AI vision-based environmental protection device energy efficiency optimization control device

Abstract

The application is an AI vision-based environmental protection equipment energy efficiency optimization control device, and relates to the technical field of environmental protection equipment control, comprising: an AI vision acquisition module, a multi-source sensing module, an AI intelligent analysis processing module, an energy efficiency optimization control module, and an equipment driving module.In the application, the AI vision acquisition module and the multi-source sensing module are synchronously collected, and the visual working condition characteristics and the sensing values are fused into a real-time working condition characteristic vector, thereby solving the technical problem of low matching degree of energy efficiency regulation and control strategy caused by limited single sensor collection dimension and inability to comprehensively reflect the field working condition, and achieving the technical effects of more complete working condition characteristic dimension and improved matching degree of regulation and control instruction and actual working condition.

Description

Technical Field

[0001] This invention relates to the field of environmental protection equipment control technology, and in particular to an energy efficiency optimization control device for environmental protection equipment based on AI vision. Background Technology

[0002] Environmental protection equipment used in industrial production (including dust removal fans, waste gas purification devices, and wastewater aeration units) operates on-site for extended periods. On-site parameters such as waste gas volume, pollutant concentration, and dust particle quantity fluctuate continuously, requiring environmental protection equipment to have the operational capability to match real-time operating conditions.

[0003] Existing environmental protection equipment generally adopts traditional control methods such as constant power or timed start-stop. Existing energy efficiency optimization schemes mostly rely on a single sensor to collect on-site operating data to obtain single physical quantities such as pollutant concentration or duct wind speed, which are then used to adjust equipment operating parameters.

[0004] However, single sensors have limited data acquisition dimensions and are easily affected by industrial environments such as high dust and high humidity. They cannot perceive multi-dimensional operating condition information such as the spatial distribution of dust, resulting in incomplete judgment of operating conditions. The control strategies formed on this basis have a low degree of matching with actual operating conditions. Under low load conditions, the equipment still maintains high power operation, generating a lot of ineffective energy consumption. When pollutants exceed the standard instantaneously, the response lag leads to the failure to meet the treatment standards. There is a technical problem of low matching between energy efficiency control strategies and actual operating conditions. Summary of the Invention

[0005] The purpose of this invention is to propose an energy efficiency optimization control device for environmental protection equipment based on AI vision in order to solve the above-mentioned problems.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: An AI vision-based energy efficiency optimization and control device for environmental protection equipment includes: The AI ​​vision acquisition module acquires images of the air outlet and treatment chamber of the environmental protection equipment, and performs noise reduction, defogging, region of interest cropping and invalid frame removal on the original images in sequence, and outputs a sequence of valid image frames. The multi-source sensing module integrates wind speed sensor, pollutant concentration sensor, temperature and humidity sensor, current and voltage sensor and pressure sensor. It synchronously collects the operating parameters of environmental protection equipment and working environment parameters with the AI ​​vision acquisition module, and aligns them with the valid image frame sequence according to a unified timestamp to form a multi-source synchronous dataset. The AI ​​intelligent analysis and processing module, equipped with a local computing chip, extracts visual operating condition features and sensor values ​​from multi-source synchronous datasets, merges them into real-time operating condition feature vectors, divides comprehensive operating condition levels, retrieves the optimal operating parameter table of the equipment using the comprehensive operating condition level as an index, and outputs the optimal operating parameter combination after emission compliance verification. The energy efficiency optimization control module executes differentiated control strategies based on the comprehensive operating condition level, converts the optimal combination of operating parameters into control signals, and continuously monitors real-time operating power and duct pressure, triggering overload protection, abnormal warnings, or shutdown protection. The equipment drive module converts control signals into drive signals for each actuator, enabling stepless speed regulation of the fan and switching of the purification power supply level.

[0007] Preferably, the AI ​​intelligent analysis and processing module includes a pollutant identification submodule, which has a built-in dust identification model. The dust identification model is a convolutional neural network model, and its output layer contains three branches: a classification branch outputs dust concentration level labels, a segmentation branch outputs dust dispersion range masks, and a detection branch outputs equipment blockage status indicators. The dust identification model uses labeled historical image frames as training samples and is trained under supervision using the Adam optimization algorithm.

[0008] Preferably, the AI ​​intelligent analysis and processing module further includes a data fusion submodule, which splices together the dust concentration level label, the area statistics of the dust dispersion range mask, the equipment blockage status indicator and the duct wind speed, the quantified value of dust concentration, the real-time operating power, the duct pressure, the operating environment temperature and humidity according to a unified timestamp to form a real-time operating condition feature vector and writes it into the real-time operating condition feature database.

[0009] Preferably, the AI ​​intelligent analysis and processing module further includes an energy efficiency matching submodule, which maintains an environmental compliance threshold table and an optimal equipment operating parameter table; the comprehensive operating condition level is determined by the quantified dust concentration value, the dust concentration level label, and the duct pressure, and is divided into four levels: high load, medium load, low load, and no load; recommended operating parameter combinations, including the target value of the fan operating frequency, are retrieved using the comprehensive operating condition level as an index. Target value of fan speed Purification level target value and start / stop status target value ,in The recommended operating frequency for the wind turbine. The recommended fan speed is... The recommended purification level. The recommended start / stop state is shown; the optimal combination of operating parameters is output after passing the emission compliance lower limit verification.

[0010] Preferably, the AI ​​intelligent analysis and processing module further includes an adaptive learning submodule, which records the governance compliance status and actual energy consumption values ​​under each operating condition level to a historical sample library, and periodically performs incremental updates on the dust identification model and the optimal operating parameter table of the equipment using data from the historical sample library.

[0011] Preferably, the differentiated control strategy executed by the energy efficiency optimization control module based on the comprehensive operating condition level is as follows: under high load conditions, output control signals to increase the fan operating frequency, increase the fan speed, and switch to a high-intensity purification level; under low load conditions, output control signals to reduce the fan operating frequency, lower the fan speed, and shut down redundant purification units; under no-load conditions, output control signals to put the equipment into a low-power sleep mode, retaining only the monitoring functions of sensors and visual acquisition; and when the operating condition is stable, output a hold signal to maintain the current energy-saving operating range.

[0012] Preferably, the device drive module includes a frequency converter drive unit, a relay control unit, and a signal conversion unit; the frequency converter drive unit converts the fan operating frequency control signal into a frequency converter drive output to drive the dust removal fan for stepless speed regulation; the relay control unit controls the on / off state and gear switching of the purification power supply; and the signal conversion unit converts the digital control signal into a drive signal format compatible with the interfaces of each actuator.

[0013] Preferably, the system further includes a data storage and transmission module, which has a built-in local storage unit and a wireless transmission unit. The local storage unit writes real-time operating condition feature vectors, optimal operating parameter combinations, actual equipment operating parameters, energy consumption data, control records, and abnormal event records in real time to form a timestamped operating log. The wireless transmission unit synchronizes the operating log to the cloud platform according to a preset upload cycle.

[0014] Preferably, the system further includes a human-machine interaction module, which is equipped with a touch screen, status indicator lights, and an audible and visual alarm unit. The touch screen displays real-time quantitative values ​​of dust concentration, real-time operating power, fan speed, purification level, energy consumption statistics, and real-time operating condition images, and receives environmental compliance thresholds, control parameter boundaries, and operating modes manually set by on-site personnel. The audible and visual alarm unit is activated when it receives an abnormal warning signal.

[0015] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention integrates visual operating condition features and sensor values ​​into a real-time operating condition feature vector through the synchronous acquisition of AI visual acquisition module and multi-source sensing module. This solves the technical problem that the limited acquisition dimension of a single sensor cannot fully reflect the on-site operating conditions, resulting in low matching degree of energy efficiency control strategy. It achieves the technical effect of more complete operating condition feature dimension and improved matching degree between control command and actual operating conditions.

[0016] 2. This invention uses an AI intelligent analysis and processing module to output the optimal combination of operating parameters in real time at the edge based on the comprehensive operating condition level, so that the operating power of the equipment can be dynamically adjusted according to the operating load. This achieves the technical effect of eliminating ineffective energy consumption under low load and no load conditions and ensuring that the treatment meets the standards under high load conditions. Attached Figure Description

[0017] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a system structure diagram of the present invention. Detailed Implementation

[0018] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0019] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0020] Example 1 Its specific implementation method is combined with the appendix Figure 1 Please provide a detailed explanation.

[0021] In this embodiment, it includes: Environmental protection equipment used in industrial production (dust collectors, exhaust gas purification devices, wastewater aerators, fume purification equipment, etc.) generally employs traditional control methods with constant power or timed start / stop. In industrial settings, operating parameters such as exhaust gas volume, pollutant concentration, and dust particle quantity fluctuate dynamically. Traditional equipment can only operate with fixed parameters, running at full power even when pollutant concentrations are low and loads are small, resulting in significant energy waste. Conversely, when pollutant levels spike or loads increase dramatically, insufficient power leads to substandard treatment. Existing energy efficiency optimization solutions often rely on single-sensor data collection, which is limited in scope, susceptible to environmental interference, and fails to comprehensively and accurately reflect real-world conditions, resulting in low matching rates for energy efficiency control strategies. This embodiment provides an AI vision-based energy efficiency optimization control device for environmental protection equipment, adapted for industrial baghouse dust collectors. The device is installed externally on the dust collector unit, with a vision camera facing the dust collector's outlet and the housing observation window. Multi-source sensors are installed in the duct, the equipment's circuitry, and inside the treatment chamber.

[0022] The AI ​​vision acquisition module is configured to acquire images of the on-site working conditions and output pre-processed image data. The AI ​​vision acquisition module includes a high-definition industrial camera, an infrared illumination unit, and an image preprocessing unit, deployed at the air outlet, treatment chamber, and operating condition monitoring area of ​​the dust removal equipment. The high-definition industrial camera acquires real-time images of dust dispersion at the air outlet and the dust accumulation status inside the chamber; the infrared illumination unit supplements the acquisition area with infrared light under dim, high-dust, and high-humidity conditions, eliminating image blurring caused by insufficient ambient light. The image preprocessing unit performs the following processing on the acquired raw images: Step 1: Perform noise reduction processing on the original image to eliminate random noise introduced by high dust and high humidity environments; Step 2: Perform dehazing on the denoised image to restore the visibility of the image under conditions of smoke or dust obscuring it; Step 3: Crop the image according to the preset region of interest coordinates to retain the effective observation area of ​​the air outlet and the treatment cavity; Step 4: Perform frame filtering on the continuous frame sequence, remove invalid frames that are blurry, repetitive, or have an occlusion ratio exceeding a preset threshold, and output a high-definition valid image frame sequence.

[0023] The multi-source sensing module is configured to simultaneously collect operating parameters of environmental protection equipment and environmental parameters. The multi-source sensing module integrates wind speed sensors, pollutant concentration sensors, temperature and humidity sensors, current and voltage sensors, and pressure sensors. These sensors are installed in the air duct, the equipment circuitry, and inside the treatment chamber, respectively, and operate synchronously with the AI ​​vision acquisition module. The wind speed sensor collects real-time wind speed in the air duct; the pollutant concentration sensor collects quantified dust concentration values ​​within the treatment chamber; the temperature and humidity sensor collects ambient temperature and humidity; the current and voltage sensor collects real-time operating power of the equipment; and the pressure sensor collects air duct pressure to detect air duct blockage. The data from these sensors are aligned with the pre-processed image frame sequence on the timeline to form a multi-source synchronous dataset for subsequent analysis and processing.

[0024] The AI ​​intelligent analysis and processing module is configured to perform condition identification and optimal operating parameter calculation on multi-source synchronous datasets. The AI ​​intelligent analysis and processing module is equipped with a local computing chip, enabling real-time computation at the edge without relying on the cloud. The module includes a pollutant identification submodule, a data fusion submodule, and an energy efficiency matching submodule.

[0025] The pollutant identification submodule is configured to extract working condition visual features from the preprocessed image frame sequence. The pollutant identification submodule incorporates a pre-trained dust identification model. This model is a convolutional neural network (CNN) model. Its input layer receives pre-processed image frames, and its output layer contains three branches: a classification branch outputs dust concentration level labels, trained using a multi-class cross-entropy loss function. The dust concentration level labels are discrete grading values ​​reflecting the visual concentration grading of dust in the current image frame; a segmentation branch outputs a dust dispersion range mask, trained using a binary cross-entropy loss function. The dust dispersion range mask is a binary map of the same size as the input image, marking the dust distribution area; and a detection branch outputs a device blockage status indicator, a Boolean value derived by comparing the dust coverage area of ​​the observation window with a preset blockage threshold, trained using a binary cross-entropy loss function. The dust identification model uses labeled historical image frames as training samples and employs the Adam optimization algorithm for supervised training. The above inference is performed frame-by-frame on a continuous image frame sequence, outputting a visual condition feature sequence.

[0026] The data fusion submodule is configured to fuse visual condition feature sequences with multi-source sensor values ​​into a real-time condition feature vector. The data fusion submodule uses dust concentration level labels, area statistics of dust dispersion range masks, and equipment blockage status indicators from the visual operating condition feature sequence, along with sensor data such as duct wind speed, quantified dust concentration values, real-time equipment operating power, duct pressure, and ambient temperature and humidity, all aligned with a unified timestamp and concatenated to form a real-time operating condition feature vector. This vector is then written into the real-time operating condition feature database. The visual feature component in the real-time operating condition feature vector compensates for the sensor's insufficient ability to perceive the spatial distribution of dust, while the sensor quantization component compensates for the limitation of visual recognition in precise numerical measurement; the two complement each other.

[0027] The energy efficiency matching submodule is configured to calculate the optimal combination of operating parameters based on real-time operating condition feature vectors. The energy efficiency matching submodule maintains a preset environmental compliance threshold table and an optimal equipment operating parameter table. The environmental compliance threshold table uses quantified dust concentration values ​​and dust concentration level labels as indexes to store the emission compliance lower limits corresponding to each operating condition level. The optimal equipment operating parameter table uses the hierarchical mapping results of the operating condition feature vectors as indexes to store the recommended combinations of corresponding fan operating frequency, fan speed, purification level, and start / stop status.

[0028] The energy efficiency matching submodule performs the following calculations: Step 1: Compare each component of the real-time operating condition feature vector with the operating condition level division boundary to output the comprehensive operating condition level; the comprehensive operating condition level is determined by the quantified dust concentration value, the dust concentration level label and the duct pressure, and is divided into four levels: high load, medium load, low load and no load. Step 2: Using the comprehensive operating condition level as an index, retrieve the corresponding recommended operating parameter combination from the equipment's optimal operating parameter table. The recommended operating parameter combination includes the target value of the fan operating frequency. Target value of fan speed Purification level target value and start / stop status target value ;in, The recommended operating frequency for the wind turbine. The recommended fan speed is... The recommended purification level. The recommended start / stop state; Step 3: Verify the recommended operating parameter combination against the emission compliance lower limit of the corresponding operating condition level in the environmental compliance threshold table to confirm that the recommended operating parameter combination is in the lowest energy consumption range while meeting the compliance requirements; if the verification fails, sequentially search the equipment optimal operating parameter table for the next level parameter combination that meets the compliance constraints until the verification passes. Step 4: Output the verified optimal combination of operating parameters and send it as a control command to the energy efficiency optimization control module.

[0029] Furthermore, the AI ​​intelligent analysis and processing module also has a built-in adaptive learning sub-module, which continuously records the actual control effects (including governance compliance and actual energy consumption values) under various operating conditions during long-term operation to the historical sample library. It periodically uses the data in the historical sample library to perform incremental updates on the dust identification model and the optimal operating parameter table of the equipment, so that the accuracy of operating condition identification and parameter matching can be continuously improved with the running time.

[0030] The energy efficiency optimization control module is configured to convert the optimal combination of operating parameters into equipment drive control signals. The energy efficiency optimization control module receives the optimal combination of operating parameters and executes differentiated control strategies based on the comprehensive operating condition level: under high load conditions (dust concentration quantification value exceeds the upper limit of the compliance threshold or the dust concentration level label is high), it outputs an increase in the fan operating frequency to... Increase the fan speed to Switch to high-intensity purification mode The control signal; under low load conditions (dust concentration quantification value is below the lower limit of the standard threshold and dust concentration level label is low), output control signals to reduce the fan operating frequency, reduce the fan speed, and shut down redundant purification units; under no load conditions (no dust generation, start / stop state target value) (For hibernation), outputs a control signal to enter low-power hibernation mode, retaining only the monitoring functions of sensors and visual acquisition; when the operating conditions are stable, outputs a hold signal to maintain the current optimal energy-saving operating range, avoiding frequent start-stop.

[0031] The energy efficiency optimization control module also continuously monitors the real-time operating power and duct pressure collected by the current and voltage sensors: when the real-time operating power exceeds the preset overload threshold, the overload protection logic is triggered and a power reduction protection command is output; when the duct pressure exceeds the preset blockage threshold and the equipment blockage status flag is true, an abnormal warning is triggered, an audible and visual alarm activation signal is output and the abnormal event is recorded; when equipment fault characteristics are detected, a shutdown protection command is output.

[0032] The device drive module is configured to convert control signals into drive signals that can be recognized by the various actuators of the dust removal equipment. The equipment drive module includes a frequency converter drive unit, a relay control unit, and a signal conversion unit. The frequency converter drive unit receives the fan operating frequency control signal and converts it into a corresponding frequency converter drive output to drive the dust collector fan for stepless speed regulation. The relay control unit receives purification level control signals and start / stop control signals to control the on / off state of the purification power supply and the switching of levels. The signal conversion unit is responsible for uniformly converting the digital control signals output by the energy efficiency optimization control module into a drive signal format compatible with the interfaces of various actuators, thereby achieving precise driving of actuators such as the fan and purification power supply.

[0033] The data storage and transmission module is configured to store runtime data and upload it to the cloud platform. The data storage and transmission module integrates a local storage unit and 4G / 5G and WiFi wireless transmission units. The local storage unit writes real-time operating condition feature vectors, optimal operating parameter combinations, actual equipment operating parameters, energy consumption data, control records, and abnormal event records, forming a complete, timestamped operating log. The wireless transmission unit synchronizes the operating log to the cloud platform according to a preset upload cycle, supporting remote data viewing, historical data tracing, energy consumption statistics, and operating condition trend analysis. It also provides cloud data backup for the historical sample library of the adaptive learning submodule.

[0034] The human-computer interaction module is configured to display real-time operating status and receive manual setting commands. The human-machine interface module is equipped with a touch screen, status indicator lights, and an audible and visual alarm unit. The touch screen displays real-time quantitative values ​​of dust concentration, dust concentration level labels, current real-time operating power of the equipment, fan speed, purification level, energy consumption statistics, and real-time operating condition images. It also allows on-site personnel to manually set environmental compliance thresholds, adjust parameter boundaries, and select operating modes via the touch interface. The status indicator lights use color to distinguish between the current overall operating condition level and the equipment's operating status. The audible and visual alarm unit activates upon receiving an abnormal warning signal from the energy efficiency optimization control module, alerting maintenance personnel to address anomalies such as instantaneous dust exceedances, fan malfunctions, or duct blockages.

[0035] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0036] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0037] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An energy efficiency optimization and control device for environmental protection equipment based on AI vision, characterized in that, include: The AI ​​vision acquisition module acquires images of the air outlet and treatment chamber of the environmental protection equipment, and performs noise reduction, defogging, region of interest cropping and invalid frame removal on the original images in sequence, and outputs a sequence of valid image frames. The multi-source sensing module integrates wind speed sensor, pollutant concentration sensor, temperature and humidity sensor, current and voltage sensor and pressure sensor. It synchronously collects the operating parameters of environmental protection equipment and working environment parameters with the AI ​​vision acquisition module, and aligns them with the valid image frame sequence according to a unified timestamp to form a multi-source synchronous dataset. The AI ​​intelligent analysis and processing module, equipped with a local computing chip, extracts visual operating condition features and sensor values ​​from multi-source synchronous datasets, merges them into real-time operating condition feature vectors, divides comprehensive operating condition levels, retrieves the optimal operating parameter table of the equipment using the comprehensive operating condition level as an index, and outputs the optimal operating parameter combination after emission compliance verification. The energy efficiency optimization control module executes differentiated control strategies based on the comprehensive operating condition level, converts the optimal combination of operating parameters into control signals, and continuously monitors real-time operating power and duct pressure, triggering overload protection, abnormal warnings, or shutdown protection. The equipment drive module converts control signals into drive signals for each actuator, enabling stepless speed regulation of the fan and switching of the purification power supply level.

2. The energy efficiency optimization and control device for environmental protection equipment based on AI vision according to claim 1, characterized in that, The AI ​​intelligent analysis and processing module includes a pollutant identification submodule, which has a built-in dust identification model. The dust identification model is a convolutional neural network model, and its output layer contains three branches: a classification branch that outputs dust concentration level labels, a segmentation branch that outputs dust dispersion range masks, and a detection branch that outputs equipment blockage status indicators. The dust identification model uses labeled historical image frames as training samples and is trained in a supervised manner using the Adam optimization algorithm.

3. The energy efficiency optimization and control device for environmental protection equipment based on AI vision according to claim 2, characterized in that, The AI ​​intelligent analysis and processing module also includes a data fusion submodule. The data fusion submodule splices together the dust concentration level label, the area statistics of the dust dispersion range mask, the equipment blockage status indicator and the duct wind speed, the quantified value of dust concentration, the real-time operating power, the duct pressure, the operating environment temperature and humidity according to a unified timestamp to form a real-time operating condition feature vector and writes it into the real-time operating condition feature database.

4. The energy efficiency optimization and control device for environmental protection equipment based on AI vision according to claim 3, characterized in that, The AI ​​intelligent analysis and processing module also includes an energy efficiency matching submodule, which maintains an environmental compliance threshold table and an optimal equipment operating parameter table; the comprehensive operating condition level is determined by the quantified dust concentration value, the dust concentration level label, and the duct pressure, and is divided into four levels: high load, medium load, low load, and no load. Recommended operating parameter combinations, including the target value of the wind turbine operating frequency, are retrieved using the comprehensive operating condition level as an index. Target value of fan speed Purification level target value and start / stop status target value ,in The recommended operating frequency for the wind turbine. The recommended fan speed is... The recommended purification level. The recommended start / stop state; After passing the emission compliance lower limit verification, the optimal combination of operating parameters is output.

5. The energy efficiency optimization and control device for environmental protection equipment based on AI vision according to claim 1, characterized in that, The AI ​​intelligent analysis and processing module also includes an adaptive learning submodule, which records the governance compliance status and actual energy consumption values ​​under each operating condition level to a historical sample library, and periodically performs incremental updates on the dust identification model and the optimal operating parameter table of the equipment using data from the historical sample library.

6. The energy efficiency optimization and control device for environmental protection equipment based on AI vision according to claim 1, characterized in that, The energy efficiency optimization control module executes differentiated control strategies based on the comprehensive operating condition level as follows: under high load conditions, it outputs control signals to increase the fan operating frequency, increase the fan speed, and switch to a high-intensity purification level; under low load conditions, it outputs control signals to reduce the fan operating frequency, lower the fan speed, and shut down redundant purification units; under no-load conditions, it outputs control signals to put the equipment into a low-power sleep mode, retaining only the monitoring functions of sensors and visual acquisition; when the operating condition is stable, it outputs a hold signal to maintain the current energy-saving operating range.

7. The energy efficiency optimization and control device for environmental protection equipment based on AI vision according to claim 1, characterized in that, The equipment drive module includes a frequency converter drive unit, a relay control unit, and a signal conversion unit. The frequency converter drive unit converts the fan operating frequency control signal into a frequency converter drive output to drive the dust removal fan with stepless speed regulation. The relay control unit controls the on / off state and gear switching of the purification power supply. The signal conversion unit converts the digital control signal into a drive signal format compatible with the interfaces of each actuator.

8. The energy efficiency optimization and control device for environmental protection equipment based on AI vision according to claim 1, characterized in that, It also includes a data storage and transmission module, which has a built-in local storage unit and a wireless transmission unit. The local storage unit writes real-time operating condition feature vectors, optimal operating parameter combinations, actual equipment operating parameters, energy consumption data, control records, and abnormal event records in real time to form a timestamped operation log. The wireless transmission unit synchronizes the operation log to the cloud platform according to a preset upload cycle.

9. The energy efficiency optimization and control device for environmental protection equipment based on AI vision according to claim 1, characterized in that, It also includes a human-machine interaction module, which is equipped with a touch screen, status indicator lights and an audible and visual alarm unit. The touch screen displays real-time quantitative values ​​of dust concentration, real-time operating power, fan speed, purification level, energy consumption statistics and real-time operating condition images, and receives environmental compliance thresholds, control parameter boundaries and operating modes manually set by on-site personnel. The audible and visual alarm unit is activated when it receives an abnormal warning signal.

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