A dynamic monitoring system for environmental emissions from cement production based on the Internet of Things

Abstract

This invention relates to the field of environmental protection supervision in cement production, aiming to solve the problems of low accuracy in pollutant concentration detection and poor adaptability of supervision strategies. It provides an IoT-based dynamic monitoring system for environmental emissions in cement production. The invention comprises an IoT sensing layer, a data transmission layer, a core processing layer, and an application layer. The sensing layer collects data such as pollutant concentration and production conditions, and transmits it to the core processing layer via 5G+LoRa dual-mode encryption. The core processing layer achieves accurate concentration detection through an improved adaptive weighted fusion algorithm, and generates dynamic monitoring strategies linked to operating conditions using an improved dynamic programming algorithm. The application layer displays data, provides early warnings, and distributes strategies. This invention improves the accuracy and dynamic adaptability of emission supervision, reduces treatment costs, and is applicable to environmental emission supervision throughout the entire cement production process.

Description

Technical Field

[0001] This invention relates to the field of environmental protection supervision in cement production, specifically to a dynamic monitoring system for environmental emissions from cement production based on the Internet of Things. Background Technology

[0002] The cement production process generates a large amount of dust and NO. x Cement production emits pollutants such as SO2, which directly impact the ecological environment and human health. Therefore, strict regulation of environmental emissions from cement production is necessary. Currently, most existing cement production environmental emission monitoring systems use single sensors to detect pollutant concentrations and combine this with fixed monitoring strategies. However, these systems suffer from the following specific technical problems:

[0003] Low accuracy and large data fluctuations in pollutant concentration detection: The cement production environment is characterized by high temperature, high humidity and high dust. A single sensor is easily affected by environmental interference, resulting in large deviations in detection data. In addition, the weights of traditional fusion algorithms are fixed and cannot be dynamically adjusted according to the real-time working status of the sensors, which further reduces the detection accuracy and easily leads to "false alarms" or "missed alarms".

[0004] Fixed regulatory strategies with poor adaptability: Existing regulatory strategies are mostly pre-set fixed rules that do not fully take into account the real-time operating conditions of cement production (such as clinker calcination temperature, raw material ratio, etc.). Changes in cement production conditions directly affect pollutant emission concentrations, causing regulatory strategies to fail to adapt to changes in operating conditions in a timely manner. This results in delayed early warnings of excessive emissions, or the use of high-intensity treatment measures even when operating conditions are stable and emissions meet standards, leading to a waste of environmental governance resources.

[0005] There are no effective solutions in the existing technology to address the specific problems mentioned above. Therefore, developing an environmental emission monitoring system that can accurately detect pollutant concentrations and dynamically adapt to production conditions has become an urgent need for the green development of the cement industry. Summary of the Invention

[0006] The purpose of this invention is to provide an IoT-based dynamic monitoring system for environmental emissions from cement production, in order to solve the problems mentioned in the background section.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a dynamic monitoring system for environmental emissions in cement production based on the Internet of Things (IoT), comprising an IoT sensing layer, a data transmission layer, a core processing layer, and an application layer; the IoT sensing layer is used to collect pollutant concentration data, production condition data, and environmental parameter data at various emission nodes in cement production; the data transmission layer is used to encrypt and transmit the data collected by the sensing layer to the core processing layer; the core processing layer is used to process and analyze the collected data and generate dynamic monitoring strategies; the application layer is used to realize monitoring data display, early warning prompts, strategy distribution, and historical data query.

[0008] The core processing layer includes an improved adaptive pollutant concentration detection module and a dynamic monitoring strategy generation module linked to operating conditions. The improved adaptive pollutant concentration detection module uses an improved adaptive weighted fusion algorithm to solve the problem that single sensors in existing detection methods are easily affected by the high temperature, high humidity, and dust interference of cement production, resulting in low detection accuracy and large data fluctuations, thus achieving accurate detection of pollutant concentrations. The dynamic monitoring strategy generation module linked to operating conditions uses an improved dynamic programming algorithm based on operating conditions and emissions to solve the problem that existing monitoring strategies are fixed and cannot be dynamically adjusted according to real-time cement production conditions (such as clinker calcination temperature, raw material ratio, and fan speed), resulting in delayed warnings of exceeding standards and wasted governance resources, thus achieving dynamic adaptation of monitoring strategies.

[0009] Preferably, the specific working logic of the IoT sensing layer is as follows:

[0010] Step A1: Node Deployment and Adaptation. Based on the key emission nodes in the entire cement production process (kiln tail chimney, raw material crushing workshop, clinker cooler outlet, raw meal grinding workshop, etc.), corresponding data collection equipment is deployed, including pollutant concentration collection equipment (dust sensors, NO...). x Sensors (SO2 sensors) are deployed at each emission node in sets of 2-3 of the same type. Operating condition acquisition equipment (operating condition sensors) is deployed in key process links such as clinker calcination and raw material proportioning. Environmental parameter acquisition equipment (temperature sensors, humidity sensors) is deployed synchronously with pollutant concentration sensors to ensure that the collected data can be matched.

[0011] Step A2: Equipment initialization calibration. Initialize and calibrate all data acquisition devices. Considering the high temperature and high dust working environment of cement production, preset equipment protection parameters. At the same time, set the initial threshold for data acquisition based on pollutant emission standards and normal operating conditions to ensure that the equipment acquisition accuracy meets the processing requirements of the subsequent improved adaptive pollutant concentration detection module and the operating condition linkage dynamic monitoring strategy generation module.

[0012] Step A3: Real-time data acquisition and control. The data acquisition device simultaneously collects three types of data at a dynamically adjusted frequency (default 3 times / minute, automatically increasing to 5 times / minute when operating conditions fluctuate significantly). Among them, the pollutant concentration data consists of dust and NO. x SO2 sensors collect data synchronously. Production condition data focuses on collecting clinker calcination temperature, raw material ratio coefficient, fan speed, and calcination time. Environmental parameter data focuses on collecting real-time temperature and humidity at each emission node.

[0013] Step A4: Preliminary data processing. The data acquisition unit converts the analog signals collected by each sensor into digital signals, performs preliminary filtering to remove minor abnormal fluctuations caused by momentary interference from the equipment, and at the same time performs preliminary classification and labeling of the collected data, associating it with the corresponding emission node, collection time and equipment number to form standardized initial collected data.

[0014] Step A5: Data Upload Preparation. After the initial processing of standardized data, the data collector organizes and summarizes the data. In accordance with the data transmission layer's transmission protocol, data encryption is prepared in advance, and data transmission commands are triggered synchronously to ensure that the collected data is transmitted to the data transmission layer in a timely and secure manner, providing reliable data support for subsequent data analysis and strategy generation in the core processing layer.

[0015] Preferably, the data transmission layer adopts a 5G+LoRa dual-mode transmission method, with 5G used for large-volume real-time data transmission and LoRa used for data transmission at remote emission nodes. The transmission process uses the AES-128 encryption algorithm to ensure data security and integrity.

[0016] Preferably, the improved adaptive pollutant concentration detection module includes a multi-sensor data acquisition unit, an abnormal data preprocessing unit, an improved adaptive weighted fusion unit, and a concentration calibration unit. The working steps of each unit are as follows:

[0017] Step B1: Multi-sensor data acquisition units are deployed at various emission points in cement production (kiln tail chimney, raw material crushing workshop, clinker cooler outlet), using dust sensors, NO... x Two to three sensors each for pollutants and SO2, simultaneously collecting real-time pollutant concentration data, denoted as... ,in For sensor type, specifically For dust, NO x , SO2 Number the sensors of the same type. , The time of data collection;

[0018] Step B2: The abnormal data preprocessing unit processes the collected data. Preprocessing is performed first by... The criterion is to remove extreme outliers if the data meets the following criteria. If it is an outlier, it will be removed. for Time of the first The average value collected by the sensor-like sensor, for Time of the first The standard deviation of the sensor data was measured; then, linear interpolation was used to fill in the missing data after outlier removal, resulting in preprocessed data. ;

[0019] Step B3: The improved adaptive weighted fusion unit processes the preprocessed... Traditional weighted fusion algorithms, which perform fusion calculations with fixed weights, are susceptible to interference. This invention improves the weight calculation method by first introducing a real-time reliability coefficient from the sensor. Combined with data fluctuation coefficient The weights are dynamically adjusted using the following formula: ,in , They are respectively Time of the first The maximum and minimum values ​​of the data after preprocessing by the sensor-like devices; ,in For weighting coefficients, ; For the first Class 1 Historical reliability of each sensor (based on detection error statistics over the past 30 days; the smaller the error, the higher the reliability). Larger); fused pollutant concentration data for: ;

[0020] Step B4: The concentration calibration unit employs an improved piecewise linear calibration algorithm. Based on standard concentration samples under different cement production conditions, a calibration model is established for the fused samples. Perform calibration; the calibration formula is: ,in , The segmented calibration coefficients are automatically matched based on real-time operating conditions (high temperature / normal temperature, high humidity / normal humidity), ultimately outputting accurate pollutant concentration detection values. .

[0021] Preferably, the dynamic monitoring strategy generation module for operating conditions includes an operating condition data parsing unit, an emission-operating condition correlation modeling unit, an improved dynamic programming strategy generation unit, and a strategy distribution unit. The working steps of each unit are as follows:

[0022] Step C1: The operating condition data analysis unit collects real-time operating condition data of cement production, including clinker calcination temperature. Raw material proportioning coefficient Fan speed Calcination time The data for each operating condition are standardized to eliminate the influence of dimensions. The standardization formula is as follows: ,in For the first The raw values ​​of the similar working condition data, , The first Historical minimum and maximum values ​​of similar operating conditions data Corresponding to four types of working condition data;

[0023] Step C2: Emission-Operating Condition Correlation Modeling Unit. This step establishes a correlation model between pollutant concentration and operating condition data, introduces an improved grey relational analysis algorithm to correct the unreasonable weight allocation problem in traditional grey relational analysis, and calculates the correlation degree between each operating condition data and pollutant concentration. The specific formula is as follows:

[0024] First, calculate the reference sequence (pollutant concentration). ) and comparison sequences (standardized operating condition data) The absolute difference of ) ;

[0025] Then calculate the correlation coefficient. ,in The resolution coefficient, ;

[0026] Finally, the influence weight of operating conditions is introduced. (Based on the cement production process, calcination temperature has the highest weight.) Raw material ratio Fan speed Calcination time ), calculate the correlation degree ,in Number of data collection groups;

[0027] Step C3: Improved dynamic programming strategy generation unit. With the goal of minimizing pollutant emissions and treatment costs, a dynamic programming model is constructed. This improves upon the fixed state transition equations in traditional dynamic programming by incorporating correlation... The state transition coefficients are dynamically adjusted as follows:

[0028] Define state variables for The emission status at any given time, where 1 = compliant, 2 = borderline exceedance, and 3 = severely exceedance; decision variables. The regulatory strategy includes: 1 = routine monitoring, 2 = enhanced monitoring + light treatment, and 3 = cessation of related processes + in-depth treatment.

[0029] The state transition equation is improved to: ,in For state transition function, correlation degree The larger the operating parameter, the greater its influence on the state transition. For example, when the correlation between calcination temperature and other parameters increases... At that time, the state transition coefficient increased by 20%, accelerating the transition from "critical exceedance" to "compliance";

[0030] The objective function is: ,in For the first The implementation cost of a real-time monitoring strategy The penalty coefficient for exceeding the limit. These are the pollutant emission standard values. The regulatory cycle is defined; the optimal regulatory strategy sequence is obtained by solving the dynamic programming model using the inverse recursive method. ;

[0031] Step C4: The strategy distribution unit will distribute the optimal regulatory strategy. This information is translated into specific control commands and sent to cement production environmental protection equipment (such as desulfurization and denitrification devices and dust recovery equipment) and production control units to achieve dynamic linkage between monitoring strategies and production conditions. At the same time, strategy information is fed back to the application layer for management personnel to view.

[0032] Preferably, in step C3, the dynamic programming model is solved using a reverse recursive method to obtain the optimal regulatory strategy sequence. The specific steps are as follows:

[0033] Step 1: Determine the recursive boundary conditions and clarify the starting node for the reverse recursion: based on the regulatory cycle. (Based on the cement production process, it is usually 24 hours, and...) The iterative correction cycle is synchronized to ensure data collaboration, with the recursive endpoint being... At this point, to ensure that pollutant emissions meet standards at the end of the regulatory cycle, boundary conditions are set: if (If the target status is met), then the terminal objective function value (No additional cost); if (Critical exceedance) or (Seriously exceeding the limit), then the terminal objective function value (Only the cost of penalties for exceeding the limit is calculated, without considering subsequent decision-making costs), among which For exceeding the penalty coefficient, for Accurate pollutant concentration detection values ​​at all times These are the pollutant emission standard values;

[0034] Step 2: Reverse recursive calculation, starting from the end of the regulatory cycle. Begin, recursively advance to the next moment. At every moment, every step is combined with the emissions-operating condition correlation. The calculation logic is dynamically adjusted as follows: For the first... time( from Decrease to 1), and iterate through all possible states at that moment. For each state Iterate through all possible decision variables According to the improved state transition equation Combined with the current working condition correlation (Output from the emission-operating condition correlation modeling unit; the greater the correlation, the greater the adjustment range of the state transition coefficient), calculate the decision under this condition. Predicted state at time Calculate the instantaneous cost corresponding to this decision, i.e. ,in The implementation cost of this decision is estimated (presumably, the cost of routine monitoring < the cost of light-level governance < the cost of deep-level governance); combined with... Predicted state at any given time The optimal objective function value Calculate the decision under this condition objective function value at time 1 ;

[0035] Step 3: Select the optimal decision and determine the optimal regulatory strategy at each time point: For the first... Each state at any given moment Compare all decision variables Corresponding objective function value The decision that minimizes the objective function value (i.e., the dual objectives of "no pollutant emissions exceeding standards and lowest treatment costs" are selected as the optimal decision for that moment and state. Simultaneously, record the objective function value corresponding to the optimal decision. To ensure that every decision achieves the optimal balance between cost and emissions compliance;

[0036] Step 4: Integrate optimal decisions to form a complete sequence of optimal regulatory strategies. This completes the process from... to After the reverse recursion, combined with Initial emission state at time (Determined by the initial concentration detection value output by the improved adaptive pollutant concentration detection module), retrieve the optimal decision under the corresponding state at each time point. Integrate them in chronological order to form a complete sequence of optimal regulatory strategies. ,Right now ;

[0037] Step 5: Strategy verification and correction to ensure sequence adaptability: The integrated optimal regulatory strategy sequence Correlation with current real-time operating data Perform a verification to determine if the optimal decision at a certain moment is... Corresponding predicted state If emission compliance requirements cannot be met (e.g., the forecast still indicates severe exceedance), then the weights of the decision variables at that point in time should be readjusted (considering the correlation). Prioritize decision adjustments for high-impact operating conditions, recalculate the objective function value, and select the optimal decision; after verification, output the final optimal regulatory strategy sequence. The strategy sequence is transmitted to the strategy distribution unit to ensure that the strategy sequence can be directly converted into control commands and accurately linked with production conditions and environmental protection equipment.

[0038] Preferably, the application layer includes a monitoring terminal (computer or mobile device) that supports real-time display of pollutant concentrations, early warning of exceeding standards (audio-visual warning + message push), query of monitoring strategies, statistical analysis of historical data, and report export.

[0039] Compared with the prior art, the beneficial effects of the present invention are:

[0040] This invention solves the specific problems of low detection accuracy and large data fluctuation of single sensors: by using an improved adaptive pollutant concentration detection module, a multi-sensor deployment and an improved adaptive weighted fusion algorithm are adopted, combined with piecewise linear calibration, to dynamically adjust the sensor weights, eliminate the influence of environmental interference and changes in operating conditions on the detection accuracy, reduce the pollutant concentration detection accuracy error, which is significantly better than existing detection methods, and reduces the occurrence of "false alarms" and "missed alarms".

[0041] This invention solves the specific problems of fixed regulatory strategies that cannot adapt to real-time operating conditions: through the dynamic regulatory strategy generation module that links operating conditions, an improved grey relational analysis algorithm is used to establish an emission-operating condition correlation model. Combined with an improved dynamic programming algorithm, an optimal regulatory strategy that links with real-time operating conditions is generated, realizing dynamic adaptation of "operating condition change - strategy adjustment". This not only avoids the lag in early warning of excessive emissions, but also reduces the waste of environmental governance resources and lowers the environmental governance costs of cement production enterprises.

[0042] This invention enables dynamic monitoring of the entire process: by using Internet of Things (IoT) technology, it achieves real-time collection, transmission, processing, and strategy distribution of pollutant concentration and production conditions, forming a closed-loop monitoring system of "collection-analysis-decision-execution". This enables full-process, dynamic, and precise monitoring of environmental emissions from cement production, contributing to the green and low-carbon development of the cement industry. Attached Figure Description

[0043] Figure 1 This is a schematic diagram of the system structure of the present invention;

[0044] Figure 2 This is a schematic diagram of the working process of the improved adaptive pollutant concentration detection module of the present invention;

[0045] Figure 3 This is a schematic diagram of the workflow of the dynamic monitoring strategy generation module for work conditions linkage of the present invention. Detailed Implementation

[0046] 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.

[0047] Please see Figure 1-3 This invention provides a technical solution: a dynamic monitoring system for environmental emissions in cement production based on the Internet of Things (IoT), comprising an IoT sensing layer, a data transmission layer, a core processing layer, and an application layer. The IoT sensing layer is used to collect pollutant concentration data, production condition data, and environmental parameter data at various emission points in cement production. The data transmission layer is used to encrypt and transmit the data collected by the sensing layer to the core processing layer. The core processing layer is used to process and analyze the collected data and generate dynamic monitoring strategies. The application layer is used to display monitoring data, provide early warnings, issue strategies, and query historical data.

[0048] The specific working logic of the IoT sensing layer is as follows:

[0049] Step A1: Node Deployment and Adaptation. Based on the key emission nodes in the entire cement production process (kiln tail chimney, raw material crushing workshop, clinker cooler outlet, raw meal grinding workshop, etc.), corresponding data collection equipment is deployed, including pollutant concentration collection equipment (dust sensors, NO...). x Sensors (SO2 sensors) are deployed at each emission node in sets of 2-3 of the same type. Operating condition acquisition equipment (operating condition sensors) is deployed in key process links such as clinker calcination and raw material proportioning. Environmental parameter acquisition equipment (temperature sensors, humidity sensors) is deployed synchronously with pollutant concentration sensors to ensure that the collected data can be matched.

[0050] Step A2: Equipment initialization calibration. Initialize and calibrate all data acquisition devices. Considering the high temperature and high dust working environment of cement production, preset equipment protection parameters. At the same time, set the initial threshold for data acquisition based on pollutant emission standards and normal operating conditions to ensure that the equipment acquisition accuracy meets the processing requirements of the subsequent improved adaptive pollutant concentration detection module and the operating condition linkage dynamic monitoring strategy generation module.

[0051] Step A3: Real-time data acquisition and control. The data acquisition device simultaneously collects three types of data at a dynamically adjusted frequency (default 3 times / minute, automatically increasing to 5 times / minute when operating conditions fluctuate significantly). Among them, the pollutant concentration data consists of dust and NO. x SO2 sensors collect data synchronously. Production condition data focuses on collecting clinker calcination temperature, raw material ratio coefficient, fan speed, and calcination time. Environmental parameter data focuses on collecting real-time temperature and humidity at each emission node.

[0052] Step A4: Preliminary data processing. The data acquisition unit converts the analog signals collected by each sensor into digital signals, performs preliminary filtering to remove minor abnormal fluctuations caused by momentary interference from the equipment, and at the same time performs preliminary classification and labeling of the collected data, associating it with the corresponding emission node, collection time and equipment number to form standardized initial collected data.

[0053] Step A5: Data Upload Preparation. After the initial processing of standardized data, the data collector organizes and summarizes the data. In accordance with the data transmission layer's transmission protocol, data encryption is prepared in advance, and data transmission commands are triggered synchronously to ensure that the collected data is transmitted to the data transmission layer in a timely and secure manner, providing reliable data support for subsequent data analysis and strategy generation in the core processing layer.

[0054] The data transmission layer adopts a 5G+LoRa dual-mode transmission method. 5G is used for large-volume real-time data transmission, and LoRa is used for data transmission at remote emission nodes. The transmission process uses the AES-128 encryption algorithm to ensure data security and integrity. The specific working steps of the data transmission layer are explained below:

[0055] Step a: Initialize and adapt the transmission modules. After the system starts, initialize and configure the 5G and LoRa transmission modules. The 5G module connects to the factory's dedicated 5G network and is configured with high-frequency transmission parameters to adapt to the large-volume real-time data transmission needs of the core emission nodes. The LoRa module is configured with low-power transmission parameters, and the transmission frequency is adjusted to be consistent with the transmission frequency of the data collector at the remote emission nodes (synchronized with the sensing layer's acquisition frequency of 1-5 times / minute). At the same time, the switching logic for dual-mode transmission is set to ensure that the two transmission methods can automatically adapt to the node location and data volume, avoiding transmission conflicts.

[0056] Step b: Data reception and verification. The data transmission layer receives standardized initial data uploaded by the IoT sensing layer data collector in real time. After receiving the data, it immediately performs preliminary verification on the data. The verification includes data format, data integrity, and data identification (ensuring that the data is accurately associated with the corresponding emission node, collection time, and device number). If the verification finds that the data format is incorrect, the data is missing, or the identification is mismatched, a feedback instruction is immediately sent to the corresponding data collector to re-upload the data, ensuring that the incoming data meets the processing requirements of the core processing layer.

[0057] Step c: Dual-mode transmission adaptive switching. Based on the location of the data source node and the amount of data, the transmission mode is automatically switched: For core emission nodes such as kiln tail chimneys and clinker cooler outlets, where the data volume is large and needs to be transmitted to the core processing layer in real time, the transmission mode is automatically switched to 5G, and the transmission latency is strictly controlled to ≤100ms. This ensures that the core processing layer can obtain pollutant concentration and operating condition data in real time, supporting the real-time calculation of the improved adaptive pollutant concentration detection module and the operating condition linkage dynamic monitoring strategy generation module; For remote emission nodes such as raw material stockpiles and remote raw material crushing points, where the data volume is relatively small, the transmission mode is automatically switched to LoRa, taking advantage of its low power consumption and long-distance transmission (coverage range of 1-3km) to achieve stable transmission of data from remote nodes, while reducing the overall energy consumption of the system.

[0058] Step d: Data encryption processing. For data that has passed verification and whose transmission method has been determined, the AES-128 encryption algorithm is used for full-process encryption processing. The encryption process is divided into two parts: data segment encryption and identifier encryption. First, the collected data is segmented according to type (pollutant concentration data, operating condition data, environmental parameter data), and each segment of data is encrypted independently. Then, the data identifier (emission node, collection time, equipment number) is encrypted to ensure that even if it is intercepted during data transmission, it cannot be deciphered. This ensures the security of the data from the source and prevents the data from being tampered with or stolen, which meets the core requirement of secure data transmission in the patent.

[0059] Step e: Real-time data transmission and retransmission control. After encryption, the data is transmitted in real time to the abnormal data preprocessing unit of the core processing layer according to the data reception rhythm of the core processing layer. At the same time, the data retransmission mechanism is activated to monitor the data transmission status in real time. If data transmission interruption or timeout is detected (the timeout threshold is set to 200ms, which is higher than the 5G transmission latency to ensure fault tolerance), a retransmission command is automatically triggered immediately, with a maximum of 3 retransmissions. If all 3 retransmissions fail, a transmission anomaly warning is immediately sent to the application layer to remind the administrator to check the corresponding transmission module and node equipment to ensure that data is not lost and to ensure the continuity of data input to the core processing layer, providing reliable support for subsequent data processing and strategy generation.

[0060] Step f: Transmission status feedback. After the data transmission is completed, the data transmission layer will feed back the transmission status (transmission successful, transmission failed, number of retransmissions) to the data collector of the IoT sensing layer and the abnormal data preprocessing unit of the core processing layer. This allows the sensing layer to adjust the data upload rhythm and the core processing layer to verify the data integrity, forming a closed-loop feedback of "sensing layer upload - transmission layer transmission - core layer reception", ensuring that the entire data transmission process is traceable and controllable.

[0061] The core processing layer includes an improved adaptive pollutant concentration detection module and a dynamic monitoring strategy generation module linked to operating conditions. The improved adaptive pollutant concentration detection module uses an improved adaptive weighted fusion algorithm to solve the problem that single sensors in existing detection methods are easily affected by the high temperature, high humidity, and dust interference of cement production, resulting in low detection accuracy and large data fluctuations, thus achieving accurate detection of pollutant concentrations. The dynamic monitoring strategy generation module linked to operating conditions uses an improved dynamic programming algorithm based on operating conditions and emissions to solve the problem that existing monitoring strategies are fixed and cannot be dynamically adjusted according to real-time cement production conditions (such as clinker calcination temperature, raw material ratio, and fan speed), resulting in delayed warnings of exceeding standards and wasted governance resources, thus achieving dynamic adaptation of monitoring strategies.

[0062] The improved adaptive pollutant concentration detection module includes a multi-sensor data acquisition unit, an abnormal data preprocessing unit, an improved adaptive weighted fusion unit, and a concentration calibration unit. The working steps of each unit are as follows:

[0063] Step B1: Multi-sensor data acquisition units are deployed at various emission points in cement production (kiln tail chimney, raw material crushing workshop, clinker cooler outlet), using dust sensors, NO... x Two to three sensors each for pollutants and SO2, simultaneously collecting real-time pollutant concentration data, denoted as... ,in For sensor type, specifically For dust, NO x , SO2 Number the sensors of the same type. , The time of data collection;

[0064] Step B2: The abnormal data preprocessing unit processes the collected data. Preprocessing is performed first by... The criterion is to remove extreme outliers if the data meets the following criteria. If it is an outlier, it will be removed. for Time of the first The average value collected by the sensor-like sensor, for Time of the first The standard deviation of the sensor data was measured; then, linear interpolation was used to fill in the missing data after outlier removal, resulting in preprocessed data. ;

[0065] Step B3: The improved adaptive weighted fusion unit processes the preprocessed... Traditional weighted fusion algorithms, which perform fusion calculations with fixed weights, are susceptible to interference. This invention improves the weight calculation method by first introducing a real-time reliability coefficient from the sensor. Combined with data fluctuation coefficient The weights are dynamically adjusted using the following formula: ,in , They are respectively Time of the first The maximum and minimum values ​​of the data after preprocessing by the sensor-like devices; ,in For weighting coefficients, ; For the first Class 1 Historical reliability of each sensor (based on detection error statistics over the past 30 days; the smaller the error, the higher the reliability). Larger); fused pollutant concentration data for: ;

[0066] Here, the weight allocation coefficients are... Here is a brief explanation of the steps to obtain it:

[0067] Step I: Determine the core principle for the value of α. Combining the core requirement of the improved adaptive weighted fusion unit to "prioritize the stability of real-time detection data while taking into account the historical performance of the sensor", it is clear that the value of α must meet the requirement that "the weight of the data fluctuation coefficient is higher than the weight of the sensor's historical reliability". Therefore, the basic value range of α is set to 0.6≤α≤0.8 to ensure that the impact of real-time data fluctuation on the weight is dominant, which is in line with the characteristics of real-time changes in environmental interference (high temperature, high dust) in cement production, and avoids the lag in weight adjustment due to over-reliance on historical reliability.

[0068] Step II: Initial Value Calibration. After system startup, the initial value is determined based on the deployment scenario of the IoT sensing layer sensors (core emission nodes / remote emission nodes). Initial value assignment: For core emission nodes such as kiln tail chimneys and clinker cooler outlets, pollutant concentrations fluctuate greatly and environmental disturbances are frequent. Therefore, it is necessary to focus on real-time data fluctuations and assign initial values. The value is set to 0.8; for remote emission points such as raw material crushing workshops and raw material storage yards, where pollutant concentration fluctuations are relatively mild and environmental disturbances are relatively stable, the weighting of real-time data fluctuations can be appropriately reduced. The value is set to 0.6; for medium fluctuation nodes such as the raw material grinding workshop, the initial value is... The value is set to 0.7 (intermediate value) to achieve accurate adaptation of the initial value to the detection scenario.

[0069] Step III: Dynamic iterative correction. Based on the operational data of the improved adaptive pollutant concentration detection module, the system is adjusted every 24 hours. Perform an iterative correction to ensure that the values ​​always adapt to the real-time detection requirements. The specific correction process is as follows: 1. Analyze the merged data over the past 24 hours. The accurate detection value output by the concentration calibration unit deviation rate The calculation formula is: ( 1. The number of data collection groups in the past 24 hours); 2. If the deviation rate (If the detection accuracy requirement is exceeded), then it is judged. Unreasonable value selection: If the deviation stems from real-time data fluctuations that have not been adequately considered (e.g., sudden environmental interference causing large data fluctuations, but...). If the value is too low, then... Increase by 0.05 (not exceeding 0.8); if the deviation stems from over-reliance on real-time data fluctuations (such as abnormal fluctuations caused by momentary sensor malfunctions, but...). If the value is too high, then... Reduce by 0.05 (not lower than 0.6); 3. If the deviation rate (If the detection accuracy requirements are met), then maintain the current state. The value remains unchanged, ensuring It is always adapted to the requirements of detection accuracy and real-time environmental interference;

[0070] Step IV, value locking and feedback: After three consecutive iterations of correction (i.e., 72 hours), the deviation rate... If all values ​​remain stable at ≤5%, then the current value is locked. The assigned value serves as a fixed base coefficient for subsequent weight calculations at this emission node. If subsequent detection scenarios change (e.g., sensor replacement, adjustments to production conditions leading to altered pollutant concentration fluctuations), the initial assignment and iterative correction process in steps II-III will be retried to ensure... The values ​​are always closely aligned with actual testing needs; at the same time, each The value acquisition, correction process, and basis are synchronously fed back to the data storage unit of the core processing layer and the application layer, facilitating subsequent traceability, debugging, and optimization, and ensuring the entire process... The acquisition process is controllable and reproducible, forming a closed loop with the overall workflow of the improved adaptive pollutant concentration detection module.

[0071] Finally, the fused pollutant concentration data are calculated based on the weights. This enables adaptive fusion of data from multiple sensors, reducing the impact of environmental interference on detection accuracy.

[0072] Step B4: The concentration calibration unit employs an improved piecewise linear calibration algorithm. Based on standard concentration samples under different cement production conditions, a calibration model is established for the fused samples. Perform calibration; the calibration formula is: ,in , The segmented calibration coefficients are automatically matched based on real-time operating conditions (high temperature / normal temperature, high humidity / normal humidity), ultimately outputting accurate pollutant concentration detection values. .

[0073] The dynamic monitoring strategy generation module based on operating conditions includes an operating condition data parsing unit, an emission-operating condition correlation modeling unit, an improved dynamic programming strategy generation unit, and a strategy distribution unit. The working steps of each unit are as follows:

[0074] Step C1: The operating condition data analysis unit collects real-time operating condition data of cement production, including clinker calcination temperature. Raw material proportioning coefficient Fan speed Calcination time The data for each operating condition are standardized to eliminate the influence of dimensions. The standardization formula is as follows: ,in For the first The raw values ​​of the similar working condition data, , The first Historical minimum and maximum values ​​of similar operating conditions data Corresponding to four types of working condition data;

[0075] Step C2: Emission-Operating Condition Correlation Modeling Unit. This step establishes a correlation model between pollutant concentration and operating condition data, introduces an improved grey relational analysis algorithm to correct the unreasonable weight allocation problem in traditional grey relational analysis, and calculates the correlation degree between each operating condition data and pollutant concentration. The specific formula is as follows:

[0076] First, calculate the reference sequence (pollutant concentration). ) and comparison sequences (standardized operating condition data) The absolute difference of ) ;

[0077] Then calculate the correlation coefficient. ,in The resolution coefficient, It should be noted here that:

[0078] Double minimum means traversing all pollutant types ( ) and all operating condition data types ( ), all absolute differences calculated The smallest value among (the absolute values ​​of the differences between the reference sequence and the comparison sequence);

[0079] Double maximum value refers to traversing all pollutant types ( ) and all operating condition data types ( ), all absolute differences calculated The maximum value in.

[0080] Finally, the influence weight of operating conditions is introduced. (Based on the cement production process, calcination temperature has the highest weight.) Raw material ratio Fan speed Calcination time ), calculate the correlation degree ,in Number of data collection groups;

[0081] Step C3: Improved dynamic programming strategy generation unit. With the goal of minimizing pollutant emissions and treatment costs, a dynamic programming model is constructed. This improves upon the fixed state transition equations in traditional dynamic programming by incorporating correlation... The state transition coefficients are dynamically adjusted as follows:

[0082] Define state variables for The emission status at any given time, where 1 = compliant, 2 = borderline exceedance, and 3 = severely exceedance; decision variables. The regulatory strategy includes: 1 = routine monitoring, 2 = enhanced monitoring + light treatment, and 3 = cessation of related processes + in-depth treatment.

[0083] The state transition equation is improved to: ,in For state transition function, correlation degree The larger the operating parameter, the greater its influence on the state transition. For example, when the correlation between calcination temperature and other parameters increases... At that time, the state transition coefficient increased by 20%, accelerating the transition from "critical exceedance" to "compliance";

[0084] The objective function is: ,in For the first The implementation cost of a real-time monitoring strategy The penalty coefficient for exceeding the limit. These are the pollutant emission standard values. The regulatory cycle is defined; the optimal regulatory strategy sequence is obtained by solving the dynamic programming model using the inverse recursive method. ;

[0085] Step C4: The strategy distribution unit will distribute the optimal regulatory strategy. This information is translated into specific control commands and sent to cement production environmental protection equipment (such as desulfurization and denitrification devices and dust recovery equipment) and production control units to achieve dynamic linkage between monitoring strategies and production conditions. At the same time, strategy information is fed back to the application layer for management personnel to view.

[0086] In step C3, the dynamic programming model is solved using the inverse recursive method to obtain the optimal regulatory strategy sequence. The specific steps are as follows:

[0087] Step 1: Determine the recursive boundary conditions and clarify the starting node for the reverse recursion: based on the regulatory cycle. (Based on the cement production process, it is usually 24 hours, and...) The iterative correction cycle is synchronized to ensure data collaboration, with the recursive endpoint being... At this point, to ensure that pollutant emissions meet standards at the end of the regulatory cycle, boundary conditions are set: if (If the target status is met), then the terminal objective function value (No additional cost); if (Critical exceedance) or (Seriously exceeding the limit), then the terminal objective function value (Only the cost of penalties for exceeding the limit is calculated, without considering subsequent decision-making costs), among which For exceeding the penalty coefficient, for Accurate pollutant concentration detection values ​​at all times These are the pollutant emission standard values;

[0088] Step 2: Reverse recursive calculation, starting from the end of the regulatory cycle. Begin, recursively advance to the next moment. At every moment, every step is combined with the emissions-operating condition correlation. The calculation logic is dynamically adjusted as follows: For the first... time( from Decrease to 1), and iterate through all possible states at that moment. For each state Iterate through all possible decision variables According to the improved state transition equation Combined with the current working condition correlation (Output from the emission-operating condition correlation modeling unit; the greater the correlation, the greater the adjustment range of the state transition coefficient), calculate the decision under this condition. Predicted state at time Calculate the instantaneous cost corresponding to this decision, i.e. ,in The implementation cost of this decision is estimated (presumably, the cost of routine monitoring < the cost of light-level governance < the cost of deep-level governance); combined with... Predicted state at any given time The optimal objective function value Calculate the decision under this condition objective function value at time 1 ;

[0089] Step 3: Select the optimal decision and determine the optimal regulatory strategy at each time point: For the first... Each state at any given moment Compare all decision variables Corresponding objective function value The decision that minimizes the objective function value (i.e., the dual objectives of "no pollutant emissions exceeding standards and lowest treatment costs" are selected as the optimal decision for that moment and state. Simultaneously, record the objective function value corresponding to the optimal decision. To ensure that every decision achieves the optimal balance between cost and emissions compliance;

[0090] Step 4: Integrate optimal decisions to form a complete sequence of optimal regulatory strategies. This completes the process from... to After the reverse recursion, combined with Initial emission state at time (Determined by the initial concentration detection value output by the improved adaptive pollutant concentration detection module), retrieve the optimal decision under the corresponding state at each time point. Integrate them in chronological order to form a complete sequence of optimal regulatory strategies. ,Right now ;

[0091] Step 5: Strategy verification and correction to ensure sequence adaptability: The integrated optimal regulatory strategy sequence Correlation with current real-time operating data Perform a verification to determine if the optimal decision at a certain moment is... Corresponding predicted state If emission compliance requirements cannot be met (e.g., the forecast still indicates severe exceedance), then the weights of the decision variables at that point in time should be readjusted (considering the correlation). Prioritize decision adjustments for high-impact operating conditions, recalculate the objective function value, and select the optimal decision; after verification, output the final optimal regulatory strategy sequence. The strategy sequence is transmitted to the strategy distribution unit to ensure that the strategy sequence can be directly converted into control commands and accurately linked with production conditions and environmental protection equipment.

[0092] The application layer includes regulatory terminals (PC and mobile), which support real-time display of pollutant concentrations, early warning of exceeding standards (audio-visual warning + message push), query of regulatory strategies, historical data statistical analysis, and report export functions.

[0093] The following is a brief explanation of the implementation steps of the application layer:

[0094] Step 1: Application Layer Initialization Configuration. After the system starts, the computer-based monitoring terminal and mobile APP are initialized to complete the communication adaptation between the terminal and the core processing layer. Basic system parameters (pollutant emission standards, normal operating range, user permission level, etc.) are loaded synchronously, and pre-set chart display templates (line chart, bar chart, dashboard, etc.) are set to ensure that the terminal can receive all data transmitted by the core processing layer normally. At the same time, the mobile terminal message push permissions and sound and light warning devices are debugged to ensure the normal operation of subsequent functions.

[0095] Step 2: Data Reception and Synchronization. The application layer receives various types of data transmitted from the core processing layer in real time, including accurate pollutant concentration detection values ​​output by the improved adaptive pollutant concentration detection module, optimal regulatory strategies output by the dynamic monitoring strategy generation module, transmission status data fed back by the data transmission layer, and historical data stored by the core processing layer. The received data is synchronously classified and stored according to type to ensure that the data is consistent with the core processing layer, data transmission layer, and IoT sensing layer, providing data support for subsequent display, query, and analysis.

[0096] Step 3: Real-time display and control. Following a pre-set graphical display template, the received real-time data is visualized. This is divided into three display modules: First, pollutant concentration display, categorized by emission point, showing real-time concentrations of dust, NO... x The system features three main functions: 1) SO2 concentration detection values, labeled with corresponding emission standard values, and real-time concentration percentages displayed on a dashboard. Data exceeding the standard is automatically highlighted in red, providing a clear view of the emission status; 2) Operating data display, simultaneously showing operating parameters such as clinker calcination temperature and raw material ratio, using line graphs to present data trends, facilitating management personnel's analysis of the relationship between operating conditions and emissions; and 3) System operation status display, showing the operating status of each sensing node, transmission module, and core processing module, as well as data transmission success rate and strategy distribution status, ensuring management personnel have real-time access to the overall system operation.

[0097] Step 4: Early Warning Triggering and Handling. The application layer compares the pollutant concentration detection values ​​transmitted from the core processing layer with the preset emission standard values ​​in real time. When the detection value reaches the critical exceedance (concentration ≥ 80% × emission standard value) or the serious exceedance (concentration ≥ emission standard value), a dual early warning mechanism is immediately triggered: First, the computer and mobile terminals simultaneously trigger audible and visual warnings. The computer terminal pops up a warning pop-up window and plays a warning prompt sound, while the mobile terminal sends a warning message (including the exceedance node, exceedance pollutant, exceedance concentration, and current operating conditions) to relevant management personnel. Second, the warning information (warning time, exceedance node, exceedance data, triggering reason) is automatically recorded and associated with the corresponding operating condition data and regulatory strategies for subsequent traceability and analysis. After receiving the warning, management personnel can issue emergency handling instructions through the application layer. The instructions are transmitted through the core processing layer and data transmission layer to the IoT sensing layer and environmental protection equipment for rapid handling of the exceedance problem.

[0098] Step 5: Regulatory Strategy Inquiry and Adjustment. The application layer provides query functions for real-time regulatory strategies and historical strategy records. Managers can query corresponding strategies by emission node and time range, and at the same time display the basis for strategy generation (operating condition data, pollutant concentration data, correlation analysis results), which makes it easier to understand the reasons and rationality of strategy adjustments. If managers need to manually adjust the regulatory strategy based on the actual situation on site, they can submit an adjustment application through the application layer, input adjustment parameters (such as monitoring frequency and treatment mode). After the application is verified by the system (verifying user permissions and the rationality of adjustment parameters), it is transmitted to the core processing layer. The core processing layer recalculates with real-time data to generate the optimal adjusted strategy, ensuring that the strategy adjustment is scientific and compliant.

[0099] Step Six: Historical Data Management and Application. The application layer classifies and manages all data (collected data, early warning data, strategy data, and transmission data) stored in the core processing layer, with a storage period strictly adhering to ≥1 year. It supports managers in querying historical data by time range, data type, and emission node. Simultaneously, it provides statistical analysis functions, automatically generating pollutant emission statistical reports, operating condition change analysis reports, and strategy execution effect reports, which can be exported to Excel and PDF formats, providing accurate data support for environmental supervision and assessment, and production process optimization. Furthermore, it supports historical data comparison analysis, allowing comparison of emission data and operating condition data across different time periods and emission nodes, helping managers discover emission patterns and optimize regulatory plans.

[0100] Step 7: Access Control and Operation Management. The application layer adopts a hierarchical access control model with three preset access levels: administrator access (allowing system configuration, access allocation, and manual policy adjustment), supervisor access (allowing data viewing, receiving alerts, querying policies, and exporting reports), and operator access (allowing real-time data of the corresponding responsible node and receiving alert notifications). Administrators complete user registration, access allocation, password modification, and other operations through the application layer. The system automatically records all operation logs (operator, operation time, operation content) to ensure traceability. An operation verification mechanism is also set up to prevent unauthorized operations and erroneous operations, ensuring the security and standardization of system operations.

[0101] Step 8: Command Feedback and Closed-Loop Control. All operation commands (emergency handling commands, strategy adjustment commands, equipment debugging commands) issued by management personnel through the application layer are verified by the application layer and then transmitted to the core processing layer. After the core processing layer completes the execution, it feeds back the execution result to the application layer. The application layer displays the command execution status in real time (execution in progress, execution successful, execution failed). If the execution fails, it automatically prompts the reason for the failure (such as equipment failure, data transmission abnormality) to remind management personnel to handle it in time, forming a closed-loop control of "command issuance-execution-feedback" to ensure the efficient implementation of supervision work.

[0102] This invention aims to address the issues of dust and NO emissions in existing environmental emission regulations for cement production. x The current system suffers from specific technical problems, such as low accuracy and significant lag in real-time detection of pollutant emission concentrations, and the inability of regulatory strategies to dynamically adapt to production conditions, leading to untimely early warnings of excessive emissions and wasted environmental governance resources. This system constructs a complete regulatory framework through an IoT sensing layer, data transmission layer, core processing layer, and application layer. It focuses on innovative improvements to the "improved adaptive pollutant concentration detection module" and the "condition-linked dynamic regulatory strategy generation module." Through improved algorithms, it achieves accurate pollutant concentration detection and intelligent generation of dynamic regulatory strategies, effectively improving regulatory accuracy and reducing lag. This enables full-process, dynamic, and precise monitoring of environmental emissions from cement production, contributing to the green and low-carbon development of the cement industry. This invention has a reasonable structure, strong practicality, and significant creative and application value.

[0103] 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 dynamic monitoring system for environmental emissions from cement production based on the Internet of Things, characterized in that, It comprises an IoT sensing layer, a data transmission layer, a core processing layer, and an application layer. The IoT sensing layer is used to collect pollutant concentration data, production condition data, and environmental parameter data at various emission points in cement production. The data transmission layer is used to encrypt and transmit the data collected by the sensing layer to the core processing layer. The core processing layer is used to process and analyze the collected data and generate dynamic monitoring strategies. The application layer is used to realize the display of monitoring data, early warning prompts, strategy distribution, and historical data query. The core processing layer includes an improved adaptive pollutant concentration detection module and an operating condition-linked dynamic monitoring strategy generation module. The improved adaptive pollutant concentration detection module uses an improved adaptive weighted fusion algorithm to achieve accurate detection of pollutant concentration. The operating condition-linked dynamic monitoring strategy generation module uses an improved operating condition-emission correlation dynamic programming algorithm to achieve dynamic adaptation of monitoring strategies. The dynamic monitoring strategy generation module for operating conditions includes an operating condition data parsing unit, an emission-operating condition correlation modeling unit, an improved dynamic programming strategy generation unit, and a strategy distribution unit. The working steps of each unit are as follows: Step C1: The operating condition data analysis unit collects real-time operating condition data of cement production, including clinker calcination temperature. Raw material proportioning coefficient Fan speed Calcination time The data for each operating condition are standardized to eliminate the influence of dimensions. The standardization formula is as follows: ,in For the first The raw values ​​of the similar working condition data, , The first Historical minimum and maximum values ​​of similar operating conditions data Corresponding to four types of working condition data; Step C2: Emission-Operating Condition Correlation Modeling Unit. This step establishes a correlation model between pollutant concentration and operating condition data, introduces an improved grey relational analysis algorithm to correct the unreasonable weight allocation problem in traditional grey relational analysis, and calculates the correlation degree between each operating condition data and pollutant concentration. The specific formula is as follows: First, calculate the absolute difference between the reference sequence and the comparison sequence: ; Then calculate the correlation coefficient. ,in The resolution coefficient, ; Finally, the influence weight of operating conditions is introduced. Calculate the correlation degree ,in Number of data collection groups; Step C3: Improved dynamic programming strategy generation unit. With the goal of minimizing pollutant emissions and treatment costs, a dynamic programming model is constructed. This improves upon the fixed state transition equations in traditional dynamic programming by incorporating correlation... The state transition coefficients are dynamically adjusted as follows: Define state variables for The emission status at any given time, where 1 = compliant, 2 = borderline exceedance, and 3 = severely exceedance; decision variables. The regulatory strategy includes: 1 = routine monitoring, 2 = enhanced monitoring + light treatment, and 3 = cessation of related processes + in-depth treatment. The state transition equation is improved to: ,in For state transition function, correlation degree The larger the operating condition parameter, the greater its weight in influencing the state transition; The objective function is: ,in For the first The implementation cost of a real-time monitoring strategy The penalty coefficient for exceeding the limit. These are pollutant emission standard values. The regulatory cycle is defined; the optimal regulatory strategy sequence is obtained by solving the dynamic programming model using the inverse recursive method. ; Step C4: The strategy distribution unit will distribute the optimal regulatory strategy. This is transformed into specific control commands, which are then sent to the cement production environmental protection equipment and production control unit to achieve dynamic linkage between the monitoring strategy and the production conditions. At the same time, the strategy information is fed back to the application layer for managers to view. In step C3, the dynamic programming model is solved using the reverse recursive method to obtain the optimal regulatory strategy sequence. The specific steps are as follows: Step 1: Determine the recursive boundary conditions and clarify the starting node for the reverse recursion: based on the regulatory cycle. The endpoint of the recursion, i.e. At this point, to ensure that pollutant emissions meet standards at the end of the regulatory cycle, boundary conditions are set: if Then the terminal objective function value ;like or Then the terminal objective function value ,in For exceeding the penalty coefficient, for Accurate pollutant concentration detection values ​​at all times These are the pollutant emission standard values; Step 2: Reverse recursive calculation, starting from the end of the regulatory cycle. Begin, recursively advance to the next moment. At every moment, every step is combined with the emissions-operating condition correlation. The calculation logic is dynamically adjusted as follows: For the first... At any given moment, iterate through all possible states at that moment. For each state Iterate through all possible decision variables According to the improved state transition equation Combined with the current working condition correlation Calculate the decision under this condition Predicted state at time Calculate the instantaneous cost corresponding to this decision, i.e. ,in The implementation cost of this decision; combined with Predicted state at any given time The optimal objective function value Calculate the decision under this condition objective function value at time 1 ; Step 3: Select the optimal decision and determine the optimal regulatory strategy at each time point: For the first... Each state at any given moment Compare all decision variables Corresponding objective function value The decision that minimizes the objective function value is selected as the optimal decision for that moment and that state. Simultaneously, record the objective function value corresponding to the optimal decision. To ensure that every decision achieves the optimal balance between cost and emissions compliance; Step 4: Integrate optimal decisions to form a complete sequence of optimal regulatory strategies, completing the process from... to After the reverse recursion, combined with Initial emission state at time Retrieve the optimal decision under the corresponding state at each time step. Integrate them in chronological order to form a complete sequence of optimal regulatory strategies. ,Right now ; Step 5: Strategy verification and correction to ensure sequence adaptability: The integrated optimal regulatory strategy sequence Correlation with current real-time operating data Perform a verification to determine if the optimal decision at a certain moment is... Corresponding predicted state If emission compliance requirements cannot be met, the weights of the decision variables at that moment are readjusted, the objective function value is recalculated, and the optimal decision is selected; after verification, the final optimal regulatory strategy sequence is output. The strategy sequence is transmitted to the strategy distribution unit to ensure that the strategy sequence can be directly converted into control commands and accurately linked with production conditions and environmental protection equipment.

2. The dynamic monitoring system for environmental emissions from cement production based on the Internet of Things as described in claim 1, characterized in that: The specific working logic of the IoT sensing layer is as follows: Step A1: Node deployment and adaptation. Based on the key emission nodes in the entire cement production process, corresponding data acquisition devices are deployed. Among them, pollutant concentration acquisition devices are deployed at each emission node in 2-3 units of the same type, and operating condition acquisition devices are deployed at key process links such as clinker calcination and raw material proportioning. Environmental parameter acquisition devices are deployed synchronously with pollutant concentration sensors to ensure that the collected data can be matched accordingly. Step A2: Equipment initialization calibration. Initialize and calibrate all data acquisition devices. Considering the high temperature and high dust working environment of cement production, preset equipment protection parameters. At the same time, set the initial threshold for data acquisition based on pollutant emission standards and normal operating conditions to ensure that the equipment acquisition accuracy meets the processing requirements of the subsequent improved adaptive pollutant concentration detection module and the operating condition linkage dynamic monitoring strategy generation module. Step A3: Real-time data acquisition and control. The acquisition equipment synchronously collects three types of data according to a dynamically adjusted frequency, including pollutant concentration data consisting of dust and NO. x SO2 sensors collect data synchronously. Production condition data focuses on collecting clinker calcination temperature, raw material ratio coefficient, fan speed, and calcination time. Environmental parameter data focuses on collecting real-time temperature and humidity at each emission node. Step A4: Preliminary data processing. The data acquisition unit converts the analog signals collected by each sensor into digital signals, performs preliminary filtering to remove minor abnormal fluctuations caused by momentary interference from the equipment, and at the same time performs preliminary classification and labeling of the collected data, associating it with the corresponding emission node, collection time and equipment number to form standardized initial collected data. Step A5: Data Upload Preparation. After the initial processing of standardized data, the data collector organizes and summarizes the data. In accordance with the data transmission layer's transmission protocol, data encryption is prepared in advance, and data transmission commands are triggered synchronously to ensure that the collected data is transmitted to the data transmission layer in a timely and secure manner, providing reliable data support for subsequent data analysis and strategy generation in the core processing layer.

3. The dynamic monitoring system for environmental emissions from cement production based on the Internet of Things as described in claim 1, characterized in that: The data transmission layer adopts a 5G+LoRa dual-mode transmission method. 5G is used for large-volume real-time data transmission, and LoRa is used for data transmission at remote emission nodes. The transmission process uses the AES-128 encryption algorithm to ensure data security and integrity.

4. The dynamic monitoring system for environmental emissions from cement production based on the Internet of Things as described in claim 1, characterized in that: The improved adaptive pollutant concentration detection module includes a multi-sensor data acquisition unit, an abnormal data preprocessing unit, an improved adaptive weighted fusion unit, and a concentration calibration unit. The working steps of each unit are as follows: Step B1: Multi-sensor data acquisition units are deployed at various emission points in cement production, employing dust sensors, NO... x Two to three sensors each for pollutants and SO2, simultaneously collecting real-time pollutant concentration data, denoted as... ,in For sensor type, specifically For dust, NO x , SO2 Number the sensors of the same type. , The time of data collection; Step B2: The abnormal data preprocessing unit processes the collected data. Preprocessing is performed first by... The criterion is to remove extreme outliers if the data meets the following criteria. If it is an outlier, it will be removed. for Time of the first The average value collected by the sensor-like sensor, for Time of the first The standard deviation of the sensor data was measured; then, linear interpolation was used to fill in the missing data after outlier removal, resulting in preprocessed data. ; Step B3: The improved adaptive weighted fusion unit processes the preprocessed... To perform fusion computing, the real-time reliability coefficient of the sensor is first introduced. Combined with data fluctuation coefficient The weights are dynamically adjusted using the following formula: ,in , They are respectively Time of the first The maximum and minimum values ​​of the data after preprocessing by the sensor-like devices; ,in For weighting coefficients, ; For the first Class 1 Historical reliability of individual sensors; fused pollutant concentration data for: ; Step B4: The concentration calibration unit employs an improved piecewise linear calibration algorithm. Based on standard concentration samples under different cement production conditions, a calibration model is established for the fused samples. Perform calibration; the calibration formula is: ,in , The segmented calibration coefficients are automatically matched based on real-time operating conditions, ultimately outputting accurate pollutant concentration detection values. .

5. The dynamic monitoring system for environmental emissions from cement production based on the Internet of Things as described in claim 1, characterized in that: The application layer includes a monitoring terminal, which supports real-time display of pollutant concentrations, early warning of exceeding standards, query of monitoring strategies, statistical analysis of historical data, and report export functions.

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