example, a linear transformation converts the product into a value between 0 and 1, which serves as the real-time risk coefficient. The real-time risk coefficient comprehensively reflects the concentration changes and risk correlation between adjacent variable pairs and is an important indicator for assessing the system's real-time risk level.

[0102] The system iterates through the remaining variable pairs to generate a set of indirect risk factors, and calculates the mean of all real-time risk coefficients as the direct risk factor. By performing the above calculation on all adjacent variable pairs, the real-time risk coefficient of each variable pair is obtained. The coefficients of these indirectly affected variable pairs are collected to form a set of indirect risk factors, while the mean of all real-time risk coefficients serves as the direct risk factor that directly reflects the overall risk level.

[0103] In practice, the factor extraction module first performs data cleaning and preprocessing on historical exhaust gas treatment records. Since the actual collected data may contain noise, missing values, and other issues, it needs to be processed to improve data quality. Data cleaning includes operations such as removing outliers, filling in missing values, and smoothing data. For example, concentration readings that are significantly outside the normal range can be considered outliers and removed; missing concentration data can be filled using interpolation methods.

[0104] After data cleaning, the system performs feature extraction on the preprocessed data. Feature extraction refers to extracting indicators and variables from the raw data that reflect its essential characteristics. In a waste gas treatment system, feature extraction can include extracting the concentration characteristics of waste gas components, the variation characteristics of equipment operating parameters, and the trend characteristics of time series data. For example, for waste gas component concentration data, statistical features such as mean, variance, maximum, and minimum values ​​can be extracted; for equipment operating parameter variation data, features such as the rate of change and trend can be extracted.

[0105] After feature extraction, the system performs variable selection and dimensionality reduction. Variable selection refers to choosing variables with a significant impact on system risk from numerous features, while dimensionality reduction involves transforming the high-dimensional feature space into a low-dimensional feature space to reduce computational complexity and improve model efficiency. In waste gas treatment systems, variable selection and dimensionality reduction can employ methods such as correlation analysis, principal component analysis, and factor analysis. For example, correlation analysis can identify variables highly correlated with system risk, allowing these variables to be further analyzed as risk-related variables; principal component analysis can transform multiple correlated variables into a few uncorrelated principal components, thus achieving dimensionality reduction.

[0106] After identifying the risk-related variables, the system calculates indirect and direct risk factors using the methods described above. During the calculation process, the system continuously optimizes the algorithm and parameters to improve the accuracy and reliability of the risk factor calculations. For example, for calculating real-time risk coefficients, different standardization methods and weighting schemes can be used, and the optimal scheme is selected by comparing the calculation results of different schemes.

[0107] The factor extraction module also verifies and evaluates the calculated indirect and direct risk factors. Verification refers to checking the rationality and accuracy of the calculation results, while evaluation refers to assessing the importance and impact of the risk factors. In waste gas treatment systems, verification and evaluation can employ methods such as expert evaluation, historical data verification, and cross-validation. For example, domain experts can be invited to evaluate the calculated risk factors to check whether they conform to reality; historical data can be used to verify the risk factors to check whether they can accurately predict system risks; and cross-validation methods can be used to evaluate the risk factors to check their stability and generalization ability.

[0108] Through the detailed implementation methods described above, the factor extraction module can accurately identify risk-related variables from historical waste gas treatment records and determine indirect and direct risk factors, providing a solid foundation for subsequent risk assessment and early warning. The entire process comprehensively utilizes various data processing and analysis techniques to ensure the accuracy and reliability of the results, effectively helping waste gas treatment systems to identify potential risks in advance and take corresponding preventative and treatment measures.

[0109] Example 3:

[0110] The assessment module plays a crucial role in the entire intelligent early warning system for waste gas treatment, primarily responsible for calculating the cumulative risk index of the waste gas treatment system based on indirect and direct risk factors. This process requires the use of various data processing and analysis techniques to ensure a comprehensive and accurate assessment of the system's risk level.

[0111] The assessment module first sets upper and lower thresholds for indirect risk factors. These thresholds are determined based on the safe operation requirements of the waste gas treatment system and historical data statistical analysis. The upper and lower thresholds may differ for different indirect risk factors. For example, for some indirect risk factors that have a significant impact on system safety, the upper threshold may be set lower to ensure timely detection of potential risks; while for some indirect risk factors that have a smaller impact on system safety, the upper threshold may be set higher.

[0112] After setting the thresholds, the evaluation module will filter the set of high-impact factors based on the upper threshold, that is, select factors among the indirect risk factors that are greater than or equal to the upper threshold to form the set of high-impact factors; at the same time, it will filter the set of low-impact factors based on the lower threshold, that is, select factors among the indirect risk factors that are less than or equal to the lower threshold to form the set of low-impact factors. For example, if there are indirect risk factors of 0.3, 0.6, 0.9, and 0.1, and the upper threshold is set to 0.8 and the lower threshold is set to 0.2, then the set of high-impact factors is {0.9} and the set of low-impact factors is {0.1, 0.3}.

[0113] The assessment module calculates the positive deviation of high-impact factors from the upper threshold. For example, if the high-impact factor is 0.9 and the upper threshold is 0.8, the positive deviation is 0.9 - 0.8 = 0.1. Then, these high-impact factors are sorted in descending order of their positive deviation to form a high-ranking queue. This clearly identifies which high-impact factors have the greatest impact on systemic risk, allowing for priority handling.

[0114] The evaluation module calculates the negative deviation of the low impact factor from the lower threshold. For example, if the low impact factor is 0.1 and the lower threshold is 0.2, the negative deviation is 0.2 - 0.1 = 0.1; if the low impact factor is 0.3, the negative deviation is 0.2 - 0.3 = -0.1 (taking the absolute value as 0.1). Then, the low impact factors are sorted in ascending order of the deviation, forming a low-order sorting queue, that is, arranged according to the absolute value of the negative deviation from the largest. This identifies low impact factors with small deviations from the lower threshold, which may have some potential risk.

[0115] After obtaining the high-order and low-order queues, the evaluation module pairs each element in the high-order and low-order queues to form influence pairs. For example, if the high-order queue has one element 0.9 (positive deviation 0.1), and the low-order queue has two elements 0.1 (negative deviation 0.1) and 0.3 (negative deviation 0.1), then influence pairs (0.9, 0.1) and (0.9, 0.3) are formed. Based on all influence pairs, the evaluation module further calculates the risk accumulation index.

[0116] The assessment module counts the total number of affected pairs and calculates the squared difference between the highest and lowest rank elements in each pair. For example, for the affected pair (0.9, 0.1), the squared difference is (0.9 - 0.1)^2 = 0.64; for the affected pair (0.9, 0.3), the squared difference is (0.9 - 0.3)^2 = 0.36. The squared difference reflects the degree of difference between the highest and lowest rank elements; the greater the difference, the greater the combined impact of these two factors on systemic risk.

[0117] The evaluation module identifies the minimum and maximum values ​​among all squared differences. For example, in the above case, the squared differences are 0.64 and 0.36, with a minimum of 0.36 and a maximum of 0.64. By determining the minimum and maximum values, the evaluation module can understand the distribution range of the squared differences, providing a basis for subsequent calculations of the dispersion parameter.

[0118] The evaluation module calculates the dispersion parameter, such as variance or standard deviation, for all squared differences. The dispersion parameter reflects the distribution of the squared differences, i.e., whether these values ​​are relatively concentrated or dispersed. A larger dispersion parameter indicates a more dispersed distribution of squared differences, with greater differences between groups affected by different effects; a smaller dispersion parameter indicates a more concentrated distribution of squared differences, with smaller differences between groups affected by different effects.

[0119] Based on the minimum, maximum, and dispersion parameters, the assessment module calculates a risk accumulation index. By comprehensively considering these parameters, a corresponding calculation formula is established to obtain a risk accumulation index that fully reflects the differences in impact on the groups. This index is used to assess the overall risk level of the waste gas treatment system, providing an important basis for subsequent early warning and decision-making.

[0120] In practice, the assessment module first preprocesses the input indirect risk factors. Since these factors may originate from different data sources, their data formats and ranges may vary. Therefore, standardization is necessary to transform all indirect risk factors to the same scale. This avoids biases in the assessment results caused by differences in data scales.

[0121] When setting the upper and lower thresholds, the evaluation module employs a comprehensive analysis using multiple methods. On one hand, it references the design standards and safety regulations of the exhaust gas treatment system to determine a basic threshold range. On the other hand, it analyzes historical data to identify which indirect risk factors were associated with system failures or accidents during past operations, adjusting the thresholds based on this historical experience. Furthermore, it considers the system's current operating status and environmental conditions, dynamically adjusting the thresholds to improve the accuracy of the evaluation.

[0122] When selecting the sets of high-impact factors and low-impact factors, the evaluation module performs rigorous logical checks. For each indirect risk factor, it is compared with predefined upper and lower thresholds, and then assigned to the appropriate set based on the comparison results. This process ensures that each indirect risk factor belongs to only one set, avoiding duplicate classification.

[0123] When calculating positive and negative deviations, the evaluation module employs precise numerical calculation methods to ensure the accuracy of the results. For the arrangement of the high-order and low-order sorting queues, efficient sorting algorithms are used to improve processing speed. When forming influence pairs, pairing is performed according to certain rules to ensure that each high-impact factor is paired with a suitable low-impact factor, thereby comprehensively reflecting all aspects of system risk.

[0124] When calculating the squared difference values, the evaluation module performs the calculation for each affected pair individually to ensure no pair is missed. When identifying the minimum and maximum values, it iterates through all squared difference values, comparing and filtering them. When calculating the dispersion parameter, it selects an appropriate calculation method, such as variance or standard deviation, based on the characteristics of the data to accurately reflect the dispersion of the data.

[0125] When calculating the risk accumulation index, the assessment module comprehensively considers multiple factors, including minimum, maximum, and dispersion parameters. A weighted combination method is used, combining these factors with different weights to obtain the final risk accumulation index. The weights are determined based on an analysis of the importance of these factors in assessing systemic risk, using methods such as expert evaluation and historical data verification to determine reasonable weight values.

[0126] The assessment module also verifies and calibrates the calculated risk accumulation indicators. It compares the results with historical data to check whether the indicators accurately reflect the system's risk level. If deviations are found, the calculation methods and parameters are adjusted to improve the accuracy and reliability of the indicators.

[0127] Example 4:

[0128] This embodiment details the working mechanism of the adjustment module. Its core function is to correct and optimize the risk level prediction table based on the risk accumulation index, ensuring that the system can dynamically adjust the early warning strategy and equipment control parameters according to the real-time risk situation. The specific implementation method is as follows:

[0129] I. Risk Threshold Setting and Correction Coefficient Configuration

[0130] The adjustment module first needs to set primary and advanced risk thresholds, which are key parameters for classifying the system's risk level. The primary risk threshold identifies a low-risk state, while the advanced risk threshold defines a high-risk state; the range between the two represents a moderate-risk state. The thresholds are set based on the waste gas treatment system's historical operating data, equipment safety parameters, and environmental standards. For example, a primary risk threshold of 0.4 indicates the system is basically safe but requires vigilance; an advanced risk threshold of 0.7 indicates the system risk is approaching or exceeding a controllable range, requiring immediate and strengthened measures.

[0131] When configuring correction coefficients, the system sets multiple levels of correction coefficients based on different risk levels, including basic correction coefficients, intermediate correction coefficients, and enhanced correction coefficients. These correction coefficients are used to adjust the weight parameters in the risk level prediction table to adapt to the early warning accuracy requirements under different risk levels. For example:

[0132] Basic adjustment coefficient: Applicable when the risk accumulation index is lower than the primary risk threshold. The value range is usually 0.8-0.95. It is used to slightly adjust the weight of the prediction table to maintain a balance between the stability and sensitivity of the system.

[0133] Intermediate adjustment coefficient: Applicable when the risk accumulation index is between the primary and advanced risk thresholds. The value range is usually 0.6-0.8. It is used to moderately adjust the weights and enhance the system's ability to respond to potential risks.

[0134] Enhanced correction coefficient: Applicable when the risk accumulation indicator exceeds the high-level risk threshold. The value range is usually 0.4-0.6. It is used to significantly adjust the weight, forcibly raise the warning level and trigger emergency control measures.

[0135] The specific value of the correction factor needs to be determined through parameter calibration during the system initialization phase. The calibration process takes into account the equipment's operating characteristics and historical risk cases to ensure that the factor setting meets the actual needs.

[0136] II. Risk Level-Based Adjustment Coefficient Triggering Mechanism

[0137] After the assessment module outputs the risk accumulation index, the adjustment module first compares the index with the preset risk threshold, and triggers the corresponding correction coefficient based on the comparison result:

[0138] Low-risk status (indicator < primary threshold):

[0139] If the cumulative risk index is 0.3 (below the primary threshold of 0.4), the system determines that the current risk state is low and activates the basic correction coefficient (e.g., 0.9). At this time, the correction weight of the risk level prediction table is based on the basic coefficient, and the mapping relationship between the adsorption device control parameters and the risk index is slightly adjusted. This aims to prevent risk escalation through subtle parameter optimization, while avoiding excessive intervention in the normal operation of the equipment.

[0140] Medium risk status (basic threshold ≤ indicator ≤ advanced threshold):

[0141] If the cumulative risk index is 0.55 (between 0.4 and 0.7), the system determines it to be a medium-risk state and activates a medium-level correction coefficient (e.g., 0.7). At this time, the adjustment range of the correction weight is greater than that of the low-risk state. The system will make a moderate correction to the key parameters in the risk level prediction table (such as the control parameters of the adsorption device in the high-risk range). For example, the target control value of a certain adsorption device may be adjusted from 80% to 70% to enhance the risk suppression effect, while reserving adjustment space for possible subsequent risk escalation.

[0142] High-risk status (indicators > advanced threshold):

[0143] If the cumulative risk index is 0.8 (exceeding the advanced threshold of 0.7), the system determines it to be a high-risk state and activates the enhanced correction coefficient (e.g., 0.5). At this time, the correction weight will significantly adjust the risk level prediction table, which may directly reset the priority of some control parameters. For example, the activation threshold of the emergency adsorption device may be advanced from the risk index of 0.6 to 0.5, and the upper limit of the equipment's operating power may be forcibly increased to quickly reduce the risk level.

[0144] III. Calculation of Target Control Values ​​and Parameter Mapping

[0145] After determining the correction coefficient, the adjustment module needs to multiply the correction weight by the adsorption unit's control parameters to obtain the target control value. Adsorption unit control parameters typically include equipment operating power, valve opening, and adsorbent replacement cycle; these parameters directly affect waste gas treatment efficiency and risk control effectiveness. For example:

[0146] If the current control parameter of an adsorption device is "operating power 90kW", after enabling the basic correction coefficient of 0.9, the target control value is 90×0.9=81kW;

[0147] If the enhanced correction factor of 0.5 is enabled, the target control value is 90 × 0.5 = 45 kW (the feasibility needs to be determined in conjunction with the equipment safety threshold; if it exceeds the lower limit, the minimum threshold value should be taken).

[0148] After calculating the target control value, the system needs to determine its corresponding expected risk index through a preset parameter mapping relationship. This parameter mapping relationship is a multi-dimensional lookup table built based on a digital twin model, trained using historical data, and includes a non-linear mapping relationship between control parameters and risk indices. For example:

[0149] When the adsorption device operates at a power of 81kW, the corresponding expected risk index is 0.35 (low risk range) as found by looking up the mapping table.

[0150] When the operating power is 45kW, if this parameter is close to the threshold for inefficient operation of the equipment, the mapping table may return an expected risk index of 0.6 (medium risk range), indicating that it is necessary to coordinate with other equipment for control.

[0151] The construction of parameter mapping relationships needs to consider multiple constraints, including equipment energy consumption limits, environmental emission standards, and process stability requirements, to ensure that the target control value can effectively reduce risks without causing new operational problems.

[0152] IV. Optimize the construction of the risk level prediction table

[0153] The adjustment module integrates the target control value and the expected risk index to construct an optimized risk level prediction table. This table is typically presented in matrix form, with the horizontal dimension representing the risk level (e.g., low, medium, high) and the vertical dimension representing the adsorption device type or control parameter category. Each cell contains the target control value, the expected risk index, and a description of the corresponding control strategy. For example:

[0154]

[0155] During the development process, the system manages historical optimization records, retaining the timestamp of each correction, correction coefficient, target adjustment value, and subsequent risk change data, forming a traceable optimization log. This log is used for subsequent system performance analysis and parameter optimization, such as dynamically adjusting thresholds and correction coefficient configurations by comparing risk evolution trends under different correction coefficients.

[0156] V. Anomaly Handling and Dynamic Calibration

[0157] During the adjustment process, if the target control value exceeds the safe operating range of the equipment (e.g., the operating power is lower than the minimum limit), the system will automatically trigger the abnormal handling mechanism:

[0158] Threshold protection: Forces the control value to be limited within the equipment's safety threshold and generates an abnormal prompt message, such as "Target power 45kW is lower than the safety lower limit of 50kW, and has been adjusted to 50kW";

[0159] Multi-device collaboration: If a single device cannot meet the control requirements, the system will automatically call other related devices (such as switching to a backup adsorption device) and recalculate the combined control parameters;

[0160] Dynamic calibration: After the anomaly is handled, the system will record the conflict data for subsequent calibration of correction coefficients and parameter mapping relationships, so as to avoid the recurrence of similar problems.

[0161] In addition, the adjustment module also has a periodic self-calibration function, which regularly updates the correction coefficients and parameter mapping relationships based on the latest historical data and equipment status, ensuring that the system can adapt to long-term changes such as equipment aging and process changes, and maintain the effectiveness of the early warning strategy.

[0162] Through the above implementation methods, the adjustment module achieves fully automated management of the entire process, from risk level determination, correction coefficient triggering, parameter calculation to prediction table optimization. This ensures that the waste gas treatment system can dynamically adjust its early warning and control strategies based on real-time risk conditions, minimizing safety risks while maintaining treatment efficiency. The entire process relies heavily on digital twin models and historical data, avoiding the limitations of subjective judgment and empiricism, and improving the system's scientific rigor and reliability.

[0163] Example 5:

[0164] This embodiment describes the extended functions and emergency response mechanism of the model building module, as follows:

[0165] I. Construction of a Three-Dimensional Dynamic Concentration Field Model

[0166] The model building module constructs a three-dimensional dynamic concentration field model by integrating multi-source data in real time. This sensor data includes exhaust gas components (such as SO2 and NO). x The system monitors real-time parameters such as VOCs concentration, temperature, and humidity; equipment logs include equipment operation data such as fan speed, valve opening, and adsorption device operating status; environmental meteorological data covers environmental factors such as wind speed, wind direction, and air pressure. The system uses spatial interpolation algorithms (such as Kriging interpolation) to convert discrete monitoring point data into a continuous three-dimensional concentration distribution field. The specific formula is as follows:

[0167]

[0168] Formula character meaning description: C(x,y,z,t): exhaust gas concentration at spatial coordinates (x,y,z) at time t; n: total number of monitoring points; λ i (t): The weight coefficient of the i-th monitoring point at time t, which is related to the reliability and timeliness of the monitoring point; c i (t): The measured concentration value at the i-th monitoring point at time t; φ(d i (x,y,z)): Distance decay function, representing the distance d between the spatial location (x,y,z) and the i-th monitoring point. i The effect on concentration is usually a function that decreases with increasing distance (such as the Gaussian function).

[0169] Using the above model, the system can dynamically visualize the diffusion pattern of exhaust gas in space and identify high-concentration accumulation areas and diffusion trends.

[0170] II. Calculation of Waste Gas Diffusion Boundary and Activation of Emergency Adsorption Device

[0171] Based on a three-dimensional dynamic concentration field model, the system calculates the waste gas diffusion boundary, i.e., the concentration equals the safety threshold C. safe The spatial curved surface. Safety thresholds are set according to environmental standards and occupational health requirements of the workshop (e.g., a safety threshold of 10 mg / m³ for a certain pollutant). 3 When the predicted diffusion range exceeds the preset safe area (such as the workshop boundary or factory boundary), the system automatically activates the emergency adsorption device. The activation logic is as follows:

[0172] Real-time prediction: Extrapolate the diffusion range in the future Δt time using a concentration field model (Δt can be set to 5-30 minutes, adjusted according to the gas diffusion rate);

[0173] Boundary breakthrough judgment: Compare the spatial position relationship between the predicted diffusion boundary and the safety area boundary. If there is an intersection or exceedance, trigger an emergency response;

[0174] Device activation: Send a start command to the nearest emergency adsorption device. The device starts running according to preset parameters (such as power, adsorbent type) and adsorbs the diffused waste gas.

[0175] III. Generation of energy consumption / purification rate matrix and construction of Pareto optimal solution set

[0176] The system generates an energy consumption / purification rate matrix under different combinations of operating parameters through Monte Carlo simulation. The operating parameters include the fan speed v, the adsorbent filling amount m, the reaction temperature T, etc. Each parameter is set at multiple discrete levels (for example, the fan speed is set at three gears: 500 rpm, 800 rpm, and 1200 rpm). Monte Carlo simulation randomly samples parameter combinations and calculates the energy consumption E (unit: kWh) and purification rate η (unit: %) under each set of parameters, forming a matrix containing thousands of sets of data.

[0177] Based on the energy consumption / purification rate matrix, the system screens out non-dominated solutions (that is, there is no other set of parameters with lower energy consumption and higher purification rate) through the Pareto optimization algorithm to form the Pareto optimal solution set. This solution set provides a multi-objective decision-making basis for the optimization of equipment operating parameters. For example:

[0178] When pursuing a high purification rate, a parameter combination with higher energy consumption but the optimal purification rate can be selected;

[0179] When energy-saving operation is required, a parameter combination with lower energy consumption but an acceptable purification rate can be selected.

[0180] IV. Three-level emergency response mechanism

[0181] The system divides the three-level emergency response according to the diffusion boundary breakthrough distance L (that is, the maximum distance that the predicted diffusion boundary exceeds the safety area):

[0182] Level 1 response (0 < L ≤ 1m): Activate the local adsorption device, such as a small mobile adsorption device set near the diffusion breakthrough point, to deal with the waste gas in the local area specifically. At this response level, the system only adjusts the local device parameters and maintains the normal operation of the main treatment process.

[0183] Level 2 response (1m < L ≤ 5m): Start the workshop isolation barrier, separate the polluted area from the clean area through physical partitions (such as rolling doors, air curtains), and at the same time increase the exhaust volume of the workshop exhaust system to reduce the pollution diffusion speed. At this stage, the system will synchronously adjust the operating parameters of the adsorption devices in the relevant areas to improve the overall treatment efficiency.

[0184] Level 3 Response (L>5m): Triggers reverse pressurization of the entire plant's exhaust ventilation system, creating a negative pressure environment inside the workshop through forced airflow to prevent exhaust gases from spreading outside the plant area. Simultaneously, all standby adsorption devices are activated to reduce the concentration of exhaust gases to the greatest extent possible. This level of response requires linkage with the plant's safety alarm system to notify personnel to evacuate or take protective measures.

[0185] V. Data Integration and Model Update

[0186] The model building module receives sensor, device, and environmental data in real time, every Δt ′ The three-dimensional dynamic concentration field model is updated every minute (e.g., every 1 minute) to ensure that the model is synchronized with actual operating conditions. At the same time, the system periodically (e.g., daily) iteratively updates the Monte Carlo simulation parameters and Pareto optimal solution set, incorporating the latest equipment operating data and environmental conditions to maintain the accuracy and adaptability of the model.

[0187] Through the above implementation methods, the model building module realizes dynamic simulation of waste gas diffusion, hierarchical control of emergency response, and optimization decision-making of operating parameters, providing underlying model support for intelligent early warning and risk prevention and control of waste gas treatment systems.

[0188] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0189] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart early warning system for waste gas treatment based on digital twins, characterized in that, include: The model building module is used to receive waste gas treatment instructions and build a dynamic risk model of the waste gas treatment process based on waste gas composition data and the operating status data of waste gas treatment equipment. The monitoring module is used to acquire historical waste gas treatment records and generate a risk level prediction table in combination with the dynamic risk model. The risk level prediction table includes a risk index and adsorption device control parameters. The factor extraction module is used to analyze the historical waste gas treatment records, identify risk-related variables, and determine indirect and direct risk factors. The assessment module is used to calculate the cumulative risk index of the waste gas treatment system based on the indirect risk factors and the direct risk factors. The adjustment module is used to set the correction weight of the risk level prediction table according to the risk accumulation index, and update the risk level prediction table based on the correction weight to form an optimized risk level prediction table. The early warning execution module is used to trigger alarm control operations of the waste gas treatment equipment based on the optimized risk level prediction table.

2. The intelligent early warning system for waste gas treatment based on digital twin as described in claim 1, characterized in that, Also includes: The instruction comparison unit is used to compare the operation type of the current exhaust gas treatment instruction with that of the previously executed instruction to determine whether the two belong to the same type of operation. The timestamp acquisition unit is used to record the initiation time of the current instruction and the execution time of the previous instruction if the operation is determined to be of the same type. The interval analysis unit is used to calculate the time interval between two instructions and compare it with a preset interval threshold to determine whether the current instruction can be processed. The instruction interception unit is used to prevent instruction execution and generate an operation conflict log if the time interval length is less than the preset interval threshold. The priority determination unit is used to allocate an independent processing thread to execute the current exhaust gas treatment instruction if the operation type comparison result does not match.

3. The intelligent early warning system for waste gas treatment based on digital twin as described in claim 1, characterized in that, The factor extraction module includes: All risk-related variables are arranged according to the monitoring time sequence to form an analysis sequence. Adjacent variable pairs are randomly selected to obtain the corresponding concentration readings and subsequent concentration readings. The absolute value of the difference between the concentration readings is calculated. Determine the concentration peaks and troughs in the full monitoring data, and calculate the overall concentration fluctuation range; Calculate the ratio of the absolute value of the difference to the fluctuation amplitude; Calculate the difference in risk association between the variable pairs; Determine the maximum and minimum correlation among all variables, and calculate the global correlation span; Calculate the ratio of the risk correlation difference to the global correlation span; The concentration ratio is multiplied by the correlation ratio and then standardized to obtain the real-time risk coefficient. Iterate through the remaining variable pairs to generate a set of indirect risk factors, and calculate the mean of all real-time risk coefficients as the direct risk factors.

4. The intelligent early warning system for waste gas treatment based on digital twin as described in claim 1, characterized in that, The evaluation module includes: Set upper and lower thresholds for indirect risk factors; The set of high-impact factors is selected based on the upper threshold, and the set of low-impact factors is selected based on the lower threshold. Calculate the positive deviation of high impact factors from the upper limit threshold, and arrange them in descending order of deviation to form a high-order sorting queue; Calculate the negative deviation of the low impact factor from the lower limit threshold, and sort them in ascending order according to the magnitude of the deviation to form a low-order sorting queue; The elements of the high-order queue and the low-order queue are paired one by one to form an impact pair group, and the risk accumulation index is calculated based on all impact pairs groups.

5. The intelligent early warning system for waste gas treatment based on digital twins according to claim 4, characterized in that, The evaluation module also includes: Count the total number of groups affected by the statistics, and calculate the squared difference between the highest and lowest elements in each group; Identify the minimum and maximum values ​​among all squared differences, and calculate the dispersion parameter of all squared differences; The risk accumulation index is calculated based on the minimum value, maximum value, and dispersion parameters.

6. The intelligent early warning system for waste gas treatment based on digital twin as described in claim 1, characterized in that, The adjustment module includes: Set primary risk thresholds and advanced risk thresholds; Configure the base correction coefficient, intermediate correction coefficient, and enhancement correction coefficient; When the risk accumulation index is lower than the primary risk threshold, the basic correction coefficient is activated; when the risk accumulation index is between the primary and advanced risk thresholds, the intermediate correction coefficient is activated; and when the risk accumulation index exceeds the advanced risk threshold, the enhanced correction coefficient is activated.

7. The intelligent early warning system for waste gas treatment based on digital twin as described in claim 1, characterized in that, The adjustment module further includes: The target control value is obtained by multiplying the corrected weight by the control parameters of the adsorption device; The expected risk index corresponding to the target control value is determined by matching the preset parameter mapping relationship; The optimized risk level prediction table is constructed by integrating the target control value and the expected risk index.

8. The intelligent early warning system for waste gas treatment based on digital twin as described in claim 1, characterized in that, The model building module also includes: Real-time integration of sensor data, equipment logs, and environmental meteorological data to construct a three-dimensional dynamic concentration field model; The waste gas diffusion boundary is calculated based on the three-dimensional dynamic concentration field model. When the predicted diffusion range exceeds the safe area, the emergency adsorption device is activated. The energy consumption / purification rate matrix under different combinations of operating parameters is generated through Monte Carlo simulation, and the Pareto optimal solution set is generated.

9. The intelligent early warning system for waste gas treatment based on digital twin as described in claim 8, characterized in that, Based on the distance of the diffusion boundary breach, the emergency response is divided into three levels: Level 1 activates the local adsorption device, Level 2 activates the workshop isolation barrier, and Level 3 triggers the reverse pressurization of the entire plant's exhaust system.

10. The intelligent early warning system for waste gas treatment based on digital twins according to claim 2, characterized in that, The instruction comparison unit includes: extracting key operational semantics from unstructured operation instructions using natural language processing technology, storing vectorized features of historical instruction conflict cases and their solutions, and when an instruction conflict is detected, retrieving suitable solutions from the conflict knowledge base through similarity matching and generating priority adjustment suggestions.

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