Workshop air purification electrical control system

Abstract

The invention relates to the technical field of electrical control and air purification, in particular to a workshop air purification electrical control system. The system comprises a data acquisition module for acquiring pollutant concentration and environmental data in real time; the data analysis module is used for determining pollutant types; the control module decides to execute reverse traceability or start fluctuation buffering according to the type and the environmental complexity; the buffer module responds to the buffer instruction, preferentially adjusts the auxiliary purification unit and keeps the frequency of the fan unchanged; the tracing module accurately locates coordinates of a pollutant generation source through a multi-sensor array and time-space correlation analysis; and the cooperative regulation and control module identifies key nodes of the propagation chain and generates a resource tilt instruction. By dynamically evaluating environmental disturbance and pollutant fluctuation characteristics, a pollutant generation source is accurately positioned, and resources are cooperatively scheduled, so that the purification efficiency and the energy-saving level are improved.

Description

Technical Field

[0001] This invention relates to the fields of electrical control and air purification technology, and in particular to an electrical control system for workshop air purification. Background Technology

[0002] In modern industrial production, the air quality in the workshop directly affects product yield and the health and safety of workers. Existing workshop air purification systems typically use fans at a fixed frequency in conjunction with filter units for continuous purification, or trigger high-speed operation through simple threshold alarms. However, these traditional control methods have the following technical drawbacks: Lack of intelligent identification capabilities for environmental disturbances. Changes in environmental factors such as temperature, humidity, air pressure, personnel activity, and equipment start-up and shutdown within the workshop often cause instantaneous fluctuations in pollutant concentrations. Traditional systems cannot distinguish whether such fluctuations are accidental environmental disturbances or continuous pollution events, easily leading to frequent malfunctions in purification equipment. This not only wastes energy but also accelerates the wear and tear on the fan inverter units. Insufficient pollution source tracing capabilities. When pollutant concentrations exceed standards, existing systems typically can only dilute them by increasing overall ventilation, failing to accurately pinpoint the actual source of the pollutants. This "exhaustion-based" approach is inefficient and cannot fundamentally cut off the source of pollutants. Lack of coordinated resource allocation. Multiple purification devices operate independently within the workshop without information exchange, resulting in over-purification in some areas and under-purification in others, failing to optimize overall purification efficiency.

[0003] Chinese Patent Publication No. CN111708301A discloses an electrical control system for a multi-functional air purifier, including a voltage adjustment circuit. The key feature is that the electrical control system includes a main control circuit, which houses a PIC32MX350F128H-1 / PT 32-bit microcontroller chip U9. Pin 8 of the microcontroller chip U9 is connected to an LED-PM25BLUE-PWMRS232 driver circuit, a light-emitting diode driver circuit, a +3.3V power supply driver circuit, an LED lighting circuit, an MCU voice control circuit, a light color changing circuit, a crystal oscillator circuit, a +3.3V MCU power supply circuit, a main control multi-functional selection circuit, a voice program circuit, a voice recognition circuit, an LED lighting driver circuit, a driver circuit, and a remote control receiver circuit. This invention solves the problems of fast and accurate function recognition, no network interference, and long service life in multi-functional air purifiers.

[0004] Therefore, the existing technology has the following problems: The lack of dynamic strategy switching based on environmental disturbance characteristics and the absence of a multi-sensor collaborative pollution tracing mechanism mean that the problem of energy waste caused by frequent misadjustments cannot be effectively solved when facing complex workshop environments. Summary of the Invention

[0005] Therefore, the present invention provides an electrical control system for workshop air purification to overcome the shortcomings of existing technologies, which do not involve dynamic strategy switching based on environmental disturbance characteristics or establish a multi-sensor collaborative pollution source tracing mechanism. As a result, the problem of energy waste caused by frequent misadjustments cannot be effectively solved when facing complex workshop environments.

[0006] To achieve the above objectives, the present invention provides an electrical control system for workshop air purification, comprising: The data acquisition module, deployed inside the workshop, is used to collect real-time data on the concentration of pollutants in the air and environmental data within the workshop. A data analysis module, connected to the data acquisition module, is used to determine the pollutant variation coefficient of the workshop based on the pollutant concentration data and the environmental data, and to determine the pollutant type of the workshop based on the pollutant variation coefficient. A control module, connected to the data analysis module, is used to determine the environmental disturbance complexity of the workshop based on the environmental data, and based on the pollutant type and / or the environmental disturbance complexity of the workshop, determine whether to perform reverse source tracing calculation on the pollutant concentration data to trace the source of pollutant generation, or to activate a fluctuation buffer mechanism. A buffer module, which is connected to the control module, responds to the activation of the fluctuation buffer mechanism by first sending adjustment commands to the auxiliary purification unit to maintain the current output frequency of the fan frequency converter unit unchanged. A traceability module, connected to the control module, includes: A multi-sensor array is deployed in different physical locations in the workshop to synchronously collect time-series data of pollutant concentrations at each monitoring point; The spatiotemporal correlation analysis unit is used to calculate the time delay and correlation coefficient of the concentration changes at each monitoring point based on the time series data of pollutant concentrations at each monitoring point. The source tracing and positioning unit is used to determine the location coordinates of the pollutant generation source based on the time delay and correlation coefficient. The collaborative regulation module, connected to the control module, is used to perform spatiotemporal correlation analysis on the location coordinates of several identified pollutant sources, construct a pollution propagation chain, identify key nodes in the pollution propagation chain, and generate resource tilt control commands based on the type and location of the key nodes.

[0007] Furthermore, the data analysis module determines the pollutant variation coefficient of the workshop based on the pollutant concentration data and the environmental data, wherein, The data analysis module establishes a dynamic baseline concentration line based on the environmental data, and calculates the degree and rate of deviation of the real-time pollutant concentration relative to the dynamic baseline concentration line. The weighted fusion value of the degree and rate of deviation is used as the pollutant change coefficient.

[0008] Furthermore, the data analysis module determines the type of pollutant in the workshop based on the pollutant variation coefficient, wherein, If the pollutant variation coefficient is greater than or equal to the preset pollutant variation coefficient, the data analysis module determines that the pollutant type in the workshop is a high-fluctuation type. If the pollutant variation coefficient is less than the preset pollutant variation coefficient, the data analysis module determines that the pollutant type in the workshop is a low-fluctuation type.

[0009] Furthermore, the control module determines the environmental disturbance complexity of the workshop based on the environmental data, wherein, The control module extracts the number of disturbance sources, the magnitude of change of each disturbance source, and the frequency of change of each disturbance source from the environmental data. After normalizing the number of disturbance sources, the magnitude of change of each disturbance source, and the frequency of change of each disturbance source, the module performs weighted fusion to obtain the environmental disturbance complexity.

[0010] Furthermore, based on the type of pollutants in the workshop and / or the complexity of the environmental disturbance, the control module determines whether to perform reverse source tracing calculation on the pollutant concentration data to trace the source of pollutant generation, or to activate a fluctuation buffering mechanism. If the pollutant type in the workshop is highly volatile or the environmental disturbance complexity is greater than the preset environmental disturbance complexity, the control module performs reverse source tracing calculation on the pollutant concentration data to trace the source of pollutant generation. If the pollutant type in the workshop is low-fluctuation type and the environmental disturbance complexity is less than or equal to the preset environmental disturbance complexity, the control module activates the fluctuation buffer mechanism.

[0011] Furthermore, the spatiotemporal correlation analysis unit calculates the time delay and correlation coefficient of concentration changes at different monitoring points, wherein, The spatiotemporal correlation analysis unit calculates the cross-correlation function for the pollutant concentration time series data of any two monitoring points, takes the offset corresponding to the peak value of the cross-correlation function as the time delay between the two monitoring points, and takes the magnitude of the peak value as the correlation coefficient between the two monitoring points.

[0012] Furthermore, the source tracing and positioning unit determines the location coordinates of the pollutant source based on the time delay and correlation coefficient, wherein, The source tracing and positioning unit removes monitoring point pairs with correlation coefficients lower than a preset correlation coefficient, and retains only monitoring point pairs with correlation coefficients reaching a preset threshold for positioning calculation; selects any one of the retained monitoring points as a reference point, and calculates the distance difference between each monitoring point and the reference point based on the time delay between other monitoring points and the reference point; based on the distance difference and the known location coordinates of each monitoring point, the location coordinates of the pollutant source are solved by the hyperbola intersection method.

[0013] Furthermore, the collaborative control module performs spatiotemporal correlation analysis on the location coordinates of several identified pollutant sources to construct a pollution propagation chain, wherein... The collaborative control module acquires airflow field distribution data within the workshop and establishes a pollutant diffusion model based on the airflow field data. Using the coordinates of the identified pollutant source locations as input, the spatial distribution of pollutants at different time points is extrapolated through the diffusion model to obtain the temporal propagation path of the pollutants, which is then used as the pollution propagation chain.

[0014] Furthermore, the coordinated control module identifies key nodes in the pollution transmission chain, wherein, The collaborative control module divides the nodes in the pollution transmission chain into source nodes, transmission nodes, and convergence nodes. The node with the highest pollutant concentration among the source nodes, the node with the largest in-degree and out-degree among the transmission nodes, and the node with the largest pollutant accumulation among the sink nodes are identified as key nodes of each type.

[0015] Furthermore, the collaborative control module generates resource tilt control instructions based on the type and location of the key nodes, wherein, The coordinated control module assigns a weight coefficient to each key node, and the weight coefficient is determined based on the node type and pollutant concentration. Based on the weighting coefficients, purification resources are allocated proportionally to the areas where each key node is located, and control commands are generated and sent to the corresponding execution agencies.

[0016] Compared with existing technologies, the beneficial effects of this invention are as follows: By introducing a dynamic baseline concentration line construction method based on operating condition pattern recognition and combining it with weighted fusion calculation of deviation degree and deviation rate, this invention achieves accurate quantification of the characteristics of pollutant changes in the workshop. This mechanism can effectively distinguish between accidental fluctuations caused by environmental disturbances and continuous pollution events, avoiding frequent misadjustments of the purification system due to instantaneous interference. Thus, while ensuring air quality, it significantly reduces the ineffective start-stop and redundant operation of the fan frequency converter unit, thereby reducing the overall energy consumption of the system. At the same time, the accurate pollutant change coefficient provides reliable data support for subsequent pollution source tracing and resource collaborative scheduling, further improving the purification efficiency and the level of intelligent energy utilization.

[0017] Furthermore, this invention introduces a pre-defined pollutant variation coefficient calibration method based on a combination of historical data statistics and on-site verification, enabling precise classification of workshop pollutant fluctuation types. This mechanism can scientifically distinguish between short-term fluctuations caused by environmental disturbances and persistent pollution events requiring intervention, providing a reliable decision-making basis for intelligent switching of subsequent control strategies. By periodically updating the pre-defined threshold, misjudgments caused by seasonal changes or process adjustments are effectively avoided, ensuring the accuracy of triggering the source tracing mechanism for high-fluctuation types and initiating the buffer mechanism for low-fluctuation types. This improves the targeting of pollution response while minimizing the ineffective operation and energy consumption of the purification system.

[0018] Furthermore, this invention achieves accurate quantitative assessment of the complexity of environmental disturbances in the workshop by constructing a weighted fusion model based on multi-dimensional environmental characteristics (number of disturbance sources, amplitude of change, and frequency of change). This mechanism can objectively reflect the comprehensive interference degree of environmental factors on pollutant concentration, providing a scientific quantitative basis for the intelligent decision-making of the control module. By eliminating the influence of different dimensions through normalization processing and combining historical data with preset weight coefficients, the adaptability and accuracy of complexity calculation under different operating conditions are ensured. The introduction of environmental disturbance complexity enables the system to effectively identify high-disturbance scenarios and prioritize pollution source tracing, while avoiding the accidental triggering of large-scale purification actions due to occasional environmental fluctuations in low-disturbance scenarios. This significantly reduces the ineffective energy consumption and equipment wear of the purification system while improving the accuracy of pollution response.

[0019] Furthermore, this invention constructs a dual-dimensional intelligent decision-making mechanism based on pollutant type and environmental disturbance complexity, enabling adaptive switching of the purification system's control strategy. When high-fluctuation pollution or a highly complex environment is detected, the system automatically initiates reverse source tracing calculations to accurately locate the source of pollutants by penetrating environmental noise interference, avoiding missed detections caused by complex disturbances masking real pollution events. When operating under stable conditions with low fluctuations and low disturbances, the system activates a fluctuation buffering mechanism, prioritizing the adjustment of auxiliary purification units for minor compensation to maintain the main fan frequency unchanged, effectively suppressing frequent erroneous adjustments caused by accidental interference. This mechanism, while ensuring accurate pollution response, significantly reduces the ineffective energy consumption and equipment wear of the purification system, achieving synergistic optimization of purification efficiency and operational economy.

[0020] Furthermore, this invention achieves precise location of pollutant sources in workshops by constructing a pollution source tracing mechanism based on spatiotemporal correlation analysis. First, cross-correlation calculations are performed on the concentration time-series data from multiple monitoring points to obtain the time delay and correlation coefficient between each monitoring point, and weakly correlated data is eliminated to ensure reliable spatiotemporal consistency of the information used for location. Then, using the selected effective monitoring point pairs, the time delay is converted into distance difference based on the pollutant propagation speed. The coordinates of the pollutant source are solved using the hyperbolic intersection method, and in cases of multiple solutions, correlation weighting or least squares optimization is introduced for optimal selection. This technique fully utilizes the spatiotemporal propagation characteristics of pollutant diffusion, can penetrate environmental noise and local interference, and quickly locates the pollutant source in complex workshop environments, providing accurate spatial coordinates for subsequent precise purification and resource allocation, thereby significantly improving the targeting and effectiveness of pollution control.

[0021] Furthermore, this invention achieves in-depth analysis of pollution diffusion paths by constructing a pollution propagation chain and identifying three key nodes: source, transmission, and convergence. Based on a dual weighting mechanism of node type and pollutant concentration, purification resources are dynamically allocated to the most critical pollution locations as needed, breaking through the limitations of traditional uniform purification or local single-point purification. This mechanism ensures priority and enhanced treatment at high pollutant sources, propagation hubs, and enriched areas, thereby achieving the fastest pollution suppression effect with minimal energy consumption. This significantly improves the overall efficiency and response speed of the purification system while avoiding resource waste, achieving synergistic optimization of precise pollution control and energy-saving operation. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the electrical control system for workshop air purification in this embodiment; Figure 2 This is a schematic diagram of the traceability module in the workshop air purification electrical control system of this embodiment; Figure 3 This is a flowchart of the data analysis module in the workshop air purification electrical control system of this embodiment. Detailed Implementation

[0023] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0024] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0025] Please see Figures 1-3 As shown, Figure 1This is a schematic diagram of the electrical control system for workshop air purification in this embodiment; Figure 2 This is a schematic diagram of the traceability module in the workshop air purification electrical control system of this embodiment; Figure 3 This is a flowchart of the data analysis module in the workshop air purification electrical control system of this embodiment.

[0026] This embodiment provides a workshop air purification electrical control system, including: The data acquisition module, deployed inside the workshop, is used to collect real-time data on the concentration of pollutants in the air and environmental data within the workshop. A data analysis module, connected to the data acquisition module, is used to determine the pollutant variation coefficient of the workshop based on the pollutant concentration data and the environmental data, and to determine the pollutant type of the workshop based on the pollutant variation coefficient. A control module, connected to the data analysis module, is used to determine the environmental disturbance complexity of the workshop based on the environmental data, and based on the pollutant type and / or the environmental disturbance complexity of the workshop, determine whether to perform reverse source tracing calculation on the pollutant concentration data to trace the source of pollutant generation, or to activate a fluctuation buffer mechanism. A buffer module, which is connected to the control module, responds to the activation of the fluctuation buffer mechanism by first sending adjustment commands to the auxiliary purification unit to maintain the current output frequency of the fan frequency converter unit unchanged. A traceability module, connected to the control module, includes: A multi-sensor array is deployed in different physical locations in the workshop to synchronously collect time-series data of pollutant concentrations at each monitoring point; The spatiotemporal correlation analysis unit is used to calculate the time delay and correlation coefficient of the concentration changes at each monitoring point based on the time series data of pollutant concentrations at each monitoring point. The source tracing and positioning unit is used to determine the location coordinates of the pollutant generation source based on the time delay and correlation coefficient. The collaborative regulation module, connected to the control module, is used to perform spatiotemporal correlation analysis on the location coordinates of several identified pollutant sources, construct a pollution propagation chain, identify key nodes in the pollution propagation chain, and generate resource tilt control commands based on the type and location of the key nodes.

[0027] In this embodiment of the invention, the pollutant concentration data includes, but is not limited to, PM2.5 concentration, PM10 concentration, volatile organic compound (VOCs) concentration, carbon dioxide concentration, ozone concentration, and specific process dust concentration; the environmental data within the workshop includes, but is not limited to, temperature, humidity, air pressure, frequency of personnel activity, equipment on / off status, door and window opening / closing status, and airflow speed; the auxiliary purification unit includes, but is not limited to, electrostatic adsorption purifiers, activated carbon filter units, ultraviolet photocatalytic oxidation devices, negative ion generators, and mobile air purification robots; the fan frequency conversion unit includes, but is not limited to, centrifugal fan frequency converters, axial flow fan frequency converters, and mixed flow fan frequency converters, whose output frequency adjustment range is preset to 0-50Hz or 0-60Hz according to the workshop purification requirements, and supports receiving locking commands sent by the buffer module to maintain the current frequency unchanged via analog signals or Modbus communication protocol.

[0028] In this embodiment of the invention, the buffer module is embedded in a PLC controller or a dedicated microcontroller, and adopts a combination of proportional-integral-derivative (PID) control algorithm and logic threshold to implement a fluctuation buffering mechanism. Upon receiving an instruction from the control module to activate the fluctuation buffering mechanism, the buffer module first locks the current output frequency of the fan inverter unit, sends a frequency hold instruction to the inverter via a 4-20mA analog output channel or RS485 Modbus communication, and simultaneously blocks any frequency adjustment requests from the upper-level control system. The buffer module monitors the pollutant concentration data fed back by the data acquisition module in real time. When it detects an instantaneous fluctuation in concentration but it does not exceed the preset safety upper limit threshold, it prioritizes sending graded adjustment instructions to the auxiliary purification units: for example, sending a voltage adjustment instruction to the electrostatic adsorption purifier to enhance adsorption efficiency, sending a fan speed fine-tuning instruction to the activated carbon filter unit (the adjustment range is limited to ±10% of the rated speed), or sending an instruction to the mobile air purification robot to stay in the current area and enhance purification. The buffer module has a preset fluctuation buffer timer, the timing duration of which is adjustable from 30 seconds to 300 seconds based on historical fluctuation statistics. If the pollutant concentration recovers to near the dynamic baseline concentration line before the buffer timer overflows, the buffer module gradually restores the auxiliary purification unit to the state before buffering and releases the frequency lock on the fan frequency converter unit. If the pollutant concentration still does not drop after the buffer timer overflows, the buffer module reports a buffer failure signal to the control module and requests to switch to the reverse source tracing mode.

[0029] Specifically, the data analysis module determines the pollutant variation coefficient of the workshop based on the pollutant concentration data and the environmental data, wherein, The data analysis module establishes a dynamic baseline concentration line based on the environmental data, and calculates the degree and rate of deviation of the real-time pollutant concentration relative to the dynamic baseline concentration line. The weighted fusion value of the degree and rate of deviation is used as the pollutant change coefficient.

[0030] In this embodiment of the invention, when establishing a dynamic baseline concentration line, the data analysis module first extracts features from the environmental data to identify the current operating mode of the workshop, including normal production periods, equipment start-up and shutdown transition periods, and personnel activity breaks. Based on the historical statistical patterns of different operating modes, an adaptive sliding window algorithm or a Kalman filter algorithm is used to fit a dynamic baseline concentration line that is updated in real time with environmental changes. The degree of deviation refers to the absolute value or relative percentage of the difference between the real-time pollutant concentration value and the dynamic baseline concentration line. The deviation rate refers to the change in the degree of deviation per unit time, which is obtained by calculating the first derivative of the pollutant concentration time series data. The data analysis module pre-calibrates the deviation degree weighting coefficient and the deviation rate weighting coefficient based on the workshop's historical operating data, and performs a weighted summation of the two to obtain the pollutant change coefficient.

[0031] This invention introduces a dynamic baseline concentration line construction method based on operating condition pattern recognition, and combines it with weighted fusion calculation of deviation degree and deviation rate to achieve precise quantification of the characteristics of pollutant changes in the workshop. This mechanism can effectively distinguish between accidental fluctuations caused by environmental disturbances and continuous pollution events, avoiding frequent erroneous adjustments of the purification system due to instantaneous interference. Thus, while ensuring air quality, it significantly reduces ineffective start-stop and redundant operation of the fan frequency converter unit, thereby reducing the overall energy consumption of the system. At the same time, the accurate pollutant change coefficient provides reliable data support for subsequent pollution source tracing and resource collaborative scheduling, further improving purification efficiency and the level of intelligence in energy utilization.

[0032] Specifically, the data analysis module determines the type of pollutants in the workshop based on the pollutant variation coefficient, wherein, If the pollutant variation coefficient is greater than or equal to the preset pollutant variation coefficient, the data analysis module determines that the pollutant type in the workshop is a high-fluctuation type. If the pollutant variation coefficient is less than the preset pollutant variation coefficient, the data analysis module determines that the pollutant type in the workshop is a low-fluctuation type.

[0033] In this embodiment of the invention, the preset pollutant variation coefficient is obtained through statistical calibration based on historical workshop operating data. Specifically, historical pollutant concentration data and corresponding environmental data are collected under typical operating conditions (including normal production, equipment start-up and shutdown, personnel activities, etc.) in the workshop, and sample values ​​of the pollutant variation coefficient for each time period are calculated. The sample values ​​are sorted from largest to smallest, and values ​​at a set percentile (e.g., the 75th or 85th percentile) are selected as initial thresholds. The initial thresholds are then corrected based on on-site measurement verification, ultimately determining a preset pollutant variation coefficient that can effectively distinguish between environmental disturbance fluctuations and persistent pollution events. This preset pollutant variation coefficient can be periodically updated according to seasonal changes or production process adjustments to ensure the accuracy and adaptability of the classification.

[0034] This invention introduces a pre-defined pollutant variation coefficient calibration method based on a combination of historical data statistics and on-site verification, enabling precise classification of pollutant fluctuation types in the workshop. This mechanism can scientifically distinguish between short-term fluctuations caused by environmental disturbances and persistent pollution events requiring intervention, providing a reliable decision-making basis for intelligent switching of subsequent control strategies. By periodically updating the pre-defined thresholds, misjudgments caused by seasonal changes or process adjustments are effectively avoided, ensuring the accuracy of triggering the source tracing mechanism for high-fluctuation types and activating the buffer mechanism for low-fluctuation types. This improves the targeting of pollution response while minimizing the ineffective operation and energy consumption of the purification system.

[0035] Specifically, the control module determines the environmental disturbance complexity of the workshop based on the environmental data, wherein, The control module extracts the number of disturbance sources, the magnitude of change of each disturbance source, and the frequency of change of each disturbance source from the environmental data. After normalizing the number of disturbance sources, the magnitude of change of each disturbance source, and the frequency of change of each disturbance source, the module performs weighted fusion to obtain the environmental disturbance complexity.

[0036] In this embodiment of the invention, when extracting the number of disturbance sources, the control module first performs abrupt change detection or activity state identification on each parameter in the environmental data (such as temperature, humidity, frequency of personnel activity, equipment on / off status, etc.), defining environmental parameters that have changed significantly or are in an active state at the current moment as disturbance sources; the change amplitude of each disturbance source refers to the absolute value of the difference between the environmental parameter at the current moment and its historical steady-state baseline, or the jump variable compared to the previous moment; the change frequency of each disturbance source refers to the number of times the disturbance source changes significantly per unit time, calculated by the zero-crossing rate of the parameter's time-series data. Alternatively, peak counts can be used to obtain the data. The control module employs maximum-minimum normalization or Z-score normalization to map the number of disturbance sources, the amplitude of change of each disturbance source, and the frequency of change of each disturbance source to the same dimension range. Based on historical operating data of the workshop or expert experience, the weight coefficients of the three factors are preset (e.g., the number of disturbance sources is weighted at 0.3, the amplitude of change at 0.4, and the frequency of change at 0.3). The normalized values ​​are then weighted and summed to obtain the quantified environmental disturbance complexity. The higher the value of this environmental disturbance complexity, the more complex the current environmental state of the workshop, and the stronger the random interference to the concentration of pollutants.

[0037] This invention achieves accurate quantitative assessment of the complexity of environmental disturbances in the workshop by constructing a weighted fusion model based on multi-dimensional environmental characteristics (number of disturbance sources, amplitude of change, and frequency of change). This mechanism can objectively reflect the comprehensive interference degree of environmental factors on pollutant concentration, providing a scientific quantitative basis for the intelligent decision-making of the control module. By eliminating the influence of different dimensions through normalization processing and combining historical data with preset weight coefficients, the adaptability and accuracy of complexity calculation under different operating conditions are ensured. The introduction of environmental disturbance complexity enables the system to effectively identify high-disturbance scenarios and prioritize pollution source tracing, while avoiding the accidental triggering of large-scale purification actions due to occasional environmental fluctuations in low-disturbance scenarios. This significantly reduces the ineffective energy consumption and equipment wear of the purification system while improving the accuracy of pollution response.

[0038] Specifically, the control module, based on the type of pollutants in the workshop and / or the complexity of the environmental disturbance, determines whether to perform reverse source tracing calculation on the pollutant concentration data to trace the source of pollutant generation, or to activate a fluctuation buffering mechanism. If the pollutant type in the workshop is highly volatile or the environmental disturbance complexity is greater than the preset environmental disturbance complexity, the control module performs reverse source tracing calculation on the pollutant concentration data to trace the source of pollutant generation. If the pollutant type in the workshop is low-fluctuation type and the environmental disturbance complexity is less than or equal to the preset environmental disturbance complexity, the control module activates the fluctuation buffer mechanism.

[0039] Understandably, when the type of pollutants in the workshop is highly volatile, it indicates that there are drastic or continuous changes in pollutant concentration. In this case, there is a high probability of a source continuously releasing pollutants, rather than a random environmental disturbance. Therefore, it is necessary to quickly locate the source through reverse tracing to fundamentally cut off the pollution. When the complexity of environmental disturbances exceeds the preset threshold, it means that environmental factors such as temperature, humidity, personnel activity, and equipment start-up and shutdown in the workshop are in a highly active or frequently changing state. Such complex environments are very likely to cause random fluctuations in pollutant concentrations, causing the traditional fixed threshold purification control to fail. At this time, starting reverse tracing calculation can penetrate environmental noise interference and accurately identify the real pollution events hidden behind complex disturbances, avoiding missed or misjudgments caused by environmental disturbances obscuring the truth of pollution.

[0040] Understandably, when the type of pollutants in the workshop is of low fluctuation and the complexity of environmental disturbances is less than or equal to the preset threshold, it indicates that the current pollutant concentration changes are gradual and the environmental factors are in a relatively stable state. At this time, the small concentration fluctuations are likely due to occasional local interference or sensor measurement noise, rather than continuous pollution events. Under this condition, the fluctuation buffering mechanism is activated, and the auxiliary purification unit is adjusted first for slight compensation, while the output frequency of the main fan inverter unit remains unchanged. This can effectively prevent the purification system from frequently starting and stopping or drastically adjusting its frequency due to instantaneous pseudo-changes. Thus, while ensuring the basic stability of air quality, unnecessary energy consumption and equipment wear are minimized, and the economy and reliability of system operation are improved.

[0041] In this embodiment of the invention, the preset environmental disturbance complexity is obtained through statistical calibration based on historical workshop operating data. Specifically, environmental data of the workshop under typical operating conditions is continuously collected, and sample values ​​of environmental disturbance complexity for each time period are calculated. Complexity samples are selected when the workshop is in a relatively stable operating state (e.g., no large equipment starts or stops, no dense personnel activity), and their mean and standard deviation are calculated. The sum of the mean and twice the standard deviation is used as the initial threshold. Combined with on-site measurement verification, the initial threshold is corrected, and finally, a preset environmental disturbance complexity that can effectively distinguish between a stable environmental state and a complex disturbance state is determined. This preset environmental disturbance complexity can be periodically updated according to seasonal changes or workshop layout adjustments to ensure the timeliness and accuracy of the decision-making criteria.

[0042] This invention constructs a dual-dimensional intelligent decision-making mechanism based on pollutant type and environmental disturbance complexity, enabling adaptive switching of the purification system's control strategy. When high-fluctuation pollution or a highly complex environment is detected, the system automatically initiates reverse source tracing calculations to accurately locate the source of pollutants, penetrating environmental noise interference and avoiding missed detections caused by complex disturbances masking actual pollution events. When operating under stable conditions with low fluctuations and disturbances, the system activates a fluctuation buffering mechanism, prioritizing the adjustment of auxiliary purification units for minor compensation to maintain the main fan frequency, effectively suppressing frequent erroneous adjustments caused by accidental interference. This mechanism ensures accurate pollution response while significantly reducing ineffective energy consumption and equipment wear in the purification system, achieving synergistic optimization of purification efficiency and operational economy.

[0043] Specifically, the spatiotemporal correlation analysis unit calculates the time delay and correlation coefficient of concentration changes at different monitoring points, wherein, The spatiotemporal correlation analysis unit calculates the cross-correlation function for the pollutant concentration time series data of any two monitoring points, takes the offset corresponding to the peak value of the cross-correlation function as the time delay between the two monitoring points, and takes the magnitude of the peak value as the correlation coefficient between the two monitoring points.

[0044] In this embodiment of the invention, before calculating the cross-correlation function, the spatiotemporal correlation analysis unit first preprocesses the time-series data of pollutant concentrations at each monitoring point, including removing obvious outliers, filling in missing data, and using a bandpass filter to remove high-frequency noise and trend terms to enhance the identifiability of pollution event characteristic signals. The cross-correlation function is calculated using Fast Fourier Transform (FFT) to improve computational efficiency and meet real-time requirements. After obtaining the cross-correlation function curve, the horizontal coordinate position corresponding to the global maximum value is identified by a peak detection algorithm. This position represents the time delay between two monitoring points; a positive value indicates that the change at the first monitoring point lags behind the change at the second monitoring point, while a negative value indicates that the change leads. The amplitude of the peak value of the cross-correlation function is normalized and used as the correlation coefficient. The closer the value is to 1, the more consistent the concentration change patterns of the two monitoring points are; the closer it is to 0, the weaker the correlation between them. This correlation coefficient will subsequently be used to screen effective monitoring point pairs, eliminating weakly correlated data caused by airflow obstruction or local interference, providing reliable basic data for accurately locating pollutant sources.

[0045] Specifically, the source tracing and positioning unit determines the location coordinates of the pollutant source based on the time delay and correlation coefficient, wherein, The source tracing and positioning unit removes monitoring point pairs with correlation coefficients lower than a preset correlation coefficient, and retains only monitoring point pairs with correlation coefficients reaching a preset threshold for positioning calculation; selects any one of the retained monitoring points as a reference point, and calculates the distance difference between each monitoring point and the reference point based on the time delay between other monitoring points and the reference point; based on the distance difference and the known location coordinates of each monitoring point, the location coordinates of the pollutant source are solved by the hyperbola intersection method.

[0046] In this embodiment of the invention, the source tracing and positioning unit first constructs a correlation matrix based on the correlation coefficients between each monitoring point. Monitoring point pairs with correlation coefficients below a preset threshold are judged as weakly correlated or invalidly correlated and are removed in subsequent positioning calculations. Only monitoring point pairs with correlation coefficients reaching the preset threshold are retained to ensure reliable spatiotemporal consistency of the data involved in positioning. The preset correlation coefficient threshold can be calibrated based on the actual layout of the workshop and airflow characteristics, for example, using 0.7 or 0.8 as empirical values, or dynamically adjusted based on historical positioning verification results. After selecting effective monitoring points, any one of these monitoring points is selected as a reference point. Based on the time delay between this reference point and other effective monitoring points, combined with the known propagation speed of pollutants in the air (which can be pre-determined through on-site tracer gas experiments or estimated in real-time based on airflow field distribution data), the time delay is converted into a distance difference, i.e., the difference in distance from other monitoring points to the pollutant source between the reference point and the reference point. Subsequently, based on the known physical coordinates of the reference point and each effective monitoring point, a system of hyperbolic equations is established: each pair of monitoring points (reference point and another monitoring point) corresponds to a hyperbola with those two points as its foci. The pollutant source must be located at the intersection of the hyperbolas that satisfy all distance difference conditions. By solving this system of hyperbolic equations, the precise location coordinates of the pollutant source in the workshop plane or space can be obtained. If multiple candidate intersection points exist, a weighted selection is performed based on the correlation coefficient, or redundant monitoring points are introduced for least squares optimization to finally determine the optimal location coordinates of the pollutant source. This location method fully utilizes the spatiotemporal propagation characteristics of pollutant diffusion and can achieve accurate source tracing in complex workshop environments.

[0047] This invention achieves precise location of pollutant sources in workshops by constructing a pollution source tracing mechanism based on spatiotemporal correlation analysis. First, cross-correlation calculations are performed on the concentration time-series data from multiple monitoring points to obtain the time delay and correlation coefficient between each monitoring point, and weakly correlated data is eliminated to ensure reliable spatiotemporal consistency of the information used for location. Then, using the selected effective monitoring point pairs, the time delay is converted into distance difference based on the pollutant propagation speed. The coordinates of the pollutant source are solved using the hyperbolic intersection method, and in cases of multiple solutions, correlation weighting or least squares optimization is introduced for optimal selection. This technique fully utilizes the spatiotemporal propagation characteristics of pollutant diffusion, can penetrate environmental noise and local interference, and quickly locates the pollutant source in complex workshop environments. This provides accurate spatial coordinates for subsequent precise purification and resource allocation, thereby significantly improving the targeting and effectiveness of pollution control.

[0048] Specifically, the collaborative control module performs spatiotemporal correlation analysis on the location coordinates of several identified pollutant sources to construct a pollution propagation chain, wherein... The collaborative control module acquires airflow field distribution data within the workshop and establishes a pollutant diffusion model based on the airflow field data. Using the coordinates of the identified pollutant source locations as input, the spatial distribution of pollutants at different time points is extrapolated through the diffusion model to obtain the temporal propagation path of the pollutants, which is then used as the pollution propagation chain.

[0049] In this embodiment of the invention, the collaborative control module first acquires real-time airflow field distribution data within the workshop through an array of airflow sensors deployed in the workshop or simulation data based on computational fluid dynamics (CFD), including wind speed vector, wind direction, turbulence intensity, and airflow organization path. Based on this airflow field data, combined with the diffusion characteristics of pollutants in the air (such as molecular diffusion coefficient, turbulent diffusion coefficient, and gravitational sedimentation effect), a pollutant diffusion model suitable for the current workshop environment is established. This model can adopt a Gaussian plume model, a Gaussian smoke plume model, or a particle tracking model based on the Lagrange method, specifically selected according to the workshop geometry and airflow complexity. After acquiring the diffusion model, the coordinates of one or more pollutant source locations identified by the source tracing and localization unit are used as initial release points input into the model. Combined with real-time airflow field data, the spatial distribution cloud map of pollutants at different time points after release from the source is projected forward along the time axis. Simultaneously, historical pollutant concentration monitoring data is used to calibrate the projection results. Through iterative optimization, the time series distribution that best matches the measured data is determined, thus obtaining the complete temporal propagation path of pollutants from source generation, migration and diffusion with airflow until capture by monitoring points. This path constitutes the pollution propagation chain. This propagation chain not only describes the spatial distribution evolution of pollutant concentration but also depicts the time delay relationship of pollutant propagation along the airflow direction, providing intuitive spatiotemporal map support for subsequent identification of key nodes and optimization of purification resource scheduling.

[0050] Specifically, the coordinated control module identifies key nodes in the pollution transmission chain, wherein, The collaborative control module divides the nodes in the pollution transmission chain into source nodes, transmission nodes, and convergence nodes. The node with the highest pollutant concentration among the source nodes, the node with the largest in-degree and out-degree among the transmission nodes, and the node with the largest pollutant accumulation among the sink nodes are identified as key nodes of each type.

[0051] In this embodiment of the invention, when identifying key nodes, the collaborative control module first divides the spatial locations on the pollution propagation chain into three types based on the topological structure of the pollution propagation chain: source nodes, which are the initial release locations of pollutants, usually corresponding to the pollutant generation source coordinates identified by the source tracing and positioning unit; transmission nodes, which are the intermediate locations on the path of pollutants migration with the airflow, where pollutants only pass through without accumulating; and convergence nodes, which are the locations where pollutants accumulate due to airflow organization or obstruction, usually located in dead corners of the workshop, recirculation areas, or near the air inlet of purification equipment. For source nodes, extract the peak or average concentration of pollutants from each source, and identify the source node with the highest concentration as the critical source node, as it contributes the most to the overall pollution. For transmission nodes, calculate the inflow / outflow degree of each node, which is the sum of the number of airflow paths flowing into and out of the node. The transmission node with the highest inflow / outflow degree indicates that it is a hub or choke point for pollutant transmission, and intervention at this node can effectively block the spread of pollution. For convergence nodes, calculate the cumulative amount of pollutants at each node (which can be obtained by integrating the concentration and residence time derived from the diffusion model), and identify the convergence node with the largest cumulative amount as the critical convergence node, as it is a concentrated and enriched area of ​​pollutants. Strengthening the purification of this node can quickly reduce the overall pollution level of the workshop.

[0052] Specifically, the collaborative control module generates resource tilt control instructions based on the type and location of the key nodes, wherein, The coordinated control module assigns a weight coefficient to each key node, and the weight coefficient is determined based on the node type and pollutant concentration. Based on the weighting coefficients, purification resources are allocated proportionally to the areas where each key node is located, and control commands are generated and sent to the corresponding execution agencies.

[0053] In this embodiment of the invention, when generating resource tilt control commands, the collaborative control module first presets basic weight coefficients based on node type, for example, a weight of 0.5 for source nodes, 0.3 for transmission nodes, and 0.2 for convergence nodes. Then, it normalizes the actual pollutant concentration values ​​(or concentration levels) of each node and multiplies them by the basic weights as dynamic adjustment coefficients to obtain the comprehensive weight coefficient for each key node. Subsequently, it calculates the sum of the comprehensive weights of all key nodes and the proportion of each node's weight to the total. Based on this proportion, it allocates the currently available purification resources (such as the number of mobile purification devices, the opening degree of fixed purification valves, and the allocated air volume of the fresh air system) to the physical areas where each key node is located. Finally, it generates specific control commands, including fan speed adjustment commands, valve opening adjustment commands, or mobile purification device path planning commands for the corresponding areas, and sends them to the corresponding actuators via fieldbus or wireless network to achieve precise tilting and dynamic scheduling of purification resources towards key locations.

[0054] This invention achieves in-depth analysis of pollution diffusion paths by constructing a pollution propagation chain and identifying three key nodes: source, transmission, and convergence. Based on a dual weighting mechanism of node type and pollutant concentration, purification resources are dynamically allocated to the most critical pollution locations as needed, breaking through the limitations of traditional uniform purification or local single-point purification. This mechanism ensures priority and enhanced treatment at high pollutant sources, propagation hubs, and enriched areas, thereby achieving the fastest pollution suppression effect with minimal energy consumption. This significantly improves the overall efficiency and response speed of the purification system, while avoiding resource waste and achieving synergistic optimization of precise pollution control and energy-saving operation.

[0055] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. An electrical control system for workshop air purification, characterized in that, include: The data acquisition module, deployed inside the workshop, is used to collect real-time data on the concentration of pollutants in the air and environmental data within the workshop. A data analysis module, connected to the data acquisition module, is used to determine the pollutant variation coefficient of the workshop based on the pollutant concentration data and the environmental data, and to determine the pollutant type of the workshop based on the pollutant variation coefficient. A control module, connected to the data analysis module, is used to determine the environmental disturbance complexity of the workshop based on the environmental data, and based on the pollutant type and / or the environmental disturbance complexity of the workshop, determine whether to perform reverse source tracing calculation on the pollutant concentration data to trace the source of pollutant generation, or to activate a fluctuation buffer mechanism. A buffer module, which is connected to the control module, responds to the activation of the fluctuation buffer mechanism by first sending adjustment commands to the auxiliary purification unit to maintain the current output frequency of the fan frequency converter unit unchanged. A traceability module, connected to the control module, includes: A multi-sensor array is deployed in different physical locations in the workshop to synchronously collect time-series data of pollutant concentrations at each monitoring point; The spatiotemporal correlation analysis unit is used to calculate the time delay and correlation coefficient of the concentration changes at each monitoring point based on the time series data of pollutant concentrations at each monitoring point. The source tracing and positioning unit is used to determine the location coordinates of the pollutant generation source based on the time delay and correlation coefficient. The collaborative regulation module, connected to the control module, is used to perform spatiotemporal correlation analysis on the location coordinates of several identified pollutant sources, construct a pollution propagation chain, identify key nodes in the pollution propagation chain, and generate resource tilt control commands based on the type and location of the key nodes.

2. The workshop air purification electrical control system according to claim 1, characterized in that, The data analysis module determines the pollutant variation coefficient of the workshop based on the pollutant concentration data and the environmental data, wherein, The data analysis module establishes a dynamic baseline concentration line based on the environmental data, and calculates the degree and rate of deviation of the real-time pollutant concentration relative to the dynamic baseline concentration line. The weighted fusion value of the degree and rate of deviation is used as the pollutant change coefficient.

3. The workshop air purification electrical control system according to claim 1, characterized in that, The data analysis module determines the type of pollutants in the workshop based on the pollutant variation coefficient, wherein, If the pollutant variation coefficient is greater than or equal to the preset pollutant variation coefficient, the data analysis module determines that the pollutant type in the workshop is a high-fluctuation type. If the pollutant variation coefficient is less than the preset pollutant variation coefficient, the data analysis module determines that the pollutant type in the workshop is a low-fluctuation type.

4. The workshop air purification electrical control system according to claim 1, characterized in that, The control module determines the environmental disturbance complexity of the workshop based on the environmental data, wherein... The control module extracts the number of disturbance sources, the magnitude of change of each disturbance source, and the frequency of change of each disturbance source from the environmental data. After normalizing the number of disturbance sources, the magnitude of change of each disturbance source, and the frequency of change of each disturbance source, the module performs weighted fusion to obtain the environmental disturbance complexity.

5. The workshop air purification electrical control system according to claim 1, characterized in that, The control module, based on the type of pollutants in the workshop and / or the complexity of the environmental disturbance, determines whether to perform reverse source tracing calculation on the pollutant concentration data to trace the source of pollutant generation, or to activate a fluctuation buffering mechanism. If the pollutant type in the workshop is highly volatile or the environmental disturbance complexity is greater than the preset environmental disturbance complexity, the control module performs reverse source tracing calculation on the pollutant concentration data to trace the source of pollutant generation. If the pollutant type in the workshop is low-fluctuation type and the environmental disturbance complexity is less than or equal to the preset environmental disturbance complexity, the control module activates the fluctuation buffer mechanism.

6. The workshop air purification electrical control system according to claim 1, characterized in that, The spatiotemporal correlation analysis unit calculates the time delay and correlation coefficient of concentration changes at different monitoring points, wherein... The spatiotemporal correlation analysis unit calculates the cross-correlation function for the pollutant concentration time series data of any two monitoring points, takes the offset corresponding to the peak value of the cross-correlation function as the time delay between the two monitoring points, and takes the magnitude of the peak value as the correlation coefficient between the two monitoring points.

7. The workshop air purification electrical control system according to claim 1, characterized in that, The source tracing and positioning unit determines the location coordinates of the pollutant source based on the time delay and correlation coefficient, wherein, The source tracing and positioning unit removes monitoring point pairs with correlation coefficients lower than a preset correlation coefficient, and retains only monitoring point pairs with correlation coefficients reaching a preset threshold for positioning calculation; selects any one of the retained monitoring points as a reference point, and calculates the distance difference between each monitoring point and the reference point based on the time delay between other monitoring points and the reference point; based on the distance difference and the known location coordinates of each monitoring point, the location coordinates of the pollutant source are solved by the hyperbola intersection method.

8. The workshop air purification electrical control system according to claim 1, characterized in that, The collaborative control module performs spatiotemporal correlation analysis on the location coordinates of several identified pollutant sources to construct a pollution propagation chain. The collaborative control module acquires airflow field distribution data within the workshop and establishes a pollutant diffusion model based on the airflow field distribution data. Using the coordinates of the identified pollutant source locations as input, the spatial distribution of pollutants at different time points is extrapolated through the diffusion model to obtain the temporal propagation path of the pollutants, which is then used as the pollution propagation chain.

9. The workshop air purification electrical control system according to claim 1, characterized in that, The coordinated control module identifies key nodes in the pollution transmission chain, wherein... The coordinated control module divides the nodes in the pollution transmission chain into source nodes, transmission nodes, and convergence nodes. The node with the highest pollutant concentration among the source nodes, the node with the largest in-degree and out-degree among the transmission nodes, and the node with the largest pollutant accumulation among the sink nodes are identified as key nodes of each type.

10. The workshop air purification electrical control system according to claim 1, characterized in that, The collaborative control module generates resource tilt control instructions based on the type and location of the key nodes, wherein, The coordinated control module assigns a weight coefficient to each key node, and the weight coefficient is determined based on the node type and pollutant concentration. Based on the weighting coefficients, purification resources are allocated proportionally to the areas where each key node is located, and control commands are generated and sent to the corresponding execution agencies.

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