Waste gas treatment intelligent early warning system based on digital twinning

Abstract

The invention relates to the technical field of waste gas treatment, and discloses a digital twinning-based waste gas treatment intelligent early warning system, 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 simulates and optimizes parameters in combination; the monitoring module generates a prediction table containing risk indexes and regulation and control parameters; the factor extraction module identifies risk association variables; the evaluation module calculates a risk accumulation index; the adjusting module updates the prediction table in sequence; and the early warning execution module triggers an alarm. The system is also provided with an instruction processing unit for realizing conflict detection and priority management. The system improves the accuracy of risk early warning and the intelligent level of regulation and control, is suitable for industrial waste gas treatment scenes, and guarantees the treatment efficiency and safety.

Description

Technical Field

[0001] The present invention relates to the technical field of waste gas treatment, and specifically to an intelligent early warning system for waste gas treatment based on digital twin. Background Art

[0002] In the process of industrial production, waste gas treatment is an important link in environmental protection and safe production. With the development of industrial intelligence, many limitations of traditional waste gas treatment systems have gradually emerged, making it difficult to meet the requirements of precise monitoring, risk early warning, and efficient regulation under current complex working conditions.

[0003] Traditional waste gas treatment systems mainly rely on manual experience and fixed thresholds for risk judgment and equipment regulation. On the one hand, when manually analyzing historical treatment records, it is difficult to comprehensively and dynamically identify risk-related variables, and indirect risk factors are easily overlooked, resulting in inaccurate risk assessment and the inability to detect potential safety hazards in a timely manner. On the other hand, fixed regulation parameters cannot be flexibly adjusted according to real-time risk conditions. When the waste gas composition, equipment operating status, or environmental conditions change, the treatment efficiency may decrease, and even safety accidents may be triggered due to untimely regulation.

[0004] In addition, traditional systems lack intelligent conflict detection and priority management mechanisms in command processing. When receiving consecutive similar operation instructions, it is unable to automatically judge whether the time interval is reasonable, which may lead to frequent start-stop of equipment or operation conflicts, affecting the equipment life and the stability of the treatment process. For different types of instructions, it is also unable to intelligently allocate processing threads according to the operation type, which may cause processing delays or resource waste.

[0005] In terms of risk model construction, traditional methods usually rely on static models and are unable to 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, the diffusion of waste gas is significantly affected by environmental meteorological conditions (such as wind speed, wind direction, temperature, etc.). Traditional models are unable to incorporate these dynamic factors in real time, resulting in inaccurate prediction of the waste gas diffusion boundary, inability to activate the emergency adsorption device in a timely manner, and difficulty in effectively coping with emergencies.

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

[0007] The purpose of the present invention is to provide an intelligent early warning system for waste gas treatment based on digital twin to solve the problems raised in the above background technology.

[0008] To achieve the above object, the present invention provides the following technical solution: An intelligent early warning system for waste gas treatment based on digital twin, the system includes:

[0009] A model construction module, configured to receive waste gas treatment instructions, and construct 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] A monitoring module, configured to obtain historical waste gas treatment records, and generate a risk level prediction table in combination with the dynamic risk model, wherein the risk level prediction table includes a risk index and adsorption device control parameters;

[0011] A factor extraction module, configured to analyze the historical waste gas treatment records, identify risk-related variables, and determine indirect risk factors and direct risk factors;

[0012] An evaluation module, configured to calculate a risk accumulation index of the waste gas treatment system according to the indirect risk factors and the direct risk factors;

[0013] An adjustment module, configured to set a 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] An early warning execution module, configured to trigger an alarm control operation of the waste gas treatment equipment based on the optimized risk level prediction table.

[0015] Preferably, the system further includes:

[0016] An instruction comparison unit, configured to compare the current waste gas treatment instruction with the operation type of the previous executed instruction to determine whether the two belong to the same type of operation;

[0017] A timestamp acquisition unit, configured to record the initiation time of the current instruction and the execution time of the previous instruction if it is determined to be the same type of operation;

[0018] An interval analysis unit, configured to calculate the time interval length between two instructions, and compare it with a preset interval threshold to determine whether to allow processing of the current instruction;

[0019] An instruction interception unit, configured to block the instruction execution and generate an operation conflict log if the time interval length is less than the preset interval threshold;

[0020] A priority determination unit, configured to allocate an independent processing thread to execute the current waste gas treatment instruction if the operation type comparison result does not match.

[0021] Preferably, the factor extraction module includes:

[0022] Arrange all risk-related variables according to the monitoring time sequence to form an analysis sequence, randomly select adjacent variable pairs, obtain the corresponding concentration readings and subsequent concentration readings, and calculate the absolute value of the difference in the concentration readings;

[0023] Determine the concentration peak and valley values in the total monitoring data, and calculate the overall concentration fluctuation range;

[0024] Obtain the ratio of the absolute value of the difference to the fluctuation range;

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

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

[0027] Obtain the proportional value of the difference in risk correlation degree to the global correlation degree span;

[0028] Multiply the concentration ratio by the correlation degree ratio and perform a normalization process to obtain the real-time risk coefficient;

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

[0030] Preferably, the evaluation module includes:

[0031] Set the upper threshold and the lower threshold for the indirect risk factor;

[0032] According to the upper threshold, screen the set of high-impact factors, and according to the lower threshold, screen the set of low-impact factors;

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

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

[0035] Pair the elements of the high-order queue and the low-order queue item by item to form an impact pair group, and calculate the risk accumulation index based on all impact pair groups.

[0036] Preferably, the evaluation module further includes:

[0037] Count the total number of impact pair groups, and calculate the square of the difference between the high-order element and the low-order element in each group respectively;

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

[0039] Calculate the risk accumulation index according to the minimum value, maximum value and dispersion parameter.

[0040] Preferably, the adjustment module includes:

[0041] Set a primary risk threshold and a high - level risk threshold;

[0042] Configure a basic correction coefficient, an intermediate correction coefficient and a strengthening correction coefficient;

[0043] When the risk accumulation index is lower than the primary risk threshold, enable the basic correction coefficient; when the risk accumulation index is between the primary and high - level risk thresholds, enable the intermediate correction coefficient; when the risk accumulation index exceeds the high - level risk threshold, enable the strengthening correction coefficient.

[0044] Preferably, the adjustment module further includes:

[0045] Multiply the correction weight by the adsorption device control parameter to obtain the target control value;

[0046] Match the preset parameter mapping relationship to determine the expected risk index corresponding to the target control value;

[0047] Integrate the target control value and the expected risk index to construct the optimized risk level prediction table.

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

[0049] Integrate sensor data, equipment logs and environmental meteorological data in real - time to construct a three - dimensional dynamic concentration field model;

[0050] Calculate the waste gas diffusion boundary according to the three - dimensional concentration field model, and activate the emergency adsorption device when the predicted diffusion range exceeds the safe area;

[0051] Generate an energy consumption / purification rate matrix under different operating parameter combinations through Monte Carlo simulation, and generate a Pareto optimal solution set.

[0052] Preferably, the system divides the three - level emergency response according to the diffusion boundary breakthrough distance: the first level activates the local adsorption device, the second level starts the workshop isolation barrier, and the third level triggers the reverse pressurization of the whole - plant exhaust system.

[0053] Preferably, the instruction comparison unit includes: extracting the key operation semantics in the unstructured operation instructions through natural language processing technology, storing the vectorized features of historical instruction conflict cases and their solutions, when detecting an instruction conflict, retrieving the adapted 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 as follows:

[0055] Based on the waste gas composition data, equipment operation status data, and multi-source environmental data, the model construction module constructs a three-dimensional dynamic concentration field model and a dynamic risk model, which can reflect the dynamic changes in the waste gas treatment process in real time. By generating an energy consumption / purification rate matrix and a Pareto optimal solution set through Monte Carlo simulation, it provides a scientific basis for optimizing the equipment operation parameters, reducing energy consumption while ensuring the purification effect. According to the diffusion boundary breakthrough distance, a three-level emergency response is divided, realizing a hierarchical and precise response to the waste gas diffusion risk and improving the emergency handling ability of the system.

[0056] The monitoring module combines the dynamic risk model to generate a risk level prediction table containing risk indexes and adsorption device control parameters, providing a direct basis for real-time risk warning and equipment control. The factor extraction module can identify risk-related variables and determine indirect risk factors and direct risk factors by deeply analyzing historical waste gas treatment records, comprehensively considering various factors affecting waste gas treatment risks, and avoiding the limitations of traditional methods that only rely on a single factor.

[0057] The evaluation module calculates the risk accumulation index based on indirect risk factors and direct risk factors, screens high and low impact factors through setting thresholds and sorts and pairs them, and combines the dispersion parameter of the squared difference value to achieve a scientific quantitative evaluation of risk accumulation, making the risk assessment result more accurate and reliable. The adjustment module sets a correction weight based on the risk accumulation index, dynamically updates the risk level prediction table, and forms an optimized risk level prediction table to ensure that the control parameters can be flexibly adjusted according to the real-time risk situation, improving the adaptability and processing efficiency of the system.

[0058] In terms of instruction processing, the system realizes the time interval analysis and conflict detection of similar operation instructions through modules such as an instruction comparison unit, a timestamp acquisition unit, and an interval analysis unit, avoiding the problems of frequent start-stop and conflicts caused by too short operation intervals of the equipment. The generated operation conflict log also provides a reference for subsequent fault analysis. For different types of instructions, the priority determination unit can allocate independent processing threads to ensure the timely and orderly execution of instructions, improving the stability and processing efficiency of the system. The instruction comparison unit also uses natural language processing technology and a conflict knowledge base to realize the intelligent parsing of unstructured instructions and the rapid retrieval of conflict solution methods, further improving the intelligence level and operation convenience of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is the working principle diagram of the intelligent early warning system for waste gas treatment based on digital twin according to the present invention;

[0060] Figure 2 A flow chart for detecting and handling conflicts in exhaust gas treatment instructions;

[0061] Figure 3 Flowchart for risk-associated variable analysis and risk factor determination;

[0062] Figure 4 A flowchart for the calculation of risk accumulation indicators based on indirect risk factors;

[0063] Figure 5 Flowchart for the calculation and analysis of the squared difference of the risk accumulation index. DETAILED DESCRIPTION

[0064] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

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

[0066] The model building module receives the exhaust gas treatment instructions and builds a dynamic risk model of the exhaust gas treatment process based on the exhaust gas composition data and the operating status data of the exhaust gas treatment equipment. Among them, the exhaust gas composition data can be collected in real time through the gas sensor installed at the entrance of the exhaust gas treatment equipment, including the concentration, humidity, temperature and other parameters of various pollutants; the operating status data of the exhaust gas treatment equipment is obtained through the monitoring device of the equipment, such as the fan speed, the working time of the adsorption device, the valve opening, etc.

[0067] The monitoring module obtains historical waste gas treatment records and generates a risk level prediction table in combination with the dynamic risk model. The risk level prediction table contains risk index and adsorption device control parameters. Historical waste gas treatment records are stored in the system database, including information such as waste gas composition, treatment time, equipment parameters used, and final treatment effect for each treatment. The dynamic risk model establishes a mapping relationship between waste gas composition, equipment operating status, and risk index by learning and analyzing these historical data, thereby predicting the risk level and corresponding adsorption device control parameters under different circumstances.

[0068] The factor extraction module analyzes historical waste gas treatment records, identifies risk-related variables, and determines indirect risk factors and direct risk factors. During the analysis process, various data in the historical records are analyzed through data mining algorithms to find variables related to the occurrence of risks, such as changes in the concentration of specific pollutants in the waste gas and fluctuations in equipment operation parameters. Then, according to the degree of association between these variables and risks, indirect risk factors and direct risk factors are distinguished.

[0069] The evaluation module calculates the risk accumulation index of the waste gas treatment system based on indirect risk factors and direct risk factors. By establishing an evaluation model, comprehensively considering the influence degree of each risk factor on the system risk, weighted calculation is performed on indirect risk factors and direct risk factors to obtain a risk accumulation index that can reflect the overall risk level of the system.

[0070] The adjustment module sets the correction weight of the risk level prediction table according to the risk accumulation index and updates the risk level prediction table based on the correction weight to form an optimized risk level prediction table. According to the size of the risk accumulation index, the weight coefficient for correcting the risk level prediction table is determined. By adjusting the weight coefficient, the risk level prediction table can more accurately reflect the current risk status of the system.

[0071] The warning execution module triggers the alarm control operation of 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 warning execution module sends an alarm signal to the waste gas treatment equipment and performs corresponding regulation operations on the equipment according to the preset control strategy, such as adjusting the fan speed, switching the adsorption device, etc., to reduce the system risk.

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

[0073] Embodiment 1:

[0074] This embodiment relates to the instruction processing function of the system, 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 the key operation semantics in the unstructured operation instructions through natural language processing technology. For example, from the instruction "Turn on the No. 1 adsorption device and adjust the fan speed to 800 revolutions per minute", it extracts key semantics such as "Turn on", "Adsorption device", "Adjust", and "Fan speed". At the same time, the system stores the vectorized features of historical instruction conflict cases and their solutions, which are obtained through the analysis and processing of historical conflict cases and contain information such as the operation type of the instruction, the equipment involved, and the form of the conflict. When an instruction conflict is detected, such as the operation type of the current instruction and the previous executed instruction may cause a device operation conflict, the system retrieves an appropriate solution from the conflict knowledge base through similarity matching. For example, if the previous instruction is to turn off the No. 1 adsorption device and perform maintenance, and the current instruction is to turn on the No. 1 adsorption device, the system will detect an operation type conflict, then search for similar conflict cases in the knowledge base. If a corresponding solution is found, such as prompting to wait until the maintenance is completed before turning on, a priority adjustment suggestion will be generated to suggest that the user suspend the execution of the current instruction.

[0075] When the timestamp acquisition unit determines that the operation types of the current waste gas treatment instruction and the previous executed instruction belong to the same type of operation, it records the initiation time of the current instruction and the execution time of the previous instruction. For example, if the previous instruction is to adjust the fan speed to 600 revolutions per minute, and the current instruction is also to adjust the fan speed, which belongs to the same type of operation. At this time, the initiation time of the current instruction is recorded as 10:00:00 on May 25, 2025, and the execution time of the previous instruction is recorded as 9:30:00 on May 25, 2025.

[0076] The interval analysis unit calculates the time interval length between the two instructions, that is, the time interval between 10:00:00 and 9:30:00 is 30 minutes, and compares it with the preset interval threshold to determine whether to allow the processing of the current instruction. The preset interval threshold is set according to the performance and operation requirements of the device. For example, for the adjustment of the fan speed, the preset interval threshold is 20 minutes. If the time interval length is 30 minutes, which is greater than the preset interval threshold of 20 minutes, the current instruction is allowed to be processed; if the time interval length is less than the preset interval threshold, such as 15 minutes, it enters the instruction interception unit.

[0077] When the time interval length is less than the preset interval threshold, the instruction interception unit blocks the execution of the instruction and generates an operation conflict log. The operation conflict log records information such as the content of the conflicting instruction, the time interval length, and the preset interval threshold, so as to analyze and optimize the system operation situation later.

[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 turn on the adsorption device and the current instruction is to close the valve, with different operation types, 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 reactor No. 2 to 85 degrees Celsius", after word segmentation, we get words such as "Adjust", "No. 2", "Reactor", "of", "Temperature", "to", "85 degrees Celsius". Then, through part-of-speech tagging and syntactic analysis, it determines the grammatical roles and semantic relationships of each word in the sentence, identifying key verbs, nouns, and numerals, etc. In this example, "Adjust" is the key verb, "Reactor" and "Temperature" are key nouns, and "No. 2" and "85 degrees Celsius" are 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 "Turn on the device", "Turn off the device", "Adjust the parameters", "Switch the mode", etc. By calculating the semantic similarity, it determines the operation type of the current instruction. For example, "Adjust the temperature" matches the operation type of "Adjust the parameters", and "Turn on Adsorption Device No. 1" matches the operation type of "Turn on 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 "Turn off Adsorption Device No. 1 for maintenance" and "Turn on Adsorption Device No. 1" 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] When an instruction conflict is detected, the system retrieves an appropriate solution from the conflict knowledge base. The conflict knowledge base stores a large number of historical conflict cases and their solutions, and each case is stored in the form of vectorized features. The system calculates the similarity between the current conflict case and the cases in the knowledge base, finds the most similar cases, and extracts their solutions. For example, for the conflict between the current instruction "Turn on Adsorption Device No. 1" and the previous instruction "Turn off Adsorption Device No. 1 for maintenance", the system retrieves a similar historical case, and its solution is "Wait until the maintenance is completed and then turn on the device", so this solution is used as the appropriate solution for the current conflict.

[0083] When the timestamp acquisition unit records the initiation time and execution time of an instruction, it uses a high-precision clock system to ensure the accuracy of time. A unified time reference is set inside the system, and all time records are based on this reference. When recording the initiation time of an instruction, the system obtains the current time at the moment the instruction is received and stores it as the initiation time of the instruction. When recording the execution time of an instruction, the system obtains the current time at the moment the instruction starts to execute and stores it as the execution time of the instruction.

[0084] To ensure the accuracy of time records, the system also performs time synchronization operations. It synchronizes with an external high-precision time source, such as a Network Time Protocol (NTP) server, at regular intervals to correct the deviation of the internal clock of the system. At the same time, the system records the time and synchronization result of each time synchronization for time calibration and verification when needed.

[0085] When the interval analysis unit calculates the time interval length between two instructions, it 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 9:30:00 on May 25, 2025), and the initiation timestamp of the current instruction is 1643076000000 milliseconds (corresponding to 10:00:00 on May 25, 2025), then the time interval length is 1643076000000 - 1643074200000 = 1800000 milliseconds, which is 30 minutes.

[0086] When the interval analysis unit determines whether to allow processing of the current instruction by comparing with the preset interval threshold, it sets different preset interval thresholds according to different device and operation types. For some devices with high requirements for operation frequency, such as the opening and closing operations of valves, the preset interval threshold may be shorter; while for some devices with low requirements for operation frequency, such as the start and stop operations of large fans, the preset interval threshold may be longer. The setting of the preset interval threshold is comprehensively considered based on factors such as the performance parameters, service life, and safety requirements of the device.

[0087] When the instruction interception unit blocks the execution of an instruction and generates an operation conflict log, it will first send an interception notice to the instruction initiator, explaining the reasons and basis for the interception of the instruction. The interception notice can be sent to the instruction initiator through the system interface, text message, email, etc., to ensure that it can timely understand the execution status of the instruction.

[0088] When generating the operation conflict log, the instruction interception unit will record detailed conflict information, including the content of the conflicting instruction, the initiation time, the execution time, the length of the time interval, the preset interval threshold, the conflict type, etc. The operation conflict log is stored in the system database in a structured manner, facilitating subsequent query, statistics, and analysis. At the same time, the system will also generate a unique identifier for each operation conflict log to facilitate the tracking and management of conflict events.

[0089] When the priority determination unit allocates an independent processing thread to execute the current waste gas treatment instruction, it will first check the current resource usage of the system. This includes CPU usage, memory usage, thread pool status, etc., to ensure that the system has sufficient resources to execute the new instruction. If the system resources are sufficient, the priority determination unit will obtain an idle thread from the thread pool and allocate the current instruction to this 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 the execution of the instruction, the thread will call the corresponding system function module according to the requirements of the instruction to complete the control operation of the waste gas treatment equipment. At the same time, the thread will monitor the execution status of the instruction in real time, such as the execution progress, whether there are any exceptions, etc., and feedback this information to the system.

[0091] After the instruction execution is completed, the processing thread will return the execution result to the system, release the occupied resources, and return 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] Embodiment 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 risk factors and direct risk factors. This process requires the comprehensive application of various data processing and analysis techniques to ensure that all factors affecting the system risk can be comprehensively and accurately captured.

[0094] Before starting the analysis, the factor extraction module will arrange all risk-related variables according to the monitoring sequence to form an orderly analysis sequence. The monitoring sequence refers to the order in which risk-related variables are collected and recorded in chronological order. For example, the concentration of each pollutant in the exhaust gas, the operating parameters of the equipment and other variables are collected every 10 minutes to form a sequence arranged in chronological order. This orderly arrangement helps the subsequent analysis of the temporal relationship and change trend between variables.

[0095] The system will randomly select adjacent variable pairs, obtain the corresponding concentration readings and subsequent concentration readings, and calculate the absolute value of the difference in concentration readings. For example, in the analysis sequence, select the variable pairs at the nth moment and the n+1th moment. Assuming that the concentration reading of a pollutant at the nth moment is C1, and the concentration reading at the n+1th moment is C2, the absolute value of the difference is |C1-C2|. The purpose of this step is to preliminarily detect the magnitude of changes between variables and provide basic data for subsequent risk analysis.

[0096] After obtaining enough absolute values of the differences between variable pairs, the system will determine the peak and valley values of the concentration in the full monitoring data and calculate the overall concentration fluctuation range. The full monitoring data refers to the concentration data of all risk-related variables collected within a certain period of time. By analyzing these data, the maximum value (peak concentration value) and the minimum value (valley concentration value) are found, and the difference between the two is the overall concentration fluctuation range. This indicator can reflect the range of changes in exhaust gas concentration throughout the monitoring period and is an important basis for evaluating system stability.

[0097] The system will calculate the ratio of the absolute value of the difference to the fluctuation range, that is, |C1-C2| / fluctuation range. This ratio reflects the degree of concentration change of adjacent variable pairs relative to the overall concentration fluctuation. The larger the ratio, the more drastic the concentration change between adjacent moments, and there may be a greater risk; the smaller the ratio, the relatively stable concentration change and the lower the risk. By calculating this ratio, the concentration changes of different variable pairs can be standardized, which is convenient for subsequent comparison and analysis.

[0098] In addition to the concentration change analysis, the system also calculates the difference in risk association between pairs of variables. The calculation of the difference in risk association is based on the correlation analysis between pairs of variables, and the difference in risk association between adjacent pairs of variables is calculated through statistical methods. Specifically, the system analyzes the synergistic change relationship between different variables, for example, whether changes in certain variables will lead to changes in other variables, and the degree and direction of such changes. Through this correlation analysis, it is possible to find out which pairs of variables have a strong risk association relationship, providing a basis for determining indirect risk factors and direct risk factors.

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

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

[0101] After obtaining the concentration ratio and the correlation degree ratio, the system multiplies these two values and performs a standardization process to obtain the real-time risk coefficient. The standardization process is to convert numerical values in different ranges into a unified range for easy comparison and analysis. For example, the product result is converted into a value between 0 and 1 through a linear transformation as the real-time risk coefficient. The real-time risk coefficient can comprehensively reflect the concentration changes and risk correlation degrees of adjacent variable pairs and is an important indicator for evaluating the real-time risk level of the system.

[0102] The system traverses 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 calculations for all adjacent variable pairs, the real-time risk coefficient for each variable pair is obtained. The coefficients of the variable pairs belonging to the indirect influence are grouped together to form a set of indirect risk factors, while the mean of all real-time risk coefficients serves as the direct risk factor directly reflecting the overall risk level.

[0103] In actual operation, the factor extraction module first performs data cleaning and preprocessing on historical waste gas treatment records. Since the actually collected data may have problems such as noise and missing values, these data need to be processed to improve data quality. Data cleaning includes operations such as removing outliers, filling in missing values, and smoothing data. For example, for some concentration readings that are significantly outside the normal range, they can be regarded as outliers and removed; for missing concentration data, interpolation methods can be used to fill them in.

[0104] After the data cleaning is completed, the system will perform feature extraction on the preprocessed data. Feature extraction refers to extracting indicators and variables that can reflect the essential characteristics of the data from the original data. In the waste gas treatment system, feature extraction can include extracting concentration characteristics of waste gas components, change characteristics of equipment operation parameters, trend characteristics of time series, etc. For example, for the concentration data of waste gas components, statistical characteristics such as mean, variance, maximum value, and minimum value can be extracted; for the change data of equipment operation parameters, characteristics such as change rate and change trend can be extracted.

[0105] After the feature extraction is completed, the system will perform variable selection and dimensionality reduction. Variable selection refers to selecting variables that have a greater impact on system risk from numerous features, and dimensionality reduction refers to converting the high-dimensional feature space into a low-dimensional feature space to reduce computational complexity and improve model efficiency. In the waste gas treatment system, methods such as correlation analysis, principal component analysis, and factor analysis can be used for variable selection and dimensionality reduction. For example, through correlation analysis, variables with high correlation with system risk can be found and these variables can be used as risk-related variables for further analysis; through principal component analysis, multiple related variables can be converted into a few uncorrelated principal components, thus achieving dimensionality reduction.

[0106] After determining the risk-related variables, the system will calculate the indirect risk factors and direct risk factors according to the above methods. During the calculation process, the system will continuously optimize the algorithms and parameters to improve the accuracy and reliability of the risk factor calculation. For example, for the calculation of real-time risk coefficients, different standardization methods and weight allocation schemes can be adopted, and by comparing the calculation results of different schemes, the optimal scheme can be selected.

[0107] The factor extraction module will also verify and evaluate the calculated indirect risk factors and direct risk factors. Verification refers to checking the rationality and accuracy of the calculation results, and evaluation refers to evaluating the importance and influence of the risk factors. In the waste gas treatment system, methods such as expert evaluation, historical data verification, and cross-validation can be used for verification and evaluation. For example, inviting domain experts to evaluate the calculated risk factors to check whether they conform to the actual situation; using historical data to verify the risk factors to check whether they can accurately predict system risk; adopting cross-validation method to evaluate the risk factors to check their stability and generalization ability.

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

[0109] Example 3:

[0110] The evaluation module undertakes a key task in the entire intelligent early warning system for waste gas treatment, mainly responsible for calculating the risk accumulation index of the waste gas treatment system based on indirect risk factors and direct risk factors. This process requires the use of a variety of data processing and analysis techniques to ensure that the risk level of the system can be comprehensively and accurately evaluated.

[0111] The evaluation module will first set the upper threshold and lower threshold of the indirect risk factors. The setting of the upper threshold and lower threshold is determined based on the safety operation requirements of the waste gas treatment system and historical data statistical analysis. For different indirect risk factors, their upper threshold and lower threshold may be different. For example, for some indirect risk factors with a greater impact on system safety, the upper threshold may be set lower to ensure that potential risks can be detected in a timely manner; while for some indirect risk factors with a smaller impact on system safety, the upper threshold may be set higher.

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

[0113] The evaluation module will calculate the positive deviation amount of the high-impact factor from the upper threshold. For example, the high-impact factor is 0.9, and the upper threshold is 0.8, the positive deviation amount is 0.9 - 0.8 = 0.1. Then, arrange them in descending order of the deviation amount to form a high-order sorting queue, that is, arrange the high-impact factors in descending order of the positive deviation amount. This can clearly understand which high-impact factors have the greatest impact on system risk, so as to give priority to treatment.

[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, a low-order sorting queue is formed by arranging them in ascending order of the deviation magnitude, that is, the low impact factors are arranged in ascending order of the absolute value of the negative deviation. This can identify those low impact factors with relatively small deviations from the lower threshold, and these factors may pose certain potential risks.

[0115] After obtaining the high-order sorting queue and the low-order sorting queue, the evaluation module pairs the elements of the high-order queue and the low-order queue item by item to form impact pair groups. For example, if there is an element 0.9 (positive deviation 0.1) in the high-order queue and two elements 0.1 (negative deviation 0.1) and 0.3 (negative deviation 0.1) in the low-order queue, then the impact pair groups (0.9, 0.1) and (0.9, 0.3) are formed. Based on all impact pair groups, the evaluation module further calculates the risk accumulation index.

[0116] The evaluation module counts the total number of impact pair groups and calculates the squared difference between the high-order element and the low-order element in each group. For example, for the impact pair group (0.9, 0.1), the squared difference is (0.9 - 0.1)^2 = 0.64; for the impact pair group (0.9, 0.3), the squared difference is (0.9 - 0.3)^2 = 0.36. The squared difference can reflect the degree of difference between the high-order element and the low-order element. The greater the difference, the greater the possible combined impact of these two factors on the system risk.

[0117] The evaluation module identifies the minimum and maximum values among all the squared difference values. For example, the above squared difference values are 0.64 and 0.36, the minimum value is 0.36, and the maximum value is 0.64. By determining the minimum and maximum values, the evaluation module can understand the distribution range of the squared difference values, providing a basis for calculating the dispersion parameter later.

[0118] The evaluation module calculates the dispersion parameter of all the squared difference values, such as variance or standard deviation. The dispersion parameter can reflect the distribution of the squared difference values, that is, whether these values are relatively concentrated or dispersed. If the dispersion parameter is large, it means that the squared difference values are more dispersed and the differences between different impact pair groups are large; if the dispersion parameter is small, it means that the squared difference values are more concentrated and the differences between different impact pair groups are small.

[0119] Based on the minimum value, maximum value, and dispersion parameter, the evaluation module calculates the risk accumulation index. By comprehensively considering these parameters, a corresponding calculation formula is established to obtain a risk accumulation index that can comprehensively reflect the impact on group differences. This index is used to evaluate the overall risk level of the waste gas treatment system and provides an important basis for subsequent early warning and decision-making.

[0120] In actual operation, the evaluation module first performs data preprocessing on the input indirect risk factors. Since indirect risk factors may come from different data sources and their data formats and ranges may vary, standardization processing is required to convert all indirect risk factors to the same scale. This can avoid deviations in evaluation results caused by different data scales.

[0121] When setting the upper limit threshold and lower limit threshold, the evaluation module uses a variety of methods for comprehensive analysis. On the one hand, it refers to the design standards and safety specifications of the waste gas treatment system to determine a basic threshold range; on the other hand, it analyzes historical data to find out which values of indirect risk factors were related to system failures or accidents during past operations, and then adjusts the threshold based on these historical experiences. In addition, it also considers the current operating state and environmental conditions of the system to dynamically adjust the threshold to improve the accuracy of evaluation.

[0122] When screening the high-impact factor set and low-impact factor set, the evaluation module conducts strict logical judgments. For each indirect risk factor, it compares it with the set upper limit threshold and lower limit threshold and classifies it into the corresponding set according to the comparison results. During this process, it ensures that each indirect risk factor belongs to only one set to avoid duplicate classification.

[0123] When calculating the positive deviation and negative deviation, the evaluation module uses precise numerical calculation methods to ensure the accuracy of the calculation results. For the arrangement of the high-order sorting queue and low-order sorting queue, it uses an efficient sorting algorithm to improve the processing speed. When forming the impact pair group, it pairs according to certain rules to ensure that each high-impact factor can be paired with a suitable low-impact factor, thus comprehensively reflecting all aspects of the system risk.

[0124] When calculating the squared difference value, the evaluation module calculates each impact pair group one by one to ensure that no pair group is missed. When identifying the minimum value and maximum value, it traverses all the squared difference values for comparison and screening. When calculating the dispersion parameter, it selects a suitable calculation method according to the characteristics of the data, such as variance or standard deviation, to accurately reflect the dispersion degree of the data.

[0125] When calculating the risk accumulation index, the evaluation module comprehensively considers multiple factors such as the minimum value, maximum value, and dispersion degree parameter. A weighted combination method will be adopted to combine these factors according to different weights to obtain the final risk accumulation index. The determination of the weights is based on the analysis of the importance of these factors in evaluating the system risk, and reasonable weight values are determined through methods such as expert evaluation and historical data verification.

[0126] The evaluation module will also verify and calibrate the calculated risk accumulation index. The calculation results will be compared with historical data to check whether the index can accurately reflect the risk level of the system. If it is found that there are deviations in the calculation results, the calculation method and parameters will be adjusted to improve the accuracy and reliability of the index.

[0127] Example 4:

[0128] This example details the working mechanism of the adjustment module. Its core function is to modify and optimize the risk level prediction table based on the risk accumulation index to ensure that the system can dynamically adjust the early warning strategy and equipment control parameters according to the real-time risk situation. The following are the specific implementation methods:

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

[0130] The adjustment module first needs to set the primary risk threshold and the advanced risk threshold. These two thresholds are the key parameters for dividing the system risk level. The primary risk threshold is used to identify the mild risk state, and the advanced risk threshold is used to define the severe risk state. The interval between the two is the moderate risk state. The setting of the thresholds is based on the historical operation data of the waste gas treatment system, equipment safety parameters, and environmental protection standard requirements. For example, assume that the primary risk threshold is set to 0.4, representing that the system is in a basically safe but vigilant state; the advanced risk threshold is set to 0.7, representing that the system risk has approached or exceeded the controllable range and immediate strengthening measures need to be taken.

[0131] When configuring the correction coefficients, the system will set multiple levels of correction coefficients according to different risk levels, including the basic correction coefficient, intermediate correction coefficient, and strengthening correction coefficient. 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 correction coefficient: Applicable to the situation where the risk accumulation index is lower than the primary risk threshold, and the value range is usually 0.8 - 0.95. It is used to slightly adjust the weights of the prediction table to maintain the balance between the stability and sensitivity of the system.

[0133] Intermediate correction coefficient: applicable to the situation where the risk accumulation index is between the primary and high - level risk thresholds, and its value range is usually 0.6 - 0.8. It is used for moderate adjustment of weights to enhance the system's response ability to potential risks.

[0134] Enhanced correction coefficient: applicable to the situation where the risk accumulation index exceeds the high - level risk threshold, and its value range is usually 0.4 - 0.6. It is used for substantial adjustment of weights to forcefully raise the warning level and trigger emergency control measures.

[0135] The specific values of the correction coefficients need to be determined through parameter calibration in the system initialization stage. The calibration process combines the operating characteristics of the equipment and historical risk cases to ensure that the coefficient settings meet the actual requirements.

[0136] II. Correction Coefficient Trigger Mechanism Based on Risk Level

[0137] After the evaluation module outputs the risk accumulation index, the adjustment module first compares this index with the preset risk thresholds and triggers the corresponding correction coefficients according to the comparison results:

[0138] Low - risk state (index < primary threshold):

[0139] If the risk accumulation index is 0.3 (lower than the primary threshold of 0.4), the system determines that the current state is a low - risk state and enables the basic correction coefficient (such as 0.9). At this time, the correction weights in the risk level prediction table are based on the basic coefficient, and the mapping relationship between the adsorption device control parameters and the risk index is slightly adjusted. The aim is to prevent risk escalation through subtle parameter optimization while avoiding excessive interference with the normal operation of the equipment.

[0140] Medium - risk state (primary threshold ≤ index ≤ high - level threshold):

[0141] If the risk accumulation index is 0.55 (between 0.4 and 0.7), the system determines it as a medium - risk state and enables the intermediate correction coefficient (such as 0.7). At this time, the adjustment range of the correction weights is greater than that in the low - risk state. The system will moderately correct the key parameters in the risk level prediction table (such as the adsorption device control parameters in the high - risk interval). For example, the target control value of a certain adsorption device is adjusted from 80% of the original value to 70% to enhance the risk suppression effect and reserve adjustment space for possible future risk escalation.

[0142] High - risk state (index > high - level threshold):

[0143] If the risk accumulation index is 0.8 (exceeding the advanced threshold of 0.7), the system determines it as a high-risk state and enables the enhanced correction factor (such as 0.5). At this time, the correction weight will significantly adjust the risk level prediction table, possibly directly resetting the priorities of some control parameters. For example, it advances the startup threshold of the emergency adsorption device from a risk index of 0.6 to 0.5 and forcibly increases the upper limit of the device's operating power to quickly reduce the risk level.

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

[0145] After determining the correction factor, the adjustment module needs to multiply the correction weight by the adsorption device control parameters to obtain the target control value. The adsorption device control parameters usually include equipment operating power, valve opening, adsorbent replacement cycle, etc. These parameters directly affect the waste gas treatment efficiency and risk control effect. For example:

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

[0147] If the enhanced correction factor of 0.5 is enabled, the target control value is 90 × 0.5 = 45 kW (it is necessary to judge whether it is feasible in combination with the equipment safety threshold. If it exceeds the lower limit, the minimum value of the threshold is taken).

[0148] After calculating the target control value, the system needs to determine the corresponding expected risk index through the preset parameter mapping relationship. The parameter mapping relationship is a multi-dimensional lookup table constructed based on the digital twin model and formed through training with historical data, including the non-linear mapping relationship between control parameters and risk indices. For example:

[0149] When the operating power of the adsorption device is 81 kW, the corresponding expected risk index is queried from the mapping table as 0.35 (low-risk interval);

[0150] When the operating power is 45 kW, if this parameter is already close to the equipment's low-efficiency operation threshold, the mapping table may return an expected risk index of 0.6 (medium-risk interval), indicating that it is necessary to coordinate control in combination with other equipment.

[0151] The construction of the parameter mapping relationship needs to consider multiple constraint conditions, including equipment energy consumption limits, environmental protection discharge standards, process stability requirements, etc., to ensure that the target control value can effectively reduce risks without causing new operation problems.

[0152] IV. Construction of the Optimized Risk Level Prediction Table

[0153] The adjustment module integrates the target regulation value and the expected risk index to construct an optimized risk level prediction table. This table is usually presented in matrix form, with the horizontal dimension being the risk level (such as low, medium, high), and the vertical dimension being the adsorption device type or the category of regulation parameters. The cell content includes the target regulation value, the expected risk index, and the corresponding description of the regulation strategy. For example:

[0154]

[0155] During the construction process, the system will manage the version of the historical optimization records, retaining the timestamp, correction coefficient, target regulation value, and subsequent risk change data of each correction, forming a traceable optimization log. This log is used for subsequent system performance analysis and parameter optimization. For example, by comparing the risk evolution trends under different correction coefficients, the thresholds and correction coefficient configurations are dynamically adjusted.

[0156] V. Exception Handling and Dynamic Calibration

[0157] During the adjustment process, if the target regulation value exceeds the safe operating range of the equipment (such as the operating power is lower than the minimum limit), the system will automatically trigger an exception handling mechanism:

[0158] Threshold protection: Force the regulation value to be limited within the safe threshold of the equipment and generate an exception prompt message, such as "The target power of 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 regulation requirements, the system will automatically call other associated devices (such as switching to a standby adsorption device) and recalculate the combined regulation parameters;

[0160] Dynamic calibration: After the exception handling is completed, the system will record the conflict data for subsequent calibration of the correction coefficient and the parameter mapping relationship 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 coefficient and the parameter mapping relationship according to the latest historical data and equipment status to ensure that the system can adapt to long-term change factors 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 realizes the full-process automated management from risk level determination, correction coefficient triggering, parameter calculation to prediction table optimization, ensuring that the waste gas treatment system can dynamically adjust the early warning and regulation strategies according to the real-time risk situation, while maximizing the reduction of safety risks while ensuring the treatment efficiency. The whole process closely relies on the digital twin model and historical data support, avoiding the limitations of subjective judgment and empiricism, and improving the scientificity and reliability of the system.

[0163] Example 5:

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

[0165] I. Construction of 3D dynamic concentration field model

[0166] The model construction module constructs a 3D dynamic concentration field model by integrating multi-source data in real time. Among them, the sensor data includes real-time monitoring parameters such as waste gas components (such as SO2, NO x , VOCs concentration), temperature, humidity, etc.; the equipment logs contain equipment operation data such as fan speed, valve opening, and adsorption device operation status; the environmental meteorological data covers environmental factors such as wind speed, wind direction, and air pressure. The system uses a spatial interpolation algorithm (such as Kriging interpolation method) to convert the discrete monitoring point data into a continuous 3D concentration distribution field. The specific formula is:

[0167]

[0168] Description of the meaning of formula characters: C(x,y,z,t): Waste gas concentration at spatial coordinates (x,y,z) at time t; n: Total number of monitoring points; λ i (t): Weight coefficient of the i-th monitoring point at time t, related to the reliability and timeliness of the monitoring point; c i (t): Measured concentration value of the i-th monitoring point at time t; φ(d i (x,y,z)): Distance attenuation function, representing the influence of the spatial position (x,y,z) on the concentration at the i-th monitoring point, usually a function that decreases with the increase of distance (such as Gaussian function). i Through the above model, the system can dynamically visualize the diffusion form of waste gas in space and identify high-concentration aggregation areas and diffusion trends.

[0169]

[0170] II. Calculation of waste gas diffusion boundary and activation of emergency adsorption device

[0171] Based on the 3D dynamic concentration field model, the system calculates the waste gas diffusion boundary, that is, the spatial surface where the concentration is equal to the safety threshold C safe . The safety threshold is set according to environmental protection standards and workshop occupational health requirements (such as the safety threshold of a certain pollutant is 10mg / m 3 ). When the predicted diffusion range exceeds the preset safety area (such as workshop boundary or factory boundary), the system automatically activates the emergency adsorption device. The activation logic is:

[0172] Real-time prediction: Use the concentration field model to extrapolate the diffusion range in the future Δt time (Δt can be set from 5 to 30 minutes, adjusted according to the waste 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 exceeding, 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) to adsorb 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 to form 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 a 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 specifically treat the waste gas in the local area. At this response level, the system only adjusts the parameters of the local device to maintain the normal operation of the main treatment process.

[0183] Level 2 response (1m < L ≤ 5m): Start the workshop isolation barrier to 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): Trigger the reverse pressurization of the whole-plant exhaust system, create a negative pressure environment inside the workshop through forced air supply to prevent the diffusion of waste gas outside the factory area, and at the same time start all standby adsorption devices to reduce the waste gas concentration with all efforts. This level of response needs to be linked to the factory safety alarm system to notify personnel evacuation or take protective measures.

[0185] V. Data Integration and Model Update

[0186] The model construction module receives sensor, equipment, and environmental data in real time, and updates the three-dimensional dynamic concentration field model every Δt ′ time (such as 1 minute) to ensure that the model is synchronized with the actual working conditions. At the same time, the system iteratively updates the Monte Carlo simulation parameters and the Pareto optimal solution set regularly (such as daily), incorporates the latest equipment operation data and environmental conditions, and maintains the accuracy and adaptability of the model.

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

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

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

Claims

1. An intelligent early warning system for waste gas treatment based on digital twin, characterized in that, Including: A model construction module, configured to receive waste gas treatment instructions and construct a dynamic risk model for the waste gas treatment process based on waste gas composition data and the operating status data of waste gas treatment equipment; A monitoring module, configured to obtain historical waste gas treatment records and generate a risk level prediction table in combination with the dynamic risk model, where the risk level prediction table includes a risk index and adsorption device control parameters; A factor extraction module, configured to analyze the historical waste gas treatment records, identify risk-related variables, and determine indirect risk factors and direct risk factors; An evaluation module, configured to calculate a risk accumulation index of the waste gas treatment system according to the indirect risk factors and the direct risk factors; An adjustment module, configured to set a 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; An early warning execution module, configured to trigger an alarm control operation 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 according to claim 1, wherein It further includes: An instruction comparison unit, configured to compare the current waste gas treatment instruction with the operation type of the previous executed instruction to determine whether the two belong to the same type of operation; A timestamp acquisition unit, configured to record the initiation time of the current instruction and the execution time of the previous instruction if it is determined to be the same type of operation; An interval analysis unit, configured to calculate the time interval length between two instructions and determine whether to allow processing of the current instruction by comparing with a preset interval threshold; An instruction interception unit, configured to block the instruction execution and generate an operation conflict log if the time interval length is less than the preset interval threshold; A priority determination unit, configured to allocate an independent processing thread to execute the current waste 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 according to claim 1, characterized in that, The factor extraction module includes: Arrange all risk-related variables in the monitoring time sequence to form an analysis sequence, randomly select adjacent variable pairs, obtain the corresponding concentration readings and subsequent concentration readings, and calculate the absolute value of the difference between the concentration readings; Determine the concentration peak value and valley value in the full amount of monitoring data, and calculate the overall concentration fluctuation range; Obtain the ratio of the absolute value of the difference to the fluctuation range; Calculate the risk correlation degree difference amount between the variable pairs; Determine the maximum correlation degree and the minimum correlation degree among all variables, and calculate the global correlation degree span; Obtain the proportional value of the risk correlation degree difference amount to the global correlation degree span; Multiply the concentration ratio by the correlation degree ratio and perform normalization processing to obtain a real-time risk coefficient; Traverse the remaining variable pairs to generate a set of indirect risk factors, and calculate the mean value of all real-time risk coefficients as the direct risk factor.

4. The intelligent early warning system for waste gas treatment based on digital twin according to claim 1, characterized in that, The evaluation module includes: Set an upper threshold and a lower threshold for indirect risk factors; Screen a set of high-impact factors according to the upper threshold, and screen a set of low-impact factors according to the lower threshold; Calculate the positive deviation amount of the high-impact factors from the upper threshold, and arrange them in descending order of the deviation amount to form a high-order sorting queue; Calculate the negative deviation amount of the low-impact factors from the lower threshold, and arrange them in ascending order of the deviation amount to form a low-order sorting queue; Pair the high-level queue and low-level queue elements item by item to form impact pair groups, and calculate the risk accumulation index based on all impact pair groups.

5. The intelligent early warning system for waste gas treatment based on digital twin according to claim 4, characterized in that, The evaluation module further includes: Count the total number of impact pair groups, and calculate the squared differences between the high-level elements and the low-level elements in each group respectively; Identify the minimum and maximum values among all the squared difference values, and calculate the dispersion parameter of all the squared difference values; Calculate the risk accumulation index according to the minimum value, maximum value and dispersion parameter.

6. The intelligent early warning system for waste gas treatment based on digital twin according to claim 1, wherein The adjustment module includes: Set a primary risk threshold and a high-level risk threshold; Configure a basic correction coefficient, an intermediate correction coefficient and an enhanced correction coefficient; Enable the basic correction coefficient when the risk accumulation index is lower than the primary risk threshold, enable the intermediate correction coefficient when the risk accumulation index is between the primary and high-level risk thresholds, and enable the enhanced correction coefficient when the risk accumulation index exceeds the high-level risk threshold.

7. The intelligent early warning system for waste gas treatment based on digital twin according to claim 1, wherein The adjustment module further includes: Multiply the correction weight by the adsorption device control parameter to obtain the target control value; Match the preset parameter mapping relationship to determine the expected risk index corresponding to the target control value; Integrate the target control value and the expected risk index to construct the optimized risk level prediction table.

8. The intelligent early warning system for waste gas treatment based on digital twin according to claim 1, wherein The model construction module further includes: Integrate sensor data, equipment logs and environmental meteorological data in real time to construct a three-dimensional dynamic concentration field model; Calculate the exhaust gas diffusion boundary according to the three-dimensional concentration field model, and activate the emergency adsorption device when the predicted diffusion range exceeds the safe area; Generate an energy consumption / purification rate matrix under different combinations of operating parameters through Monte Carlo simulation, and generate a Pareto optimal solution set.

9. The intelligent early warning system for waste gas treatment based on digital twin according to claim 8, characterized in that, Divide the three-level emergency response according to the diffusion boundary breakthrough distance: activate the local adsorption device at the first level, start the workshop isolation barrier at the second level, and trigger the reverse pressure of the whole plant exhaust system at the third level.

10. The intelligent early warning system for waste gas treatment based on digital twin according to claim 2, wherein, The instruction comparison unit includes: extracting the key operation semantics in the unstructured operation instructions through natural language processing technology, storing the vectorized features of historical instruction conflict cases and their solutions, and when detecting an instruction conflict, retrieving the adapted solution from the conflict knowledge base through similarity matching and generating a priority adjustment suggestion.

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