Suction and exhaust energy efficiency real-time optimization system and method based on multivariable frequency conversion technology

Abstract

The invention provides a suction and exhaust energy efficiency real-time optimization system and method based on a multivariable frequency conversion technology, and belongs to the technical field of multivariable frequency conversion, and the method comprises the steps: carrying out the real-time collection through a multi-source sensor, and carrying out the synchronous processing to generate a state vector and a wind resistance particle index; establishing a wind resistance prediction model based on the state vector and the wind resistance particle index, and outputting the current predicted wind resistance and the actual motor output torque; in combination with the current predicted wind resistance, the actual motor output torque and the temperature array, generating a two-dimensional hot area map, and dynamically marking a hot area position needing to be intervened as a control sensitive area; and according to the two-dimensional hot area map and the control sensitive area, establishing an optimized objective function by taking hot area risk minimization, energy consumption minimization and control stability as objectives, and coordinating and outputting fan rotating speed, torque and air guide angle control instructions. The method is suitable for suction and exhaust system scenes with severe oil smoke fluctuation, complex smoke exhaust space and sensitive wind resistance feedback delay.

Description

Technical Field

[0001] The present invention belongs to the technical field of multivariable frequency conversion, and in particular relates to a system and method for real-time optimization of suction and exhaust energy efficiency based on multivariable frequency conversion technology. Background Art

[0002] With increasing demands for energy efficiency, intelligence, and safety in kitchen exhaust systems, traditional exhaust systems relying on fixed-frequency motors and fixed control logic are struggling to adapt to the dynamic operating conditions of complex kitchen environments, such as multiple stoves, high fume loads, and frequent thermal disturbances. In real-world scenarios, fume particle size, duct resistance, fan load, and local temperature rise exhibit a strongly coupled, nonlinear relationship, which varies dramatically with cooking behavior. Existing systems, however, mostly rely solely on fume concentration as a control reference for fan control, ignoring the dynamic interactions between particle size distribution, pressure differential, hot zone accumulation, and electromagnetic torque. This leads to problems such as delayed fan response, motor overload, reduced exhaust efficiency, and uncontrolled thermal diffusion in highly volatile and high-interference environments. Furthermore, while some systems have incorporated variable-frequency motors and PID control mechanisms for energy-saving regulation, they still lack a modeling mechanism for the multi-physical state of the exhaust process, the ability to predict motor and windage loads online, and the inability to incorporate hot zone spatial feedback mechanisms and multi-objective optimization strategies at the control level. This hinders overall system energy efficiency improvement and inadequate response speed and accuracy.

[0003] In order to achieve the true "on-demand output, structural adaptation, dynamic response and multi-objective coordination" of the suction and exhaust system, a new suction and exhaust control system with multi-variable state perception capability, online identification coupling model, spatial hot zone identification capability and high real-time predictive control framework is urgently needed. Summary of the Invention

[0004] The purpose of the present invention is to propose a real-time optimization system and method for suction and exhaust energy efficiency based on multivariable frequency conversion technology to solve the above problems.

[0005] To achieve the above objectives, a first aspect of the present invention provides an intelligent optimization method for an enterprise management system based on digital twins, comprising the following steps: Multi-source sensors collect real-time data on the average fan speed, average duct pressure difference, motor power sliding mean, total particle size concentration, fume concentration sliding mean, and duct temperature mean. These data are then processed synchronously to generate a state vector and wind resistance particle index. Based on the state vector and the wind resistance particle index, a wind resistance prediction model is established to express the nonlinear relationship between the electromagnetic torque and wind resistance in the suction and exhaust system in real time, and output the current predicted wind resistance and actual motor output torque; Combining the current predicted wind resistance, actual motor output torque and temperature array, a two-dimensional hot zone map is generated and the hot zone positions requiring intervention are dynamically marked as control sensitive areas; According to the two-dimensional hot zone map and control sensitive areas, an optimization objective function is constructed with the goals of minimizing hot zone risks, minimizing energy consumption and controlling stability, and the output fan speed, torque and air guide angle control instructions are coordinated.

[0006] Furthermore, the wind resistance particle index represents the wind resistance particle index at unit speed, which is calculated based on the average pressure difference in the air duct, the total concentration of the particle size spectrum, and the average speed of the fan, and is used to reflect the combined effect of pressure difference and particle size concentration on wind resistance.

[0007] Furthermore, the wind resistance prediction model calculates the current predicted wind resistance by weighted summation based on the preset resistance, the average pressure difference in the wind duct, the total concentration of the particle size spectrum, the average temperature in the wind duct, and the average speed of the fan, and the square of the first-order derivative of the wind resistance particle index; The actual motor output torque is calculated by combining the current predicted wind resistance, the average speed of the fan, the sliding mean of the motor power, and the predicted theoretical output power of the motor.

[0008] Furthermore, the operating logic of the wind resistance prediction model is all deployed in the edge control board, where the model parameters are updated online through the recursive least squares algorithm, the perturbation derivative based on the wind resistance particle index is obtained through sliding difference, and the regularization term for predicting the theoretical output power of the motor is updated once per cycle by the main control logic. The wind resistance prediction model can be iterated twice per second.

[0009] Furthermore, the method combines the current predicted wind resistance, actual motor output torque and temperature array to generate a two-dimensional hot zone map and dynamically marks the hot zone position requiring intervention as a control sensitive area, specifically including: Perform spatial local difference and time derivative processing on the temperature array to obtain the degree of heat concentration per unit time in each area; A nonlinear wind resistance amplification factor is introduced based on the current predicted wind resistance and the actual motor output torque to calculate a two-dimensional heat zone map; Extracting a control sensitive area according to the two-dimensional heat map; wherein the sensitive area is a set of all points exceeding a dynamic threshold; The control sensitive area represents the location of the area that currently requires key risk control intervention.

[0010] Furthermore, if the control sensitive area is actually used, the control sensitive area will be mapped to the corresponding area of ​​the stove layout and converted into a secondary air duct airflow angle adjustment suggestion or an auxiliary target wind speed enhancement area.

[0011] Furthermore, based on the two-dimensional hot zone map and the control sensitive area, an optimization objective function is constructed with the goals of minimizing hot zone risks, minimizing energy consumption, and controlling stability, and the output of fan speed, torque, and wind guide angle control instructions are coordinated, specifically including: For the prediction of the future window, the fan speed, motor output torque and auxiliary air duct angle are used as the control variable group; The design optimizes the control variable group based on the optimization objective functions of minimizing total energy consumption, minimizing the risk of two-dimensional hot zone maps, and smoothing the controller response. At the same time, during the optimization, the controller designs physical constraints to reflect the hardware boundaries of the actual equipment and outputs control instructions for fan speed, torque, and air guide angle.

[0012] Furthermore, the physical constraints are that the fan speed is within the upper and lower limits of the equipment safety wind speed, and the maximum response change rate of the auxiliary air duct angle cannot exceed a preset threshold.

[0013] Furthermore, the controller runs an MPC solver once in a 250ms period and updates in real time through a rolling window approach; the optimization process is implemented using a single-layer QP structure.

[0014] In a second aspect of the present invention, a real-time optimization system for suction and exhaust energy efficiency based on multivariable frequency conversion technology is provided, the system comprising: The sensor acquisition module is used to collect the fan average speed, duct average pressure difference, motor power sliding mean, particle size spectrum total concentration, fume concentration sliding mean and duct temperature mean in real time through multi-source sensors, and generate the state vector and wind resistance particle index through synchronous processing; An online modeling module is used to establish a wind resistance prediction model based on the state vector and the wind resistance particle index, so as to express the nonlinear relationship between the electromagnetic torque and the wind resistance in the suction and exhaust system in real time, and output the current predicted wind resistance and the actual motor output torque; A hot zone analysis module is used to generate a two-dimensional hot zone map by combining the current predicted wind resistance, actual motor output torque and temperature array, and dynamically mark the hot zone positions that need intervention as control sensitive areas; The predictive control module is used to construct an optimization objective function based on the two-dimensional hot zone map and the control sensitive area, with the goals of minimizing hot zone risks, minimizing energy consumption and controlling stability, and coordinate the output of fan speed, torque and wind guide angle control instructions.

[0015] The beneficial technical effects of the present invention are at least as follows: The invention provides a real-time optimization system and method for suction and exhaust energy efficiency based on multivariable frequency conversion technology, constructing a closed-loop optimization structure from multi-dimensional state acquisition, online modeling of coupling models, hot zone distribution identification to multi-objective model prediction controller output. It can realize dynamic, precise and coordinated energy efficiency control strategies for typical problems such as high disturbance, high particle size, and high heat local accumulation in kitchen suction and exhaust conditions. The system first constructs a unified state vector by integrating multi-source real-time sensor data such as fan speed, duct pressure difference, particle size spectrum, oil smoke concentration and temperature distribution, and combines it with a particle size-wind resistance index construction mechanism based on a sliding window to effectively improve the state expression dimension and predictability; secondly, based on the state vector and the particle size trend term, an online identification model of wind resistance and electromagnetic torque is constructed, and the disturbance derivative term and energy consumption regularization term are used to enhance the model's ability to suppress rapid working condition switching and misjudgment scenarios; further, by fusing the predicted wind resistance, motor load and thermal array data, a two-dimensional thermal zone map that can be updated in real time is constructed, and the wind resistance-thermal flow coupling term and nonlinear risk amplification factor are introduced to achieve accurate extraction of control-sensitive areas; finally, by establishing an MPC controller with the goals of minimizing energy consumption, minimizing hot zone risk and ensuring control stability, the three-variable coordinated control of wind speed, torque and wind guide angle under local spatial disturbance conditions is achieved, forming a complete prediction-identification-execution closed-loop control path. The hierarchical modeling, hot zone identification and multi-objective predictive control mechanism proposed in the present invention has a clear deployment structure, physical interpretability and improved control accuracy, and is particularly suitable for suction and exhaust system scenarios with severe oil fume fluctuations, complex exhaust space, and sensitive wind resistance feedback delay. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention is further described with reference to the accompanying drawings. However, the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative effort.

[0017] Figure 1 This is a flow chart of the real-time optimization method for suction and exhaust energy efficiency based on multivariable frequency conversion technology of the present invention. DETAILED DESCRIPTION

[0018] The following describes embodiments of the present invention in detail. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended only to explain the present invention and are not to be construed as limiting the present invention.

[0019] like Figure 1 As shown, the embodiment of the present invention provides a method for real-time optimization of suction and exhaust energy efficiency based on multivariable frequency conversion technology, which includes: S1. Use multi-source sensors to collect real-time data on the average fan speed, average duct pressure difference, motor power sliding mean, total particle size concentration, fume concentration sliding mean, and duct temperature mean. After synchronous processing, the state vector and wind resistance particle index are generated.

[0020] Specifically, this step aims to construct a multivariable state space that reflects the actual operating state of the kitchen exhaust system, providing the input foundation for subsequent modeling and predictive control. Because the operating conditions of the kitchen exhaust system fluctuate dramatically, transient behaviors such as igniting a fire, adding a wok, and stir-frying significantly affect the heat flow structure, wind resistance, and motor load. Therefore, it is necessary to establish a state vector with high temporal resolution and structural integrity for key physical variables. Furthermore, this state space must rely solely on engineering-feasible sensing methods, low-computing resource processing mechanisms, and online feature processing that can be deployed on edge devices (such as the range hood main control board or air controller).

[0021] The raw data sources for this step are the various sensors deployed in the suction and exhaust system. Their installation locations and sampling methods are based on current kitchen appliance hardware standards. The following is the source of each input variable and the processing logic: Current fan speed : Obtained through a Hall effect magnetic encoder installed coaxially with the motor shaft. The encoder has a 600-line resolution and is converted into a speed signal through the controller counting method. The update frequency is 10Hz.

[0022] Duct pressure difference : Measured by two MEMS differential pressure sensors embedded at each end of the air duct. For example, a certain model (such as the Honeywell HSC series) has a measurement range of ±500 Pa, an update period of 100 ms, and the pressure differential is read via the analog-to-digital conversion channel of the main controller.

[0023] Motor power : The power is estimated based on the current and voltage sampling modules in the inverter (i.e., directly sampling the drive current and bus voltage). The controller generates the power value by calculating the voltage-current product and filtering it. The update frequency is synchronized with the PWM carrier (typically 10kHz, but this step uses the 1Hz cycle average).

[0024] Oil fume particle size distribution : An infrared particle light scattering sensor module (such as the Plantower PMS5003) deployed directly in front of the air intake provides particle concentrations within six particle size ranges: PM0.3, PM0.5, PM1.0, PM2.5, PM5.0, and PM10.0. It is updated once a second and outputs a 6-dimensional vector.

[0025] Total fume concentration : Also provided by the above sensors, the weighted average of PM2.5 and PM10 is used as the oil fume intensity index to reflect the current oil fume generation situation.

[0026] Duct temperature :The temperature is measured by a bridge composed of NTC thermistors distributed at two locations on the inner wall of the air duct. The temperature signal is sampled by the ADC of the main control chip and converted into a temperature value by combining the lookup table method. The average of the two temperatures represents the overall temperature level in the air duct.

[0027] Due to differences in sampling periods and physical response speeds among sensors, a synchronization window mechanism must be established within the master control system. Using a sliding window of 1 second, a sliding average is performed on all sampled data within each control cycle (typically 500ms), and the results are combined to form a time-aligned state vector. The specific state vector construction is as follows (this calculation is dimensionless): ; in: Indicates the average speed of the fan at time t within the current 1 second; is the average pressure difference in the air duct at time t; is the sliding mean of the motor power at time t; is the six-dimensional particle size distribution vector at time t ; is the sliding mean of oil smoke concentration at time t; is the mean duct temperature at time t.

[0028] On this basis, in order to construct an indicator variable that can more effectively reflect the actual load pressure, the following composite physical quantity construction formula is proposed (this calculation is dimensionless): ; in: Indicates the wind resistance particle index at unit speed; is the average duct pressure difference; Indicates the Particle concentration within each size range; is the average fan speed.

[0029] This formula reflects the combined effect of pressure difference and particle size concentration on wind resistance. When the wind speed is low, the particles are large and the concentration is high, will increase significantly, reflecting the increase in system resistance. To ensure a uniform numerical scale, this quantity is normalized together with the state vector using standard deviation normalization.

[0030] Output: The output of this step is the following two quantities, which serve as input for the next step modeling: state vector : Standardized multi-dimensional state data; wind resistance particle index : Reflects the resistance strength index of the system at unit speed.

[0031] This step forms a dynamic and reproducible multivariable state space with practical significance by arranging a mature and mass-producible sensor array at the core nodes of the suction and exhaust system, combining the sliding window mechanism with the composite quantity construction method. In particular, in terms of the expression of the relationship between particle size distribution and wind resistance, a design This independent construct enables the system to evaluate resistance strength in real time without model reliance, providing significantly higher expression efficiency for subsequent motor-load matching modeling.

[0032] S2. Based on the state vector and the wind resistance particle index, a wind resistance prediction model is established to express the nonlinear relationship between the electromagnetic torque and wind resistance in the suction and exhaust system in real time, and output the current predicted wind resistance and actual motor output torque.

[0033] Specifically, the core task of this step is to build a motor-air duct coupling model with adaptive capabilities , in order to express the nonlinear relationship between the electromagnetic torque and wind resistance in the suction and exhaust system in real time. Unlike the traditional static matching curve, the suction and exhaust system in the actual kitchen environment is continuously affected by physical disturbances such as sudden changes in oil fume particle size, obstruction by pots and pans, and temperature rise accumulation. The system load state shows obvious non-steady-state characteristics. Especially in the case of "rapid changes in particle size spectrum + sudden increase in duct pressure difference", the traditional model cannot reflect the trend of load surge in time, which can easily cause fan control lag or over-compensation, affecting the suction and exhaust efficiency and energy consumption performance. Based on the previous state space, this step proposes a model with a clear structure, online correction, and the ability to incorporate disturbance compensation and coupled nonlinear characteristics. , providing predictive support for subsequent control strategies.

[0034] Furthermore, the model Aims to predict current wind duct resistance The output torque required by the motor , to achieve dynamic feedforward regulation at the control layer. Traditional drag modeling relies primarily on static function mapping of pressure differential or rotational speed, ignoring the high-order coupling effects of the particle size spectrum and the coupling trends between smoke, heat flow, and pressure differential, making it difficult to capture rapid disturbances in the suction and exhaust scenarios. The online modeling scheme proposed in this paper consists of two parts: the main model is a regression structure with physical constraints, supplemented by a disturbance gain correction term to reflect abnormal operating conditions in real time.

[0035] The wind resistance prediction model uses the following structure (this calculation is dimensionless): ; in: It is the inherent resistance of the system; is the contribution weight of each physical quantity to wind resistance; , is the duct pressure difference; , total concentration of particle size spectrum; , duct temperature, reflects the trend of heat diffusion and flue gas buoyancy; , average fan speed; is the disturbance gain coefficient; It is the square of the first-order derivative of the wind resistance particle index, which is used to capture the sudden increase trend of wind resistance caused by sudden disturbances (such as oil smoke spray).

[0036] in, is the square of the first derivative of the drag particle index and is calculated as follows: Known , Its time derivative is ; in: : Wind resistance particle index defined by the above formula.

[0037] : The time window average value of the duct pressure difference, which is obtained by sliding average of the differential pressure sensor signal.

[0038] : No. Particle counts for each size channel are obtained from the segmented output of the infrared particle sensor.

[0039] : The sum of six particle size counts, used to simplify writing.

[0040] : The time window average value of the fan speed is obtained from the shaft end encoder count and the sliding average.

[0041] 、 、 : The first-order derivative of the corresponding signal with respect to time is obtained by approximating the difference between adjacent sampling points in the controller and directly substituted into the above formula for calculation.

[0042] Next, the wind resistance prediction results are used to calculate the actual required electromagnetic torque Here, the energy consumption-load linkage formula based on aerodynamic work and similarity law is adopted, the motor power value in the state vector is used as a constraint term, and the load coordination regularization term is introduced (this calculation is dimensionless): ; in: The predicted wind resistance output by the previous formula; is the current average speed of the fan; Depend on Converted to get, is the operating efficiency used as a constant parameter in the current period (the following calculation is dimensionless); The actual sampled power of the current motor; is the regularization term coefficient, which is used to penalize the power estimation error and prevent the model from overfitting under high load; is the predicted torque of the previous control cycle; is the angular velocity of the previous control cycle. Based on the aerodynamic work and similarity law: the aerodynamic side satisfies the volume work and pipe network characteristics, and the axial flow fan approximately satisfies the similarity law and mechanical work relationship. Combined with formula (4), we have: and and (Right now ) is negatively correlated, where Based on The predicted theoretical output power of the motor, = , because the larger the two terms in the denominator of formula (4), the higher the effective conversion capacity under the same aerodynamic work demand ( The required shaft torque estimate will be increased (increased) or the model uncertainty is larger and needs to be conservatively shrunk (the bias term increases). decreases accordingly; while the numerator The physical meaning is the aerodynamic load torque scale obtained by combining the pipe network system curve with the fan similarity law, that is, the current predicted wind resistance and speed The magnitude of the shaft-side torque required to overcome the aerodynamic drag of the duct is then used, and the motor-side uncertainty correction is then performed using the efficiency correction and power consistency regularization of the denominator.

[0043] in, The initial value of (i.e., the value before the first control cycle) is usually set to zero or estimated based on the idle state, i.e., the angular velocity and torque before the first control cycle. The physical meaning of is the "aerodynamic load torque scale" obtained by combining the pipe network system curve and the fan similarity law, that is, the current predicted wind resistance and speed The magnitude of the shaft-side torque required to overcome the aerodynamic drag of the duct is then used, and the motor-side uncertainty correction is then performed using the efficiency correction and power consistency regularization of the denominator.

[0044] in, ; in, The motor output torque in the previous cycle (calculated by the motor current and speed); is the angular velocity; They are the bus voltage and current sampled by the inverter in real time; is the power factor, which is given by the inverter phase measurement module.

[0045] This ratio is input into formula (4) as the efficiency of the current cycle after first-order digital filtering.

[0046] in, The actual sampling power of the current motor is directly calculated by the inverter And it is obtained by smoothing for 500 ms. Theoretical output power based on the predicted torque (this calculation is dimensionless): ; in is the current iteration value of formula (4), is the average speed of the previous cycle; this product reflects the mechanical power estimation of "predicted torque × real-time angular velocity" and is used to Make deviation constraints.

[0047] (Here is a special explanation of the relationship between formula (4) and formula (6): The controller first uses the angular velocity of the previous control cycle and the initial estimated torque Calculation formula (6) yields , and then Substitute into formula (4) to generate the new torque ; Only 1–2 iterations are needed to Converge to the minimum value, then the converged value and Output. Among them, .

[0048] Load coordination regularization coefficient, given by system debugging, is used to amplify Bias, suppressing the risk of overfitting in high-load scenarios.

[0049] This torque formula embodies a key innovation: introducing the physical measurement value (power) in the state space as a check item for windage torque inference, thereby avoiding the output of excessively high invalid torque in special scenarios where the particle size and pressure difference are both large but the suction and exhaust efficiency is low.

[0050] For example, if the cooker blocks the air intake but the concentration of oil smoke particles is extremely high, only It will be mistakenly judged as high load, but If the actual value does not increase, it means that the suction and exhaust resistance is not actually transmitted to the motor. At this time, the regular term will increase the denominator and automatically reduce , thus avoiding the erroneous output of high-energy consumption signals. This design accurately reflects the particularity of the suction and exhaust system scenario and is also one of the most technically valuable highlights of this step.

[0051] The model operation logic can be deployed in the edge control board. The parameters are updated online using the recursive least squares (RLS) algorithm, and the perturbation derivatives Obtained through sliding difference, the regularization term can be updated once per cycle by the main control logic, and the entire model can be iterated twice per second.

[0052] This step outputs two physical prediction quantities: Predicting wind resistance :It will be used as the basis for judging the hot zone model and wind flow configuration and input into step 3; Predict motor output torque :It will be used as one of the target variables of control optimization and participate in energy consumption regulation and motor response strategy design.

[0053] S3. Combining the current predicted wind resistance, actual motor output torque and temperature array, a two-dimensional hot zone map is generated and the hot zone positions requiring intervention are dynamically marked as control sensitive areas.

[0054] Specifically, this step is based on the motor-windage coupling model The output of and , combined with the temperature array , construct a two-dimensional spatial hot zone map , and extract control sensitive areas .

[0055] Input: Predicted wind resistance : From the model , represents the total wind resistance response at the current moment; electromagnetic torque : From the model , reflecting the load response caused by wind resistance; temperature array :The infrared thermal sensor array is arranged above the stove, inside the smoke hood and near the air duct entrance to collect Two-dimensional temperature field matrix, update frequency 200ms, resolution can be 4×4 or 6×6; In addition, in order to introduce the trend of heat accumulation within the time scale, this step also introduces the temperature sliding mean in the state vector Serves as a global thermal benchmark to construct local relative thermal risks.

[0056] The goal of this step is to generate , used to reflect the degree of thermal risk at different spatial points under the current wind resistance background. Traditional temperature distribution judgment is based solely on absolute values ​​or local differences, which makes it difficult to reflect the dynamic characteristics of "hot spots driven by wind resistance changes" in the suction and exhaust system. This solution achieves spatial structure mapping across physical fields by constructing a spectrum function with temperature difference, wind resistance response, torque load, and local heat dissipation capacity as joint inputs. The details are as follows: First, perform spatial local difference and time derivative processing on the temperature array to obtain the degree of heat accumulation per unit time in each area (this calculation is dimensionless): ; in: is the global average temperature, representing the ambient thermal benchmark; is the time-derived weight, reflecting the proportion of temperature rise rate in thermal risk assessment; Indicates in Rank The thermal sensors at the measuring points are at the time temperature; The infrared thermal array is Rank List the measurement points in the previous control cycle (with the current moment Separated ) Historical temperature samples collected and cached after the same calibration / smoothing process; The first term captures the relative heating location in space, and the second term captures the local heating rate in time.

[0057] Then based on the wind resistance prediction value and torque A nonlinear windage amplification factor is introduced to reflect the coupled trend that “the risk of hot spots increases sharply with the increase of windage pressure” (this calculation is dimensionless): ; in: It is a two-dimensional heat zone map, representing the heat risk map, covering a two-dimensional coordinate plane; Control the amplifying effect of wind resistance changes on the expansion speed of the hot zone; Control the effect of electromagnetic load on the heat accumulation "extrapolation zone" and use a logarithmic function to balance the nonlinear growth trend under high load response.

[0058] Among them, formula (8) is based on the energy balance and the similarity law of the fan: the motor shaft power is When the duct resistance is high or there is local backflow, a larger proportion of the shaft power is consumed as turbulence and wall friction loss and converted into heat locally. These losses change in the same direction as the load torque, so the risk of hot spots increases with to increase and improve; Formula (8) adopts The reason is to maintain the "monotonically increasing but gradually saturating" effect - avoiding excessive amplification in the high load area, making the thermal risk amplification attenuated sensitive to extreme torque, which is consistent with the actual saturation characteristics and improves numerical stability.

[0059] Indicates that in the prediction step Grid points The thermal risk intensity (scalar) calculated by formula (8) is the local temperature rise at this point. Determined together with the windage / torque amplification term, it reflects the relative risk of heat accumulation and backflow at that point.

[0060] This formula is a key innovation in the context of the present invention: it maps windage load to temperature change weighting logic in a structural way, so that the same temperature rise is assigned a higher thermal risk score when windage suddenly increases or motor load increases. A 2°C rise is considered a medium risk, but =1.9 (much higher than the reference value of 1.0), =0.8N·m, then It will be amplified nonlinearly and included in the thermal control sensitive area.

[0061] Next, according to Extract control sensitive areas , the region is defined as exceeding the dynamic threshold The set of all points of (this calculation is dimensionless): ; in, Represents the global temperature reference: at time Infrared thermal array First do spatial averaging (all The temperature obtained by averaging the short window time is used as the threshold Baseline for adaptive regulation; It is not a fixed constant, but is based on the current and Adaptive adjustments, such as: If the wind resistance is 30% higher than the reference value, the threshold value is reduced by 10%, increasing the sensitivity; If the global temperature rises slowly, the threshold is raised to avoid misjudgment; The adjustment strategy is executed online through table lookup without introducing complex calculations.

[0062] In the actual control strategy, The corresponding areas of the stove layout are mapped (such as left stove, middle stove, right stove), and converted into auxiliary air duct airflow angle adjustment suggestions or auxiliary target wind speed enhancement areas. For example, if Contains coordinates , it can be inferred that there is serious accumulation in the upper right heat zone of the stove, and the system should lower the main wind speed on the right side or increase the jet angle of the middle auxiliary air duct.

[0063] Output: Spatial heat map :one The floating point matrix is ​​used to describe the thermal risk weight of each point. According to formula (8) at time Grid points The calculated thermal risk intensity (scalar) is directly used as the weight of the point in the summation; Control sensitive areas : Point set array, representing the location of the area that currently requires key risk control intervention, providing regional weight support for the next step of MPC objective function generation.

[0064] S4. Based on the two-dimensional hot zone map and the control sensitive area, an optimization objective function is constructed with the goals of minimizing hot zone risks, minimizing energy consumption and controlling stability, and the output fan speed, torque and air guide angle control instructions are coordinated.

[0065] Specifically, the core task of this step is to build a multi-objective model predictive controller (MPC) to control the hot zone identified in the previous step to the sensitive area and hot zone maps Compared with the previously predicted wind resistance and electromagnetic torque Incorporate them into an optimized control framework to achieve variable frequency output of the fan , motor response torque And the angle of the auxiliary air duct The entire MPC structure needs to accomplish three things in a short period of time: predict the evolution trend of the target variable, evaluate the hot zone and energy consumption objective function, and output a real-time executable fan speed and wind direction control strategy.

[0066] The controller goal is to calculate the future window [t,t+ ], where t+ Represents the current moment plus the prediction window length; Under the constraints, the control variable group The following objectives are achieved: (1) minimum total energy consumption, (2) minimum hot zone risk, and (3) smooth controller response. To this end, this step designs the following optimization objective function (this calculation is dimensionless): ; in: To predict the electromagnetic torque value within the cycle, supply; Indicates the thermal risk intensity, which is calculated as the thermal risk weight of each point and is given by the thermal zone map in step 3; is the set of sensitive point coordinates, directly from the previous step; Indicates the rate of change of fan speed and controls air volume fluctuations; Indicates the rate of change of the auxiliary air duct angle, affecting the local wind direction; are the weight coefficients of the objective function, corresponding to energy consumption, thermal zone balance and control smoothness.

[0067] The objective function structure embodies three distinct innovative features for the suction and exhaust system invention scenario: For the first time, the “weight of spatially sensitive points in hot zones” is directly embedded into the control function as a control target, achieving a direct response to spatially uneven heat flow disturbances. The introduction of electromagnetic torque prediction value instead of traditional power index is more in line with the motor physical model and can better control the energy consumption trend; A smooth control item for the wind speed / direction change rate has been added to ensure that the system remains stable in high-speed disturbance scenarios, such as opening and closing a pot lid, or sudden explosion of food.

[0068] For example, if the current Including the upper left corner of the stove and central , and the thermal intensities are , , and at this time It has risen significantly to 1.8. If it rises from 0.5 in the previous cycle to 0.9, the controller will increase the weight of the second target function during the prediction process, guide the angle of the secondary air duct to deflect to this area, and control the main wind speed to rise slowly within an acceptable range, rather than significantly increasing the air volume and causing energy waste.

[0069] At the same time, this controller sets the following physical constraints on the control variables to reflect the hardware boundaries of the actual device (this calculation is dimensionless): ; in, It is the upper and lower limits of the equipment's safe wind speed. It is the maximum response change rate of the auxiliary air duct angle, which is generally set by factory parameters to not exceed .

[0070] The controller runs an MPC solver with a 250ms period, updating in real time via a rolling window approach. The optimization process uses a single-layer QP architecture and can be implemented using open-source embedded MPC solvers (such as ACADO and FORCES). The computation time is less than 50ms and can be deployed on platforms such as the STM32F4 and TI C2000 series with floating-point support.

[0071] The controller outputs the following two variables per cycle to directly drive the physical actuator response: fan speed and electromagnetic torque :Serves as direct control input of the variable frequency drive module; auxiliary air duct angle : Output to the wind guide blade servo mechanism to control wind direction deflection.

[0072] An embodiment of the present invention further provides a real-time optimization system for suction and exhaust energy efficiency based on multivariable frequency conversion technology, the system comprising: The sensor acquisition module is used to collect the fan average speed, duct average pressure difference, motor power sliding mean, particle size spectrum total concentration, fume concentration sliding mean and duct temperature mean in real time through multi-source sensors, and generate the state vector and wind resistance particle index through synchronous processing; An online modeling module is used to establish a wind resistance prediction model based on the state vector and the wind resistance particle index, so as to express the nonlinear relationship between the electromagnetic torque and the wind resistance in the suction and exhaust system in real time, and output the current predicted wind resistance and the actual motor output torque; A hot zone analysis module is used to generate a two-dimensional hot zone map by combining the current predicted wind resistance, actual motor output torque and temperature array, and dynamically mark the hot zone positions that need intervention as control sensitive areas; The predictive control module is used to construct an optimization objective function based on the two-dimensional hot zone map and the control sensitive area, with the goals of minimizing hot zone risks, minimizing energy consumption and controlling stability, and coordinate the output of fan speed, torque and wind guide angle control instructions.

[0073] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0074] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a division of logical functions. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0075] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0076] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A real-time optimization method for suction and exhaust energy efficiency based on multivariable frequency conversion technology, characterized in that: The method comprises the following steps: Multi-source sensors collect real-time data on the average fan speed, average duct pressure difference, motor power sliding mean, total particle size concentration, fume concentration sliding mean, and duct temperature mean. These data are then processed synchronously to generate a state vector and wind resistance particle index. Based on the state vector and the wind resistance particle index, a wind resistance prediction model is established to express the nonlinear relationship between the electromagnetic torque and wind resistance in the suction and exhaust system in real time, and output the current predicted wind resistance and actual motor output torque; Combining the current predicted wind resistance, actual motor output torque, and temperature array, a two-dimensional heat map is generated and the locations of the hot zones requiring intervention are dynamically marked as control sensitive zones; wherein the temperature array is acquired by an infrared thermal sensor; According to the two-dimensional thermal map and the control sensitive area, an optimization objective function is constructed with the goals of minimizing the risk of the two-dimensional thermal map, minimizing energy consumption and controlling stability, and coordinating the output of fan speed, torque and air guide angle control instructions.

2. The method for real-time optimization of suction and exhaust energy efficiency based on multivariable frequency conversion technology according to claim 1 is characterized in that: The wind resistance particle index represents the wind resistance particle index at unit speed, which is calculated based on the average pressure difference in the air duct, the total concentration of the particle size spectrum, and the average speed of the fan, and is used to reflect the combined effect of pressure difference and particle size concentration on wind resistance.

3. The method for real-time optimization of suction and exhaust energy efficiency based on multivariable frequency conversion technology according to claim 1 is characterized in that: The wind resistance prediction model calculates the current predicted wind resistance by weighted summation based on the preset resistance, the average pressure difference in the wind duct, the total concentration of the particle size spectrum, the average temperature in the wind duct, and the average speed of the fan, and the square of the first-order derivative of the wind resistance particle index; The actual motor output torque is calculated by combining the current predicted wind resistance, the average speed of the fan, the sliding mean of the motor power, and the predicted theoretical output power of the motor.

4. The method for real-time optimization of suction and exhaust energy efficiency based on multivariable frequency conversion technology according to claim 3 is characterized in that: The operating logic of the wind resistance prediction model is entirely deployed in the edge control board, where the model parameters are updated online using a recursive least squares algorithm. The perturbation derivative based on the wind resistance particle index is obtained through sliding difference. The regularization term for predicting the theoretical output power of the motor is updated once per cycle by the main control logic. The wind resistance prediction model can be iterated twice per second; wherein, the perturbation derivative is the first-order derivative of the wind resistance particle index.

5. The method for real-time optimization of suction and exhaust energy efficiency based on multivariable frequency conversion technology according to claim 1 is characterized in that: The method combines the current predicted wind resistance, the actual motor output torque, and the temperature array to generate a two-dimensional hot zone map and dynamically mark the hot zone position requiring intervention as a control sensitive area, specifically including: Perform spatial local difference and time derivative processing on the temperature array to obtain the degree of heat concentration per unit time in each area; A nonlinear wind resistance amplification factor is introduced based on the current predicted wind resistance and the actual motor output torque to calculate a two-dimensional heat zone map; Extracting a control sensitive area according to the two-dimensional heat map; wherein the sensitive area is a set of all points exceeding a dynamic threshold; The control sensitive area represents the location of the area that currently requires key risk control intervention.

6. The method for real-time optimization of suction and exhaust energy efficiency based on multivariable frequency conversion technology according to claim 5 is characterized in that: If the control sensitive area is actually used, the control sensitive area is mapped to the corresponding area of ​​the stove layout and converted into a secondary air duct airflow angle adjustment suggestion or an auxiliary target wind speed enhancement area.

7. The method for real-time optimization of suction and exhaust energy efficiency based on multivariable frequency conversion technology according to claim 1 is characterized in that: According to the two-dimensional thermal map and the control sensitive area, an optimization objective function is constructed with the goals of minimizing the risk of the two-dimensional thermal map, minimizing energy consumption and controlling stability, and coordinating the output of fan speed, torque and wind guide angle control instructions, specifically including: For the prediction of the future window, the fan speed, motor output torque and auxiliary air duct angle are used as the control variable group; The design optimizes the control variable group based on the optimization objective functions of minimizing total energy consumption, minimizing the risk of two-dimensional hot zone maps, and smoothing the controller response. At the same time, during the optimization, the controller designs physical constraints to reflect the hardware boundaries of the actual equipment and outputs control instructions for fan speed, torque, and air guide angle.

8. The method for real-time optimization of suction and exhaust energy efficiency based on multivariable frequency conversion technology according to claim 7 is characterized in that: The physical constraints are that the fan speed is within the upper and lower limits of the equipment's safe wind speed, and the maximum response change rate of the auxiliary air duct angle cannot exceed a preset threshold.

9. The method for real-time optimization of suction and exhaust energy efficiency based on multivariable frequency conversion technology according to claim 7 is characterized in that: The controller runs an MPC solver with a period of 250 ms and updates in real time using a rolling window approach.

10. The real-time optimization system for suction and exhaust energy efficiency based on multivariable frequency conversion technology is characterized by: The system comprises: The sensor acquisition module is used to collect the fan average speed, duct average pressure difference, motor power sliding mean, particle size spectrum total concentration, fume concentration sliding mean and duct temperature mean in real time through multi-source sensors, and generate the state vector and wind resistance particle index through synchronous processing; An online modeling module is used to establish a wind resistance prediction model based on the state vector and the wind resistance particle index, so as to express the nonlinear relationship between the electromagnetic torque and the wind resistance in the suction and exhaust system in real time, and output the current predicted wind resistance and the actual motor output torque; a hot zone analysis module, configured to generate a two-dimensional hot zone map based on the current predicted wind resistance, actual motor output torque, and temperature array, and dynamically mark the hot zone locations requiring intervention as control sensitive areas; wherein the temperature array is acquired by an infrared thermal sensor; The predictive control module is used to construct an optimization objective function based on the two-dimensional hot zone map and the control sensitive area, with the goals of minimizing the risk of the two-dimensional hot zone map, minimizing energy consumption and controlling stability, and coordinate the output of fan speed, torque and wind guide angle control instructions.

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