Factory dust removal energy efficiency optimization system based on digital twinning

Abstract

The invention relates to the technical field of factory dust removal, and discloses a factory dust removal energy efficiency optimization system based on digital twinning. The multi-source data sensing module obtains multi-dimensional operation data of target dust removal equipment, wherein the multi-dimensional operation data comprises air volume distribution, particulate matter concentration and energy consumption characteristic data. And the energy efficiency analysis decision module performs dynamic energy efficiency mapping processing on the data and inputs the data into the optimization strategy generation layer. The optimization strategy generation layer comprises a data cleaning module and a strategy construction module, the data cleaning module is used for cleaning and calibrating data, and the strategy construction module is used for generating an energy efficiency optimization strategy through three-layer collaborative modeling of state perception, energy efficiency optimization and strategy execution. The state sensing layer processes an operation sequence to obtain working condition associated feature data, the energy efficiency optimization layer performs modeling to determine optimization strategy feature data, and the strategy execution layer performs multi-level decision making according to the optimization strategy feature data. The system can accurately obtain equipment operation data, scientifically analyze and generate an optimization strategy, improve the energy efficiency of the dust removal equipment, guarantee the dust removal effect, improve the reliability and stability of the equipment, and reduce the energy consumption and the production cost.

Description

Technical Field

[0001] The present invention relates to the technical field of factory dust removal, and specifically to a factory dust removal energy efficiency optimization system based on digital twins. Background Art

[0002] In modern industrial production, factory dust removal is a critical step in maintaining a high quality production environment, maintaining equipment operation, and ensuring product quality. With increasingly stringent environmental standards and businesses placing increasing emphasis on energy conservation and emission reduction, improving the energy efficiency of factory dust removal systems has become a critical issue that needs to be addressed.

[0003] Traditional factory dust removal equipment has many drawbacks during operation. On the one hand, data acquisition methods are limited, making it difficult to fully and accurately grasp the operating status of dust removal equipment. In the past, they mostly relied on a single type of sensor, which could only obtain a small amount of key data such as particle concentration. There was a lack of detailed monitoring of air volume distribution, a key factor affecting dust removal effectiveness and energy consumption. For example, in large industrial plants, the airflow conditions in different areas are complex and changeable. Traditional sensors are unable to accurately measure the differences in wind pressure in each area, making it difficult for dust removal equipment to be adjusted according to actual airflow conditions. This results in poor dust removal performance in some areas and high energy consumption.

[0004] On the other hand, traditional approaches to energy efficiency analysis and strategy development are relatively crude. Adjusting equipment operating parameters is typically based on manual experience and simple data analysis, a method that lacks scientificity and dynamic adaptability. Faced with frequent changes in production conditions, it's impossible to promptly and accurately develop optimal energy efficiency optimization strategies. For example, when dust generation fluctuates due to production process adjustments, manual adjustments often lag behind, failing to quickly match the optimal dust removal parameters under the new conditions. This results in wasted energy and reduced dust removal efficiency.

[0005] Furthermore, traditional dust removal systems lack effective coordination between their components. Information exchange between the multi-zone air pressure regulator and sensors, and between different functional modules, is poor, preventing them from forming an integrated whole. This makes it difficult to comprehensively consider various factors when adjusting the equipment, further impacting the overall performance of the dust removal system.

[0006] With the advancement of intelligent manufacturing, digital twin technology is gaining popularity. However, its application in factory dust removal is still in its exploratory stages, and a mature and comprehensive digital twin-based factory dust removal energy efficiency optimization system has yet to be established. Existing attempts have either lacked data processing accuracy or suffered from flaws in the effectiveness and real-time nature of optimization strategies, failing to fully leverage the enormous potential of digital twin technology to improve the energy efficiency of dust removal equipment. Therefore, the development of a new, efficient digital twin-based factory dust removal energy efficiency optimization system is urgently needed to meet the demands of modern industrial production for high-performance, low-energy dust removal systems. Summary of the Invention

[0007] The purpose of the present invention is to provide a factory dust removal energy efficiency optimization system based on digital twins to solve the problems raised in the above background technology.

[0008] To achieve the above objectives, the present invention provides the following technical solution: a factory dust removal energy efficiency optimization system based on digital twins, the system comprising: a multi-source data perception module for acquiring multi-dimensional dust removal data of the target dust removal equipment in operation, the multi-dimensional dust removal data including a first operating sequence corresponding to air volume distribution data, a second operating sequence corresponding to particulate matter concentration data, and a third operating sequence corresponding to energy consumption characteristic data; the air volume distribution data including a first control parameter generated by a multi-zone wind pressure regulating device and a second flow characteristic collected by an array sensor; An energy efficiency analysis and decision module is used to perform dynamic energy efficiency mapping processing on the multi-dimensional dust removal data and input it into the optimization strategy generation layer for feature analysis, and generate an energy efficiency optimization strategy for the target dust removal equipment according to the output results of the optimization strategy generation layer; The optimization strategy generation layer includes a data cleaning module and a strategy construction module, wherein the data cleaning module is used to align data and eliminate anomalies in the original operation data stream, and the strategy construction module is obtained by collaborative modeling based on historical control parameters and historical energy consumption data of multiple historical operation cycles; the strategy construction module includes a state perception layer, an energy efficiency optimization layer and a strategy execution layer connected in sequence.

[0009] Preferably, the state perception layer is used to perform spatiotemporal matching processing on multiple operation sequences contained in the original operation data stream to obtain operating condition-related feature data; the energy efficiency optimization layer is used to model the dynamic constraint relationship between the operating condition-related feature data corresponding to each operation sequence to obtain optimization strategy feature data; the strategy execution layer is used to make multi-level decisions based on the optimization strategy feature data and the operating condition-related feature data to generate an energy efficiency optimization strategy.

[0010] Preferably, the dynamic constraint relationship between the operating condition-related characteristic data corresponding to each operating sequence is modeled to obtain the optimization strategy characteristic data, including: A dynamic energy efficiency mapping algorithm is used to identify key energy consumption nodes in the operating condition association characteristic data, and an energy consumption association sequence corresponding to each operating sequence is determined based on the operating mode corresponding to each key energy consumption node; The energy efficiency deviation between energy consumption nodes with the same operating mode in energy consumption association sequences corresponding to any two operating sequences is calculated, and the optimization strategy characteristic data between the any two operating sequences is determined based on the energy efficiency deviation.

[0011] Preferably, the calculating of the energy efficiency deviation between energy consumption nodes having the same operating mode in the energy consumption association sequences corresponding to any two operating sequences includes: When there is a difference in the number of energy consumption nodes in the energy consumption association sequence corresponding to any two operating sequences, virtual node compensation is performed based on the operating mode corresponding to the terminal energy consumption node among the ones with fewer energy consumption nodes, and the energy efficiency deviation between the energy consumption nodes with the same operating mode is calculated based on the compensated data.

[0012] Preferably, the data cleaning module is specifically used to: Performing equal-dimensional division on the feature information included in the first operating sequence, the second operating sequence, and the third operating sequence according to a preset alignment rule to obtain a standardized first operating sequence, a standardized second operating sequence, and a standardized third operating sequence; The standardized first operating sequence and the standardized second operating sequence are calibrated in real time using a dynamic air volume compensation method, and the standardized third operating sequence is statically corrected using a fixed air volume filtering method to generate a first processing sequence, a second processing sequence, and a third processing sequence; wherein the first processing sequence includes a calibrated first control parameter and a calibrated second flow characteristic.

[0013] Preferably, the data cleaning module is further used to: Calculating an energy efficiency correlation between the calibrated first control parameter and the calibrated second flow characteristic within a historical operation cycle; Predicting a predicted energy consumption value of the calibrated second flow characteristic in the real-time operation cycle according to the energy efficiency correlation and the operating condition characteristics of the calibrated first control parameter in the real-time operation cycle; Target operating state data is generated according to the calibrated second flow characteristic and the predicted energy consumption value thereof, and an operating sequence corresponding to the target operating state data is used as a first processing sequence.

[0014] Preferably, the energy efficiency optimization layer specifically includes: a constraint analysis unit, configured to perform energy efficiency path analysis on each operation sequence included in the operating condition-related characteristic data, so as to extract a corresponding energy consumption conduction chain from each operation sequence; The strategy matching unit is used to dynamically associate the energy consumption conduction chain extracted from each operation sequence with the corresponding operating condition characteristic data to generate optimization strategy characteristic data.

[0015] Preferably, the energy efficiency optimization layer further comprises: The redundancy elimination unit is used to eliminate invalid decision components from the optimization strategy feature data.

[0016] Preferably, the policy execution layer specifically includes: A multi-level decision-making unit, comprising a plurality of strategy execution nodes, each strategy execution node being connected to each operation sequence in the optimization strategy characteristic data and the working condition correlation characteristic data through an association configuration; A dynamic adjustment unit, configured to adjust the associated configuration by a dynamic adjustment algorithm to minimize the deviation between the energy efficiency optimization strategy and actual operating data; The abnormal response unit is used to predict the equipment state degradation based on the optimization strategy characteristic data and the working condition related characteristic data, and generate an energy efficiency optimization strategy.

[0017] Preferably, the dynamic air volume compensation method specifically includes: Generate adaptive adjustment strategies based on the airflow disturbance characteristics in the real-time operating environment; The standardized first running sequence is calibrated section by section using a rolling optimization mechanism.

[0018] Compared with the prior art, the present invention has the following beneficial effects: From the perspective of data acquisition, the multi-source data perception module can fully obtain multi-dimensional dust removal data during the operation of the target dust removal equipment. It covers multiple key dimensions such as air volume distribution data, particulate matter concentration data, and energy consumption characteristic data, and the air volume distribution data is accurate to the first control parameter generated by the multi-zone wind pressure regulating device and the second flow characteristic collected by the array sensor. This enables the system to have a comprehensive and detailed understanding of the operating status of the dust removal equipment, providing a solid data foundation for subsequent precise control. Compared with the traditional method of obtaining a single or small amount of data, it can more accurately grasp the operation of the equipment under complex working conditions and promptly discover potential problems.

[0019] In terms of data processing and analysis, the energy efficiency analysis and decision-making module works in tandem with the optimization strategy generation layer, demonstrating exceptional performance. The data cleaning module aligns and removes anomalies from the raw operational data stream. It then uses preset alignment rules to divide multi-dimensional data into equal dimensions. It then employs methods such as dynamic air volume compensation and fixed air volume filtering for calibration and correction, ensuring data accuracy and consistency. This effectively avoids analysis errors caused by data deviations or anomalies, improving system reliability.

[0020] The strategy construction module is collaboratively modeled based on historical control parameters and historical energy consumption data from multiple historical operating cycles. The state perception layer, energy efficiency optimization layer, and strategy execution layer it contains have clear division of labor and work closely together. The state perception layer performs spatiotemporal matching processing on the operating sequence to obtain working condition-related characteristic data, providing more targeted information for subsequent analysis; the energy efficiency optimization layer models the dynamic constraint relationship between working condition-related characteristic data, and uses technologies such as dynamic energy efficiency mapping algorithms to accurately identify key energy consumption nodes and calculate energy efficiency deviations. It can also generate more scientific and reasonable optimization strategy characteristic data through operations such as constraint analysis, strategy matching, and redundancy elimination; the strategy execution layer makes multi-level decisions based on this data. Through the collaborative work of multi-level decision-making units, dynamic adjustment units, and abnormal response units, it can not only generate energy efficiency optimization strategies, but also dynamically adjust related configurations to minimize the deviation between the strategy and actual operating data. At the same time, it predicts equipment state degradation, achieving real-time and precise control of equipment operation.

[0021] From the perspective of improving overall system performance, the system significantly improves the energy efficiency of dust removal equipment. Through precise data analysis and optimization strategy formulation, it is possible to timely adjust equipment parameters according to different production conditions and equipment operating status, so that the dust removal equipment can minimize energy consumption while ensuring the dust removal effect. This not only saves the company a lot of energy costs, but also responds to the national call for energy conservation and emission reduction, and is in line with the concept of sustainable development. In terms of improving dust removal effectiveness, the system can accurately adjust the multi-zone air pressure regulating device based on real-time air volume distribution and particulate matter concentration data to ensure that dust in each area is effectively treated, improve the quality of the production environment, protect the health of employees and the normal operation of production equipment. At the same time, the system's abnormal response mechanism and equipment status degradation prediction function effectively improve the reliability and stability of the equipment, reduce equipment downtime, improve production efficiency, and enhance the company's market competitiveness. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 This is a working principle diagram of the factory dust removal energy efficiency optimization system based on digital twins according to the present invention; Figure 2 Workflow diagram for determining feature data for optimization strategies; Figure 3 Workflow diagram for calculating energy efficiency deviation (handling special cases); Figure 4 Workflow diagram for data processing in the data cleaning module; Figure 5 This is the workflow diagram of the strategy execution layer. DETAILED DESCRIPTION

[0023] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. 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 making creative efforts are within the scope of protection of the present invention.

[0024] See also Figure 1-Figure 5 The present invention provides a factory dust removal energy efficiency optimization system based on digital twins, and the specific implementation steps are as follows: The system includes a multi-source data perception module and an energy efficiency analysis and decision module. The function of the multi-source data perception module is to obtain multi-dimensional dust removal data of the target dust removal equipment in the operating state. Among them, the multi-dimensional dust removal data covers the first operating sequence corresponding to the air volume distribution data, the second operating sequence corresponding to the particulate matter concentration data, and the third operating sequence corresponding to the energy consumption characteristic data. The air volume distribution data also includes the first control parameter generated by the multi-zone wind pressure regulating device and the second flow characteristics collected by the array sensor. The multi-zone wind pressure regulating device will adjust the wind pressure according to the actual conditions of different areas in the factory, such as spatial layout, distribution of dust generation sources, etc., and then generate the first control parameter. The array sensor will be arranged at the key position of the dust removal equipment to collect the second flow characteristics of the airflow in real time. These characteristics can reflect the actual flow of the airflow inside the dust removal equipment, such as the speed and direction changes of the airflow.

[0025] The energy efficiency analysis and decision-making module is responsible for processing multi-dimensional dust removal data. It first performs dynamic energy efficiency mapping on this data, then inputs the processed data into the optimization strategy generation layer for feature analysis. Ultimately, based on the output of the optimization strategy generation layer, it generates an energy efficiency optimization strategy for the target dust removal equipment. The optimization strategy generation layer includes a data cleaning module and a strategy construction module. The data cleaning module is used to align and eliminate anomalies in the raw operating data stream. The strategy construction module is derived through collaborative modeling based on historical control parameters and historical energy consumption data from multiple historical operating cycles. This strategy construction module comprises a state perception layer, an energy efficiency optimization layer, and a strategy execution layer, all connected in sequence.

[0026] The present invention will be further described below in conjunction with Examples 1 to 5: Example 1: The state perception layer performs spatiotemporal matching on the multiple operating sequences contained in the original operating data stream. When performing spatiotemporal matching, factors in both time and space are comprehensively considered. From a temporal perspective, the data change trends of different operating sequences at the same time point or within a similar time period are analyzed; from a spatial perspective, the data characteristics of different operating sequences at different locations in the dust removal equipment are studied. This comprehensive analysis yields operating condition-related characteristic data. For example, correlation analysis is performed on the air volume distribution data, particulate matter concentration data, and energy consumption characteristic data for different regions at a given moment to identify the inherent connections between them and determine the operating condition-related characteristic data at that moment.

[0027] For example, a large machinery manufacturing plant has multiple large-scale dust removal systems installed in its production workshops. During the factory's daily production processes, the dust removal equipment operates continuously, and the multi-source data perception module continuously collects various data, forming multiple operation sequences. For example, during a single day's production period, a series of air volume distribution data, particulate matter concentration data, and energy consumption characteristic data are collected every five minutes. These data constitute the first, second, and third operation sequences, respectively.

[0028] The state perception layer begins to perform spatiotemporal matching processing on multiple operation sequences in these raw operation data streams. From a temporal perspective, the air volume distribution data collected every 5 minutes between 9:00 AM and 9:30 AM shows that the air volume is gradually increasing. This may be due to the increase in the number of production equipment in the workshop during this period, resulting in an increase in dust generation, which in turn requires a larger air volume for effective dust removal. At the same time, the particle concentration data also shows a synchronous upward trend, and the power consumption in the energy consumption characteristic data also increases over time. Through the analysis of this set of time series data, it is found that there is a clear temporal correlation between air volume, particle concentration, and energy consumption. That is, the changing trends of these three parameters are consistent over time.

[0029] From a spatial perspective, the dust removal equipment covers multiple production areas. Different areas have different functions, generating varying amounts and types of dust. For example, in the welding area, due to the large amount of metal dust generated by welding operations, the particle concentration in this area is significantly higher than in other areas. In the machining area, however, impurities such as metal debris and oil are primarily generated, and the particle concentration and composition of these impurities differ from those in the welding area. The state perception layer performs correlation analysis on the air volume distribution data, particle concentration data, and energy consumption characteristics of different areas. At a certain moment, the welding area has a high air volume to meet the need for rapid metal dust removal, but also has a high particle concentration and correspondingly high energy consumption. In the machining area, the air volume is relatively low, with lower particle concentrations and energy consumption. This spatial analysis allows the characteristics of the working conditions in different areas to be determined, as well as the differences between them.

[0030] Combining the analysis results of both time and space, the state perception layer performs time-space matching processing. For example, at 10:00 a.m., combined with the air volume distribution, particulate matter concentration, and energy consumption data of each area, it was found that the overall operating conditions showed high production intensity and high dust generation. At this time, the various data of different areas at that point in time were integrated and correlated to determine the operating condition correlation feature data at that moment. These operating condition correlation feature data comprehensively reflect the operating status of the dust removal equipment at that moment, including the working status of each area and the relationship between different parameters, providing important basic data for subsequent energy efficiency optimization analysis.

[0031] Example 2: After obtaining the operating condition correlation feature data, the energy efficiency optimization layer models the dynamic constraint relationships between the operating condition correlation feature data corresponding to each operating sequence. First, a dynamic energy efficiency mapping algorithm is used to identify key energy consumption nodes within the operating condition correlation feature data. This algorithm determines which nodes have the greatest impact on energy consumption based on factors such as the energy consumption data's changing trends and its correlation with other data. These nodes are designated as key energy consumption nodes. Then, based on the operating mode corresponding to each key energy consumption node, the energy consumption correlation sequence corresponding to each operating sequence is determined. The energy efficiency deviation between energy consumption nodes with the same operating mode in the energy consumption correlation sequence corresponding to any two operating sequences is calculated. When the number of energy consumption nodes in two energy consumption correlation sequences differs, virtual node compensation is performed based on the operating mode corresponding to the terminal energy consumption node in the sequence with the fewer energy consumption nodes. The energy efficiency deviation is then calculated based on this compensated data. Finally, the optimization strategy feature data between any two operating sequences is determined based on the energy efficiency deviation. These steps enable in-depth analysis of the energy consumption relationships between operating sequences, providing a basis for developing optimization strategies.

[0032] Take the dust removal system of a thermal power plant as an example. The coal combustion process in the boilers of this power plant generates a large amount of dust, which needs to be processed by dust removal equipment. The multi-source data perception module continuously collects dust removal equipment operating data, forming multiple operating sequences. For example, during the 8-hour operating period on a particular workday, data is collected every 15 minutes, resulting in a first operating sequence corresponding to air volume distribution data, a second operating sequence corresponding to particulate matter concentration data, and a third operating sequence corresponding to energy consumption characteristic data.

[0033] The energy efficiency optimization layer begins to model the dynamic constraint relationship between the working condition-related characteristic data corresponding to each operating sequence. First, the dynamic energy efficiency mapping algorithm is used to identify the key energy consumption nodes in the working condition-related characteristic data. When analyzing the correlation between the energy consumption characteristic data and other data, it was found that in the filter unit part of the dust removal equipment, when the air volume increases, the resistance of the filter unit will increase, resulting in a significant increase in energy consumption, and this part of the energy consumption has a greater impact on the energy consumption of the overall dust removal system. Therefore, the energy consumption node of the filter unit is determined as the key energy consumption node. Based on the operating mode corresponding to this key energy consumption node, the energy consumption association sequence corresponding to each operating sequence is determined. In this example, the air volume distribution data and the particle concentration data are closely related to the filter unit energy consumption data. Therefore, with the filter unit energy consumption as the core, the related air volume, particle concentration and other data are constructed to form an energy consumption association sequence.

[0034] Next, the energy efficiency deviation between energy consumption nodes with the same operating mode in the energy consumption association sequences corresponding to any two operating sequences is calculated. For example, the energy consumption association sequences corresponding to 10:00 AM and 11:00 AM are selected for analysis. At 10:00 AM, the air volume of the filter unit is X1 cubic meters per minute, the particulate matter concentration is Y1 mg / m3, and the energy consumption is Z1 kWh. At 11:00 AM, the air volume is X2 cubic meters per minute, the particulate matter concentration is Y2 mg / m3, and the energy consumption is Z2 kWh. If there is a difference in the number of energy consumption nodes between the two energy consumption association sequences, assuming that the energy consumption association sequence at 10:00 AM has relatively few energy consumption nodes, virtual node compensation is performed based on the operating mode corresponding to the terminal energy consumption node in the energy consumption association sequence with fewer energy consumption nodes at 10:00 AM. Here, it is assumed that the operating mode at 10:00 AM is such that air volume, particulate matter concentration, and energy consumption are in a stable correspondence. Based on this relationship, virtual nodes are added to the corresponding positions in the energy consumption association sequence at 11:00 AM to make it as structurally similar as possible to the energy consumption association sequence at 10:00 AM. Then, based on the compensated data, the energy efficiency deviation between energy-consuming nodes with the same operating mode is calculated. For example, the energy efficiency deviation between the filter unit energy consumption at 10 o'clock and 11 o'clock is calculated. By comparing the energy consumption difference at the two time points under similar operating modes (i.e., operating conditions that are as similar as possible after virtual node compensation), the energy efficiency difference between the two operating sequences is evaluated.

[0035] Finally, based on the energy efficiency deviation, the optimization strategy characteristic data between any two operating sequences is determined. If the calculated energy efficiency deviation between 10 o'clock and 11 o'clock is large, it means that there are large fluctuations in the operating energy efficiency of the dust removal system between these two time points. After further analysis, it was found that at 11 o'clock, due to the unstable boiler combustion conditions, the amount and concentration of dust entering the dust removal equipment changed greatly, which in turn affected the energy consumption of the filter unit. Based on this, the determined optimization strategy characteristic data may include adjusting the air volume control strategy. When the dust volume changes greatly, a more precise air volume adjustment method is used to maintain the energy consumption of the filter unit stable, thereby achieving energy efficiency optimization of the overall dust removal system.

[0036] Example 3: When the data cleaning module processes the data, it first divides the characteristic information contained in the first operating sequence, the second operating sequence, and the third operating sequence into equal dimensions according to the preset alignment rules, and obtains a standardized first operating sequence, a standardized second operating sequence, and a standardized third operating sequence. The preset alignment rules are formulated according to the characteristics of the data and the needs of subsequent processing, such as division according to time intervals or the physical meaning of the data. Then, the dynamic air volume compensation method is used to perform real-time calibration on the standardized first operating sequence and the standardized second operating sequence, and the fixed air volume filtering method is used to perform static correction on the standardized third operating sequence to generate the first processing sequence, the second processing sequence, and the third processing sequence. Among them, the first processing sequence contains the calibrated first control parameters and the calibrated second flow characteristics. When the dynamic air volume compensation method is used, an adaptive adjustment strategy will be generated according to the airflow disturbance characteristics in the real-time operating environment, and a rolling optimization mechanism will be used to perform segment-by-segment calibration on the standardized first operating sequence. This can ensure the accuracy and reliability of the data and provide better data support for subsequent analysis and decision-making.

[0037] Take a metal smelting plant as an example. During the production process, the plant generates a large amount of dust, which needs to be treated by dust removal equipment.

[0038] During the metal smelting process, the multi-source data perception module continuously collects operating data from the target dust removal equipment, generating a first operating sequence corresponding to air volume distribution data, a second operating sequence corresponding to particle concentration data, and a third operating sequence corresponding to energy consumption characteristic data. For example, during a single day of production, data is collected every 10 minutes, forming each operating sequence.

[0039] The data cleaning module begins by performing an equal-dimensional segmentation of the feature information contained in the first, second, and third run sequences according to the preset alignment rules. Assuming the preset rule uses time as the basis, the data collected every 10 minutes is divided into a data segment. This results in the standardized first, second, and third run sequences. This equal-dimensional segmentation ensures consistency across different data types on a temporal scale, facilitating subsequent processing.

[0040] Next, the standardized first and second operating sequences are calibrated in real time using the dynamic air volume compensation method, and the standardized third operating sequence is statically corrected using the fixed air volume filtering method to generate the first, second, and third processing sequences. When the dynamic air volume compensation method is used, an adaptive adjustment strategy is generated based on the airflow disturbance characteristics in the real-time operating environment. Assuming that the airflow disturbance in the real-time operating environment will cause the air volume to fluctuate, let the actual air volume at the current moment be , the ideal stable air volume is , then the wind volume fluctuation Here It is the wind volume data obtained by real-time measurement of the sensor. It is a stable air volume value set according to the design parameters and normal operating conditions of the dust removal equipment.

[0041] According to the wind volume fluctuation , the standardized first operation sequence is calibrated section by section using a rolling optimization mechanism. For example, the standardized first operation sequence is divided into multiple small segments according to the time sequence, and each small segment represents a short time interval. For each small segment, according to the current wind volume fluctuation , adjust the air volume data in this section. If it is positive, it means that the actual air volume is greater than the ideal stable air volume, and the air volume setting value in this small section should be appropriately reduced; if By means of this step-by-step calibration process, the air volume data in the first operation sequence is made more in line with actual needs, thereby improving the accuracy of the data.

[0042] When calibrating the standardized second operating sequence, the wind volume fluctuation is also referenced. Because particulate matter concentration is closely related to air volume, adjusting air volume also requires corresponding corrections to the particulate matter concentration data. For example, when air volume increases, the particulate matter concentration data collected during the same period may be lower due to the dilution effect of the airflow. Therefore, the particulate matter concentration data needs to be adjusted appropriately based on the change in air volume.

[0043] For the third standardized operation sequence, a fixed air volume filtering method is used for static correction. Assume that the principle of the fixed air volume filtering method is to set a filter coefficient , filtered energy consumption data ,in is the raw energy consumption data in the standardized third operation sequence, It is the energy consumption data after filtering and correction. Filter coefficient It is a constant predetermined based on historical data and equipment operating characteristics. It is used to remove noise and abnormal fluctuations in the original energy consumption data, making the energy consumption data smoother and more stable, and better reflecting the actual energy consumption of the equipment.

[0044] After the above processing, the first processing sequence, which includes the calibrated first control parameters and the calibrated second flow characteristics, the calibrated second processing sequence, and the corrected third processing sequence are finally generated. This processed data provides a more reliable foundation for subsequent energy efficiency analysis and optimization strategy formulation.

[0045] Example 4: The data cleaning module will also perform a series of calculations and predictions. Calculate the energy efficiency correlation between the calibrated first control parameter and the calibrated second flow characteristic in the historical operating cycle, and find out the degree of correlation between the two parameters by analyzing historical data. Predict the predicted energy consumption value of the calibrated second flow characteristic in the real-time operating cycle based on the energy efficiency correlation and the operating condition characteristics of the calibrated first control parameter in the real-time operating cycle. Then, generate the target operating status data based on the calibrated second flow characteristic and its predicted energy consumption value, and use the operating sequence corresponding to the target operating status data as the first processing sequence. Through these steps, the operating status and energy consumption of the equipment can be understood more accurately, providing more accurate data for the formulation of optimization strategies.

[0046] Taking a building materials production factory as an example, the factory's dust removal equipment is used to handle the large amount of dust generated during the production process to ensure the normal operation of the production environment and equipment.

[0047] During the building materials production process, the multi-source data perception module continuously collects operational data from the target dust removal equipment, generating a first operational sequence corresponding to air volume distribution data, a second operational sequence corresponding to particle concentration data, and a third operational sequence corresponding to energy consumption characteristic data. After preliminary processing by the data cleaning module, calibrated first control parameters and calibrated second flow characteristics are obtained.

[0048] The data cleaning module begins calculating the energy efficiency correlation between the calibrated first control parameter and the calibrated second flow characteristic over the historical operating cycle. Assume that production has been conducted daily for the past week according to a fixed production process and equipment operating hours. The daily operating data is divided into multiple hourly operating cycles, recording the calibrated first control parameter (e.g., the air pressure adjustment value for each area) and the calibrated second flow characteristic (e.g., airflow velocity and direction data for each monitoring point) for each hour. Through in-depth analysis of this historical data, we study how changes in air pressure adjustment values affect airflow velocity and direction, and the relationship between this effect and energy consumption. For example, when the air pressure adjustment value in a certain area increases, the airflow velocity at nearby monitoring points generally increases. Statistical findings show that, in most cases, increased airflow velocity improves dust removal efficiency in that area, but also increases energy consumption. By quantifying this relationship, we calculate the energy efficiency correlation between the two, measuring their mutual impact on energy consumption.

[0049] The predicted energy consumption value of the calibrated second flow characteristic during the real-time operating cycle is predicted based on the energy efficiency correlation and the operating condition characteristics of the calibrated first control parameter during the real-time operating cycle. Assume that the current real-time operating cycle is the third hour of a new production day. During this hour, the calibrated first control parameter indicates that a temporary increase in output on a certain production line has caused the wind pressure adjustment value in the corresponding area to increase by a certain percentage compared to normal. Combined with the previously calculated energy efficiency correlation, the energy consumption value corresponding to the calibrated second flow characteristic (i.e., airflow state) under the current operating conditions is predicted, taking into account the impact of increased wind pressure on airflow speed and direction, and the correlation between this impact and energy consumption. The prediction process comprehensively considers multiple factors, including the real-time operating status of the production equipment and environmental factors (such as the impact of temperature and humidity on airflow), to estimate energy consumption as accurately as possible.

[0050] The target operating status data is generated based on the calibrated second flow characteristics and their predicted energy consumption values, and the operating sequence corresponding to the target operating status data is used as the first processing sequence. For example, the current calibrated second flow characteristics show that after the wind pressure is adjusted, the airflow velocity in some areas exceeds the normal range. Combined with the predicted energy consumption value, it is found that the energy consumption is also higher than the expected level. These actual airflow status data and predicted energy consumption data are integrated to form the target operating status data. This target operating status data reflects the actual operating conditions of the current dust removal equipment and possible energy consumption problems. The corresponding operating sequence is used as the first processing sequence to provide an accurate and reliable data basis for subsequent energy efficiency optimization analysis and strategy formulation, so as to further analyze how to adjust the equipment operating parameters, reduce energy consumption and improve dust removal efficiency.

[0051] Embodiment 5: The policy execution layer consists of multiple components. The multi-level decision-making unit contains multiple policy execution nodes, each of which is connected to each operating sequence in the optimization policy feature data and the operating condition-related feature data through an associated configuration. The dynamic adjustment unit is used to adjust the associated configuration through a dynamic adjustment algorithm to minimize the deviation between the energy efficiency optimization strategy and the actual operating data. The abnormal response unit is used to predict the degradation of the equipment state based on the optimization policy feature data and the operating condition-related feature data and generate an energy efficiency optimization strategy. During actual operation, the various policy execution nodes of the multi-level decision-making unit will make decisions based on the data received, and the dynamic adjustment unit will continuously adjust the associated configuration to make the system operation more consistent with the optimization strategy. The abnormal response unit will monitor the equipment status in real time. Once it finds signs of equipment status degradation, it will promptly generate a corresponding energy efficiency optimization strategy to ensure stable operation and efficient energy saving of the equipment.

[0052] Take the dust removal system in the paint shop of an automotive parts manufacturing plant as an example. The paint shop generates a large amount of paint mist and volatile organic compounds during the painting process. Efficient dust removal equipment is needed to purify the air and protect the workshop environment and employee health.

[0053] In the dust removal system of this workshop, the strategy execution layer plays a key role. The multi-level decision-making unit contains multiple strategy execution nodes, each of which is connected to each operation sequence in the optimization strategy feature data and the working condition association feature data through association configuration. For example, in the paint shop, different paint spraying stations produce different amounts and concentrations of paint mist, corresponding to different working conditions. Strategy execution node 1 is connected to the dust removal equipment branch responsible for collecting dust near paint spraying station A. The air volume, paint mist concentration, and energy consumption data of this branch constitute the corresponding operation sequence; strategy execution node 2 is associated with the dust removal branch corresponding to paint spraying station B. These strategy execution nodes will receive their associated operation sequence data in real time, as well as the optimization strategy feature data generated by the energy efficiency analysis decision module.

[0054] The dynamic adjustment unit is used to adjust associated configurations through a dynamic adjustment algorithm to minimize the deviation between the energy efficiency optimization strategy and actual operating data. Assume that at a certain moment, according to the optimization strategy, the dust removal equipment branch corresponding to paint station A should maintain a specific air volume to ensure dust removal effectiveness while minimizing energy consumption. However, actual operating data shows that due to a temporary adjustment to the air pressure of the spray gun at paint station A, the amount of paint mist generated suddenly increased. The current air volume was unable to fully collect the paint mist, affecting the dust removal effect and causing fluctuations in energy consumption. At this time, the dynamic adjustment unit is activated and, through a preset dynamic adjustment algorithm, automatically adjusts the associated configuration of strategy execution node 1 and the relevant operating sequence based on the actual paint mist concentration and air volume data, increasing the air volume of the dust removal branch at paint station A. During the adjustment process, the dynamic adjustment unit continuously monitors actual operating data and continuously optimizes the adjustment parameters to ensure that the air volume adjustment not only meets the dust removal requirements but also stays as close as possible to the energy consumption standard set by the energy efficiency optimization strategy, making the system operation more stable and efficient.

[0055] The abnormal response unit is used to predict equipment degradation based on optimization strategy characteristic data and operating condition-related characteristic data, and generate energy efficiency optimization strategies. For example, after a period of data monitoring and analysis, the abnormal response unit discovered that the energy consumption of the dust removal equipment branch corresponding to paint station B continued to rise, even when the amount of paint mist generated was stable. At the same time, the resistance of the filter equipment in this branch gradually increased, indicating that the filter equipment may be clogged, a sign of equipment degradation. Based on this data, combined with historical experience and optimization strategy characteristic data, the abnormal response unit predicted that if not addressed promptly, not only would the dust removal effect be reduced, but energy consumption would also increase further. Therefore, the abnormal response unit generates a new energy efficiency optimization strategy, such as immediately activating a backup filter equipment and arranging maintenance personnel to clean or replace the clogged filter equipment. While ensuring dust removal effectiveness, this reduces system energy consumption, maintains normal equipment operation, and ensures that the production environment in the paint shop always meets standards.

[0056] It should be noted that, in this document, relational terms such as first and second, etc., are used only 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 "comprises," "includes," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0057] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A factory dust removal energy efficiency optimization system based on digital twin, characterized by: include: a multi-source data perception module for acquiring multi-dimensional dust removal data of the target dust removal equipment in operation, the multi-dimensional dust removal data including a first operating sequence corresponding to air volume distribution data, a second operating sequence corresponding to particulate matter concentration data, and a third operating sequence corresponding to energy consumption characteristic data; the air volume distribution data including a first control parameter generated by a multi-zone wind pressure regulating device and a second flow characteristic collected by an array sensor; An energy efficiency analysis and decision module is used to perform dynamic energy efficiency mapping processing on the multi-dimensional dust removal data and input it into the optimization strategy generation layer for feature analysis, and generate an energy efficiency optimization strategy for the target dust removal equipment according to the output results of the optimization strategy generation layer; The optimization strategy generation layer includes a data cleaning module and a strategy construction module, wherein the data cleaning module is used to align data and eliminate anomalies in the original operation data stream, and the strategy construction module is obtained by collaborative modeling based on historical control parameters and historical energy consumption data of multiple historical operation cycles; the strategy construction module includes a state perception layer, an energy efficiency optimization layer and a strategy execution layer connected in sequence.

2. The factory dust removal energy efficiency optimization system based on digital twin according to claim 1 is characterized in that: The state perception layer is used to perform spatiotemporal matching processing on multiple operation sequences contained in the original operation data stream to obtain operating condition-related feature data; the energy efficiency optimization layer is used to model the dynamic constraint relationship between the operating condition-related feature data corresponding to each operation sequence to obtain optimization strategy feature data; the strategy execution layer is used to make multi-level decisions based on the optimization strategy feature data and the operating condition-related feature data to generate an energy efficiency optimization strategy.

3. The factory dust removal energy efficiency optimization system based on digital twin according to claim 2 is characterized in that: The dynamic constraint relationship between the operating condition-related characteristic data corresponding to each operating sequence is modeled to obtain the optimization strategy characteristic data, including: A dynamic energy efficiency mapping algorithm is used to identify key energy consumption nodes in the operating condition association characteristic data, and an energy consumption association sequence corresponding to each operating sequence is determined based on the operating mode corresponding to each key energy consumption node; The energy efficiency deviation between energy consumption nodes with the same operating mode in energy consumption association sequences corresponding to any two operating sequences is calculated, and the optimization strategy characteristic data between the any two operating sequences is determined based on the energy efficiency deviation.

4. The factory dust removal energy efficiency optimization system based on digital twin according to claim 3 is characterized in that: The calculating of the energy efficiency deviation between energy consumption nodes having the same operating mode in the energy consumption association sequences corresponding to any two operating sequences includes: When there is a difference in the number of energy consumption nodes in the energy consumption association sequence corresponding to any two operating sequences, virtual node compensation is performed based on the operating mode corresponding to the terminal energy consumption node among the ones with fewer energy consumption nodes, and the energy efficiency deviation between the energy consumption nodes with the same operating mode is calculated based on the compensated data.

5. The factory dust removal energy efficiency optimization system based on digital twin according to claim 1 is characterized in that: The data cleaning module is specifically used for: Performing equal-dimensional division on the feature information included in the first operating sequence, the second operating sequence, and the third operating sequence according to a preset alignment rule to obtain a standardized first operating sequence, a standardized second operating sequence, and a standardized third operating sequence; The standardized first operating sequence and the standardized second operating sequence are calibrated in real time using a dynamic air volume compensation method, and the standardized third operating sequence is statically corrected using a fixed air volume filtering method to generate a first processing sequence, a second processing sequence, and a third processing sequence; wherein the first processing sequence includes a calibrated first control parameter and a calibrated second flow characteristic.

6. The factory dust removal energy efficiency optimization system based on digital twin according to claim 5 is characterized in that: The data cleaning module is also used for: Calculating an energy efficiency correlation between the calibrated first control parameter and the calibrated second flow characteristic within a historical operation cycle; Predicting a predicted energy consumption value of the calibrated second flow characteristic in the real-time operation cycle according to the energy efficiency correlation and the operating condition characteristics of the calibrated first control parameter in the real-time operation cycle; Target operating state data is generated according to the calibrated second flow characteristic and the predicted energy consumption value thereof, and an operating sequence corresponding to the target operating state data is used as a first processing sequence.

7. The factory dust removal energy efficiency optimization system based on digital twin according to claim 2, characterized in that: The energy efficiency optimization layer specifically includes: a constraint analysis unit, configured to perform energy efficiency path analysis on each operation sequence included in the operating condition-related characteristic data, so as to extract a corresponding energy consumption conduction chain from each operation sequence; The strategy matching unit is used to dynamically associate the energy consumption conduction chain extracted from each operation sequence with the corresponding operating condition characteristic data to generate optimization strategy characteristic data.

8. The factory dust removal energy efficiency optimization system based on digital twin according to claim 7 is characterized in that: The energy efficiency optimization layer also includes: The redundancy elimination unit is used to eliminate invalid decision components from the optimization strategy feature data.

9. The factory dust removal energy efficiency optimization system based on digital twin according to claim 2, characterized in that: The strategy execution layer specifically includes: A multi-level decision-making unit, comprising a plurality of strategy execution nodes, each strategy execution node being connected to each operation sequence in the optimization strategy characteristic data and the working condition correlation characteristic data through an association configuration; A dynamic adjustment unit, configured to adjust the associated configuration by a dynamic adjustment algorithm to minimize the deviation between the energy efficiency optimization strategy and actual operating data; The abnormal response unit is used to predict the equipment state degradation based on the optimization strategy characteristic data and the working condition related characteristic data, and generate an energy efficiency optimization strategy.

10. The factory dust removal energy efficiency optimization system based on digital twin according to claim 5, characterized in that: The dynamic air volume compensation method specifically includes: Generate adaptive adjustment strategies based on the airflow disturbance characteristics in the real-time operating environment; The standardized first running sequence is calibrated section by section using a rolling optimization mechanism.

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