Multi-equipment cooperative dust removal control system for environment-friendly production line

Abstract

The invention relates to the technical field of environment-friendly production equipment control, and discloses a multi-equipment cooperative dust removal control system for an environment-friendly production line. The system comprises a dust data acquisition module for acquiring and dividing operation data flow of production line equipment; the dust state reconstruction module is used for generating a dust and airflow distribution situation map; the dedusting efficiency evaluation module is associated with the situation map to determine a dedusting level; the regulation and control strategy generation module is used for matching control strategies; and the cooperative control unit performs bidirectional verification to generate a cooperative dust removal instruction. The system is also provided with an industrial communication gateway for realizing protocol interaction, an equipment linkage module for optimizing a dust removal process, an instruction issuing module for controlling equipment operation, and a data archiving module for storing data and generating a record chain. The system can comprehensively collect dust data, accurately evaluate the dust removal efficiency, efficiently cooperate with multi-equipment dust removal, improve the dust removal effect and management level of an environment-friendly production line and reduce energy consumption, and has a good application prospect.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental protection production equipment control, and in particular to a multi-equipment collaborative dust removal control system for an environmental protection production line. Background Art

[0002] In the modern environmentally friendly production sector, with the continuous expansion of industrial production and increasingly stringent environmental standards, controlling dust pollution during the production process has become a key link. Traditional dust removal methods have exposed many difficult-to-overcome shortcomings when faced with complex production line environments.

[0003] In the early days, many production lines relied on single dust removal equipment. This approach only addressed dust in a localized area and failed to fully account for dust generation, diffusion, and distribution throughout the entire production process. Large factories, such as steel mills and cement plants, feature numerous production equipment and complex processes, resulting in significant variations in the properties and concentrations of dust generated by each piece of equipment. This single dust removal system not only suffers from low dust removal efficiency but also struggles to meet the varying requirements for dust removal across different production processes. This leads to high levels of dust emissions, causing serious environmental pollution and posing a threat to worker health.

[0004] With technological advancements, some production lines have begun to utilize multiple independent dust removal devices. However, these devices lack effective coordination mechanisms, operating independently. In actual operation, some equipment often over-operates, while dust in other areas remains untreated. This not only wastes energy but also reduces overall dust removal effectiveness. For example, on chemical production lines, dust concentrations and airflow conditions generated during different reaction stages fluctuate frequently. Because the various dust removal devices fail to coordinate and adjust dust removal strategies based on actual conditions, dust removal effectiveness is significantly reduced.

[0005] Furthermore, existing dust removal control systems also have shortcomings in data collection and analysis. Most systems can only collect simple dust concentration data, failing to fully capture dust particle size distribution, diffusion rate, sedimentation, humidity, temperature, and other parameters that critically influence dust characteristics. Furthermore, the collected data lacks effective analysis and processing methods, failing to accurately reflect the actual distribution and changing patterns of dust. This results in a lack of scientific basis for controlling dust removal equipment, making precise dust removal control difficult.

[0006] At the same time, when multiple devices are working together, problems arise, such as poor communication between devices and delayed command issuance. Because different devices may come from different manufacturers and utilize different communication protocols and control standards, compatibility between devices is poor, making efficient collaborative operation difficult. These issues severely restrict the production efficiency and environmental performance of environmentally friendly production lines, necessitating a new multi-device collaborative dust removal control system to address them. Summary of the Invention

[0007] The purpose of the present invention is to provide an environmentally friendly production line multi-equipment collaborative dust removal control system 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 multi-device coordinated dust removal control system for an environmentally friendly production line, the system comprising: A dust data acquisition module is used to obtain real-time operation data streams of production line equipment and divide the operation data streams into concentration parameter data streams and airflow parameter data streams based on dust distribution characteristics; a dust state reconstruction module, configured to perform dynamic trajectory tracking on the concentration parameter data stream to generate a first distribution situation diagram, and perform path analysis on the airflow parameter data stream to generate a second distribution situation diagram; a dust removal efficiency evaluation module, configured to spatially superimpose the first distribution situation diagram and the second distribution situation diagram according to a preset collaborative control matrix, and associate the result with a corresponding dust removal level, and use the dust removal level as an operating indicator of the current equipment; A control strategy generation module is used to match the target control strategy in the preset strategy library based on the dust removal level, and use the target control strategy as the execution parameter of the current device; The collaborative control unit is used to perform a bidirectional verification on the operating index and the execution parameter to generate a collaborative dust removal instruction.

[0009] Preferably, the dust state reconstruction module performs path parsing on the airflow parameter data stream, including: Dividing the dynamic change sequence in the airflow parameter data stream into a stable airflow group and a turbulent disturbance group, and performing motion trajectory calculation on the turbulent disturbance group based on a preset vortex analytical model to generate a path feature set; Performing segmentation processing on the pressure gradient data in the airflow parameter data stream for the dust removal area, extracting the airflow density characteristics of each area and constructing a partition map; The path feature set is matched with the partition map in three-dimensional space to generate the second distribution situation map.

[0010] Preferably, the dust distribution characteristics include basic monitoring dimensions and auxiliary monitoring dimensions; the basic monitoring dimensions include particle size distribution identifiers, diffusion rate identifiers and sedimentation amount identifiers; the auxiliary monitoring dimensions include humidity association identifiers and temperature fluctuation identifiers, and each identifier corresponds to an independent data parsing thread.

[0011] Preferably, the system further comprises an industrial communication gateway, which is used to implement protocol interaction between the dust data acquisition module, the dust state reconstruction module, the dust removal efficiency evaluation module and the control strategy generation module and the device Internet of Things network respectively; The dust data collection module divides the operation data flow based on the dust distribution characteristics, including: Receiving a composite signal packet from the device Internet of Things network through the industrial communication gateway, and matching a protocol tag of the composite signal packet according to an identifier in the basic monitoring dimension to separate a basic signal segment; traversing the extended tag of the composite signal packet according to the identifier in the auxiliary monitoring dimension to extract the auxiliary signal segment; The basic signal segment and the auxiliary signal segment are time-aligned according to the acquisition frequency and then written into the concentration parameter storage area and the airflow parameter buffer area respectively.

[0012] Preferably, when the preset collaborative control matrix adopts a fixed coupling model, the dust removal level is an interval mapping result of the superimposed weights of the first distribution situation diagram and the second distribution situation diagram; When the preset collaborative control matrix adopts a dynamic coupling model, the dust removal level is a collaborative state set obtained by real-time correction of the correlation features of the first distribution situation diagram and the second distribution situation diagram through an iterative algorithm.

[0013] Preferably, the system further comprises an equipment linkage module connected to the collaborative control unit, and the equipment linkage module is connected to the dust removal equipment database through the industrial communication gateway; The equipment linkage module is used to screen the adaptation equipment queue from the dust removal equipment database according to the equipment collaboration requirements in the collaborative dust removal instruction, and generate an execution priority sequence to optimize the dust removal operation process.

[0014] Preferably, the device linkage module generates an execution priority sequence including: Loading a three-dimensional coordinate model of the production line, and locating the spatial position node of each device in the adaptation device queue in the coordinate model; Calculate the optimal linkage path from the current position of each device to the target dust removal area based on the energy consumption optimization algorithm, and divide the adaptive device queue into collaborative levels according to the dust removal efficiency; The optimal linkage path and the collaborative level division are integrated into the coordinate model to generate a visual execution sequence.

[0015] Preferably, when the collaborative control unit performs bidirectional verification on the operating indicators and execution parameters, a two-layer verification mode of data anomaly interception mechanism and parameter compatibility verification mechanism is adopted. The data anomaly interception mechanism is used to verify the timeliness of data collection, and the parameter compatibility verification mechanism is used to eliminate control conflicts between devices.

[0016] Preferably, the system also includes an instruction issuing module connected to the collaborative control unit, and the instruction issuing module is used to convert the collaborative dust removal instruction into a device control instruction set, and send the device control instruction set to the target dust removal equipment through the industrial communication gateway to start collaborative operation.

[0017] Preferably, the system also includes a data archiving module connected to the collaborative control unit, and the data archiving module is used to store the real-time operation data stream, the first distribution situation diagram, the second distribution situation diagram, the dust removal level and the collaborative dust removal instructions, and generate a complete dust removal operation record chain according to the operation cycle.

[0018] Compared with the prior art, the present invention has the following beneficial effects: In terms of dust data collection and analysis, the system's dust data collection module can obtain the real-time operation data stream of the production line equipment, and based on the unique dust distribution characteristics, it can be finely divided into concentration parameter data stream and airflow parameter data stream. Among them, the dust distribution characteristics cover basic monitoring dimensions (particle size distribution identification, diffusion rate identification and sedimentation amount identification) and auxiliary monitoring dimensions (humidity correlation identification and temperature fluctuation identification), and each identification corresponds to an independent data analysis thread. This comprehensive data collection and classification method can more accurately grasp the actual situation of dust in the production environment compared to the traditional method of only collecting single dust concentration data. Through the analysis of these rich data, the system can more accurately understand the source of dust generation, diffusion path and possible sedimentation area, providing a solid data foundation for subsequent dust removal work; The dust state reconstruction module further performs dynamic trajectory tracking on the concentration parameter data stream to generate a first distribution situation diagram, and performs path analysis on the airflow parameter data stream to generate a second distribution situation diagram. This process can intuitively and vividly display the distribution status of dust and airflow, allowing operators and managers to clearly see the real-time dynamics of dust in the production space, as if they have a pair of "X-ray eyes", providing a visual basis for the formulation of dust removal strategies, greatly improving the scientific nature and accuracy of decision-making. In terms of dust removal efficiency evaluation and regulation, the dust removal efficiency evaluation module spatially superimposes the two distribution situation maps and associates them with the corresponding dust removal level based on the preset collaborative control matrix as the operating indicator of the current equipment. Whether a fixed coupling model or a dynamic coupling model is used, the dust removal effect can be evaluated flexibly and accurately. The regulation strategy generation module matches the target control strategy in the preset strategy library based on the dust removal level as the execution parameter of the current equipment. This method of automatically matching the control strategy according to the actual situation can adapt to the changes in dust and airflow in the production process more quickly and effectively than the traditional fixed control method, significantly improving the pertinence and effectiveness of dust removal, and ensuring that the production environment is always kept in a good state; The collaborative control unit uses a dual-layer verification mode, combining a data anomaly interception mechanism and a parameter compatibility verification mechanism, to perform bidirectional verification of operating indicators and execution parameters to generate collaborative dust removal instructions. The data anomaly interception mechanism ensures the timeliness of data collection, avoiding erroneous decisions caused by outdated data; the parameter compatibility verification mechanism eliminates control conflicts between devices, enabling multiple devices to work together and improving system stability and reliability. The equipment linkage module selects matching equipment queues based on collaborative dust removal instructions and generates an execution priority sequence, optimizing the dust removal process. By loading the production line's three-dimensional coordinate model, locating equipment spatial nodes, and calculating the optimal linkage path based on an energy consumption optimization algorithm, the module divides the coordination levels, and generates a visual execution sequence. This enables efficient equipment linkage, improving dust removal efficiency while reducing energy consumption, achieving both environmental and energy-saving advantages. The command issuance module converts collaborative dust removal instructions into device control instruction sets and sends them to the target dust removal equipment, ensuring accurate command transmission and timely equipment response. The data archiving module stores various data and generates a complete dust removal operation record chain, providing rich historical data for subsequent data analysis, system optimization, and production management, helping to continuously improve the performance of the dust removal system. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a working principle diagram of the multi-equipment coordinated dust removal control system for an environmentally friendly production line according to the present invention; Figure 2 This is a diagram showing the working principle of the dust state reconstruction module in parsing the airflow parameter data flow path; Figure 3 This is a working principle diagram of the dust data acquisition module that divides the operating data flow based on dust distribution characteristics; Figure 4 A diagram showing the working principle of determining dust removal levels based on different preset collaborative control matrices; Figure 5 Generates a working diagram of the execution priority sequence for the device linkage module. DETAILED DESCRIPTION

[0020] 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.

[0021] See also Figure 1-Figure 5 The present invention provides an environmentally friendly production line multi-equipment collaborative dust removal control system, the specific implementation steps are as follows: Dust data acquisition module: This module is used to obtain real-time operating data streams from production line equipment. During actual operation, production line equipment generates operating data streams containing a variety of information. This module classifies these data streams based on dust distribution characteristics, dividing them into concentration parameter data streams and airflow parameter data streams. Dust distribution characteristics cover both basic and auxiliary monitoring dimensions. The basic monitoring dimension includes particle size distribution identifiers, diffusion rate identifiers, and sedimentation identifiers, while the auxiliary monitoring dimension includes humidity association identifiers and temperature fluctuation identifiers. Each identifier corresponds to an independent data parsing thread.

[0022] Dust state reconstruction module: Dynamically track the concentration parameter data stream to generate a first distribution situation diagram. By continuously tracking the changing trajectory of the concentration parameters in time and space, the distribution situation of the dust concentration can be clearly presented. At the same time, the airflow parameter data stream is path parsed to generate a second distribution situation diagram. When performing path parsing on the airflow parameter data stream, the dynamic change sequence is first divided into a stable airflow group and a turbulent disturbance group. The motion trajectory of the turbulent disturbance group is calculated based on the preset vortex analysis model to obtain a path feature set. Next, the pressure gradient data in the airflow parameter data stream is segmented into dust removal areas, the airflow density characteristics of each area are extracted, and a partition map is constructed. Finally, the path feature set and the partition map are matched in three-dimensional space to form a second distribution situation diagram, which intuitively displays the motion path and distribution of the airflow.

[0023] Dust removal efficiency evaluation module: Based on the preset collaborative control matrix, the first distribution situation diagram and the second distribution situation diagram are spatially superimposed. The preset collaborative control matrix has two cases: a fixed coupling model and a dynamic coupling model. When the fixed coupling model is adopted, the dust removal level is the interval mapping result of the superposition weights of the first distribution situation diagram and the second distribution situation diagram; when the dynamic coupling model is adopted, the dust removal level is a collaborative state set in which the correlation features of the first distribution situation diagram and the second distribution situation diagram are corrected in real time through an iterative algorithm. The dust removal level is then used as the operating indicator of the current equipment to evaluate the working efficiency of the current dust removal system.

[0024] Control Strategy Generation Module: Based on the dust removal level, the module matches the corresponding target control strategy in the preset strategy library. The preset strategy library stores a variety of control strategies for different dust removal levels. This module will select the most appropriate strategy and use it as the execution parameter of the current device, providing a control basis for subsequent device operation.

[0025] Collaborative Control Unit: This system performs bidirectional verification of operating indicators and execution parameters, employing a dual-layer verification model consisting of a data anomaly interception mechanism and a parameter compatibility verification mechanism. The data anomaly interception mechanism verifies the timeliness of data collection, ensuring that collected data is up-to-date and valid. The parameter compatibility verification mechanism eliminates control conflicts between devices, ensuring that conflicting control instructions are not issued when the devices work together. After verification, collaborative dust removal instructions are generated, enabling coordinated control of the entire dust removal system.

[0026] The implementation of the present invention is further described in detail below through specific embodiments: Example 1: During operation of a multi-device collaborative dust removal control system on an actual environmental production line, the dust data acquisition module receives composite signal packets from the device IoT network via an industrial communication gateway. Since dust distribution characteristics include both basic and auxiliary monitoring dimensions, the particle size distribution identifier, diffusion rate identifier, and sedimentation amount identifier in the basic monitoring dimension correspond to different protocol tags in the composite signal packet. The dust data acquisition module matches these identifiers with the protocol tags of the composite signal packet to isolate the basic signal segment. Simultaneously, the humidity association identifier and temperature fluctuation identifier in the auxiliary monitoring dimension correspond to the extended tags of the composite signal packet. The dust data acquisition module traverses the extended tags based on these identifiers to extract the auxiliary signal segments. The basic and auxiliary signal segments are then time-aligned according to the acquisition frequency and written to the concentration parameter storage area and airflow parameter buffer, respectively, completing the division and storage of the operational data stream and providing accurate data support for subsequent modules.

[0027] In a hypothetical environmentally friendly metal processing production line, multiple devices operate simultaneously, such as cutting machines, grinding equipment, etc. These devices will generate a large amount of dust during operation. At this time, the dust data collection module function of the environmentally friendly production line multi-device collaborative dust removal control system in this embodiment is used.

[0028] Equipment on metal processing production lines transmit data via the IoT network. The transmitted data is in the form of composite signal packets, which contain a variety of dust-related information.

[0029] The dust data acquisition module receives these composite signal packets by connecting to the device's IoT network via an industrial communication gateway. In terms of basic monitoring dimensions, the particle size distribution identifier appears as a specific code within the protocol tag of the composite signal packet. For example, "0x01" represents particle size distribution information. The diffusion rate identifier and sedimentation amount identifier also have corresponding codes, such as "0x02" for diffusion rate and "0x03" for sedimentation amount. The dust data acquisition module uses these codes to accurately match the protocol tag of the composite signal packet, successfully separating the basic signal segment, which contains raw data related to particle size distribution, diffusion rate, and sedimentation amount.

[0030] In the auxiliary monitoring dimension, the humidity association flag and temperature fluctuation flag correspond to the extended tags of the composite signal packet. Assume that the extended tag name of the humidity association flag is "humidity_info" and the extended tag name of the temperature fluctuation flag is "temperature_info." The dust data collection module traverses the extended tags of the composite signal packet based on these tag names and extracts the auxiliary signal segment, which contains humidity and temperature-related data.

[0031] After collecting the basic signal segment and the auxiliary signal segment, since there may be differences in their collection times, the dust data collection module will time-align them according to the collection frequency. For example, if the collection frequency of the basic signal segment and the auxiliary signal segment is once per second, but due to transmission delays and other reasons, there is a deviation in the timestamps of the two. The data acquisition module will rearrange the data according to the timestamp at a frequency of once per second, and write the aligned basic signal segment into the concentration parameter storage area, because the particle size distribution, diffusion rate, and sedimentation data in the basic signal segment are closely related to the dust concentration. At the same time, the auxiliary signal segment is written into the airflow parameter buffer, because humidity and temperature will affect the airflow state, and these data are important bases for analyzing airflow parameters. In this way, the division and storage of the operating data stream based on the dust distribution characteristics are completed, providing accurate and orderly data support for subsequent dust state reconstruction, dust removal efficiency evaluation and other modules, and ensuring the stable operation of the multi-device collaborative dust removal control system of the entire environmental protection production line.

[0032] Example 2: When performing path analysis on the airflow parameter data stream, the dust state reconstruction module first processes the dynamic change sequence within the data stream. In actual operation, airflow motion is complex and variable, with some sections experiencing relatively stable flow and others experiencing turbulent disturbances. The dust state reconstruction module divides the dynamic change sequence into a stable flow group and a turbulent disturbance group. For the turbulent disturbance group, the module calculates its trajectory using a pre-set vortex analysis model to generate a path feature set. Next, the module processes the pressure gradient data within the airflow parameter data stream. Within the dust removal area, pressure gradients vary at different locations. Based on this pressure gradient data, the module segments the dust removal area and extracts airflow density features for each segment. These airflow density features reflect information such as the density of airflow in different regions, and a partition map is constructed based on these features. Finally, the path feature set and the partition map are matched in three-dimensional space, aligning the path features with the corresponding partition locations and features. This generates a second distribution map, clearly displaying the airflow path and distribution in three-dimensional space. Taking the boiler workshop of a thermal power plant as an example, a large amount of dust-laden airflow is generated during the coal combustion process. These airflows flow in the boiler and related piping systems, and their paths need to be parsed to achieve effective dust removal control. This involves the processing of the airflow parameter data stream by the dust state reconstruction module in this embodiment.

[0033] In the boiler room of a thermal power plant, numerous sensors collect airflow parameter data in real time and aggregate it into a data stream. This data stream contains rich information, such as real-time wind speed, pressure changes, and temperature fluctuations. This data reflects the dynamic changes in airflow throughout the system.

[0034] After the dust state reconstruction module obtains the airflow parameter data stream, it divides the dynamic change sequence therein. The movement of airflow inside the boiler and in the pipeline is relatively complex. The airflow in some areas is relatively stable, while in other areas there are obvious turbulent disturbances. For example, in the pipeline section close to the boiler combustion zone, the airflow will produce strong turbulent disturbances due to the violent reaction of the combustion process; while in some smoother pipeline sections away from the combustion zone, the airflow is relatively stable. The dust state reconstruction module uses specific algorithms and data analysis to accurately divide the dynamic change sequence into a stable airflow group and a turbulent disturbance group.

[0035] For the turbulent disturbance group, the dust state reconstruction module calculates its motion trajectory based on a preset vortex analytical model. The preset vortex analytical model is established based on a large amount of experimental data and theoretical research, and can simulate the formation, development, and movement of vortices in turbulent disturbances. During actual calculations, the model will combine data such as wind speed and pressure gradient in the turbulent disturbance group to calculate parameters such as the center position, rotation direction, and movement speed of the vortex, and then generate a path feature set. This path feature set records in detail the motion path and change trend of the airflow vortex in the turbulent disturbance area.

[0036] At the same time, the dust state reconstruction module will process the pressure gradient data in the airflow parameter data stream. At different locations in the boiler workshop, the pressure gradient of the airflow is different. For example, at the air inlet and outlet of the boiler, the pressure gradient changes more obviously; while in some intermediate pipe sections, the pressure gradient is relatively small. The dust state reconstruction module segments the dust removal area according to the changes in the pressure gradient data. Each area has unique pressure gradient characteristics, and the module will extract the airflow density characteristics of these areas. The airflow density is related to factors such as pressure and temperature. Through calculation and analysis, the airflow density value of each area can be obtained. Based on these airflow density characteristics, a partition map is constructed, which clearly shows the location, range and corresponding airflow density of different areas.

[0037] Finally, the dust state reconstruction module matches the generated path feature set with the partition map in three-dimensional space. In this three-dimensional space, the horizontal, vertical, and vertical coordinates represent different dimensions of spatial position, respectively. The motion path information of the vortex in the path feature set is combined with the position and airflow density characteristics of each region in the partition map to determine the motion of the vortex in different regions. Through this matching, a second distribution situation map is generated. This map presents the motion path and distribution situation of the airflow in three-dimensional space in an intuitive way, providing a key basis for the subsequent dust removal efficiency evaluation and control strategy formulation, helping staff to better understand the airflow situation, thereby optimizing dust removal operations and improving dust removal efficiency.

[0038] Example 3: In the dust removal efficiency evaluation module, the preset collaborative control matrix plays a key role in determining the dust removal level. When the preset collaborative control matrix adopts a fixed coupling model, the first distribution situation diagram and the second distribution situation diagram each have a certain weight. Through a specific calculation method, the information of the two situation diagrams is superimposed to obtain the superposition weight. This superposition weight will be mapped according to the pre-set interval rules. For example, different weight intervals are set to correspond to different dust removal levels, so that the superposition weight is mapped to a specific dust removal level. When a dynamic coupling model is adopted, the correlation features of the first distribution situation diagram and the second distribution situation diagram are corrected in real time through an iterative algorithm. As the dust and airflow states continue to change during the operation of the production line, the correlation features will also change accordingly. The iterative algorithm continuously adjusts these features to form a collaborative state set, which is ultimately determined as the current dust removal level, more accurately reflecting the real-time performance of the dust removal system.

[0039] Taking a large ceramic production plant as an example, a large amount of dust will be generated during the production process of ceramic firing, polishing, etc., and this dust needs to be effectively controlled, which involves the working process of the dust removal efficiency evaluation module in this embodiment.

[0040] This ceramic production factory has multiple production workshops, and the production equipment in each workshop generates varying degrees of dust during operation. Through the dust data acquisition module and the dust state reconstruction module, a first distribution diagram reflecting the distribution of dust concentration and a second distribution diagram reflecting the airflow path and distribution are obtained. These two diagrams visually present the dust and airflow conditions in different areas. For example, in the ceramic polishing workshop, areas with higher dust concentrations are displayed as darker areas in the first distribution diagram, while the second distribution diagram can show the direction of airflow from the polishing equipment to the vents.

[0041] Next, the dust removal efficiency evaluation module processes the two graphs according to the preset collaborative control matrix to determine the dust removal level. When the preset collaborative control matrix adopts a fixed coupling model, assuming that the weight of the first distribution situation graph is , the weight of the second distribution situation map is ( , 、 Indicates the importance of different situation diagrams in the comprehensive evaluation. The value range is between 0 and 1. It is determined by the factory based on long-term production experience and dust removal test data. For example, after multiple tests, it is found that the dust concentration distribution has a greater impact on the dust removal effect. The value is 0.6, The first distribution situation map and the second distribution situation map are spatially superimposed, and the superposition weight is The calculation formula is: ,in Represents the value of the first distribution situation diagram after quantization (this value is obtained by mathematically quantifying the dust concentration distribution characteristics in the first distribution situation diagram, for example, assigning corresponding values according to the dust concentration in different areas, and the higher the concentration, the larger the value). Represents the numerical value of the second distribution situation diagram after quantification (it is also the result of mathematical quantification of the airflow movement path and distribution characteristics in the second distribution situation diagram, for example, higher numerical values are assigned to areas with fast airflow speed and strong dust carrying capacity).

[0042] Get the superposition weight After that, the dust removal level is determined based on the pre-set interval rules. In the range of 0-30, the dust removal level is "good", which means that the current dust removal system can deal with the dust problem well; when In the range of 31-60, the dust removal level is "medium", which means that the dust removal system needs to be adjusted; When it is greater than 60, the dust removal level is "poor", indicating that the dust removal system is under great pressure and immediate measures need to be taken.

[0043] The situation is different when the pre-set collaborative control matrix uses a dynamic coupling model. As the ceramic production process continues, the state of dust and airflow constantly changes. For example, dust generation and airflow characteristics vary during different firing stages. An iterative algorithm continuously adjusts the correlation features between the first and second distribution maps in real time. The iterative algorithm continuously adjusts the weights and calculation methods of the correlation features between the two maps based on newly collected data. For example, in the early stages of ceramic firing, dust is primarily generated by the volatilization of impurities from the raw materials. At this time, the weight of the dust concentration distribution features related to impurities in the first distribution map is higher. However, in the later stages of firing, due to the increase in dust generated by ceramic surface treatment, the weight of the corresponding dust concentration distribution features changes. After multiple iterative calculations, a collaborative state set is formed, which ultimately determines the current dust removal level. This dynamic adjustment method more accurately reflects the real-time performance of the dust removal system in complex and changing production environments, providing a more reliable basis for the formulation of subsequent control strategies.

[0044] Example 4: The system's device linkage module connects to the collaborative control unit and, via an industrial communication gateway, to the dust removal equipment database. When the collaborative control unit generates a collaborative dust removal instruction, the device linkage module executes according to the device collaboration requirements specified in the instruction. The device linkage module selects a queue of compatible devices from the dust removal equipment database, which contains detailed information on numerous dust removal devices, including device type, performance parameters, and applicable scenarios. Based on collaboration requirements, such as dust removal area and dust concentration, the device linkage module selects eligible devices and forms a queue of compatible devices. The device linkage module then loads a three-dimensional coordinate model of the production line and locates the spatial location node of each device in the queue. Based on this model, an energy optimization algorithm is used to calculate the optimal linkage path from each device's current position to the target dust removal area. The queue of compatible devices is then classified into collaborative levels based on dust removal efficiency. Finally, the optimal linkage path and collaborative level classification are integrated into the coordinate model to generate a visual execution sequence, providing clear guidance for optimizing the dust removal process.

[0045] Take the dust removal system of a large cement plant as an example. During the production process, a large amount of dust is generated in multiple links such as raw material crushing, clinker calcination, and cement grinding. Multiple dust removal devices need to work together to ensure the production environment and air quality. When the collaborative control unit issues a collaborative dust removal command, the equipment linkage module begins to work. The specific implementation method is as follows: A cement plant's dust removal equipment database stores information on various types of dust removal equipment, including bag filters and electrostatic precipitators. Detailed information on each device, including model, performance parameters, applicable dust types, and installation location, is recorded. Suppose the coordinated dust removal directive requires high-efficiency dust removal in a specific area of the cement grinding plant because of the high concentration and fine dust particles in that area.

[0046] After receiving the command, the device linkage module begins selecting compatible devices from the dust removal equipment database based on the device coordination requirements. This selection is based on requirements such as dust concentration, particle size, and workshop layout. For example, for high-concentration, fine-particle dust, bag filters are more effective in collecting fine dust. The device linkage module prioritizes bag filters with appropriate filtration accuracy and air handling capacity, selecting those that meet the requirements and forming a matching device queue.

[0047] Next, the equipment linkage module loads a 3D coordinate model of the cement plant's production line. This model accurately depicts the entire production line layout, including the specific locations of each workshop and equipment. Within this coordinate model, the equipment linkage module accurately locates the spatial location nodes of each device in the equipment queue. For example, the selected bag filters are installed on different floors and locations, and their specific spatial orientation can be clearly seen through the 3D coordinate model.

[0048] Based on an energy consumption optimization algorithm, the device linkage module calculates the optimal linkage path from each device's current location to the target dust removal area (i.e., a specific area in the cement grinding workshop). The energy consumption optimization algorithm comprehensively considers factors such as the distance the device must move, the resistance it must overcome, and the energy consumption of the device during operation. For example, a bag dust collector located on a lower floor and close to the target area may have a relatively short path to the target area, resulting in lower energy consumption. For another device located on a farther floor, the algorithm will plan a path that avoids obstacles and reduces unnecessary energy consumption. At the same time, the device linkage module divides the coordination level of the matching device queue according to dust removal efficiency. Devices with high dust removal efficiency are assigned a higher coordination level because, in the coordinated dust removal process, highly efficient devices play a more critical role in ensuring the dust removal effect.

[0049] Finally, the equipment linkage module integrates the calculated optimal linkage path and coordination level division into the three-dimensional coordinate model. Through visualization technology, a visual execution sequence is generated. In this visual interface, operators can clearly see the startup sequence, movement path and coordination level of each device. For example, equipment with a high coordination level will be started first and quickly move to the target area according to the planned optimal path to perform dust removal operations, while equipment with a low coordination level will follow in sequence, and the entire process is clear at a glance. Such a visual execution sequence provides clear guidance for optimizing the dust removal operation process, enabling dust removal equipment to work together more efficiently and orderly, improving the dust removal effect of the entire cement plant and reducing energy consumption and operating costs.

[0050] Example 5: The data archiving module in the system is connected to the collaborative control unit. During the operation of the entire dust removal system, the data archiving module undertakes important data storage and organization tasks. It is responsible for storing real-time operation data streams. These data streams contain various raw data generated during the operation of the production line equipment and are the basis for subsequent analysis and optimization. At the same time, the first distribution situation diagram and the second distribution situation diagram are stored. These two situation diagrams intuitively show the distribution of dust concentration and airflow, which helps to understand the working status of the dust removal system. The data archiving module also stores dust removal levels and collaborative dust removal instructions. These data reflect the work efficiency evaluation results and control instructions of the dust removal system. The data archiving module generates a complete dust removal operation record chain according to the operation cycle, organizes and connects the relevant data in each operation cycle to form a complete record chain, which is convenient for subsequent tracing and analysis of the operation of the dust removal system, and provides strong data support for the optimization and improvement of the system.

[0051] For example, a wood processing plant generates a large amount of sawdust during wood cutting and sanding, requiring a comprehensive dust removal system to maintain a healthy production environment. The data archiving module plays a crucial role in this process, as detailed below: During operation, various sensors continuously collect real-time operational data streams from wood processing plants' production equipment. This data covers information such as the equipment's operating status, dust generation, and airflow velocity. For example, the motor speed of cutting equipment and the degree of wear on cutting tools affect dust generation, while airflow velocity at different locations within ventilation ducts influences dust transmission and dispersion. The data archiving module continuously collects these real-time operational data streams, providing the raw data foundation for subsequent analysis.

[0052] During the production process, the dust state reconstruction module will generate the first distribution situation diagram and the second distribution situation diagram based on the collected data. The first distribution situation diagram shows the concentration distribution of sawdust. For example, in the cutting area, a large amount of dust is generated instantly when the wood is cut, and this area is shown as a high-concentration area in the diagram; in the corners far away from the cutting equipment and with good ventilation, the dust concentration is lower. The second distribution situation diagram shows the movement path and distribution situation of the airflow, such as the direction of the airflow in the ventilation duct, the strength of the airflow in different areas of the workshop, and other information. The data archiving module stores these two situation diagrams so that the distribution of dust and airflow at different times can be viewed at any time, providing an intuitive basis for analyzing the dust removal effect.

[0053] The dust removal efficiency evaluation module determines the dust removal level based on a pre-set collaborative control matrix. This level reflects the current performance of the dust removal system. For example, as orders increase at a wood processing plant, production intensity rises, and dust generation increases, the dust removal level may decrease accordingly, indicating that the dust removal system is facing greater pressure and needs to adjust its operating status. The data archiving module records this dust removal level data. By comparing dust removal levels at different time points, it can clearly understand the dust removal system's performance under different production conditions.

[0054] The collaborative control unit generates collaborative dust removal instructions based on operating indicators and execution parameters. These instructions are used to control the operation of each dust removal device, such as adjusting fan speed and opening or closing specific suction ports. The data archiving module stores these collaborative dust removal instructions, facilitating subsequent tracing of the specific control information for each dust removal operation and understanding the measures taken by the system to ensure dust removal effectiveness in different situations.

[0055] Based on an operating cycle (for example, a single day), the data archiving module organizes the day's real-time operating data stream, primary and secondary distribution diagrams, dust removal levels, and coordinated dust removal instructions to create a complete dust removal operation record chain. Within this record chain, data is arranged chronologically to form a coherent record system. By reviewing this record chain, managers can review dust removal operations throughout the day, analyzing whether the dust removal system is operating normally during different production periods and any anomalies, such as a sudden increase in dust concentration during a certain period without the dust removal equipment responding promptly. This data also provides powerful data support for subsequent equipment maintenance and system optimization. For example, if a specific area frequently experiences excessively high dust concentrations, the dust removal equipment in that area can be inspected, and the suction port position or suction power can be adjusted to continuously improve the performance and efficiency of the entire dust removal system.

[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. An environmental protection production line multi-equipment collaborative dust removal control system, characterized in that: include: A dust data acquisition module is used to obtain real-time operation data streams of production line equipment and divide the operation data streams into concentration parameter data streams and airflow parameter data streams based on dust distribution characteristics; a dust state reconstruction module, configured to perform dynamic trajectory tracking on the concentration parameter data stream to generate a first distribution situation diagram, and perform path analysis on the airflow parameter data stream to generate a second distribution situation diagram; a dust removal efficiency evaluation module, configured to spatially superimpose the first distribution situation diagram and the second distribution situation diagram according to a preset collaborative control matrix, and associate the result with a corresponding dust removal level, and use the dust removal level as an operating indicator of the current equipment; A control strategy generation module is used to match the target control strategy in the preset strategy library based on the dust removal level, and use the target control strategy as the execution parameter of the current device; The collaborative control unit is used to perform a bidirectional verification on the operating index and the execution parameter to generate a collaborative dust removal instruction.

2. The multi-device coordinated dust removal control system for an environmentally friendly production line according to claim 1 is characterized in that: The dust state reconstruction module performs path parsing on the airflow parameter data stream, including: Dividing the dynamic change sequence in the airflow parameter data stream into a stable airflow group and a turbulent disturbance group, and performing motion trajectory calculation on the turbulent disturbance group based on a preset vortex analytical model to generate a path feature set; Performing segmentation processing on the pressure gradient data in the airflow parameter data stream for the dust removal area, extracting the airflow density characteristics of each area and constructing a partition map; The path feature set is matched with the partition map in three-dimensional space to generate the second distribution situation map.

3. The multi-device coordinated dust removal control system for an environmentally friendly production line according to claim 1 is characterized in that: The dust distribution characteristics include basic monitoring dimensions and auxiliary monitoring dimensions; the basic monitoring dimensions include particle size distribution identifiers, diffusion rate identifiers, and sedimentation amount identifiers; the auxiliary monitoring dimensions include humidity association identifiers and temperature fluctuation identifiers, and each identifier corresponds to an independent data parsing thread.

4. The multi-device coordinated dust removal control system for an environmentally friendly production line according to claim 3 is characterized in that: The system further includes an industrial communication gateway, which is used to implement protocol interaction between the dust data acquisition module, the dust state reconstruction module, the dust removal efficiency evaluation module, and the control strategy generation module and the device Internet of Things network respectively; The dust data collection module divides the operation data flow based on the dust distribution characteristics, including: Receiving a composite signal packet from the device Internet of Things network through the industrial communication gateway, and matching a protocol tag of the composite signal packet according to an identifier in the basic monitoring dimension to separate a basic signal segment; traversing the extended tag of the composite signal packet according to the identifier in the auxiliary monitoring dimension to extract the auxiliary signal segment; The basic signal segment and the auxiliary signal segment are time-aligned according to the acquisition frequency and then written into the concentration parameter storage area and the airflow parameter buffer area respectively.

5. The multi-equipment coordinated dust removal control system for an environmentally friendly production line according to claim 1 is characterized in that: When the preset collaborative control matrix adopts a fixed coupling model, the dust removal level is an interval mapping result of the superimposed weights of the first distribution situation map and the second distribution situation map; When the preset collaborative control matrix adopts a dynamic coupling model, the dust removal level is a collaborative state set obtained by real-time correction of the correlation features of the first distribution situation diagram and the second distribution situation diagram through an iterative algorithm.

6. The multi-device coordinated dust removal control system for an environmentally friendly production line according to claim 1 is characterized in that: It also includes an equipment linkage module connected to the collaborative control unit, and the equipment linkage module is connected to the dust removal equipment database through the industrial communication gateway; The equipment linkage module is used to screen the adaptation equipment queue from the dust removal equipment database according to the equipment collaboration requirements in the collaborative dust removal instruction, and generate an execution priority sequence to optimize the dust removal operation process.

7. The multi-device coordinated dust removal control system for an environmentally friendly production line according to claim 6 is characterized in that: The device linkage module generates an execution priority sequence including: Loading a three-dimensional coordinate model of the production line, and locating the spatial position node of each device in the adaptation device queue in the coordinate model; Calculate the optimal linkage path from the current position of each device to the target dust removal area based on the energy consumption optimization algorithm, and divide the adaptive device queue into collaborative levels according to the dust removal efficiency; The optimal linkage path and the collaborative level division are integrated into the coordinate model to generate a visual execution sequence.

8. The multi-equipment coordinated dust removal control system for an environmentally friendly production line according to claim 1 is characterized in that: When the collaborative control unit performs bidirectional verification on the operating indicators and execution parameters, a two-layer verification mode of data anomaly interception mechanism and parameter compatibility verification mechanism is adopted. The data anomaly interception mechanism is used to verify the timeliness of data collection, and the parameter compatibility verification mechanism is used to eliminate control conflicts between devices.

9. The multi-device coordinated dust removal control system for an environmentally friendly production line according to claim 1 is characterized in that: It also includes an instruction issuing module connected to the collaborative control unit, which is used to convert the collaborative dust removal instruction into a device control instruction set, and send the device control instruction set to the target dust removal equipment through the industrial communication gateway to start collaborative operation.

10. The multi-equipment coordinated dust removal control system for an environmentally friendly production line according to claim 1, characterized in that: It also includes a data archiving module connected to the collaborative control unit, which is used to store the real-time operation data stream, the first distribution situation map, the second distribution situation map, the dust removal level and the collaborative dust removal instructions, and generate a complete dust removal operation record chain according to the operation cycle.

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