An intelligent control system for high-efficiency energy-saving air heating unit

Abstract

The application relates to the technical field of air heating unit control, and particularly discloses an efficient and energy-saving air heating unit intelligent control system. The application builds a multi-dimensional data acquisition system covering operation parameters, environment parameters and load parameters, establishes state evaluation models of heating elements, fans and circulating pumps and a heat load dynamic compensation model, and builds a multi-objective collaborative optimization function with the heat exchange efficiency boundary condition of an energy-saving heat exchange device as a coupling constraint, so that the problems of traditional on-off control or simple PID regulation strategy, such as difficulty in fine regulation and control according to dynamic changes, low matching degree of heating output and actual demand and prominent energy waste, are solved, joint optimization and Pareto optimal control of target power of the heating element, target rotating speed of the fan and target flow of the circulating pump are realized, heating quality and overall operation efficiency of the unit are improved, and operation energy consumption and equipment maintenance cost are reduced.

Description

Technical Field

[0001] This invention relates to the field of air heating unit control technology, and in particular to a high-efficiency and energy-saving intelligent control system for air heating units. Background Technology

[0002] In the fields of industrial heating and building heating, air conditioning units, as key heat energy conversion equipment, account for a significant proportion of total industrial energy consumption. Existing air conditioning unit control systems mostly employ traditional on / off control or simple PID regulation strategies, making it difficult to perform precise control based on dynamic changes in ambient temperature, load demand, and equipment operating status. This results in a low match between heating output and actual demand, leading to significant energy waste. Furthermore, existing systems generally lack the ability to collaboratively optimize multiple operating parameters of the unit. The operating states of key components such as heating elements, fans, and circulating pumps are independent, failing to establish effective linkage regulation strategies. This limits the overall operating efficiency of the unit, exacerbates equipment wear, and increases maintenance costs. Therefore, developing an air conditioning unit control system capable of multi-parameter collaborative optimization and intelligent regulation has significant engineering application value for improving heating quality and reducing operating energy consumption.

[0003] Therefore, there is an urgent need to provide a technical solution to address the above problems. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention provides an intelligent control system for a high-efficiency and energy-saving air heating unit. The technical solution of this intelligent control system is as follows:

[0005] The data acquisition module is used to collect the operating parameters, environmental parameters and load parameters of the air heating unit in real time. The operating parameters include the current power of the heating element, the current operating temperature of the heating element, the current speed of the fan, the current flow rate of the circulating pump, the outlet air temperature and the return air temperature. The environmental parameters include the outdoor ambient temperature. The load parameters include the set temperature and the measured temperature of the heating area.

[0006] The status assessment module is used to generate a heating element status assessment value by weighting the ratio of the current power of the heating element to the upper limit of the rated power of the heating element and the ratio of the current operating temperature of the heating element to the upper limit of the operating temperature of the heating element; to generate a fan status assessment value by weighting the ratio of the current speed of the fan to the upper limit of the rated speed of the fan; to generate a circulation pump status assessment value by weighting the ratio of the current flow rate of the circulation pump to the upper limit of the rated flow rate of the circulation pump; and to construct a dynamic heat load compensation model based on the temperature difference between the outlet air temperature and the return air temperature, the temperature difference between the outdoor ambient temperature and the measured temperature, and the deviation between the measured temperature and the set temperature, and output a predicted heat load demand value from the dynamic heat load compensation model.

[0007] The collaborative optimization module is used to construct a multi-objective collaborative optimization function by using the state evaluation values ​​of the heating element, the fan, the circulating pump, and the predicted heat load demand as constraint boundaries, and the heat exchange efficiency boundary condition of the energy-saving heat exchange device as coupling constraints. The multi-objective collaborative optimization function aims to minimize total energy consumption, minimize heating response time, and minimize the steady-state deviation between the measured temperature and the set temperature. By jointly optimizing the target power of the heating element, the target speed of the fan, and the target flow rate of the circulating pump, the module outputs a collaborative control strategy that makes the multi-objective collaborative optimization function Pareto optimal. In the collaborative control strategy, the target power of the heating element, the target speed of the fan, and the target flow rate of the circulating pump form a nonlinear correlation constraint through the heat exchange efficiency boundary condition.

[0008] The drive control module is used to generate a heating element drive command based on the target power of the heating element, a fan drive command based on the target speed of the fan, and a circulating pump drive command based on the target flow rate of the circulating pump. The module outputs the heating element drive command to the heating element driver, the fan drive command to the fan frequency converter, and the circulating pump drive command to the circulating pump frequency converter.

[0009] Furthermore, the status assessment module generates a status assessment value for the heating element based on the ratio of the current power of the heating element to the upper limit of its rated power, the ratio of the current operating temperature of the heating element to the upper limit of its operating temperature, and the ratio of the rate of change of the current power of the heating element to a preset upper limit of the rate of change of power, according to the following weighted formula:

[0010]

[0011] in, This is the condition assessment value of the heating element. The current power of the heating element. This is the upper limit of the rated power of the heating element. The current operating temperature of the heating element. This is the upper limit of the operating temperature of the heating element. The rate of change of the current power of the heating element. The upper limit of the preset power change rate, , , The preset weighting coefficients, and .

[0012] Furthermore, the status assessment module dynamically adjusts the weighting coefficient based on the ratio of the current operating temperature of the heating element to the upper limit of the operating temperature of the heating element. , , When the current operating temperature of the heating element exceeds a preset temperature threshold, the temperature will increase. and reduce and .

[0013] Furthermore, the dynamic heat load compensation model constructed by the state assessment module outputs the predicted heat load demand value according to the following formula:

[0014]

[0015] in, The predicted heat load demand value is... The outlet air temperature is... The return air temperature, The outdoor ambient temperature is... The measured temperature is... For the set temperature, , , , The preset compensation coefficient, It is a time variable.

[0016] Furthermore, the state assessment module dynamically adjusts the compensation coefficient based on the rate of change of the outdoor ambient temperature. When the rate of change of the outdoor ambient temperature exceeds a preset rate of change threshold, the compensation coefficient is increased. .

[0017] Furthermore, the multi-objective collaborative optimization function constructed by the collaborative optimization module uses the target power of the heating element, the target speed of the fan, and the target flow rate of the circulating pump as decision variables. The total energy consumption is expressed as the sum of the product of the target power of the heating element and time, the product of the cube of the target speed of the fan and time, and the product of the cube of the target flow rate of the circulating pump and time.

[0018] Furthermore, the collaborative optimization module employs a multi-objective particle swarm optimization algorithm to jointly optimize the multi-objective collaborative optimization function. In each iteration, an adaptive inertial weight is constructed based on the state evaluation values ​​of the heating element, the fan, the circulating pump, and the predicted heat load demand. The optimal position of the individual particle and the global optimal position are updated according to the Pareto dominance relationship.

[0019] Furthermore, in the collaborative control strategy output by the collaborative optimization module, the nonlinear correlation constraint between the target power of the heating element and the target flow rate of the circulating pump is determined by the power function relationship between the heat transfer coefficient and the flow rate of the energy-saving heat exchange device. The heat transfer coefficient is proportional to the power exponent of the target flow rate of the circulating pump, and the power exponent is dynamically adjusted as the target power of the heating element changes.

[0020] Furthermore, it also includes a fault self-diagnosis module, which is connected to the data acquisition module. The fault self-diagnosis module is used to generate a heating element fault warning signal based on the deviation between the current operating temperature of the heating element and the theoretical operating temperature corresponding to the target power of the heating element; to generate a fan fault warning signal based on the deviation between the current speed of the fan and the theoretical speed corresponding to the target speed of the fan; and to generate a circulating pump fault warning signal based on the deviation between the current flow rate of the circulating pump and the theoretical flow rate corresponding to the target flow rate of the circulating pump.

[0021] Furthermore, when the fault self-diagnosis module generates the heating element fault warning signal, the fan fault warning signal, or the circulating pump fault warning signal, it marks the collaborative control strategy output by the collaborative optimization module as a failure state and triggers the collaborative optimization module to regenerate an emergency control strategy with minimizing total energy consumption as the single optimization objective.

[0022] The technical solution of this invention constructs a multi-dimensional data acquisition system covering operating parameters, environmental parameters, and load parameters, establishes a state assessment model and a dynamic heat load compensation model for heating elements, fans, and circulating pumps, and constructs a multi-objective collaborative optimization function using the heat exchange efficiency boundary conditions of energy-saving heat exchange devices as coupling constraints. This solves the problems of traditional on / off control or simple PID regulation strategies, which are difficult to finely regulate according to dynamic changes, have low matching degree between heating output and actual demand, and have significant energy waste. It achieves joint optimization and Pareto optimal control of target power of heating elements, target speed of fans, and target flow of circulating pumps, thereby improving heating quality and overall unit operating efficiency, and reducing operating energy consumption and equipment maintenance costs.

[0023] Compared to the independent control of heating elements, fans, and circulating pumps in existing technologies, this invention uses the state evaluation values ​​of heating elements, fans, and circulating pumps, along with the predicted heat load demand, as the constraint boundaries. It also introduces the heat exchange efficiency boundary conditions of an energy-saving heat exchange device as a coupling constraint. This allows the three decision variables to form nonlinear correlation constraints during the optimization process, thereby achieving a unified approach of multi-component linkage regulation and global energy consumption optimization. This solves the efficiency loss problem caused by the lack of coupling relationships in independent control modes.

[0024] The above description is merely an overview of the technical solution of the present invention. In order to better understand the technical means of the present invention and to implement it in accordance with the contents of the specification, and in order to make the above and other objects, features and advantages of the present invention more apparent and understandable, specific embodiments of the present invention are described below. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative effort.

[0026] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Furthermore, the same reference numerals denote the same parts throughout the drawings. In the drawings:

[0027] Figure 1 This is a schematic diagram of an embodiment of an intelligent control system for a high-efficiency and energy-saving air heating unit according to the present invention. Detailed Implementation

[0028] Exemplary embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0029] Figure 1 A schematic diagram of an embodiment of an intelligent control system for a high-efficiency and energy-saving air heating unit provided by the present invention is shown. Figure 1 As shown, the intelligent control system of this high-efficiency and energy-saving air heating unit includes:

[0030] The data acquisition module is used to collect the operating parameters, environmental parameters, and load parameters of the air heating unit in real time. The operating parameters include the current power of the heating element, the current operating temperature of the heating element, the current speed of the fan, the current flow rate of the circulating pump, the outlet air temperature, and the return air temperature. The environmental parameters include the outdoor ambient temperature. The load parameters include the set temperature and the measured temperature of the heating area.

[0031] Among them, air heating units refer to: integrated equipment used in industrial heating and building heating scenarios, which heats air through heating elements and uses fans and circulating pumps to drive air flow to achieve heat energy transmission and distribution; for example, in the winter heating system of Factory C in Region B, the air heating unit heats the -10℃ cold air drawn from the outside to 45℃ through the heating elements, and then sends it into the factory building through the air duct by the fan. At the same time, the circulating pump drives the heat medium to circulate to ensure the continuous and stable operation of the heating elements.

[0032] In this context, "heating element" refers to the component in an air heating unit that converts electrical energy or fuel chemical energy into heat energy, used to directly heat the air flowing over its surface. For example, the air heating unit in Factory C's heating system uses an electric heating tube as its heating element. After being powered on, the surface temperature of the heating tube rises, and when cold air flows over its surface at a speed of 2.5 m / s, the air is heated to the set temperature. "Current power" refers to the electrical power consumed by the heating element during real-time operation, calculated by multiplying voltage and current. For example, the operating voltage of the electric heating tube in Factory C during normal heating is 380V, and the operating current is 52.6A, resulting in a calculated current power of 20kW. "Current operating temperature" refers to the surface or internal temperature of the heating element during real-time operation, acquired by a temperature sensor installed on the heating element. For example, after 20 minutes of continuous power-on, the surface temperature of the electric heating tube in Factory C reaches 280℃, and the temperature sensor transmits this value to the data acquisition unit in real time.

[0033] The current fan speed refers to the rotational speed of the fan driving airflow in the air heating unit during real-time operation, expressed in r / min. For example, if the fan in Factory C rotates at 1450 r / min under normal operating conditions, the airflow rate into the plant is 5000 m³ / h. The current circulation pump flow rate refers to the volume of medium transported per unit time by the pump driving the heat medium circulation in the air heating unit during real-time operation, expressed in m³ / h. For example, if the circulation pump in Factory C delivers heat medium to the heating element at a flow rate of 15 m³ / h, ensuring that the heat on the surface of the heating element is promptly carried away and transferred to the air. The outlet air temperature refers to the temperature of the air flowing out of the air heating unit after being heated by the heating element, obtained by a temperature sensor installed at the unit's outlet. For example, if the outdoor ambient temperature is -10℃, the temperature sensor at the outlet of the air heating unit in Factory C displays a temperature of 45℃; this value is the outlet air temperature. Return air temperature refers to the temperature of the air returning to the inlet of the air heater unit after the air has completed its heating cycle. This temperature is collected by a temperature sensor installed at the unit's return air inlet. For example, after being cooled inside the factory building, the air temperature sensor at the return air inlet of the air heater unit in Factory C displays a temperature of 18°C; this value is the return air temperature. Outdoor ambient temperature refers to the atmospheric temperature of the environment outside the installation location of the air heater unit. This temperature is collected by a temperature sensor installed on the outdoor part of the unit. For example, in the winter heating system of Factory C, a temperature sensor installed on the exterior wall of the factory building displays a current outdoor temperature of -10°C; this value is the outdoor ambient temperature.

[0034] The heating area refers to the target space served by the air heating unit, which is a closed or semi-closed space that requires temperature regulation through the delivery of hot air. For example, the workshop of Factory C is the heating area, with an area of ​​2000m² and a height of 8m. The set temperature refers to the target temperature value preset by the operator or automatic control system for the heating area, serving as the reference for the control system to adjust the heating output. For example, based on the comfort requirements of the workers in the workshop, the operator of Factory C sets the set temperature of the heating area to 22℃ on the control panel. The measured temperature refers to the actual air temperature value of the area, collected in real time by temperature sensors installed within the heating area. For example, a temperature sensor installed in the center of the workshop of Factory C displays the current actual temperature as 18℃; this value is the measured temperature.

[0035] The status assessment module is used to generate a weighted status assessment value for the heating element based on the ratio of the current power of the heating element to the upper limit of the rated power of the heating element and the ratio of the current operating temperature of the heating element to the upper limit of the operating temperature of the heating element; to generate a fan status assessment value based on the ratio of the current speed of the fan to the upper limit of the rated speed of the fan; to generate a circulation pump status assessment value based on the ratio of the current flow rate of the circulation pump to the upper limit of the rated flow rate of the circulation pump; and to construct a dynamic heat load compensation model based on the temperature difference between the outlet air temperature and the return air temperature, the temperature difference between the outdoor ambient temperature and the measured temperature, and the deviation between the measured temperature and the set temperature, and output a predicted heat load demand value from the dynamic heat load compensation model.

[0036] The heating element status assessment value refers to a quantitative indicator output by the status assessment section after weighting the ratio of the heating element's current power to its rated power limit, the ratio of its current operating temperature to its operating temperature limit, and the ratio of its current power change rate to a preset power change rate limit. This indicator characterizes the current load level and health status of the heating element. For example, in Factory C, the current power of the heating element is 20kW, the rated power limit is 25kW, the current operating temperature is 280℃, the operating temperature limit is 320℃, the power change rate is 2kW per minute, the preset power change rate limit is 5kW per minute, and the preset weighting coefficient... =0.5、 =0.2、 When the value is 0.3, the condition assessment section calculates the condition assessment value of the heating element as 0.5×0.8+0.2×0.875+0.3×0.4=0.4+0.175+0.12=0.695, indicating that the heating element is in a medium load operating state.

[0037] The upper limit of the rated speed of the fan refers to the maximum rotational speed that the fan in the air heating unit can reach under continuous operation. This speed is determined by the fan manufacturer based on mechanical strength and electrical performance. For example, the upper limit of the rated speed marked on the nameplate of the fan in Factory C is 1800 r / min, and the control system is not allowed to exceed this value when adjusting the fan speed. The upper limit of the rated flow rate of the circulating pump refers to the maximum flow rate of the medium that the circulating pump in the air heating unit can deliver under continuous operation. This flow rate is determined by the circulating pump manufacturer based on the pump body structure and motor power. For example, the upper limit of the rated flow rate marked on the nameplate of the circulating pump in Factory C is 25 m³ / h, and the control system is not allowed to exceed this value when adjusting the circulating pump flow rate.

[0038] The circulating pump status assessment value refers to a quantitative index calculated by the status assessment section based on the ratio of the current flow rate of the circulating pump to its rated flow rate limit. This index characterizes the current load level and health status of the circulating pump. For example, in Factory C, with a current circulating pump flow rate of 15 m³ / h and a rated flow rate limit of 25 m³ / h, the status assessment section calculates a status assessment value of 0.6, indicating that the circulating pump is operating under medium load. The dynamic heat load compensation model refers to a mathematical relationship constructed by the status assessment section using proportional, integral, and differential operations based on the temperature difference between the outlet and return air temperatures, the temperature difference between the outdoor ambient temperature and the measured temperature, and the deviation between the measured temperature and the set temperature. This model dynamically estimates the current heat supply required by the heating area. For example, under the conditions of an outlet air temperature of 45℃, a return air temperature of 18℃, an outdoor ambient temperature of -10℃, a measured temperature of 18℃, and a set temperature of 22℃, the dynamic heat load compensation model calculates that the current additional heat demand requiring compensation is 85 kW.

[0039] The predicted heat load demand refers to the value output by the dynamic heat load compensation model after calculation, which represents the amount of heat supply required to maintain the set temperature in the heating area under the current operating conditions. For example, under the above operating conditions in Plant C, the predicted heat load demand output by the dynamic heat load compensation model is 85kW, which serves as the input constraint for the optimization calculation in the collaborative optimization part.

[0040] The collaborative optimization module is used to construct a multi-objective collaborative optimization function by using the state evaluation values ​​of the heating element, the fan, the circulating pump, and the predicted heat load demand as constraint boundaries, and the heat exchange efficiency boundary condition of the energy-saving heat exchange device as coupling constraints. The multi-objective collaborative optimization function aims to minimize total energy consumption, minimize heating response time, and minimize the steady-state deviation between the measured temperature and the set temperature. By jointly optimizing the target power of the heating element, the target speed of the fan, and the target flow rate of the circulating pump, the module outputs a collaborative control strategy that makes the multi-objective collaborative optimization function Pareto optimal. In the collaborative control strategy, the target power of the heating element, the target speed of the fan, and the target flow rate of the circulating pump form a nonlinear correlation constraint through the heat exchange efficiency boundary condition.

[0041] Among them, the constraint boundary refers to the restriction conditions set by the collaborative optimization part for the range of values ​​of decision variables or the feasible domain of optimization results when constructing the multi-objective collaborative optimization function. These include the state evaluation values ​​of heating elements, fans, circulating pumps, and predicted heat load demand. For example, when solving the problem, the collaborative optimization part uses the state evaluation values ​​of heating elements (0.72), fans (0.68), circulating pumps (0.60), and predicted heat load demand (85kW) as constraint boundaries to ensure that the solved target power, target speed, and target flow rate do not exceed the safe operating range of the equipment and meet the heating demand.

[0042] Energy-saving heat exchange devices refer to components in air heating units used to efficiently transfer heat from heating elements to the air. Their structural design and material selection prioritize improving heat exchange efficiency and reducing heat loss. For example, Factory C's air heating units utilize finned tube energy-saving heat exchange devices. By adding aluminum fins to the outer surface of the heating tubes, the heat exchange efficiency is increased to 92%, reducing heat loss by 8% compared to ordinary bare tube heat exchange devices. The heat exchange efficiency boundary condition refers to the functional relationship between the heat exchange efficiency of the energy-saving heat exchange device and the changes in circulating pump flow rate, fan speed, and heating element power during operation. This relationship characterizes the actual working capacity limit of the heat exchange device. For instance, Factory C's finned tube energy-saving heat exchange device achieves a heat exchange efficiency of 92% when the circulating pump flow rate is 15 m³ / h and the fan speed is 1450 r / min. When the circulating pump flow rate decreases to 10 m³ / h, the heat exchange efficiency drops to 86%. This functional relationship constitutes the heat exchange efficiency boundary condition. Coupling constraints refer to the mathematical relationships in a multi-objective collaborative optimization function that link the target power of the heating element, the target speed of the fan, and the target flow rate of the circulating pump through the boundary conditions of the heat exchange efficiency of the energy-saving heat exchange device. This ensures that the values ​​of the three variables cannot be determined independently and must satisfy the physical coupling characteristics. For example, when the boundary conditions of the heat exchange efficiency of Plant C require that the flow rate of the circulating pump must match the power of the heating element, if the collaborative optimization part sets the target power of the heating element to 22kW, then the target flow rate of the circulating pump must be adjusted to 18m³ / h to ensure that the heat exchange efficiency is not less than 90%. Otherwise, the optimization result cannot be physically realized.

[0043] The multi-objective collaborative optimization function refers to the functional expression constructed by the collaborative optimization part, which describes the mathematical relationship between the three optimization objectives (total energy consumption, heating response time, and steady-state deviation) and the three decision variables (target power of heating element, target speed of fan, and target flow rate of circulating pump). For example, the multi-objective collaborative optimization function constructed by the collaborative optimization part expresses the total energy consumption as the product of the target power of heating element and time, plus the product of the cube of the target speed of fan and time, plus the product of the cube of the target flow rate of circulating pump and time; expresses the heating response time as the time required for the measured temperature to rise from 18℃ to 22℃; and expresses the steady-state deviation as the root mean square value of the temperature fluctuation after the measured temperature reaches 22℃. The optimization objective refers to the target quantity that needs to be minimized or maximized in the multi-objective collaborative optimization function, including minimizing total energy consumption, minimizing heating response time, and minimizing the steady-state deviation between the measured temperature and the set temperature. For example, in the optimization problem of Factory C, the collaborative optimization part simultaneously pursues three optimization objectives: minimizing the total electrical energy consumed by the heating element in 24 hours, minimizing the heating time of the factory temperature from 18℃ to 22℃, and minimizing the temperature fluctuation after reaching 22℃ to no more than ±0.5℃.

[0044] The target power refers to the optimal power value that the heating element should operate at, determined by the collaborative optimization unit after joint optimization, and is used to generate the heating element drive command. For example, in the collaborative control strategy output by the collaborative optimization unit, the target power of the heating element is 18.5kW, which is lower than the current power of 20kW, indicating that energy-saving operation can be achieved while meeting heating demand. The target speed refers to the optimal rotational speed value that the fan should operate at, determined by the collaborative optimization unit after joint optimization, and is used to generate the fan drive command. For example, in the collaborative control strategy output by the collaborative optimization unit, the target speed of the fan is 1300r / min, which is lower than the current speed of 1450r / min, indicating that fan energy consumption can be reduced while ensuring the outlet air temperature. The target flow rate refers to the optimal medium flow rate value that the circulating pump should operate at, determined by the collaborative optimization part after joint optimization. It is used to generate the circulating pump drive command. For example, in the collaborative control strategy output by the collaborative optimization part, the target flow rate of the circulating pump is 16.5 m³ / h, which is an increase compared to the current flow rate of 15 m³ / h. This indicates that in order to match the reduced heating element power, the heat medium circulation volume needs to be increased to maintain heat exchange efficiency.

[0045] Joint optimization refers to the process where the collaborative optimization part treats the target power of the heating element, the target speed of the fan, and the target flow rate of the circulating pump as a set of decision variables, and simultaneously solves for the optimal combination of the three variables within the framework of a multi-objective collaborative optimization function. For example, the collaborative optimization part treats the target power of the heating element, the target speed of the fan, and the target flow rate of the circulating pump as points in three-dimensional space, and searches for the point in three-dimensional space that simultaneously achieves the optimal values ​​for the three optimization objectives using a multi-objective particle swarm optimization algorithm, ultimately finding a combination point with a target power of 18.5kW, a target speed of 1300r / min, and a target flow rate of 16.5m³ / h. The collaborative control strategy refers to the complete control scheme output by the collaborative optimization part, which includes the target power of the heating element, the target speed of the fan, and the target flow rate of the circulating pump. This scheme guides the drive control part in generating corresponding drive commands. For example, the collaborative control strategy output by the collaborative optimization part explicitly indicates that the heating element operates at 18.5kW power, the fan operates at 1300r / min speed, and the circulating pump operates at a flow rate of 16.5m³ / h. The drive control part generates corresponding drive commands based on the strategy. Nonlinear correlation constraints refer to the mutual constraints among the target power of the heating element, the target speed of the fan, and the target flow rate of the circulating pump in a collaborative control strategy that cannot be described by a linear relationship. These constraints are determined by the heat exchange efficiency boundary conditions of the energy-saving heat exchange device. For example, in the strategy of Plant C, when the target power of the heating element is adjusted from 20kW to 18.5kW, the target flow rate of the circulating pump is not adjusted nonlinearly from 15m³ / h to 14m³ / h, but needs to be adjusted to 16.5m³ / h after calculation based on the heat exchange efficiency boundary conditions. This nonlinear adjustment relationship is a nonlinear correlation constraint.

[0046] The drive control module is used to generate a heating element drive command based on the target power of the heating element, a fan drive command based on the target speed of the fan, and a circulating pump drive command based on the target flow rate of the circulating pump. The module outputs the heating element drive command to the heating element driver, the fan drive command to the fan frequency converter, and the circulating pump drive command to the circulating pump frequency converter.

[0047] The heating element drive command refers to the electrical signal generated by the drive control unit based on the target power of the heating element, used to control the operation of the heating element driver. This signal can be in the form of a pulse-width modulation (PWM) signal or an analog signal. For example, based on a target power of 18.5kW, the drive control unit calculates the corresponding PWM signal duty cycle to be 74% and outputs this 74% duty cycle PWM signal to the heating element driver. The fan drive command refers to the electrical signal generated by the drive control unit based on the target fan speed, used to control the operation of the fan inverter. This signal can be in the form of a frequency setting signal or an analog signal. For example, based on a target speed of 1300r / min, the drive control unit calculates the corresponding inverter output frequency to be 43.3Hz and outputs the frequency setting signal to the fan inverter. The circulating pump drive command refers to the electrical signal generated by the drive control unit based on the target flow rate of the circulating pump to control the operation of the circulating pump frequency converter. The signal can be in the form of a frequency setting signal or an analog signal. For example, the drive control unit calculates the corresponding frequency converter output frequency of 41.2Hz based on the target flow rate of 16.5m³ / h and outputs the frequency setting signal to the circulating pump frequency converter.

[0048] The heating element driver refers to an actuator that receives drive commands from the drive control unit and adjusts the input voltage or current of the heating element accordingly. It is typically implemented using a solid-state relay or a thyristor power regulator. For example, in the air heating unit of Factory C, a thyristor power regulator is used as the heating element driver. After receiving a pulse width modulation signal, it adjusts the conduction angle of the heating element to achieve an 18.5kW power output. The fan frequency converter refers to an actuator that receives drive commands from the drive control unit and adjusts the operating frequency of the fan motor accordingly, thereby regulating the airflow by changing the motor speed. For example, in the air heating unit of Factory C, the fan frequency converter receives a 43.3Hz frequency setting signal and adjusts the fan motor's operating frequency to 43.3Hz, reducing the fan speed to 1300 r / min. A circulating pump frequency converter is an actuator that receives drive commands from the drive control unit and adjusts the operating frequency of the circulating pump motor accordingly, thereby regulating the flow rate by changing the motor speed. For example, in the air heating unit of Factory C, a circulating pump frequency converter receives a 41.2Hz frequency setting signal and adjusts the operating frequency of the circulating pump motor to 41.2Hz, thereby increasing the circulating pump flow rate to 16.5m³ / h.

[0049] The technical solution of this embodiment constructs a multi-dimensional data acquisition system covering operating parameters, environmental parameters, and load parameters, establishes a state assessment model and a dynamic heat load compensation model for heating elements, fans, and circulating pumps, and constructs a multi-objective collaborative optimization function using the heat exchange efficiency boundary conditions of energy-saving heat exchange devices as coupling constraints. This solves the problems of traditional on / off control or simple PID regulation strategies, which are difficult to finely regulate according to dynamic changes, have low matching degree between heating output and actual demand, and have significant energy waste. It realizes the joint optimization and Pareto optimal control of the target power of heating elements, the target speed of fans, and the target flow of circulating pumps, improves the heating quality and the overall operating efficiency of the unit, and reduces operating energy consumption and equipment maintenance costs.

[0050] In one optional manner, the status assessment module generates a status assessment value for the heating element based on the ratio of the current power of the heating element to the upper limit of its rated power, the ratio of the current operating temperature of the heating element to the upper limit of its operating temperature, and the ratio of the rate of change of the current power of the heating element to a preset upper limit of the rate of change of power, according to the following weighted formula:

[0051]

[0052] in, This is the condition assessment value of the heating element. The current power of the heating element. This is the upper limit of the rated power of the heating element. The current operating temperature of the heating element. This is the upper limit of the operating temperature of the heating element. The rate of change of the current power of the heating element. The upper limit of the preset power change rate, , , The preset weighting coefficients, and .

[0053] It should be noted that the calculation formula for the heating element status assessment value assigns preset weighting coefficients to the ratio of the heating element's current power to the rated power limit, the ratio of the current operating temperature to the operating temperature limit, and the ratio of the current power change rate to the preset power change rate limit, and then performs a weighted summation. By fusing parameters from these three dimensions, a comprehensive quantification of the heating element's operating status is achieved. The above formula outputs a normalized heating element status assessment value, which characterizes the current load level and health status of the heating element. This provides a constraint basis for the safe operation boundary of the equipment for the collaborative optimization module, preventing the heating element from exceeding its rated operating range during subsequent optimization processes.

[0054] In the above-mentioned optional methods, the power change rate of the heating element is further introduced as a third-dimensional evaluation parameter. The state evaluation value is calculated by weighted fusion of power ratio, temperature ratio and power change rate ratio, which enhances the tracking ability of the dynamic adjustment process of the heating element and provides a more refined state constraint boundary for the collaborative optimization module.

[0055] In an alternative approach, the state assessment module also dynamically adjusts the weighting coefficient based on the ratio of the current operating temperature of the heating element to the upper limit of the operating temperature of the heating element. , , When the current operating temperature of the heating element exceeds a preset temperature threshold, the temperature will increase. and reduce and .

[0056] In the above-mentioned optional methods, an adaptive adjustment rule for the weight coefficients is further established. When the operating temperature of the heating element exceeds the preset threshold, the temperature weight is automatically increased and the power and power change rate weights are reduced. This enables the state assessment model to prioritize the response to thermal safety constraints under high-temperature conditions, thereby improving the adaptability of the assessment strategy to extreme operating conditions.

[0057] In an alternative approach, the dynamic heat load compensation model constructed by the state assessment module outputs the predicted heat load demand value according to the following formula:

[0058]

[0059] in, The predicted heat load demand value is... The outlet air temperature is... The return air temperature, The outdoor ambient temperature is... The measured temperature is... For the set temperature, , , , The preset compensation coefficient, It is a time variable.

[0060] It should be noted that the formula for calculating the heat load demand forecast linearly superimposes the following: the temperature difference between the supply air temperature and the return air temperature multiplied by the first compensation coefficient; the temperature difference between the outdoor ambient temperature and the measured temperature multiplied by the second compensation coefficient; the time integral of the deviation between the measured temperature and the set temperature multiplied by the third compensation coefficient; and the time derivative of the deviation between the measured temperature and the set temperature multiplied by the fourth compensation coefficient. This forms a composite compensation structure that includes proportional, feedforward, integral, and derivative terms. The above formula dynamically estimates the amount of heat supply required to maintain the set temperature in the heating area under the current operating conditions. By introducing an integral term to eliminate steady-state deviations, introducing a derivative term to suppress temperature fluctuations, and introducing an ambient temperature difference term to achieve feedforward compensation, the accuracy and response speed of the heat load demand forecast are improved.

[0061] In the above-mentioned optional methods, the deviation integral term and deviation differential term are further introduced into the dynamic heat load compensation model. Through the four-element coupling operation of supply and return air temperature difference, ambient temperature difference, cumulative deviation and deviation change rate, comprehensive compensation for steady-state heat load and dynamic heat disturbance is realized, which improves the accuracy and timeliness of heat load demand forecasting.

[0062] In one alternative approach, the state assessment module dynamically adjusts the compensation coefficient based on the rate of change of the outdoor ambient temperature. When the rate of change of the outdoor ambient temperature exceeds a preset rate of change threshold, the compensation coefficient is increased. .

[0063] In the above-mentioned optional methods, a dynamic correction mechanism for the compensation coefficient k2 is further established. Based on the comparison between the outdoor ambient temperature change rate and the preset threshold, the influence weight of the ambient temperature difference on the heat load prediction is automatically adjusted, thereby enhancing the forward-looking response capability of the prediction model when climate conditions fluctuate drastically.

[0064] In one alternative approach, the multi-objective collaborative optimization function constructed by the collaborative optimization module uses the target power of the heating element, the target speed of the fan, and the target flow rate of the circulating pump as decision variables. The total energy consumption is expressed as the sum of the product of the target power of the heating element and time, the product of the cube of the target speed of the fan and time, and the product of the cube of the target flow rate of the circulating pump and time.

[0065] In this context, decision variables refer to adjustable parameters that are used as optimization objects in the multi-objective collaborative optimization function, including the target power of the heating element, the target speed of the fan, and the target flow rate of the circulating pump. For example, in the optimization problem of Factory C, the three decision variables are the target power of the heating element, the target speed of the fan, and the target flow rate of the circulating pump. The collaborative optimization part changes the value of the optimization objective by adjusting the values ​​of the three variables.

[0066] Among the above-mentioned optional methods, the functional mapping relationship between total energy consumption and three types of decision variables is further clarified. The linear function of heating element power, the cubic function of fan speed, and the cubic function of circulating pump flow are accumulated in the time dimension to form a comprehensive energy consumption index, and an energy consumption quantitative evaluation benchmark that conforms to the physical characteristics of the equipment is established.

[0067] In one alternative approach, the collaborative optimization module employs a multi-objective particle swarm optimization algorithm to jointly optimize the multi-objective collaborative optimization function. In each iteration, an adaptive inertial weight is constructed based on the state evaluation values ​​of the heating element, the fan, the circulating pump, and the predicted heat load demand. The optimal position of the individual particles and the global optimal position are updated according to the Pareto dominance relationship.

[0068] The multi-objective particle swarm optimization algorithm refers to a heuristic optimization algorithm used in the collaborative optimization part. It searches for Pareto optimal solutions by simulating the motion of a swarm of particles in the solution space. Its key feature is that in each iteration, the particle's velocity and position are updated based on its own historical best position and the swarm's historical best position. For example, the collaborative optimization part initializes 50 particles, each representing a set of decision variable values. The particles move in the three-dimensional solution space, converging to the Pareto optimal front after 200 iterations. The adaptive inertia weight refers to a coefficient used in the multi-objective particle swarm optimization algorithm to balance the global and local search capabilities of the particles. During iteration, it is dynamically adjusted based on the heating element state evaluation value, fan state evaluation value, circulating pump state evaluation value, and predicted heat load demand value. For example, when the heating element state evaluation value of 0.72 approaches 1, the algorithm reduces the adaptive inertia weight from 0.9 to 0.4, shifting the particle swarm from global exploration to local fine-tuning search to avoid exceeding the safe operating range of the equipment.

[0069] In multi-objective particle swarm optimization (MPS), the optimal position of an individual particle refers to the position found by a single particle in its search history that optimizes the multi-objective collaborative optimization function. This position represents the Pareto optimal solution experienced by the particle. For example, particle number 5 visits multiple combinations of decision variables during iterations, and the set of decision variable values ​​that optimally combines the three optimization objectives of total energy consumption, heating response time, and steady-state deviation is recorded as the particle's optimal position. The global optimal position, on the other hand, refers to the position found by the entire particle swarm in its search history that optimizes the multi-objective collaborative optimization function. This position represents the Pareto optimal solution among all the optimal positions of individual particles. For example, after 200 iterations, the optimal positions of all particles in the swarm, after non-dominated sorting, are determined to be the solutions on the Pareto front, which are then identified as the global optimal positions. The set of decision variable values ​​corresponding to these positions represents the collaborative control strategy output by the collaborative optimization component.

[0070] In the above-mentioned optional methods, a multi-objective particle swarm optimization algorithm is further specified as the solution tool. During the iteration process, an adaptive inertial weight is constructed based on the state evaluation value and the heat load prediction value, and the optimal position of the particle is updated based on the Pareto dominance relationship, which improves the solution efficiency and solution set quality of the multi-objective optimization problem.

[0071] In one alternative approach, in the collaborative control strategy output by the collaborative optimization module, the nonlinear correlation constraint between the target power of the heating element and the target flow rate of the circulating pump is determined by the power function relationship between the heat transfer coefficient and the flow rate of the energy-saving heat exchange device. The heat transfer coefficient is proportional to the power exponent of the target flow rate of the circulating pump, and the power exponent is dynamically adjusted as the target power of the heating element changes.

[0072] Among the above optional methods, the nonlinear correlation constraint between the target power of the heating element and the target flow rate of the circulating pump is further refined. A physical coupling model between the two is established through the power function relationship between the heat transfer coefficient and the flow rate, and a rule for the power exponent to be dynamically adjusted with power is introduced to improve the modeling realism of the boundary conditions for heat exchange efficiency.

[0073] In an optional embodiment, a fault self-diagnosis module is further included. The fault self-diagnosis module is connected to the data acquisition module and is used to generate a heating element fault warning signal based on the deviation between the current operating temperature of the heating element and the theoretical operating temperature corresponding to the target power of the heating element, generate a fan fault warning signal based on the deviation between the current speed of the fan and the theoretical speed corresponding to the target speed of the fan, and generate a circulation pump fault warning signal based on the deviation between the current flow rate of the circulation pump and the theoretical flow rate corresponding to the target flow rate of the circulation pump.

[0074] Among them, the heating element fault warning signal refers to the alarm signal generated by the fault self-diagnosis part when it detects that the deviation between the current operating temperature of the heating element and the theoretical operating temperature corresponding to the target power of the heating element exceeds a preset threshold. For example, when the target power of the heating element is 18.5kW, the theoretical operating temperature corresponding to the equipment characteristic curve should be 240℃, but the actual current operating temperature is only 185℃, the deviation reaches 55℃, which exceeds the preset threshold of 30℃, and the fault self-diagnosis part generates a heating element fault warning signal.

[0075] Among them, the wind turbine fault warning signal refers to the alarm signal generated by the fault self-diagnosis part when it detects that the deviation between the current speed of the wind turbine and the theoretical speed corresponding to the target speed of the wind turbine exceeds a preset threshold. For example, when the target speed of the wind turbine is 1300 r / min, the theoretical speed calculated according to the output frequency of the frequency converter should be 1300 r / min, but the actual current speed of the wind turbine is only 980 r / min, the deviation reaches 320 r / min, which exceeds the preset threshold of 100 r / min, and the fault self-diagnosis part generates a wind turbine fault warning signal.

[0076] The circulating pump fault warning signal refers to the alarm signal generated by the fault self-diagnosis section when it detects that the deviation between the current flow rate of the circulating pump and the theoretical flow rate corresponding to the target flow rate of the circulating pump exceeds a preset threshold. For example, when the target flow rate of the circulating pump is 16.5 m³ / h, the theoretical flow rate calculated according to the output frequency of the frequency converter should be 16.5 m³ / h, but the actual current flow rate of the circulating pump is only 10.2 m³ / h, and the deviation reaches 6.3 m³ / h, which exceeds the preset threshold of 2 m³ / h. The fault self-diagnosis section generates a circulating pump fault warning signal.

[0077] In the above-mentioned optional methods, a fault self-diagnosis module is further added and three types of fault early warning logic are established. By comparing the deviations between the actual working temperature and theoretical temperature of the heating element, the actual speed and theoretical speed of the fan, and the actual flow rate and theoretical flow rate of the circulating pump, real-time identification and early warning of abnormal operation of key components are realized.

[0078] In one alternative approach, when the fault self-diagnosis module generates the heating element fault warning signal, the fan fault warning signal, or the circulating pump fault warning signal, it marks the collaborative control strategy output by the collaborative optimization module as a failure state and triggers the collaborative optimization module to regenerate an emergency control strategy with minimizing total energy consumption as the single optimization objective.

[0079] In this context, the failure state refers to the following: after generating any fault warning signal, the fault self-diagnosis part marks the currently output collaborative control strategy of the collaborative optimization part as unexecutable. At this time, the control system no longer executes according to the original collaborative control strategy. For example, after generating a fan fault warning signal, the fault self-diagnosis part marks the set of strategies in the collaborative control strategy, such as the fan target speed of 1300 r / min, the heating element target power of 18.5 kW, and the circulating pump target flow of 16.5 m³ / h, as a failure state, and the drive control part suspends the execution of the strategy. Emergency control strategy refers to a simplified control scheme regenerated by the collaborative optimization part after the collaborative control strategy is marked as a failure. The single optimization objective is to minimize total energy consumption, without simultaneously considering heating response time and steady-state deviation. For example, after the fan failure warning signal is triggered, the collaborative optimization part reduces the optimization objective from three to one, with the sole objective of minimizing total energy consumption. Under the premise of ensuring that the circulating pump operates at a safe flow rate and the heating element operates at a safe power, the emergency control strategy outputs a target power of 12kW for the heating element, a target flow rate of 12m³ / h for the circulating pump, and maintains the target fan speed at the current 980r / min.

[0080] Among the above-mentioned optional methods, a linkage switching mechanism between fault warning and optimization strategy is further established. When any type of fault warning signal is generated, the collaborative control strategy is automatically marked as failed and the emergency control mode is triggered. The control command is regenerated with the single objective of minimizing total energy consumption, which ensures the operational safety and energy consumption controllability under fault conditions.

[0081] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this invention is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-disclosed concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this invention.

[0082] It should be noted that the terms "first," "second," etc., used in the specification and claims of this application are used to distinguish similar objects and do not imply a specific order or sequence. Where appropriate, the order of use for similar objects can be interchanged so that the embodiments of this application described herein can be implemented in an order other than that shown or described.

[0083] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A high-efficiency and energy-saving intelligent control system for air heating units, characterized in that, The system includes: The data acquisition module is used to collect the operating parameters, environmental parameters and load parameters of the air heating unit in real time. The operating parameters include the current power of the heating element, the current operating temperature of the heating element, the current speed of the fan, the current flow rate of the circulating pump, the outlet air temperature and the return air temperature. The environmental parameters include the outdoor ambient temperature. The load parameters include the set temperature and the measured temperature of the heating area. The status assessment module is used to generate a heating element status assessment value by weighting the ratio of the current power of the heating element to the upper limit of the rated power of the heating element and the ratio of the current operating temperature of the heating element to the upper limit of the operating temperature of the heating element; to generate a fan status assessment value by weighting the ratio of the current speed of the fan to the upper limit of the rated speed of the fan; to generate a circulation pump status assessment value by weighting the ratio of the current flow rate of the circulation pump to the upper limit of the rated flow rate of the circulation pump; and to construct a dynamic heat load compensation model based on the temperature difference between the outlet air temperature and the return air temperature, the temperature difference between the outdoor ambient temperature and the measured temperature, and the deviation between the measured temperature and the set temperature, and output a predicted heat load demand value from the dynamic heat load compensation model. The collaborative optimization module is used to construct a multi-objective collaborative optimization function by using the state evaluation values ​​of the heating element, the fan, the circulating pump, and the predicted heat load demand as constraint boundaries, and the heat exchange efficiency boundary condition of the energy-saving heat exchange device as coupling constraints. The multi-objective collaborative optimization function aims to minimize total energy consumption, minimize heating response time, and minimize the steady-state deviation between the measured temperature and the set temperature. By jointly optimizing the target power of the heating element, the target speed of the fan, and the target flow rate of the circulating pump, the module outputs a collaborative control strategy that makes the multi-objective collaborative optimization function Pareto optimal. In the collaborative control strategy, the target power of the heating element, the target speed of the fan, and the target flow rate of the circulating pump form a nonlinear correlation constraint through the heat exchange efficiency boundary condition. The drive control module is used to generate a heating element drive command based on the target power of the heating element, a fan drive command based on the target speed of the fan, and a circulating pump drive command based on the target flow rate of the circulating pump. The module outputs the heating element drive command to the heating element driver, the fan drive command to the fan frequency converter, and the circulating pump drive command to the circulating pump frequency converter.

2. The intelligent control system for the high-efficiency energy-saving air heating unit according to claim 1, characterized in that, The status assessment module generates a status assessment value for the heating element based on the following weighted formula: the ratio of the current power of the heating element to its rated power limit, the ratio of the current operating temperature of the heating element to its operating temperature limit, and the ratio of the rate of change of the current power of the heating element to a preset power change rate limit. in, This is the condition assessment value of the heating element. The current power of the heating element. This is the upper limit of the rated power of the heating element. The current operating temperature of the heating element. This is the upper limit of the operating temperature of the heating element. The rate of change of the current power of the heating element. The upper limit of the preset power change rate, , , The preset weighting coefficients, and .

3. The intelligent control system for the high-efficiency energy-saving air heating unit according to claim 2, characterized in that, The status assessment module also dynamically adjusts the weighting coefficient based on the ratio of the current operating temperature of the heating element to the upper limit of the operating temperature of the heating element. , , When the current operating temperature of the heating element exceeds a preset temperature threshold, the temperature will increase. and reduce and .

4. The intelligent control system for the high-efficiency energy-saving air heating unit according to claim 1, characterized in that, The dynamic heat load compensation model constructed by the state assessment module outputs the predicted heat load demand value according to the following formula: in, The predicted heat load demand value is... The outlet air temperature is... The return air temperature, The outdoor ambient temperature is... The measured temperature is... For the set temperature, , , , The preset compensation coefficient, It is a time variable.

5. The intelligent control system for the high-efficiency energy-saving air heating unit according to claim 4, characterized in that, The status assessment module dynamically adjusts the compensation coefficient based on the rate of change of the outdoor ambient temperature. When the rate of change of the outdoor ambient temperature exceeds a preset rate of change threshold, the compensation coefficient is increased. .

6. The intelligent control system for the high-efficiency energy-saving air heating unit according to claim 1, characterized in that, The multi-objective collaborative optimization function constructed by the collaborative optimization module uses the target power of the heating element, the target speed of the fan, and the target flow rate of the circulating pump as decision variables. The total energy consumption is expressed as the sum of the product of the target power of the heating element and time, the product of the cube of the target speed of the fan and time, and the product of the cube of the target flow rate of the circulating pump and time.

7. The intelligent control system for the high-efficiency energy-saving air heating unit according to claim 6, characterized in that, The collaborative optimization module employs a multi-objective particle swarm optimization algorithm to jointly optimize the multi-objective collaborative optimization function. In each iteration, an adaptive inertial weight is constructed based on the state evaluation values ​​of the heating element, the fan, the circulating pump, and the predicted heat load demand. The optimal position of each particle and the global optimal position are updated according to the Pareto dominance relationship.

8. The intelligent control system for the high-efficiency energy-saving air heating unit according to claim 1, characterized in that, In the collaborative control strategy output by the collaborative optimization module, the nonlinear correlation constraint between the target power of the heating element and the target flow rate of the circulating pump is determined by the power function relationship between the heat transfer coefficient and the flow rate of the energy-saving heat exchange device. The heat transfer coefficient is proportional to the power exponent of the target flow rate of the circulating pump, and the power exponent is dynamically adjusted as the target power of the heating element changes.

9. The intelligent control system for the high-efficiency energy-saving air heating unit according to claim 1, characterized in that, It also includes a fault self-diagnosis module, which is connected to the data acquisition module. The fault self-diagnosis module is used to generate a heating element fault warning signal based on the deviation between the current operating temperature of the heating element and the theoretical operating temperature corresponding to the target power of the heating element, generate a fan fault warning signal based on the deviation between the current speed of the fan and the theoretical speed corresponding to the target speed of the fan, and generate a circulation pump fault warning signal based on the deviation between the current flow rate of the circulation pump and the theoretical flow rate corresponding to the target flow rate of the circulation pump.

10. The intelligent control system for the high-efficiency energy-saving air heating unit according to claim 9, characterized in that, When the fault self-diagnosis module generates the heating element fault warning signal, the fan fault warning signal, or the circulating pump fault warning signal, it marks the collaborative control strategy output by the collaborative optimization module as a failure state and triggers the collaborative optimization module to regenerate an emergency control strategy with minimizing total energy consumption as the single optimization objective.

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