Multi-load adaptive AI energy-saving control device and method
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-04-29
- Publication Date
- 2026-07-21
Smart Images
Figure CN122431137A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to intelligent energy-saving control, and more particularly to an AI energy-saving control device and method that is multi-load adaptive. Background Technology
[0002] With the deepening of industrial automation and energy conservation and emission reduction policies, energy-saving control technologies for industrial and commercial electrical equipment are receiving increasing attention. Currently, common energy-saving control technologies mainly include frequency converter control, thyristor voltage regulation control, and intelligent relay control. However, existing technologies have the following significant drawbacks in practical applications: (1) Lack of load identification capability Traditional energy-saving control algorithms lack the ability to automatically identify load types, requiring professionals to manually adjust parameters on-site based on load characteristics. Since different loads exhibit significant differences in electrical characteristics, operating patterns, and energy consumption patterns, manual adjustment is not only labor-intensive but also poorly adaptable, making it difficult to accommodate changes in load type or operating conditions.
[0003] (2) Lack of self-learning capability for operating conditions and load forecasting capability Existing energy-saving control technologies mainly employ passive signal response control strategies, which trigger corresponding control actions based on preset threshold conditions. This control method cannot actively learn and predict load change trends, nor can it adjust control parameters in advance to optimize energy consumption performance, resulting in control lag and limited energy-saving effects.
[0004] (3) The algorithm is disconnected from the hardware control unit. Traditional energy-saving control schemes typically design the control algorithm separately from the hardware control unit. The algorithm runs in a host computer or independent controller, lacking tight coupling with the underlying drive circuits and actuators. This architecture not only increases system complexity and response latency but also reduces operational stability and reliability.
[0005] (4) The control logic is simple and compatibility issues are prominent. Existing technologies often design dedicated control logic for specific types of loads; for example, frequency converters are specifically for motor speed control, and thyristor voltage regulators are specifically for resistive heating control. When dealing with mixed load scenarios, multiple independent control systems need to be configured, increasing equipment costs and management complexity.
[0006] (5) Lack of multi-parameter collaborative optimization capability Energy consumption optimization in real-world industrial scenarios often involves the coordinated adjustment of multiple parameters such as voltage, current, power, temperature, pressure, flow rate, and rotational speed. Existing technologies typically focus only on the control of a single parameter, lacking the ability to conduct comprehensive multi-parameter analysis and coordinated optimization, thus failing to achieve globally optimal energy-saving effects.
[0007] (6) Lack of fault prediction and adaptive protection logic Traditional protection mechanisms rely primarily on simple threshold comparisons, triggering protection actions only after a fault occurs. This passive protection approach cannot prevent potential faults and may cause equipment damage or safety accidents. Summary of the Invention
[0008] Purpose of the invention: The purpose of this invention is to provide an AI energy-saving control device and method with multi-load adaptive capability to solve the technical problems existing in the prior art, such as lack of load identification capability, insufficient self-learning capability of working conditions, low load prediction accuracy, disconnect between algorithm and hardware, single control mode, poor multi-parameter collaborative optimization capability, and lack of fault prediction mechanism.
[0009] Technical solution: A multi-load adaptive AI energy-saving control device, comprising: The AI underlying algorithm computing core module includes an industrial-grade embedded AI chip, and a multi-type load automatic identification algorithm module, a multi-condition historical data self-learning algorithm module, and a global energy-saving collaborative optimization algorithm module, which are respectively integrated in the industrial-grade embedded AI chip. A multi-dimensional data acquisition module, which is communicatively connected to the AI underlying algorithm core module, is used to collect voltage, current, active power, temperature, pressure, flow rate and speed parameters of the load device; A multi-protocol drive output module is communicatively connected to the AI underlying algorithm operation core module and is used to output frequency conversion drive protocol signals, thyristor trigger drive protocol signals and general industrial control communication protocol signals. The data storage and self-learning module is communicatively connected to the AI underlying algorithm operation core module and the multi-dimensional data acquisition module, respectively. It is used to store historical load operation data, operating condition fluctuation patterns and energy-saving optimization parameters, and continuously iterate the AI control model. The protection and communication module is connected to the AI underlying algorithm core module for real-time monitoring of abnormal data, prediction of fault risks and execution of overload protection. It also realizes data interaction through RS485 interface, Modbus protocol and Ethernet communication interface.
[0010] Furthermore, the industrial-grade embedded AI chip adopts a heterogeneous multi-core processor architecture, integrating a neural network acceleration unit and a digital signal processing unit, supporting real-time online inference computing and edge deployment.
[0011] Furthermore, the multi-dimensional data acquisition module includes a voltage transformer, a current transformer, a power factor sensor, a temperature sensor, a pressure sensor, a flow sensor, and a speed sensor. Each sensor is connected to the communication bus through an isolation amplifier circuit and an analog-to-digital converter circuit.
[0012] Furthermore, the multi-protocol driver output module supports four output modes: 0-10V analog output, 4-20mA current loop output, PWM pulse width modulation output, and RS485 digital communication output.
[0013] Furthermore, the data storage and self-learning module uses a non-volatile storage medium with a storage capacity of no less than 8GB, and supports data retention during power outages and historical data backtracking and querying.
[0014] A multi-load adaptive AI energy-saving control method includes the following control stages: Automatic load identification stage: The electrical characteristics and operating parameters of the load are collected by the multi-dimensional data acquisition module, and the load type is automatically identified and matched with the initial optimal control model by the multi-type load automatic identification algorithm module. The load types include variable frequency motor loads, thyristor resistive heating loads and mixed loads. Multi-condition self-learning stage: Real-time collection of operating data, environmental parameters and operating condition change data, and deep learning analysis using the multi-condition historical data self-learning algorithm module to establish a dedicated operating condition optimization model for energy consumption patterns and operating condition fluctuation cycles; Active prediction and energy-saving regulation stage: Based on the dedicated operating condition optimization model, predict the load change and energy consumption fluctuation trend, use the global energy-saving collaborative optimization algorithm module to perform global optimization and regulation, and output frequency conversion control command, thyristor conduction angle control command and start-stop timing control command; Model iteration and optimization phase: Continuously collect operational data and compare actual energy consumption with theoretical optimal energy consumption, and automatically iterate and optimize the AI control model; Fault prediction and safety protection phase: Real-time monitoring of abnormal values of operating parameters, prediction of equipment overload, over-temperature and electrical fault risks, and early output of protection commands.
[0015] Furthermore, in the active prediction energy-saving control stage, the adjustment range of the frequency conversion control command is 0-400Hz, and the adjustment accuracy is not less than 0.01Hz; the adjustment range of the thyristor conduction angle control command is 0-180 degrees, and the adjustment accuracy is not less than 0.1 degrees.
[0016] Furthermore, in the fault prediction and safety protection phase, when the current is detected to exceed 120% of the rated value, an overcurrent protection command is output within 100ms; when the temperature is detected to exceed the set threshold, an over-temperature protection command is output within 500ms; and when the voltage drop is detected to exceed the set range, an undervoltage protection command is output within 200ms.
[0017] Furthermore, the method also includes a remote monitoring stage, which connects to a host computer system via an Ethernet communication interface to achieve real-time monitoring of operating status, remote parameter configuration, and historical data export.
[0018] Beneficial effects: (1) Through the multi-type load automatic identification algorithm module, the device can automatically identify different types of loads and match the optimal control model, eliminating the need for manual on-site debugging and greatly reducing installation and debugging costs and workload.
[0019] (2) By establishing a multi-condition self-learning algorithm module and a dedicated condition optimization model, the device can proactively predict load change trends, adjust control parameters in advance, achieve predictive energy-saving control, and significantly improve energy-saving effect.
[0020] (3) The AI algorithm runs directly in the underlying hardware control unit, and is tightly coupled with the drive circuit and actuator, which reduces signal transmission delay, improves system response speed and control accuracy, and enhances operational stability.
[0021] (4) The multi-protocol drive output module supports multiple protocols such as frequency conversion drive, thyristor voltage regulation, and general industrial control communication. It can be compatible with multiple types of equipment such as frequency conversion motors, thyristor resistance heating, and mixed loads. One system can meet the needs of multiple scenarios.
[0022] (5) The global energy-saving collaborative optimization algorithm module can comprehensively analyze multi-dimensional parameters such as voltage, current, power, temperature, pressure, flow rate, and speed to generate a globally optimal control strategy, avoiding the local optimum problem caused by single parameter optimization.
[0023] (6) Through real-time monitoring and historical data analysis, the device can predict potential risks such as equipment overload, over-temperature, and electrical faults, and output protection commands in advance, transforming passive protection into active prevention, thereby improving equipment safety and service life.
[0024] (7) It connects to the host computer system through the Ethernet communication interface, supports real-time monitoring of operating status, remote configuration of parameters and export of historical data, and provides convenience for intelligent operation and maintenance management. Attached Figure Description
[0025] Figure 1 This is a block diagram of the overall structure of the AI energy-saving control device; Figure 2 This is a flowchart of the core control process of the underlying AI algorithm. Figure 3 This is a flowchart of automatic load identification and self-learning process. Detailed Implementation
[0026] To make the technical solution of the present invention clearer, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. Example 1
[0027] In an energy-saving renovation project of a large industrial water pump station, the device of this invention was used to control the energy saving of six water pump motors with a rated power of 75kW.
[0028] The device is configured as follows: The core AI underlying algorithm computing module uses an industrial-grade embedded AI chip with a main frequency of 1.2GHz and a built-in neural network acceleration unit, providing a computing power of no less than 8 TOPS. All three core algorithm modules have pre-built load identification model libraries and energy-saving optimization model libraries.
[0029] Multi-dimensional data acquisition module: configured with voltage transformer (accuracy class 0.5), current transformer (accuracy class 0.5), power factor sensor, active power transmitter, temperature sensor (for monitoring bearing temperature and stator temperature), pressure sensor (for monitoring pipeline pressure) and speed sensor (for monitoring motor speed).
[0030] Multi-protocol drive output module: Configured with a dedicated drive output interface for frequency converters, supporting 0-10V analog output and Modbus communication output, seamlessly connecting with ABB ACS580 series frequency converters installed in the field.
[0031] Data storage and self-learning module: Equipped with a 16GB industrial-grade solid-state storage chip, with ample space reserved for model storage and historical data storage.
[0032] Protection and Communication Module: Equipped with RS485 and Ethernet interfaces, it connects to the central control room's host computer system via Ethernet to achieve centralized monitoring and management.
[0033] Work process: (1) Automatic load identification stage: After the device is powered on, it automatically collects electrical parameters such as the starting current waveform, operating voltage, operating current, and power factor of the water pump motor. Through the analysis of the multi-type load automatic identification algorithm, it is identified as a variable frequency motor type load and the variable frequency drive control model is automatically loaded.
[0034] (2) Multi-condition self-learning stage: The device enters the learning mode and continuously collects 30 days of operating data, including load change patterns, working cycle, water demand changes, etc., to establish an energy consumption optimization model for the pumping station.
[0035] (3) Proactive prediction and energy-saving control stage: Based on the optimization model established by learning, predict the peak and valley distribution of water demand the next day, adjust the output frequency of the inverter in advance, and optimize the motor operating frequency to the economic zone under the premise of meeting water demand.
[0036] (4) Model Iteration and Optimization Stage: Continuously compare actual energy consumption with theoretical optimal energy consumption, and automatically update the control model parameters once a week.
[0037] (5) Fault prediction and safety protection stage: Real-time monitoring of bearing temperature, and reduction of operating frequency in advance when the predicted temperature will exceed the set threshold; monitoring of abnormal pipeline pressure and timely adjustment of control strategy.
[0038] Results: After the renovation, the pumping station achieved an annual power saving rate of 23.5%, saving approximately 180,000 yuan in electricity costs annually. At the same time, the equipment failure rate decreased by 60%, and maintenance costs decreased significantly. Example 2
[0039] In the temperature control system of the tempering furnace of a glass deep processing enterprise, the device of this invention is used to control the heating elements of three tempering furnaces for energy saving.
[0040] The device is configured as follows: The core module for AI underlying algorithm computation uses an industrial-grade embedded AI chip of the same specifications and has a pre-set dedicated control model for silicon controlled rectifier resistive heating loads.
[0041] Multi-dimensional data acquisition module: configured with voltage transformer, current transformer, active power transmitter, temperature sensor (multiple points arranged inside the furnace) and thermocouple signal conditioning circuit.
[0042] Multi-protocol drive output module: Configured with a thyristor trigger drive output interface, supporting 0-10V analog output and zero-crossing trigger pulse output, and interfacing with the three-phase thyristor voltage regulator installed in the field.
[0043] Data storage and self-learning module: configured with an 8GB industrial-grade storage chip.
[0044] Protection and communication module: Configured with RS485 interface and Ethernet interface.
[0045] Work process: (1) Automatic load identification stage: The device automatically collects the resistance characteristics, starting current waveform and temperature response characteristics of the heating element, identifies it as a thyristor resistive heating load, and automatically loads the thyristor voltage regulation control model.
[0046] (2) Multi-condition self-learning stage: Collect the heating and cooling curves of the tempering furnace, the variation law of product specifications, the influence of ambient temperature, and other factors to establish a temperature control optimization model.
[0047] (3) Proactive prediction of energy-saving regulation stage: Based on the production plan and process requirements, predict the furnace temperature regulation demand, optimize the thyristor conduction angle in advance, and reduce the energy loss of the over-temperature regulation process while ensuring product quality.
[0048] (4) Model Iteration and Optimization Stage: Compare the theoretical heating energy consumption and actual energy consumption of products of the same specifications, and continuously optimize the control model.
[0049] (5) Fault prediction and safety protection stage: Monitor potential faults such as open circuit faults of heating elements and abnormal thermocouples, issue early warnings and take protective measures.
[0050] Implementation results: The heating energy consumption of the tempering furnace was reduced by 18.2%, the temperature control accuracy was improved to ±2°C, and the product quality stability was significantly improved. Example 3
[0051] In the HVAC system of a general hospital's logistics building, the device of this invention is used to perform unified energy-saving control on mixed loads including chillers, cooling tower fans, circulating water pumps, and other equipment.
[0052] The device is configured as follows: The core module for AI underlying algorithm computation is equipped with a high-performance industrial-grade AI chip and a pre-built hybrid load comprehensive optimization control model.
[0053] Multi-dimensional data acquisition module: current sensor and power factor sensor for chiller unit; inverter communication interface for cooling tower fan; pressure sensor and flow sensor for circulating water pump; indoor and outdoor temperature and humidity sensor and air conditioning terminal temperature sensor.
[0054] Multi-protocol drive output module: configured with a frequency converter drive output interface (for fans and water pumps), a thyristor trigger drive output interface (for auxiliary electric heating), and a Modbus communication interface (for interfacing with the chiller unit controller).
[0055] Data storage and self-learning module: Configured with a 16GB storage chip to establish a complete HVAC system energy consumption database.
[0056] Protection and Communication Module: Configured with an Ethernet interface to interface with the building automation system via the BACnet protocol.
[0057] Work process: (1) Automatic load identification stage: The device automatically scans and identifies various load devices connected in the system, including variable frequency motor loads and thyristor resistive heating loads, and establishes a device list and communication connection.
[0058] (2) Multi-condition self-learning stage: Collect the operation data of the entire HVAC system, including meteorological parameters, building heating and cooling load variation patterns, and energy consumption characteristics of each equipment, and establish a comprehensive optimization model.
[0059] (3) Active prediction and energy-saving control stage: Access weather forecast data, predict the cooling and heating load demand of the next day, and generate a collaborative optimization control scheme based on the global optimization algorithm for the chiller outlet water temperature setpoint, cooling tower fan speed and circulating water pump flow rate.
[0060] (4) Model Iteration and Optimization Stage: The model parameters are continuously updated and optimized based on seasonal changes and equipment aging.
[0061] (5) Fault prediction and safety protection stage: Monitor the operating status of each device, predict fault risks, and ensure the safe and reliable operation of the system.
[0062] Implementation results: The annual comprehensive energy saving rate of the hospital's logistics building HVAC system reached 21.8%, the equipment operating efficiency was improved by 15%, and the workload of maintenance personnel was reduced by 40%. Example 4
[0063] The process of an AI energy-saving control method is as follows: Step 1: System Initialization The device performs a power-on self-test to confirm that all modules are working properly. Load the pre-built load identification model library and energy-saving optimization model library; Initialize the communication interface and establish a connection with the host computer.
[0064] Step 2: Automatic Load Identification The multi-dimensional data acquisition module begins collecting load electrical parameters; AI chip executes multi-type load automatic identification algorithms Determine the load type: variable frequency motor, thyristor resistive heating, or mixed load; Load the corresponding initial control model; If it cannot be automatically identified, a prompt will appear indicating that the load type needs to be manually configured.
[0065] Step 3: Data Acquisition Collect various operational data according to the preset sampling period; The data includes: voltage, current, active power, power factor, temperature, pressure, flow rate, and rotational speed; The collected data is filtered, calibrated, and its validity is verified. Valid data is stored in the data storage module.
[0066] Step 4: Training the self-learning model When the accumulated data reaches a preset threshold, the self-learning model training is started; Execute a self-learning algorithm based on historical data under multiple operating conditions; Analyze energy consumption patterns and the cyclical characteristics of operating conditions; Generate parameters for a custom operating condition optimization model; Replace the original initial control model.
[0067] Step 5: Proactive prediction and energy-saving control Load forecasting is based on a dedicated operating condition optimization model; Calculate the optimal control parameters for the next control cycle; Generate frequency conversion instructions, thyristor conduction angle instructions, and start / stop timing instructions; Control commands are output via a multi-protocol driven output module. Monitor the response status of the implementing agency.
[0068] Step 6: Iterative Model Optimization Continuously collect operational data; Calculate the deviation between actual energy consumption and theoretical optimal energy consumption; When the deviation exceeds the set threshold, the model parameters are updated. Execute online learning algorithms to update the control model; Record model update logs.
[0069] Step 7: Fault Prediction and Protection Real-time monitoring of the instantaneous values and trends of each parameter; Analyze potential failure risks based on a historical failure case library; When a failure risk is predicted, an early warning message is issued in advance; When an emergency anomaly is detected, a protection command is immediately output; Record the fault events and upload them to the host computer.
[0070] Step 8: Remote Monitoring and Data Management Real-time operation data is pushed to the host computer via Ethernet interface; Respond to parameter query and configuration commands from the host computer; Regularly export historical data to generate reports; Supports remote firmware upgrades and model updates. Example 5
[0071] In practical applications, the load type may change (such as when equipment is replaced). This invention provides the following processing method: Step 1: Continuously monitor the electrical characteristics of the load. Perform load identification and detection periodically; When a significant change in electrical characteristic parameters is detected, a re-identification process is triggered.
[0072] Step 2: Re-identify the load type Collect new load electrical parameters; Execute the automatic load identification algorithm; If the recognition result matches the current model, continue operating normally; If the recognition result changes, proceed to step 3.
[0073] Step 3: Model Switching Save the current control model parameters as a backup; Load the control model corresponding to the newly identified load type; Execute the initialization process for the new model; Gradually transition to the control strategy of the new model to avoid control mutations.
[0074] Step 4: Observation during the transition period Strengthen operational data monitoring during the transition period; Collect control effect data for the new model; If the control effect is normal, the new model will be officially implemented. If the control effect is abnormal, revert to the backup model and issue an alarm. Example 6
[0075] In mixed load scenarios, the present invention provides the following collaborative optimization control method: Step 1: Establish the equipment association model Identify the process relationships between various devices in the system; Establish a database of constraints for equipment linkage control; Define device priorities and interlock relationships.
[0076] Step 2: Construct the global optimization objective function Taking into account factors such as overall system energy efficiency, equipment lifespan, and operating costs; Construct a weighted objective function that includes multiple optimization objectives; Define the weight coefficients for each optimization objective.
[0077] Step 3: Distributed Collaborative Computing Each device sub-controller performs local optimization calculations; Upload the local optimization results to the core module of AI underlying algorithm computation; The core module performs global coordination and optimization calculations; Generate the globally optimal set of control instructions.
[0078] Step 4: Issuance and Execution of Commands Control commands are issued according to equipment priority and timing requirements; Each device sub-controller receives and executes instructions; Monitor execution feedback to ensure instructions are executed correctly; If an error occurs, execute the fault handling procedure.
[0079] Step 5: Effectiveness Evaluation and Feedback Calculate the achievement of the global optimization objective; Compare system performance metrics before and after optimization; Adjust and optimize the objective function parameters based on the evaluation results; Continuously improve the collaborative optimization algorithm. Example 7
[0080] Multi-type load automatic identification algorithm This invention employs a deep learning-based automatic load identification algorithm, which mainly includes the following steps: Data preprocessing: The collected electrical parameter data are normalized and feature extracted. The extracted features include peak starting current, starting duration, steady-state current waveform, power factor range, voltage and current phase difference, etc.
[0081] Feature vector construction: The extracted features are combined into a multi-dimensional feature vector, which is then used as input to the classifier.
[0082] Neural Network Classification: A convolutional neural network is used for load type classification. The input layer of the network receives feature vectors, and the output layer outputs the probability distribution of load types.
[0083] Post-processing of classification results: The reliability of the recognition results is judged based on the probability threshold. If the reliability is insufficient, a manual confirmation prompt is output.
[0084] Multi-condition historical data self-learning algorithm This invention employs a time series prediction algorithm based on Long Short-Term Memory (LSTM) networks for self-learning of operating conditions: Data serialization processing: Organize historical operating data into time series samples, including historical operating condition data and environmental parameter data.
[0085] LSTM network training: Construct a multi-layer LSTM network and train the network parameters through the backpropagation algorithm so that the network learns the temporal patterns of operating conditions.
[0086] Prediction model generation: After training, the weight parameters of the LSTM network are saved to generate a model file that can be used for real-time prediction.
[0087] Online update mechanism: The model is incrementally trained using newly collected data on a regular basis, enabling continuous learning and updating of the model.
[0088] Global energy-saving collaborative optimization algorithm This invention employs a collaborative optimization algorithm based on deep reinforcement learning: State space definition: Defines the system state space containing all relevant parameters, including load operating parameters, environmental parameters, prediction parameters, etc.
[0089] Action space definition: Defines the executable optimized action space, including the adjustment range and step size of each control parameter.
[0090] Reward function design: Design a reward function that comprehensively considers energy efficiency improvement, equipment protection, and control stability.
[0091] Deep Q-Network Training: A deep Q-network is used to learn the optimal control policy and continuously optimize the policy parameters through interaction with the environment.
[0092] Strategy Application: Deploy the trained strategy model to the edge to achieve real-time online optimization and control.
[0093] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.
Claims
1. A multi-load adaptive AI energy-saving control device, characterized in that, include: The AI underlying algorithm computing core module includes an industrial-grade embedded AI chip, and a multi-type load automatic identification algorithm module, a multi-condition historical data self-learning algorithm module, and a global energy-saving collaborative optimization algorithm module, which are respectively integrated in the industrial-grade embedded AI chip. A multi-dimensional data acquisition module, which is communicatively connected to the AI underlying algorithm core module, is used to collect voltage, current, active power, temperature, pressure, flow rate and speed parameters of the load device; A multi-protocol drive output module is communicatively connected to the AI underlying algorithm operation core module and is used to output frequency conversion drive protocol signals, thyristor trigger drive protocol signals and general industrial control communication protocol signals. The data storage and self-learning module is communicatively connected to the AI underlying algorithm operation core module and the multi-dimensional data acquisition module, respectively. It is used to store historical load operation data, operating condition fluctuation patterns and energy-saving optimization parameters, and continuously iterate the AI control model. The protection and communication module is connected to the AI underlying algorithm core module for real-time monitoring of abnormal data, prediction of fault risks and execution of overload protection. It also realizes data interaction through RS485 interface, Modbus protocol and Ethernet communication interface.
2. The multi-load adaptive AI energy-saving control device according to claim 1, characterized in that, The industrial-grade embedded AI chip adopts a heterogeneous multi-core processor architecture, integrating a neural network acceleration unit and a digital signal processing unit, supporting real-time online inference computing and edge deployment.
3. The multi-load adaptive AI energy-saving control device according to claim 1, characterized in that, The multi-dimensional data acquisition module includes a voltage transformer, a current transformer, a power factor sensor, a temperature sensor, a pressure sensor, a flow sensor, and a speed sensor. Each sensor is connected to the communication bus through an isolation amplifier circuit and an analog-to-digital converter circuit.
4. The multi-load adaptive AI energy-saving control device according to claim 1, characterized in that, The multi-protocol driver output module supports four output modes: 0-10V analog output, 4-20mA current loop output, PWM pulse width modulation output, and RS485 digital communication output.
5. The multi-load adaptive AI energy-saving control device according to claim 1, characterized in that, The data storage and self-learning module uses non-volatile storage media with a storage capacity of no less than 8GB, and supports data retention during power outages and historical data backtracking queries.
6. A multi-load adaptive AI energy-saving control method, characterized in that, Includes the following control phases: Automatic load identification stage: The electrical characteristics and operating parameters of the load are collected by the multi-dimensional data acquisition module, and the load type is automatically identified and matched with the initial optimal control model by the multi-type load automatic identification algorithm module. The load types include variable frequency motor loads, thyristor resistive heating loads and mixed loads. Multi-condition self-learning stage: Real-time collection of operating data, environmental parameters and operating condition change data, and deep learning analysis using the multi-condition historical data self-learning algorithm module to establish a dedicated operating condition optimization model for energy consumption patterns and operating condition fluctuation cycles; Active prediction and energy-saving regulation stage: Based on the dedicated operating condition optimization model, predict the load change and energy consumption fluctuation trend, use the global energy-saving collaborative optimization algorithm module to perform global optimization and regulation, and output frequency conversion control command, thyristor conduction angle control command and start-stop timing control command; Model iteration and optimization phase: Continuously collect operational data and compare actual energy consumption with theoretical optimal energy consumption, and automatically iterate and optimize the AI control model; Fault prediction and safety protection phase: Real-time monitoring of abnormal values of operating parameters, prediction of equipment overload, over-temperature and electrical fault risks, and early output of protection commands.
7. The multi-load adaptive AI energy-saving control method according to claim 6, characterized in that, In the active prediction and energy-saving control stage, the adjustment range of the frequency conversion control command is 0-400Hz, and the adjustment accuracy is not less than 0.01Hz; the adjustment range of the thyristor conduction angle control command is 0-180 degrees, and the adjustment accuracy is not less than 0.1 degrees.
8. The multi-load adaptive AI energy-saving control method according to claim 6, characterized in that, During the fault prediction and safety protection phase, when the current is detected to exceed 120% of the rated value, an overcurrent protection command is output within 100ms; when the temperature is detected to exceed the set threshold, an over-temperature protection command is output within 500ms; and when the voltage drop is detected to exceed the set range, an undervoltage protection command is output within 200ms.
9. The multi-load adaptive AI energy-saving control method according to claim 6, characterized in that, The method also includes a remote monitoring phase, which connects to a host computer system via an Ethernet communication interface to achieve real-time monitoring of operating status, remote parameter configuration, and historical data export.