Power regulation method and device based on load feedforward, medium and product

Abstract

The invention provides a power regulation method and device based on load feedforward, a medium and a product, and relates to the technical field of power regulation, and the method comprises the steps: carrying out the real-time monitoring of the working condition state of a target load, so as to obtain a load state signal, and the load state signal is used for representing the actual control information of the target load; the load state signal is input to a preset logic classifier to obtain a load type output by the preset logic classifier, and the load type is the type of a target load output after the preset logic classifier classifies the load state signal; predicting the state change trend of the target load according to the load type and the load state signal to obtain a load feedforward signal; and performing a power regulation operation according to the load feed-forward signal. According to the invention, the technical problem of low power regulation efficiency of the generator set in the case of load fluctuation in the prior art is solved, and the technical effect of improving the power regulation efficiency of the generator set in the case of load fluctuation is achieved.

Description

Technical Field

[0001] This application relates to the technical field of power regulation, and particularly to a power regulation method, device, medium, and product based on load feedforward. Background Art

[0002] With the continuous growth of modern power demand, as the core component of the power supply system, the stable operation and rapid response ability of the generator set are crucial for ensuring power quality and meeting load requirements.

[0003] In the related art, the generator set mainly adjusts the output power by sensing the changes in current and voltage. Specifically, when the load in the power system changes, it will directly cause fluctuations in current and voltage. The generator set controller controls the generator to increase or decrease the output power by the changes in current and voltage to cope with the increase or decrease in load demand. However, in this power regulation process, when the output power of the generator increases, the engine as the power source will also face an increase in load. To cope with this increase in load, the engine controller needs to dynamically adjust the supply of fuel and air to ensure that the engine can output sufficient power to meet the load demand.

[0004] However, adopting the above method of controlling the output power by sensing the changes in current and voltage, since there are multiple links (such as load change sensing, generator output power adjustment, engine load increase and sensing, etc.) between the load change and the increase in the engine output power, the existence of these links may cause a delay in the control of the output power. This delay makes the generator set unable to respond quickly to load fluctuations, and thus the power regulation efficiency of the generator set in the related art when facing load fluctuations is relatively low. Summary of the Invention

[0005] This application provides a power regulation method, device, medium, and product based on load feedforward, which is used to improve the power regulation efficiency of the generator set when facing load fluctuations.

[0006] In a first aspect, this application provides a power regulation method based on load feedforward, which is applied to the above-mentioned electronic device. The method includes: monitoring the working condition state of the target load in real time to obtain a load state signal, where the load state signal is used to represent the actual control information of the target load; inputting the load state signal into a preset logic classifier to obtain the load type output by the preset logic classifier, where the load type is the type of the target load output after the preset logic classifier classifies and processes the load state signal; predicting the state change trend of the target load according to the load type and the load state signal to obtain a load feedforward signal; and performing a power regulation operation according to the load feedforward signal.

[0007] By adopting the above technical solutions, the working condition state of the target load can be monitored in real time, and the load status signal can be obtained accordingly, which can provide basic data support for subsequent load type classification and status change trend prediction. The accurate acquisition of the load status signal can reflect the actual control information of the target load in real time, and can lay a foundation for the timeliness and accuracy of power regulation. By presetting a logic classifier to classify the load status signal, the load type can be accurately and automatically identified, so as to perform refined power regulation for different load types, thereby improving the accuracy and efficiency of power regulation. Furthermore, the technical problem of low power regulation efficiency of the generator set in the face of load fluctuations in the related art is solved, and the technical effect of improving the power regulation efficiency of the generator set in the face of load fluctuations is achieved.

[0008] Optionally, input the load status signal into a preset logic classifier to obtain the load type output by the preset logic classifier, which specifically includes: performing timing synchronization processing on the load status signal; extracting features from the load status signal after timing synchronization processing to obtain key features, where the key features are used to characterize the load characteristics of the target load; inputting the key features into the preset logic classifier to control the preset logic classifier to perform the following operations: when the preset logic classifier determines that the key features are received, call the preset classification strategy to perform classification detection on the key features to output the load type; when the preset logic classifier determines that the fluctuation range of the first status signal is continuously less than the first preset fluctuation range within the first preset duration according to the classification detection result, output the first load corresponding to the first status signal as the continuously unchanged load type, where the load status signal includes the first status signal, the target load includes the first load, and the load type includes the continuously unchanged load type; when the preset logic classifier determines that the second status signal fluctuates periodically within the second preset duration according to the classification detection result, output the second load corresponding to the second status signal as the continuously changing load type, where the load status signal includes the second status signal, the target load includes the second load, and the load type includes the continuously changing load type; when the preset logic classifier determines that the third status signal fluctuates stepwise within the third preset duration according to the classification detection result, output the third load corresponding to the third status signal as the intermittent load type, where the load status signal includes the third status signal, the target load includes the third load, and the load type includes the intermittent load type; when the preset logic classifier determines that the fluctuation range of the fourth status signal is greater than the second preset fluctuation range and the duration is less than the fourth preset duration according to the classification detection result, output the fourth load corresponding to the fourth status signal as the short-term load type, where the second preset fluctuation range is greater than the first preset fluctuation range, the load status signal includes the fourth status signal, the target load includes the fourth load, and the load type includes the short-term load type; obtain the continuously unchanged load type, continuously changing load type, intermittent load type, and short-term load type output by the preset logic classifier.

[0009] By adopting the above technical solutions, performing timing synchronization processing and feature extraction on the load status signal can ensure the accuracy and reliability of the key features. Inputting the key features into the preset logic classifier and calling the preset classification strategy can accurately output the load type. By classifying the continuously unchanged load, continuously changing load, intermittent load, and short-term load in detail, an accurate classification basis can be provided for predicting the subsequent load status change trend. Through meticulous classification, the characteristics of the load can be understood more accurately, thereby providing a more accurate feedforward signal for power regulation.

[0010] Optionally, predict the state change trend of the target load according to the load type and the load status signal to obtain a load feedforward signal, specifically including: obtaining the historical operation data of the first load, and predicting the stability trend of the first load according to the historical operation data, the first state signal, and the continuously unchanged load type to obtain a first feedforward signal, where the load feedforward signal includes the first feedforward signal; establishing a time series analysis model, and predicting the periodic fluctuation trend of the second load according to the time series analysis model, the second state signal, and the continuously changing load type to obtain a second feedforward signal, where the load feedforward signal includes the second feedforward signal; obtaining the historical mutation point data of the third load, and predicting the step change trend of the third load according to the historical mutation point data, the third state signal, and the intermittent load type to obtain a third feedforward signal, where the load feedforward signal includes the third feedforward signal; obtaining the historical burst demand data of the fourth load, and predicting the burst demand trend of the fourth load according to the historical burst demand data, the fourth state signal, and the short-term load type to obtain a fourth feedforward signal, where the load feedforward signal includes the fourth feedforward signal.

[0011] By adopting the above technical solution, it is possible to predict the state change trend of the target load according to the load type and the load status signal, and obtain a load feedforward signal. For continuously unchanged loads, predict their stability trend through historical operation data; for continuously changing loads, establish a time series analysis model to predict their periodic fluctuation trend; for intermittent loads, predict their step change trend through historical mutation point data; for short-term loads, predict their burst demand trend through historical burst demand data. This enables the state change trend of different types of loads to be obtained in advance, providing forward-looking guidance for power regulation, and thus improving the efficiency and accuracy of power regulation.

[0012] Optionally, perform a power regulation operation according to the load feedforward signal, specifically including: when it is determined that the load feedforward signal is the first feedforward signal and the stability trend of the first load is continuously stable or stable within a first preset fluctuation range, maintain the current first output power of the target engine and monitor the fluctuation range of the first state signal; when it is monitored that the fluctuation range of the first state signal continuously exceeds the first preset fluctuation range within a fifth preset time period, determine that the first load has been switched to another load, and trigger a preset logic classifier to reclassify the first load.

[0013] By adopting the above technical solution, when it is determined that the load feedforward signal is the first feedforward signal, the current first output power of the target engine is maintained, and the fluctuation range of the first state signal is monitored. When the load stability trend remains stable or is stable within the first preset fluctuation range, the output power of the engine can be maintained stable, thereby avoiding unnecessary power adjustments and further saving energy. At the same time, by continuously monitoring the fluctuation range of the load state signal, potential changes in the load can be detected in a timely manner, triggering reclassification, and thus ensuring the sensitivity and response speed to changes in the load state.

[0014] Optionally, performing a power adjustment operation according to the load feedforward signal specifically includes: in the case where it is determined that the load feedforward signal is the second feedforward signal, extracting the periodic parameters of the second load from the second state signal by using a timing analysis model; calculating the phase compensation time for triggering the power adjustment operation according to the periodic parameters; at the starting moment of the trough stage of the periodic fluctuation of the second load, triggering the target power adjustment function before the phase compensation time, and controlling the target engine to synchronously adjust the current second output power according to the periodic parameters.

[0015] By adopting the above technical solution, when it is determined that the load feedforward signal is the second feedforward signal, the periodic parameters of the continuously changing load are extracted by using a timing analysis model, and the phase compensation time is calculated according to the periodic parameters. It is possible to trigger the power adjustment operation in advance before the starting moment of the trough stage of the load periodic fluctuation, so as to ensure that the output power of the target engine changes synchronously with the load demand. Through phase compensation, the hysteresis of power adjustment can be effectively reduced, and thus the real-time performance and accuracy of power adjustment are improved.

[0016] Optionally, performing a power adjustment operation according to the load feedforward signal specifically includes: in the case where it is determined that the load feedforward signal is the third feedforward signal, determining the start-stop time interval of the third load according to the historical mutation point data; performing a transition detection on the third state signal according to the start-stop time interval; when the rising edge of the transition of the third state signal is detected, controlling the target engine to increase the current third output power to a preset safety margin value; when the falling edge of the transition of the third state signal is detected, controlling the target engine to reduce the third output power to a first reference power according to a preset attenuation rate, where the first reference power is the first steady-state output power of the third load before the rising edge of the transition is triggered; in the case where the steepness of the detected rising edge or falling edge of the transition is greater than a preset steepness threshold, performing a damping compensation process on the rising edge or falling edge of the transition.

[0017] By adopting the above technical solution, the start-stop time interval of the intermittent load can be accurately identified, and the power can be adjusted according to the result of the transition detection. When the rising edge of the transition is detected, the output power of the target engine is promptly increased to the preset safety margin value. When the falling edge of the transition is detected, the output power is reduced to the reference power at the preset attenuation rate, which can avoid power shortage or excess caused by sudden load changes. At the same time, damping compensation processing is performed on the transition edge with a large slope, which can effectively reduce the fluctuations and impacts during the power adjustment process.

[0018] Optionally, performing a power adjustment operation according to the load feedforward signal specifically includes: when it is determined that the load feedforward signal is the fourth feedforward signal, determining the transient impact amplitude and duration of the fourth load according to the historical burst demand data; performing burst fluctuation detection on the fourth state signal according to the transient impact amplitude and duration; when it is detected that the amplitude of the fourth state signal exceeds the transient impact amplitude and the duration is less than the duration, determining the start of the burst demand, and controlling the target engine to increase the current fourth output power to the transient impact amplitude within the target time period of the duration; when it is detected that the amplitude of the fourth state signal drops back to the preset baseline value or the duration reaches the duration, determining the end of the burst demand, and controlling the target engine to reduce the fourth output power to the second reference power according to the preset inertia time constant, where the second reference power is the second steady-state output power of the fourth load before the start of the burst demand.

[0019] By adopting the above technical solution, the transient impact amplitude and duration of the short-term load can be accurately identified, and the power can be adjusted according to the result of the burst fluctuation detection. When the start of the burst demand is detected, the output power of the target engine is promptly increased to the transient impact amplitude to meet the instantaneous demand of the load; when the end of the burst demand is detected, the output power is reduced to the reference power according to the preset inertia time constant to ensure stability and reliability under burst demands, which can not only improve the adaptability to short-term load changes, but also effectively reduce the impact of power fluctuations on the overall performance.

[0020] In a second aspect, an embodiment of the present application provides an electronic device, which includes: one or more processors and a memory; the memory is coupled to the one or more processors, and the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to cause the electronic device to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0021] In a third aspect, an embodiment of the present application provides a computer program product containing instructions. When the above computer program product runs on an electronic device, it causes the above electronic device to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0022] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium including instructions, which, when running on an electronic device, cause the electronic device to execute the method described in the first aspect and any possible implementation manner in the first aspect.

[0023] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages: 1. The power regulation method based on load feedforward provided by the present application can monitor the operating condition of the target load in real time, obtain the load status signal accordingly, and provide basic data support for subsequent load type classification and state change trend prediction. The accurate acquisition of the load status signal can reflect the actual control information of the target load in real time, laying a foundation for the timeliness and accuracy of power regulation. By classifying the load status signal through a preset logic classifier, the load type can be accurately and automatically identified, so as to perform refined power regulation for different load types, thereby improving the accuracy and efficiency of power regulation.

[0024] 2. The power regulation method based on load feedforward provided by the present application performs time series synchronization processing and feature extraction on the load status signal, which can ensure the accuracy and reliability of key features. Inputting the key features into a preset logic classifier and calling the preset classification strategy can accurately output the load type. By classifying continuous constant loads, continuous variable loads, intermittent loads, and short-term loads in detail, a precise classification basis can be provided for subsequent prediction of load state change trends. Through detailed classification, the characteristics of the load can be understood more accurately, thereby providing a more accurate feedforward signal for power regulation.

[0025] 3. The power regulation method based on load feedforward provided by the present application can predict the state change trend of the target load according to the load type and load status signal to obtain the load feedforward signal. For continuous constant loads, predict their stability trend through historical operation data; for continuous variable loads, establish a time series analysis model to predict their periodic fluctuation trend; for intermittent loads, predict their step change trend through historical mutation point data; for short-term loads, predict their sudden demand trend through historical sudden demand data. This enables the state change trend of different types of loads to be obtained in advance, providing forward-looking guidance for power regulation, and further improving the efficiency and accuracy of power regulation. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 is a flowchart of the power regulation method based on load feedforward in an embodiment of the present application; Figure 2 is a schematic structural diagram of an entity device of an electronic device in an embodiment of the present application. Detailed implementation manners

[0027] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the present application. As used in the specification and appended claims of the present application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are also intended to include the plural forms, unless the context clearly indicates otherwise. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations including one or more of the listed items.

[0028] Hereinafter, the terms "first" and "second" are only used for descriptive purposes, and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first" and "second" may explicitly or implicitly include one or more of such features. In the description of the embodiments of the present application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0029] The present application provides a power regulation method based on load feedforward. Refer to Figure 1 , Figure 1 which is a flow schematic diagram of the power regulation method based on load feedforward in the embodiments of the present application, and includes the following steps: Step S101, monitor the working condition state of the target load in real time to obtain a load state signal, where the load state signal is used to characterize the actual control information of the target load; In the above embodiment, the target load refers to a device or apparatus that actually bears power or performs work. For example, an electric motor, a heater, a hydraulic station, etc. The working condition state refers to the specific operating state or conditions during the operation of the target load, including but not limited to running, stopping, overloading, underloading, malfunction, etc. The load state signal is used to characterize the actual control information of the target load (such as motor start / stop signal, frequency command of the frequency converter, hydraulic valve opening signal, etc.), that is, it reflects the direct control command or operation state of the target load. The direct control command or operation state is obtained by monitoring the actual control information of the target load, rather than relying on the voltage and current feedback of the generator set. Specifically, assuming that the working condition state of the hydraulic station is monitored in real time, the pressure, flow rate, temperature change and other data of the hydraulic station can be monitored in real time through the pressure sensor, flow sensor and temperature sensor installed on the hydraulic station, and these real-time monitoring data are used as the load state signal.

[0030] Step S102, input the load state signal into a preset logic classifier to obtain the load type output by the preset logic classifier, where the load type is the type of the target load output after the preset logic classifier classifies the load state signal; In the above embodiments, the preset logic classifier refers to an algorithm or model that is designed and trained in advance and can perform logical analysis and classification processing on the input load status signal. The load type is the category of the target load output after the preset logic classifier classifies the load status signal. For example, the motor type, heater type, hydraulic station type, etc., which helps the engine control system to perform differential management and control on different types of target loads. Specifically, assuming that the load status signal is input into the trained preset logic classifier, the preset logic classifier can identify the type of the hydraulic station (for example, hydraulic stations of different types and specifications) according to the input pressure, flow rate, and temperature data.

[0031] Step S103: Predict the state change trend of the target load according to the load type and the load status signal to obtain a load feedforward signal; In the above embodiments, the load feedforward signal is predicted based on the load type and the load status signal, and is used to feed back in advance the possible state changes or development trends of the target load in a future period of time. Specifically, predict the possible state changes (such as pressure fluctuations, flow rate changes, etc.) of the hydraulic station in a future period of time according to the hydraulic station type and the load status signal, and generate a load feedforward signal.

[0032] Step S104: Perform a power adjustment operation according to the load feedforward signal.

[0033] In the above embodiments, the power adjustment operation refers to an operation of adjusting or controlling the power according to the load feedforward signal to meet specific requirements or optimize the operating state of the load. The power adjustment operation can directly predict and control through the target load conditions, rather than passively responding to changes in voltage and current. Specifically, the power output or control strategy of the hydraulic station can be adjusted according to the load feedforward signal to optimize the operating state of the hydraulic station or avoid potential problems. For example, if it is predicted that the hydraulic station is about to be overloaded, the power output can be reduced in advance or protection measures can be started, and so on.

[0034] Through the above steps, the operating conditions of the target load can be monitored in real time, and the load status signal can be obtained accordingly, which can provide basic data support for subsequent load type classification and prediction of the state change trend. The accurate acquisition of the load status signal can reflect the actual control information of the target load in real time, and can lay a foundation for the timeliness and accuracy of power regulation. By classifying the load status signal through a preset logic classifier, the load type can be accurately and automatically identified, so as to perform refined power regulation for different load types, thereby improving the accuracy and efficiency of power regulation. Furthermore, the technical problem of low power regulation efficiency of the generator set in the face of load fluctuations in the related art is solved, and the technical effect of improving the power regulation efficiency of the generator set in the face of load fluctuations is achieved.

[0035] Among them, the execution subject of the above steps can be an engine control system with power regulation capabilities, or an engine control device with power regulation capabilities, or an engine controller or engine processor in a device or system, or a separate engine controller or engine processor, or it can also be other processing devices or processing units with similar processing functions, etc., but not limited thereto.

[0036] In an optional embodiment, the load status signal is input into a preset logic classifier to obtain the load type output by the preset logic classifier, which specifically includes: performing timing synchronization processing on the load status signal; extracting features from the load status signal after timing synchronization processing to obtain key features, where the key features are used to characterize the load characteristics of the target load; inputting the key features into the preset logic classifier to control the preset logic classifier to perform the following operations: when the preset logic classifier determines that the key features are received, it calls a preset classification strategy to perform classification detection on the key features to output the load type; when the preset logic classifier determines that the fluctuation range of the first status signal continuously is less than the first preset fluctuation range within the first preset duration according to the classification detection result, it outputs that the first load corresponding to the first status signal is of the continuously unchanged load type, where the load status signal includes the first status signal, the target load includes the first load, and the load type includes the continuously unchanged load type; when the preset logic classifier determines that the second status signal fluctuates periodically within the second preset duration according to the classification detection result, it outputs that the second load corresponding to the second status signal is of the continuously changing load type, where the load status signal includes the second status signal, the target load includes the second load, and the load type includes the continuously changing load type; when the preset logic classifier determines that the third status signal fluctuates stepwise within the third preset duration according to the classification detection result, it outputs that the third load corresponding to the third status signal is of the intermittent load type, where the load status signal includes the third status signal, the target load includes the third load, and the load type includes the intermittent load type; when the preset logic classifier determines that the fluctuation range of the fourth status signal is greater than the second preset fluctuation range and the continuous duration is less than the fourth preset duration according to the classification detection result, it outputs that the fourth load corresponding to the fourth status signal is of the short-time load type, where the second preset fluctuation range is greater than the first preset fluctuation range, the load status signal includes the fourth status signal, the target load includes the fourth load, and the load type includes the short-time load type; obtaining the continuously unchanged load type, the continuously changing load type, the intermittent load type, and the short-time load type output by the preset logic classifier.

[0037] In the above embodiments, it is assumed that in an industrial environment, there are various types of target loads (for example, motors, hydraulic pumps, lighting equipment, etc.). In order to accurately classify and manage these target loads, a solution based on a preset logic classifier can be adopted. The specific implementation steps are as follows: Install a current sensor and a speed sensor on the motor, install a pressure sensor and a flow sensor on the hydraulic pump, and install a light intensity sensor and a power consumption sensor on the lighting equipment. These sensors continuously monitor the operating conditions of each target load and generate corresponding load status signals. Due to possible time synchronization problems among different sensors, the load status signals are subjected to time series synchronization processing to ensure that all load status signals are consistent on the time axis. Feature extraction is performed on the load status signals after time series synchronization processing. For example, key features such as average current, maximum current, and average speed are extracted from the current and speed signals of the motor, features such as pressure fluctuation range and flow stability are extracted from the pressure and flow signals of the hydraulic pump, and features such as light intensity stability and average power consumption are extracted from the light intensity and power consumption signals of the lighting equipment. The key features are input into the preset logic classifier. After receiving the key features, the preset logic classifier classifies the target loads according to the preset classification strategy. When the average current and speed of the motor remain stable for a long time (i.e., the fluctuation range within the first preset time period is continuously less than the first preset fluctuation range), it is determined that the motor is a continuously unchanged load type. When the pressure and flow signals of the hydraulic pump show periodic fluctuations within a certain period of time (i.e., periodic fluctuations occur within the second preset time period), it is determined that the hydraulic pump is a continuously variable load type. For example, some hydraulic pumps will periodically adjust the pressure and flow to meet production requirements under specific operating conditions. When the light intensity signal of the lighting equipment frequently undergoes step-like fluctuations within a certain period of time (i.e., step-like fluctuations occur within the third preset time period), it is determined that the lighting equipment is an intermittent load type, which may be caused by frequent switching or brightness adjustment of the lighting equipment. When the fluctuation range of the status signal of a certain target load (such as a motor or a hydraulic pump, etc.) is abnormally large within a short period of time (i.e., greater than the second preset fluctuation range and the duration is less than the fourth preset time period), it is determined that the load is a short-term load type, which may be caused by a sudden impact or failure of the load.

[0038] In the above embodiments, according to the classification results of the preset logic classifier, different types of loads can be managed and controlled differentially. For example, for continuously invariant load types, a stable power supply strategy can be adopted; for continuously variable load types, the power supply strategy can be adjusted according to their periodic fluctuations to improve energy efficiency; for intermittent load types, their switching control strategies can be optimized to reduce energy consumption; for short-term load types, potential faults or impacts can be detected and processed in a timely manner. Specifically, the current and rotational speed signals of motor A remain stable for a long time, and the fluctuation range is less than the set first preset fluctuation range, so it is classified as a continuously invariant load type. The pressure and flow signals of hydraulic pump B have a peak period in the morning and afternoon every day, showing periodic fluctuations, so it is classified as a continuously variable load type. The light intensity signal of lighting device C frequently switches at night, showing step-like fluctuations, so it is classified as an intermittent load type. During a certain operation, motor D was suddenly impacted, resulting in a sharp fluctuation of the current and rotational speed signals within a short time and exceeding the set second preset fluctuation range, so it is classified as a short-term load type and a fault detection mechanism is triggered. Through the above specific implementation steps, different types of target loads can be accurately classified, providing strong support for subsequent management and control.

[0039] In an alternative embodiment, the state change trend of the target load is predicted according to the load type and the load state signal to obtain a load feedforward signal, which specifically includes: obtaining the historical operation data of the first load, and predicting the stability trend of the first load according to the historical operation data, the first state signal, and the continuously invariant load class to obtain a first feedforward signal, where the load feedforward signal includes the first feedforward signal; establishing a time series analysis model, and predicting the periodic fluctuation trend of the second load according to the time series analysis model, the second state signal, and the continuously variable load class to obtain a second feedforward signal, where the load feedforward signal includes the second feedforward signal; obtaining the historical mutation point data of the third load, and predicting the step-like change trend of the third load according to the historical mutation point data, the third state signal, and the intermittent load type to obtain a third feedforward signal, where the load feedforward signal includes the third feedforward signal; obtaining the historical sudden demand data of the fourth load, and predicting the sudden demand trend of the fourth load according to the historical sudden demand data, the fourth state signal, and the short-term load type to obtain a fourth feedforward signal, where the load feedforward signal includes the fourth feedforward signal.

[0040] In the above embodiments, assume that in an intelligent factory environment, there are multiple types of target loads, including motors on the production line (first load), hydraulic lifting platforms for material handling (second load), intermittent working welding robots (third load), and temporary power supply equipment added to handle emergency orders (fourth load). To optimize the factory operation efficiency, it is necessary to accurately predict the state change trends of these target loads and adjust the resource allocation and energy management strategies in advance accordingly. The specific implementation steps are as follows: The motor is a continuously invariant load type, and the historical operation data is to collect the operation data of the motor over a period of time in the past, including but not limited to time series data of parameters such as current, voltage, and speed. The first state signal is the real-time current signal of the current motor. Machine learning algorithms can be used to train the historical operation data to establish a stability prediction model. Input the real-time current signal of the current motor (i.e., the first state signal) into the stability prediction model. Combining the characteristics of the continuously invariant load type, predict the stability trend of the motor in the future for a period of time. Output the predicted stability trend (i.e., the first feedforward signal) for adjusting the maintenance plan and energy allocation strategy of the motor.

[0041] In the above embodiments, the hydraulic lifting platform is a continuously changing load type, and the time series analysis model can be a seasonal time series analysis model, etc. The second state signal is the real-time pressure signal of the current hydraulic lifting platform. Use historical data to train the time series analysis model to identify the periodic fluctuation pattern of the hydraulic lifting platform. Input the current pressure signal (i.e., the second state signal) into the time series analysis model. Combining the periodic characteristics of the continuously changing load type, predict the periodic fluctuation trend of the hydraulic lifting platform in the future for a period of time. Output the predicted periodic fluctuation trend (i.e., the second feedforward signal) for optimizing the material handling plan and energy scheduling.

[0042] In the above embodiments, the welding robot is an intermittent load type (welding robot), and record the data of the state mutation points (i.e., historical mutation point data) of the welding robot caused by changes in work tasks in the past. The third state signal is the real-time welding current signal of the current welding robot. Analyze the historical mutation point data to extract the rules and characteristics of the state mutation of the welding robot. Input the current welding current signal (i.e., the third state signal) into the mutation prediction model. Combining the stepwise characteristics of the intermittent load type, predict the possible stepwise change trend of the welding robot in the future. Output the predicted stepwise change trend (i.e., the third feedforward signal) for adjusting the welding task allocation and energy management strategy.

[0043] In the above embodiments, the temporarily added power supply device is of the short-term load type, and data on past sudden increases in power demand caused by emergency orders or other unexpected events (i.e., historical sudden demand data) is recorded. The fourth state signal is the real-time power signal of the current power supply device. The historical sudden demand data is used to train an anomaly detection model to identify the patterns of sudden increases in power demand. The current power signal (i.e., the fourth state signal) is input into the anomaly detection model. Combining the sudden demand characteristics of the short-term load type, the possible future trend of sudden increase in power demand is predicted. The predicted sudden demand trend (i.e., the fourth feedforward signal) is output for advance scheduling of power resources and optimization of energy distribution. Through the above specific implementation steps, the intelligent factory can accurately predict the trend of state changes of different types of target loads, and adjust the resource allocation and energy management strategies in advance according to the prediction results, which helps to reduce energy consumption, improve production efficiency, reduce equipment failure rates, etc.

[0044] In an alternative embodiment, performing a power adjustment operation according to the load feedforward signal specifically includes: when it is determined that the load feedforward signal is the first feedforward signal and the stability trend of the first load is continuously stable or stable within a first preset fluctuation range, maintaining the current first output power of the target engine and monitoring the fluctuation range of the first state signal; when it is monitored that the fluctuation range of the first state signal continuously exceeds the first preset fluctuation range within a fifth preset time period, determining that the first load has been switched to another load and triggering a preset logic classifier to reclassify the first load.

[0045] In the above embodiments, in a large data center, the cooling system is one of the key infrastructures for ensuring the normal operating temperature of servers and other devices. The load of the cooling system varies according to various factors such as the operating status of the servers, ambient temperature, and humidity. To operate the cooling system efficiently and energy - savingly, a power regulation strategy based on the load feed - forward signal is adopted. The sensor network is used to monitor the status signals such as the temperature, humidity, and cooling system flow rate in the data center. The engine controller is responsible for regulating the output power of the cooling system engine. The load feed - forward signal generator predicts the future load demand of the cooling system based on the sensor data and generates the load feed - forward signal. The preset logic classifier is used to classify the load so as to adjust the power regulation strategy according to different types of loads. The specific implementation steps are as follows: The sensor network continuously collects information such as the temperature and humidity in the data center. The load feed - forward signal generator predicts the cooling demand in the future period based on the collected data and generates the load feed - forward signal. The preset logic classifier preliminarily classifies the load feed - forward signal, assuming it is divided into three categories: normal load, peak load, and abnormal load. When the load feed - forward signal is identified as a normal load, it is assumed that this corresponds to the stable load state during the daily operation of the data center. The engine controller maintains the current first output power of the cooling system (for example, set as the minimum power to maintain the data center temperature within a certain range). Monitor the fluctuation range of the cooling system status signals (such as cooling water flow rate, outlet temperature). If within the fifth preset duration (such as 8 minutes, 10 minutes, 15 minutes, etc., not limited here) continuously, it is monitored that the fluctuation range of the cooling system status signals continuously exceeds the first preset fluctuation range (such as the flow rate fluctuation exceeds ±10%, ±11%, ±12%, etc., the temperature fluctuation exceeds ±2°C, ±3°C, ±4°C, etc., not limited here), then it is determined that the load status may change. When it is determined that the load may change, trigger the preset logic classifier to re - classify the first load. The preset logic classifier re - evaluates the load type according to the latest sensor data. If the load is re - classified as a peak load or an abnormal load, the engine controller adjusts the output power accordingly to meet the new cooling demand. For example, if it is determined as a peak load, increase the output power to ensure that the data center temperature does not rise. If it is determined as an abnormal load (possibly due to overheating of a certain server), then start an emergency cooling strategy. Through the above specific implementation steps, by monitoring the fluctuation range of the load feed - forward signal and the cooling system status signal, and combining the dynamic adjustment of the preset logic classifier, precise regulation of the power of the data center cooling system is achieved, which not only improves the energy efficiency of the cooling system but also helps to ensure the stable operation of the data center equipment.

[0046] In an alternative embodiment, a power regulation operation is performed according to a load feedforward signal, which specifically includes: when it is determined that the load feedforward signal is a second feedforward signal, extracting periodic parameters of a second load from the second state signal by using a timing analysis model; calculating a phase compensation time for triggering the power regulation operation according to the periodic parameters; triggering a target power regulation function at a phase compensation time before the starting moment of a trough stage of the periodic fluctuation of the second load, and controlling a target engine to synchronously adjust a current second output power according to the periodic parameters.

[0047] In the above embodiments, in a certain industrial production line, a power regulation system (such as an electric motor, etc.) is responsible for driving various mechanical equipment, such as conveyor belts, agitators, etc. The target loads of these mechanical equipment usually show periodic changes as the production line operates. For example, the target load is relatively high during specific periods on weekdays and relatively low during other periods. To optimize energy use and improve production efficiency, a power regulation strategy based on load feedforward signals and a time series analysis model is adopted. A sensor network is used to monitor the operating states of various devices on the production line, including state signals such as motor current and rotational speed. The load feedforward signal generator predicts the load demand in the future period according to historical data and the current production plan, and generates a load feedforward signal. The time series analysis model is used to extract the periodic parameters of the load from historical state signals, such as the cycle length, the positions of peaks and valleys, etc. The power regulation system is responsible for adjusting the output power of the power system to meet the load demand. The specific implementation steps are as follows: According to the production plan and historical data, the load feedforward signal generator predicts the load demand in the future period and generates a second feedforward signal. When it is determined that the received signal is the second feedforward signal (indicating that a periodic load change is predicted), proceed to the next step. Using the time series analysis model, extract the periodic parameters of the second load from historical state signals (such as motor current, rotational speed, etc.) (including but not limited to the length of the load cycle, the positions of peaks and valleys in each cycle, the fluctuation amplitude, etc.). According to the extracted periodic parameters, calculate the phase compensation time for triggering the power regulation operation. The phase compensation time is to ensure that the power regulation operation can be accurately triggered before the starting moment of the trough stage of the load periodic fluctuation, thereby optimizing energy use. At the phase compensation time before the starting moment of the trough stage of the periodic fluctuation of the second load, trigger the power regulation system. The power regulation system synchronously adjusts the current second output power of the target engine according to the periodic parameters (such as the fluctuation amplitude). For example, if it is predicted that the load will significantly decrease in the upcoming trough stage, the power regulation system can pre-reduce the output power of the engine to reduce unnecessary energy consumption. After the power regulation operation is executed, continuously monitor the changes in the state signals and load feedforward signals. If the actual load does not match the prediction or the periodic parameters change, adjust the power regulation strategy in a timely manner. Through the above specific implementation steps, by combining the load feedforward signal, the time series analysis model, and the power regulation system, precise regulation of the power of the power system of the industrial production line is achieved, which not only helps to optimize energy use but also improves the stability and efficiency of the production line.

[0048] In an optional embodiment, power regulation operations are performed according to a load feedforward signal, which specifically includes: when it is determined that the load feedforward signal is a third feedforward signal, determining the start-stop time interval of a third load according to historical mutation point data; detecting a transition of a third status signal according to the start-stop time interval; when a rising edge of the transition of the third status signal is detected, controlling a target engine to increase the current third output power to a preset safety margin value; when a falling edge of the transition of the third status signal is detected, controlling the target engine to reduce the third output power to a first reference power at a preset attenuation rate, where the first reference power is the first steady-state output power of the third load before the rising edge of the transition is triggered; when the steepness of the detected rising edge or falling edge of the transition is greater than a preset steepness threshold, performing damping compensation processing on the rising edge or falling edge of the transition.

[0049] In the above embodiments, in a certain data center, the cooling system is responsible for maintaining a suitable temperature in the server room to ensure the stable operation of the servers. Since the load of the servers fluctuates over time (e.g., the morning and evening rush hours on weekdays), the load of the cooling system also changes accordingly. To save energy and improve the system response speed, a power regulation strategy based on the load feedforward signal is adopted, with particular attention paid to the sudden start-stop changes in the load. The sensor network monitors the operating status of the cooling system, including status signals such as the rotational speed of the cooling fans, the flow rate and temperature of the coolant, etc. The load feedforward signal generator generates a load feedforward signal based on the server load prediction and cooling demand of the data center. The historical mutation point database stores the start-stop time interval data of the past cooling system load for predicting future mutation situations. The power regulation system is responsible for adjusting the output power of the cooling system (e.g., cooling fans and pumps). The specific implementation steps are as follows: The load feedforward signal generator generates a third feedforward signal based on the real-time load and cooling demand of the data center. When it is determined that the received signal is the third feedforward signal (indicating the possibility of predicting a start-stop mutation in the load), proceed to the next step. According to the data in the historical mutation point database, analyze the start-stop time interval pattern of the third load (i.e., the cooling system). Predict the possible future start-stop time points to prepare for power regulation. Continuously monitor the third status signal (e.g., the rotational speed of the cooling fans or the flow rate of the coolant, etc.). According to the start-stop time interval prediction, perform a transition detection on the third status signal to detect the mutation points where the load changes from shutdown to startup (i.e., the transition rising edge) or from running to shutdown (i.e., the transition falling edge). When the transition rising edge is detected, immediately control the target engine (e.g., the motor of the cooling fan, etc.) to increase the current third output power to the preset safety margin value to ensure a quick response and maintain the stable temperature of the machine room when the load suddenly increases. When the transition falling edge is detected, gradually reduce the third output power at a preset attenuation rate until the first reference power is reached. The first reference power is the output power of the cooling system in the stable operating state before the transition rising edge is triggered. When the steepness of the detected transition rising edge or transition falling edge is greater than the preset steepness threshold, it indicates that the load change is too drastic and may impact the cooling system. At this time, perform a damping compensation process on the transition rising edge or transition falling edge, for example, by adjusting the parameters in the control algorithm to slow down the rate of power change, thereby protecting the cooling system and other equipment in the data center. Through the above specific implementation steps, combined with the load feedforward signal, historical mutation point data, and the power regulation system, the dynamic regulation of the power of the data center cooling system is achieved. Especially in the case where there are start-stop mutations in the load, through transition detection and damping compensation processing, the quick response and stable operation of the cooling system are ensured, while the energy utilization efficiency is improved.

[0050] In an optional embodiment, power regulation operations are performed according to a load feedforward signal, which specifically includes: when it is determined that the load feedforward signal is the fourth feedforward signal, determining the transient impact amplitude and duration of the fourth load according to historical burst demand data; performing burst fluctuation detection on the fourth state signal according to the transient impact amplitude and duration; when it is detected that the amplitude of the fourth state signal exceeds the transient impact amplitude and the duration is less than the duration, determining the start of the burst demand, and controlling the target engine to increase the current fourth output power to the transient impact amplitude within the target time period of the duration; when it is detected that the amplitude of the fourth state signal drops back to a preset baseline value or the duration reaches the duration, determining the end of the burst demand, and controlling the target engine to reduce the fourth output power to the second reference power according to a preset inertia time constant, where the second reference power is the second steady-state output power of the fourth load before the start of the burst demand.

[0051] In the above embodiments, in a certain power system, the stability of the power grid depends on the timely response of each power generation unit. When there is a transient load impact in the power grid (for example, the sudden startup or shutdown of large industrial equipment, etc.), it is necessary for a fast-response power generation unit (such as a gas turbine or an energy storage system, etc.) to quickly adjust its output power to maintain the frequency and voltage stability of the power grid. The power grid monitoring system monitors the load conditions of the power grid in real time, including status signals such as the load change rate and frequency fluctuation. The load feedforward signal generator predicts possible future transient load impacts based on the historical data and real-time monitoring information of the power grid, and generates a fourth feedforward signal. The historical sudden demand database stores the amplitude and duration data of transient load impacts in the past power grid, which is used to predict future transient demands. A fast-response power generation unit is a power generation device that can quickly adjust its output power to respond to the power grid demand. The power regulation control system controls the output power of the fast-response power generation unit according to the load feedforward signal and historical data. The specific implementation steps are as follows: The load feedforward signal generator predicts possible future transient load impacts based on the real-time monitoring information and historical data of the power grid, and generates a fourth feedforward signal. When it is determined that the received signal is the fourth feedforward signal, proceed to the next step. Analyze the amplitude and duration patterns of past transient load impacts in the power grid according to the data in the historical sudden demand database. Predict the amplitude and duration of possible future transient impacts to prepare for power regulation. Monitor the status signals of the power grid in real time (such as the load change rate, frequency fluctuation, etc.). According to the predicted amplitude and duration of the transient impact, perform a sudden fluctuation detection on the status signals, that is, detect whether the load has a predicted transient impact. When it is detected that the amplitude of the status signal exceeds the predicted amplitude of the transient impact and the duration is less than the predicted duration, determine that the sudden demand starts. Control the fast-response power generation unit to quickly increase the current fourth output power to the predicted amplitude of the transient impact within the target time period of the predicted duration to respond to the transient load impact of the power grid. When it is detected that the amplitude of the status signal drops back to the preset baseline value (indicating that the power grid has returned to stability) or the duration reaches the predicted duration, determine that the sudden demand ends. Control the fast-response power generation unit to gradually reduce the fourth output power according to the preset inertia time constant until it reaches the second reference power. The second reference power is the output power of the power generation unit in the stable operation state before the sudden demand starts. Through the above specific implementation steps, combined with the load feedforward signal, historical sudden demand data, and power regulation control system, the dynamic regulation of the power of the fast-response power generation unit in the power grid is achieved. Especially in the case of a transient load impact in the power grid, through sudden fluctuation detection and rapid power adjustment, the frequency and voltage stability of the power grid are ensured, and the reliability and resilience of the power system are improved.

[0052] Through the embodiments of the present application, it is possible to directly predict the obtained load feedforward signal according to the load type and load condition (i.e., the actual control information of the target load), bypass the voltage and current feedback links of the generator set, and perform refined power adjustment for different load types to improve the accuracy and efficiency of power adjustment.

[0053] It should be noted that the above-described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The present application will be specifically described below in conjunction with specific embodiments.

[0054] The engine controller serving as the power source actively and real-time senses the working condition of the terminal load (corresponding to the above-mentioned target load) through specially installed sensors, and then predicts the load condition through a specific algorithm. When it senses that the load condition changes, it immediately actively pre-adjusts the engine output power. As a result, the timeliness and effectiveness of the output response of the entire generator set can be significantly improved when the load condition changes, and the response delay can be significantly reduced. At the same time, when it senses that the load power decreases, the engine power output can be reduced in a timely manner to significantly improve the energy-saving effect.

[0055] The specific implementation steps are as follows: Step 1, assume that the load fluctuation can be expressed as an external disturbance D(s), which affects the voltage and current of the system; Step 2, the voltage and current changes caused by the electrical appliance load fluctuation can be expressed as a transfer function G VC (s); Step 3, the generator controller detects the changes in voltage and current and converts them into changes in power requests, which can be expressed as a transfer function G G (s). This transfer function can be a simple proportional controller or a PID (Proportional-Integral-Derivative) controller, depending specifically on the dynamic characteristics of the power generation system.

[0056] Step 4, the engine controller performs power closed-loop control according to the request of the generator controller, which can be expressed as another transfer function G E (s). The transfer function of the entire power generation system can be expressed as: Among them, C(s) is the output (the stabilized voltage and current), and D(s) is the input (load fluctuation). e -τsThe delay from load change to power request can be seen. From the load change to the stable voltage and current, multiple transfer functions (i.e., subsystems) are involved. The more subsystems there are, the greater the introduced delay and interference. However, the above embodiments of the present application can reduce this part of the delay and interference, that is, directly introduce the load change into the engine control system. Then, the delay from load change to power change will no longer affect the power generation system. At this time, the closed-loop transfer function of the power generation system becomes: The delay here has been eliminated, and the control is more direct and rapid. When the generator set controller detects the voltage and current changes, the engine controller has completed or has started the power regulation, thereby greatly reducing the voltage and current fluctuations to protect the power generation system.

[0057] The following describes the electronic device in the embodiments of the present invention application from the perspective of hardware processing. Refer to Figure 2 , Figure 2 is a schematic structural diagram of an entity device of the electronic device in the embodiments of the present application.

[0058] It should be noted that Figure 2 The structure of the electronic device shown is only an example and should not bring any limitations to the functions and usage scopes of the embodiments of the present invention.

[0059] As Figure 2 shown, the electronic device includes a central processing unit (CPU) 201, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 202 or the program loaded from the storage part 208 into the random access memory (RAM) 203, such as executing the method described in the above embodiments. In the RAM 203, various programs and data required for system operation are also stored. The CPU 201, ROM 202, and RAM 203 are connected to each other through the bus 204. The input / output (I / O) interface 205 is also connected to the bus 204.

[0060] The following components are connected to the I / O interface 205: an input section 206 including an audio input device, a button switch, etc.; an output section 207 including a liquid crystal display (LCD), an audio output device, an indicator light, etc.; a storage section 208 including a hard disk, etc.; and a communication section 209 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 209 performs communication processing via a network such as the Internet. The drive 210 is also connected to the I / O interface 205 as required. A removable medium 211 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc. is installed on the drive 210 as required so that a computer program read from it can be installed into the storage section 208 as required.

[0061] Specifically, according to an embodiment of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, an embodiment of the present invention includes a computer program product that includes a computer program carried on a computer-readable medium, and the computer program includes a computer program for performing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication section 209, and / or installed from the removable medium 211. When the computer program is executed by the central processing unit (CPU) 201, various functions defined in the present invention are executed.

[0062] It should be noted that specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM), a flash memory, an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present invention, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, device, or component.

[0063] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present invention. Among them, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code, and the above-mentioned module, program segment, or part of code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than that marked in the accompanying drawings.

[0064] Specifically, the electronic device of this embodiment includes a processor and a memory. A computer program is stored on the memory. When the computer program is executed by the processor, the power adjustment method based on load feedforward provided in the above-mentioned embodiment is implemented.

[0065] On the other hand, the present invention also provides a computer-readable storage medium, which may be included in the electronic device described in the above-mentioned embodiment; or it may exist separately and not be assembled into the electronic device. The above storage medium carries one or more computer programs. When the one or more computer programs are executed by a processor of an electronic device, the electronic device is enabled to implement the power adjustment method based on load feedforward provided in the above-mentioned embodiment.

[0066] As mentioned above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the various embodiments of the present application.

[0067] Those of ordinary skill in the art can understand all or part of the processes in the methods of the above embodiments. These processes can be completed by relevant hardware instructed by a computer program. The program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the above method embodiments. The foregoing storage medium includes: various media such as ROM or random access memory RAM, magnetic disk, or optical disc that can store program codes.

Claims

1. A power regulation method based on load feedforward, characterized in that: include: Performing real-time monitoring on the working state of the target load to obtain a load state signal, wherein the load state signal is used to represent actual control information of the target load; Inputting the load status signal into a preset logic classifier to obtain a load type output by the preset logic classifier, wherein the load type is the type of the target load output by the preset logic classifier after classifying the load status signal; Predicting a state change trend of the target load according to the load type and the load state signal to obtain a load feedforward signal; A power regulation operation is performed according to the load feed-forward signal.

2. The method according to claim 1, characterized in that Inputting the load state signal into a preset logic classifier to obtain the load type output by the preset logic classifier specifically includes: Performing timing synchronization processing on the load status signal; Extracting features from the load state signal after the timing synchronization processing to obtain key features, wherein the key features are used to characterize the load characteristics of the target load; The key feature is input into the preset logic classifier to control the preset logic classifier to perform the following operations: when the preset logic classifier determines that the key feature is received, the preset logic classifier calls a preset classification strategy to classify and detect the key feature to output the load type; The preset logic classifier determines, according to the classification detection result, that the fluctuation range of the first state signal within the first preset time period is continuously smaller than the first preset fluctuation range, and outputs that the first load corresponding to the first state signal is a continuous constant load type, wherein the load state signal includes the first state signal, the target load includes the first load, and the load type includes the continuous constant load type; The preset logic classifier determines, according to the classification detection result, that the second state signal undergoes periodic fluctuations within a second preset time period, and outputs that the second load corresponding to the second state signal is a continuously changing load type, wherein the load state signal includes the second state signal, the target load includes the second load, and the load type includes the continuously changing load type; The preset logic classifier determines, according to the classification detection result, that when the third state signal undergoes a step fluctuation within a third preset time period, that the third load corresponding to the third state signal is output as an intermittent load type, wherein the load state signal includes the third state signal, the target load includes the third load, and the load type includes the intermittent load type; When the preset logic classifier determines, according to the classification detection result, that the fluctuation range of the fourth state signal is greater than the second preset fluctuation range and the duration is less than the fourth preset duration, the fourth load corresponding to the fourth state signal is output as a short-term load type, wherein the second preset fluctuation range is greater than the first preset fluctuation range, the load state signal includes the fourth state signal, the target load includes the fourth load, and the load type includes the short-term load type; The continuous constant load type, the continuously variable load type, the intermittent load type, and the short-time load type output by the preset logic classifier are obtained.

3. The method according to claim 2, characterized in that The predicting the state change trend of the target load according to the load type and the load state signal to obtain a load feedforward signal specifically includes: Acquire historical operation data of the first load, and predict the stability trend of the first load according to the historical operation data, the first state signal, and the continuous constant load class to obtain a first feedforward signal, wherein the load feedforward signal includes the first feedforward signal; Establishing a timing analysis model, and predicting the periodic fluctuation trend of the second load according to the timing analysis model, the second state signal, and the continuously changing load type, so as to obtain a second feedforward signal, wherein the load feedforward signal includes the second feedforward signal; Acquire historical mutation point data of the third load, and predict the step change trend of the third load according to the historical mutation point data, the third state signal, and the intermittent load type to obtain a third feedforward signal, wherein the load feedforward signal includes the third feedforward signal; Acquire historical burst demand data of the fourth load, and predict the burst demand trend of the fourth load according to the historical burst demand data, the fourth state signal, and the short-term load type to obtain a fourth feedforward signal, wherein the load feedforward signal includes the fourth feedforward signal.

4. The method according to claim 3, characterized in that The performing of the power regulation operation according to the load feedforward signal specifically includes: When it is determined that the load feedforward signal is the first feedforward signal and the stability trend of the first load is continuously stable or stable within the first preset fluctuation range, maintaining the current first output power of the target engine and monitoring the fluctuation range of the first state signal; When it is monitored that the fluctuation range of the first state signal continues to exceed the first preset fluctuation range within a fifth preset time period, it is determined that the first load is switched to other loads, and a preset logic classifier is triggered to reclassify the first load.

5. The method according to claim 3, characterized in that: The performing of the power regulation operation according to the load feedforward signal specifically includes: In the case where it is determined that the load feedforward signal is the second feedforward signal, extracting the periodic parameter of the second load from the second state signal using the timing analysis model; Calculating a phase compensation time for triggering the power regulation operation according to the periodic parameter; The phase compensation time before the start time of the trough phase of the periodic fluctuation of the second load triggers the target power adjustment function, and controls the target engine to synchronously adjust the current second output power according to the periodic parameter.

6. The method according to claim 3, characterized in that The performing of the power regulation operation according to the load feedforward signal specifically includes: In the case where it is determined that the load feedforward signal is the third feedforward signal, determining a start / stop time interval of the third load according to the historical mutation point data; Performing transition detection on the third state signal according to the start-stop time interval; When a rising edge of the transition of the third state signal is detected, the target engine is controlled to increase the current third output power to a preset safety margin value; When the transition falling edge of the third state signal is detected, the target engine is controlled to reduce the third output power to a first reference power according to a preset attenuation rate, wherein the first reference power is the first steady-state output power of the third load before the transition rising edge is triggered; When it is detected that the steepness of the transition rising edge or the transition falling edge is greater than a preset steepness threshold, damping compensation processing is performed on the transition rising edge or the transition falling edge.

7. The method according to claim 3, characterized in that The performing of the power regulation operation according to the load feedforward signal specifically includes: In the case where it is determined that the load feedforward signal is the fourth feedforward signal, determining the transient impact amplitude and duration of the fourth load according to the historical burst demand data; Performing sudden fluctuation detection on the fourth state signal according to the transient impact amplitude and the duration; When it is detected that the amplitude of the fourth state signal exceeds the transient impact amplitude and the duration is less than the duration, determining that the sudden demand begins, and controlling the target engine to increase the current fourth output power to the transient impact amplitude within a target period of the duration; When it is detected that the amplitude of the fourth state signal drops back to the preset baseline value or the duration reaches the duration, it is determined that the sudden demand has ended, and the target engine is controlled to reduce the fourth output power to a second reference power according to a preset inertia time constant, wherein the second reference power is the second steady-state output power of the fourth load before the sudden demand begins.

8. An electronic device, characterized in that: The electronic device comprises: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code comprises computer instructions, and the one or more processors call the computer instructions so that the electronic device executes the method as described in any one of claims 1-7.

9. A computer-readable storage medium comprising instructions, characterized in that: When the instructions are executed on an electronic device, the electronic device is caused to execute the method as claimed in any one of claims 1 to 7.

10. A computer program product, characterized in that When the computer program product is executed on an electronic device, the electronic device is enabled to execute the method according to any one of claims 1 to 7.

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