An embedded control system for efficient waste heat reuse management

Abstract

This invention provides an embedded control system for efficient waste heat recovery management, comprising: a data acquisition module that monitors waste heat parameters in real time using temperature, flow, and pressure sensors; an embedded control module with built-in waste heat analysis algorithms and control strategies for data processing and generating control commands; a communication module supporting wired and wireless communication protocols; actuators including an electric regulating valve, a variable frequency pump, and a waste heat generator set that execute operations according to control commands; and a heat energy processing module for waste heat storage and distribution. Through adaptive control strategies and algorithm optimization, this invention can dynamically adjust the operating mode of the heat exchanger and other key parameters based on real-time environmental data, ensuring optimal heat recovery efficiency under various operating conditions. This not only reduces energy waste but also lowers the power consumption of the heating and cooling systems, thereby significantly saving operating costs.

Description

Technical Field

[0001] This invention relates to the field of energy management technology, and in particular to an embedded control system for efficient waste heat reuse management. Background Technology

[0002] A large amount of waste heat is generated during boiler production. If these resources are not effectively managed and reused, it will result in huge energy waste. Traditional waste heat recovery systems often suffer from insufficient flexibility and low level of intelligence, and cannot be precisely controlled according to actual needs. For example, when faced with fluctuations in parameters such as temperature and flow rate, traditional control systems may have difficulty responding quickly, resulting in low heat recovery efficiency and mismatch. With increasingly stringent environmental regulations and technological advancements, it has become particularly important to develop an intelligent control system that can efficiently manage and reuse waste heat resources.

[0003] Therefore, in order to address the above problems, the proposed solution is to develop an embedded control system for efficient waste heat reuse management. Summary of the Invention

[0004] In order to overcome the shortcomings of existing technologies, such as insufficient flexibility, low level of intelligence, and inability to accurately control according to actual needs, this invention provides an embedded control system for efficient waste heat reuse management.

[0005] The technical solution of this invention is: an embedded control system for efficient waste heat reuse management, comprising:

[0006] The data acquisition module includes a temperature sensor, a flow sensor, and a pressure sensor, used to monitor the temperature, flow rate, and pressure parameters of the waste heat recovery equipment in real time.

[0007] An embedded control module, connected to the data acquisition module, employs a high-performance microprocessor and incorporates waste heat analysis algorithms and control strategies to process the acquired data and generate control commands.

[0008] A communication module, connected to the embedded control module, supports wired and wireless communication protocols, and is used to enable interconnection between the system and a host computer or other devices.

[0009] The actuator, connected to the embedded control module, includes an electric regulating valve, a variable frequency pump, and a waste heat generator set, used to perform corresponding operations according to control commands to realize the recovery and reuse of waste heat;

[0010] The thermal energy processing module is used to further process the recovered high-efficiency waste heat, including a thermal energy storage unit and a thermal energy distribution unit.

[0011] The human-machine interaction module, connected to the embedded control module, provides a visual interface to display waste heat parameters, system operating status, and control command execution results, and supports manual intervention and parameter adjustment.

[0012] As a preferred embodiment of the present invention, the waste heat analysis algorithm built into the embedded control module includes a waste heat quality assessment algorithm based on waste heat temperature, flow rate, and pressure data, a waste heat utilization optimization algorithm based on historical and real-time data, and a feedback control algorithm for dynamically adjusting the control strategy. The waste heat quality assessment algorithm calculates the waste heat energy value by inputting waste heat temperature T, waste heat flow rate Q, and waste heat pressure P, as shown in the following formula:

[0013] E=ρ·Q·c p ·(TT env );

[0014] Where ρ is the density of the fluid, c p T is the specific heat capacity of the fluid. env Using the current ambient temperature data, after obtaining the waste heat energy value, the difference between the waste heat temperature and the current ambient temperature data is compared with the difference between the maximum waste heat temperature and the current ambient temperature data to obtain the waste heat quality coefficient. The closer the waste heat quality coefficient is to 1, the higher the waste heat temperature and the stronger the availability. The closer the waste heat quality coefficient is to 0, the lower the waste heat temperature and the weaker the availability. The scale of waste heat resources and the availability of waste heat are assessed based on the waste heat energy value and waste heat quality coefficient, providing data support for energy allocation and optimizing waste heat utilization methods.

[0015] The waste heat utilization optimization algorithm based on historical and real-time data needs to first calculate the waste heat recovery efficiency. The objective function for the waste heat recovery efficiency is set as f(x), and its expression is:

[0016]

[0017] Where η is the heat exchanger efficiency, ranging from 0 to 1, Q is the heat medium flow rate, and c p Let T1 be the specific heat capacity of the heat medium, T2 be the inlet temperature of the heat medium, and E1 be the total input heat energy. After obtaining the waste heat recovery efficiency, the system will autonomously optimize the waste heat recovery and utilization method based on historical and real-time data to maximize the waste heat recovery efficiency. The calculation steps are as follows:

[0018] First, input the waste heat temperature, flow rate, pressure, and utilization efficiency for the past hour, as well as the current waste heat temperature, flow rate, and pressure. Then, calculate using the formula. Constraints must be placed on the waste heat temperature, flow rate, and pressure; that is, the waste heat temperature, flow rate, and pressure must be maintained between the system's maximum and minimum values. The formula is as follows:

[0019]

[0020] Where, η k Let w be the efficiency of the k-th waste heat utilization method, i.e., the efficiency of power generation, heating, and cooling. i Let be the weighting coefficient for the i-th utilization method. This weighting coefficient needs to be dynamically adjusted based on historical data and real-time requirements. The adaptive adjustment formula for the weighting coefficient is:

[0021]

[0022] Among them, s i Data is provided for each sensor, i = 1, 2, 3…n, F is an exponential function, a is an adjustment parameter used to control the sensitivity of the weights to the variance of the sensor data, and V is a variance function. The weight contribution of the i-th sensor is calculated based on the data variance of the i-th sensor and the preset adjustment parameter. This contribution is then compared with the sum of the weight contributions of all sensors to obtain the proportion data. Based on the calculated maximum waste heat recovery efficiency, the system automatically matches the optimal waste heat recovery method. Relying on the feedback control algorithm of the dynamic adjustment control strategy, the system operation is automatically adjusted and controlled to ensure stable operation and maximize waste heat recovery efficiency. The feedback control algorithm uses a PID control algorithm, and the calculation formula is as follows:

[0023]

[0024] Where u(t) represents the control output data, indicating the control command generated by the system at time t, i.e., valve opening, pump speed, and power generation; e(t) represents the error signal value, i.e., the difference between the waste heat temperature, flow rate, or pressure and the target value; and K... p K i K d These are the proportional, integral, and differential coefficients, respectively. This is the integral term of the error signal, representing the cumulative error from system startup to the current time t. If the waste heat temperature has remained consistently low over a period of time, the integral term will gradually increase, prompting the system to adjust valve openings or pump speeds. The derivative of the error signal represents the rate of change of the error over time. That is, if the waste heat temperature is rising rapidly, the derivative term will reduce the valve opening in advance to prevent the temperature from exceeding the target value.

[0025] As a preferred embodiment of the present invention, the thermal energy storage unit employs a phase change material or a high-temperature heat storage device to store the recovered waste heat.

[0026] As a preferred embodiment of the present invention, the heat energy distribution unit distributes the stored heat energy to the heating system, cooling system or industrial production line according to actual needs.

[0027] As a preferred embodiment of the present invention, the embedded control module also incorporates an environment adaptation algorithm to dynamically adjust the waste heat recovery and utilization strategy based on external environmental parameters.

[0028] As a preferred embodiment of the present invention, the environmental adaptation algorithm includes a multi-objective optimization algorithm based on ambient temperature, humidity, and load demand. This algorithm dynamically adjusts waste heat recovery efficiency, thermal energy storage ratio, and allocation priority by analyzing environmental parameters and system operating status in real time. The calculation formula for the environmental status data is as follows:

[0029]

[0030] Where H represents environmental condition data, namely ambient temperature, humidity, and load demand, l(s) i The formula for calculating the sum of nonlinear functions is used to process data from a single sensor.

[0031] Where a, b, and c are the coefficients of the polynomial, x i h(s) is the i-th input value, n is the total number of inputs, and h(s) is the ith input value. i This is used for preprocessing sensor data, and the specific formula is as follows: Where μ is the mean of the sensor data and σ is the standard deviation of the sensor data, after obtaining the environmental state data, the logic rules are adjusted according to the waste heat temperature, flow rate, pressure and current thermal energy storage capacity to match the current waste heat recovery efficiency.

[0032] As a preferred embodiment of the present invention, the logical rules of the environment adaptation algorithm are as follows:

[0033] In low-temperature environments, the priority of the heating system is increased, waste heat is allocated to the heating system first, the proportion of heat energy storage is increased, and more heat energy is reserved for the heating system.

[0034] In high-temperature environments, the priority of the refrigeration system is increased, and waste heat is allocated to the refrigeration system first, reducing the proportion of heat energy storage.

[0035] When load demand fluctuates, priorities and storage ratios are dynamically adjusted to ensure supply and demand balance and stable heat supply.

[0036] As a preferred embodiment of the present invention, the error signal is calculated as follows:

[0037] e(t) = r(t) - y(y), where r(t) is the target value and y(t) is the actual output value.

[0038] By adopting the above technical solution, the present invention has the following advantages:

[0039] 1. This invention, through adaptive control strategies and algorithm optimization, can dynamically adjust the working mode and other key parameters of the heat exchanger based on real-time environmental data, ensuring optimal heat recovery efficiency under various working conditions. This not only reduces energy waste but also lowers the power consumption of the heating and cooling systems, thereby significantly saving operating costs.

[0040] 2. This invention adopts a closed-loop feedback mechanism and a distributed control architecture, which can quickly respond to any abnormal situation and automatically take corrective measures to ensure the continuous and stable operation of the system. The modular hardware design makes installation and maintenance simpler and faster, reducing the time and difficulty of troubleshooting. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of the structure of the present invention.

[0042] Figure 2 This is a schematic diagram of the data acquisition module of the present invention.

[0043] Figure 3 This is a schematic diagram of the embedded control module of the present invention.

[0044] Figure 4 This is a schematic diagram of the communication module of the present invention.

[0045] Figure 5 This is a schematic diagram of the structure of the actuator of the present invention.

[0046] Figure 6 This is a schematic diagram of the thermal energy processing module of the present invention. Detailed Implementation

[0047] References to embodiments herein mean that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0048] An embedded control system for efficient waste heat recovery management, such as Figures 1-6As shown, the system includes a data acquisition module, an embedded control module, a communication module, an actuator, a heat energy processing module, and a human-machine interface module. The data acquisition module includes temperature sensors, flow sensors, and pressure sensors for real-time monitoring of the temperature, flow rate, and pressure parameters of the waste heat recovery equipment. The waste heat recovery equipment includes a plate heat exchanger, a thermoelectric generator, an absorption chiller, and a heat storage system. Temperature sensors are installed at the inlet and outlet of the heat exchanger, near the heating equipment, and at temperature change points to monitor heat transfer efficiency in real time and detect temperature fluctuations to adjust the heat exchange process. Flow sensors are installed on the pipeline, near pumps and valves, to monitor fluid flow, ensure stable operation of the heat energy transfer system, and assist in calculating energy recovery. Pressure sensors are located in the pipeline system, installed in high-pressure areas and at control points to prevent overpressure and protect system safety. The embedded control module is connected to the data acquisition module and uses a high-performance microprocessor with built-in waste heat analysis algorithms and control strategies to process the acquired data. The system processes and generates control commands. A communication module connects to the embedded control module, supporting both wired and wireless communication protocols. Wired communication protocols supported include Modbus and Ethernet, while wireless communication protocols include Wi-Fi and LoRa. This enables interconnection between the system and a host computer or other devices. Actuators connected to the embedded control module include electric regulating valves, variable frequency pumps, and waste heat generator sets. These actuators perform corresponding operations based on control commands, enabling waste heat recovery and reuse. A thermal energy processing module further processes the recovered high-efficiency waste heat, including a thermal energy storage unit and a thermal energy distribution unit. The thermal energy storage unit uses phase change materials or high-temperature heat storage devices to store the recovered waste heat. The thermal energy distribution unit distributes the stored thermal energy to heating systems, cooling systems, or industrial production lines according to actual needs. A human-machine interface module connects to the embedded control module, providing a visual interface to display waste heat parameters, system operating status, and control command execution results, and supporting manual intervention and parameter adjustment.

[0049] The embedded control module's built-in waste heat analysis algorithms include a waste heat quality assessment algorithm based on waste heat temperature, flow rate, and pressure data; a waste heat utilization optimization algorithm based on historical and real-time data; and a feedback control algorithm that dynamically adjusts the control strategy. The waste heat quality assessment algorithm calculates the waste heat energy value by inputting waste heat temperature T, waste heat flow rate Q, and waste heat pressure P, as shown in the following formula:

[0050] E=ρ·Q·c p ·(TT env );

[0051] Where ρ is the density of the fluid, c p T is the specific heat capacity of the fluid. envUsing the current ambient temperature data, after obtaining the waste heat energy value, the difference between the waste heat temperature and the current ambient temperature data is compared with the difference between the maximum waste heat temperature and the current ambient temperature data to obtain the waste heat quality coefficient. The closer the waste heat quality coefficient is to 1, the higher the waste heat temperature and the stronger the availability. The closer the waste heat quality coefficient is to 0, the lower the waste heat temperature and the weaker the availability. The scale of waste heat resources and the availability of waste heat are assessed based on the waste heat energy value and waste heat quality coefficient, providing data support for energy allocation and optimizing waste heat utilization methods.

[0052] The waste heat utilization optimization algorithm based on historical and real-time data needs to first calculate the waste heat recovery efficiency. If the objective function for waste heat recovery efficiency is set as f(x), then the function expression is:

[0053]

[0054] Where η is the heat exchanger efficiency, ranging from 0 to 1, Q is the heat medium flow rate, and c p Let T1 be the specific heat capacity of the heat medium, T2 be the inlet temperature of the heat medium, and E1 be the total input heat energy. After obtaining the waste heat recovery efficiency, the system will autonomously optimize the waste heat recovery and utilization method based on historical and real-time data to maximize the waste heat recovery efficiency. The calculation steps are as follows:

[0055] First, input the waste heat temperature, flow rate, pressure, and utilization efficiency for the past hour, as well as the current waste heat temperature, flow rate, and pressure. Then, calculate using the formula. Constraints must be placed on the waste heat temperature, flow rate, and pressure; that is, the waste heat temperature, flow rate, and pressure must be maintained between the system's maximum and minimum values. The formula is as follows:

[0056]

[0057] Where, η k Let w be the efficiency of the k-th waste heat utilization method, i.e., the efficiency of power generation, heating, and cooling. i Let be the weighting coefficient for the i-th utilization method. This weighting coefficient needs to be dynamically adjusted based on historical data and real-time requirements. The adaptive adjustment formula for the weighting coefficient is:

[0058]

[0059] Among them, s i The data provided for each sensor, i = 1, 2, 3…n, F is an exponential function e x 'a' is an adjustment parameter used to control the sensitivity of the weights to the variance of the sensor data, and 'V' is the variance function. The weight contribution of the i-th sensor is calculated based on the data variance of the i-th sensor and preset adjustment parameters. This weight contribution is then compared with the sum of the weight contributions of all sensors to obtain the proportion data. Based on the calculated maximum waste heat recovery efficiency, the system automatically matches the optimal waste heat recovery method and uses a feedback control algorithm with dynamic adjustment control strategy to automatically adjust and control the system operation, ensuring stable system operation and maximizing waste heat recovery efficiency. The calculation formula for the feedback control algorithm is as follows:

[0060]

[0061] Where u(t) represents the control output data, indicating the control command generated by the system at time t, i.e., valve opening, pump speed, and power generation; e(t) represents the error signal value, i.e., the difference between the waste heat temperature, flow rate, or pressure and the target value, calculated as: e(t) = r(t) - y(t), where r(t) is the target value, y(t) is the actual output value, and K... p K i K d These are the proportional, integral, and differential coefficients, respectively. This is the integral term of the error signal, representing the cumulative error from system startup to the current time t. If the waste heat temperature has remained consistently low over a period of time, the integral term will gradually increase, prompting the system to adjust valve openings or pump speeds. The derivative of the error signal represents the rate of change of the error over time. That is, if the waste heat temperature is rising rapidly, the derivative term will reduce the valve opening in advance to prevent the temperature from exceeding the target value.

[0062] The embedded control module also incorporates an environment adaptation algorithm to dynamically adjust waste heat recovery and utilization strategies based on external environmental parameters. This algorithm includes a multi-objective optimization algorithm based on ambient temperature, humidity, and load demand. By analyzing environmental parameters and system operating status in real time, it dynamically adjusts waste heat recovery efficiency, thermal energy storage ratio, and allocation priority. The formula for calculating environmental status data is as follows:

[0063]

[0064] Where H represents environmental condition data, namely, comprehensive index data of environmental temperature, humidity, and load demand, and f(s) i () is the formula for calculating the sum of nonlinear functions, used to process data from a single sensor. The formula for calculating the sum of nonlinear functions is:

[0065] Where a, b, and c are the coefficients of the polynomial, x i It is the i-th input value, and n is the total number of inputs;

[0066] h(s iThis is used for preprocessing sensor data, and the specific formula is as follows: , where μ is the mean of the sensor data and σ is the standard deviation of the sensor data;

[0067] After obtaining the environmental status data, the system combines the waste heat temperature, flow rate, pressure and current thermal energy storage to adjust the logic rules corresponding to the current waste heat recovery efficiency. Specifically, when the environmental status data is less than 20, the system will determine that it is in a low temperature state; between 20 and 40, it is a comfortable environment; and greater than 40, it is a high temperature environment.

[0068] The logic rules of the environment adaptation algorithm are as follows: In low-temperature environments, the priority of the heating system is increased, waste heat is allocated to the heating system first, the heat storage ratio is increased, and more heat energy is reserved for the heating system; in high-temperature environments, the priority of the cooling system is increased, waste heat is allocated to the cooling system first, and the heat storage ratio is reduced; when the load demand fluctuates, the priority and storage ratio are dynamically adjusted to ensure supply and demand balance and stable heat energy supply.

[0069] Specifically, in low-temperature environments, the priority of the heating system is increased, the priority of the cooling system is decreased, the hot water circulation pump of the heating system is started, and the hot water is delivered to the radiator or the underfloor heating system. Excess heat energy is stored in the heat storage tank, and the target storage capacity of the heat storage tank is set at 80%-90%.

[0070] In high-temperature environments, waste heat is preferentially distributed to the refrigeration system. The waste heat drives the absorption chiller to generate cooling capacity to supply the refrigeration system. Excess heat energy is stored in the heat storage tank, with the target storage capacity of the heat storage tank set at 20%-30%.

[0071] Within the comfortable temperature range, waste heat distribution is dynamically adjusted according to real-time load demand.

[0072] Specifically, the operational logic of the executing agency is as follows:

[0073] The valves of the heating system are fully open in low-temperature environments to ensure that waste heat flows preferentially to the heating system; in high-temperature environments, they are partially closed to reduce the heat distribution of the heating system.

[0074] The valves of the refrigeration system are fully open in high-temperature environments to ensure that waste heat flows preferentially to the absorption chiller; in low-temperature environments, they are partially closed to reduce the heat distribution of the refrigeration system.

[0075] The valves of the thermal storage tank are dynamically adjusted according to storage needs to ensure that thermal energy is stored or released as needed;

[0076] The hot water circulation pump operates at high speed in low-temperature environments to ensure the heat supply of the heating system; it operates at low speed or stops in high-temperature environments.

[0077] The chilled water circulation pump operates at high speed in high-temperature environments to ensure the cooling supply of the refrigeration system; it operates at low speed or stops in low-temperature environments.

[0078] If the amount of waste heat recovered is insufficient to meet the demand, the system will automatically start the gas boiler to supplement it. If the amount of waste heat recovered exceeds the demand, the system will store the excess heat energy in the heat storage tank. If the heat storage tank is full, the system will reduce the boiler load or adjust the operating parameters of the waste heat recovery device.

[0079] For example, during system operation, a low temperature environment of 10℃ and a current humidity of 60% are encountered, with high heating demand and low cooling demand. Based on waste heat parameters obtained from sensors—waste heat temperature 200℃, waste heat flow rate 500 kg / h, waste heat pressure 1.5 bar, and the current storage capacity of the heat storage tank at 40%—the waste heat energy value is calculated to be 397,100 kJ / h, with a waste heat quality coefficient of 0.79. In this case, the waste heat resource is relatively large and highly usable. Furthermore, the waste heat recovery efficiency is calculated to be 0.63. The system begins to adaptively adjust and calculate weighting coefficients, adjusting the heating weight to 0.7, increasing the valve opening of the heating system to 80%, increasing the speed of the hot water circulation pump to 90%, and reducing the power generation to 10%. Then, it calculates environmental status data, and the system will match the calculation results and adjust the waste heat distribution ratio: 70% for heating, 10% for cooling, and 20% for power generation. The storage capacity of the heat storage tank is increased from 40% to 60% to reserve heat energy for subsequent heating needs, and the waste heat recovery efficiency is improved to 73%. The system operates stably.

[0080] The above embodiments are provided for those skilled in the art to implement or use the present invention. Those skilled in the art can make various modifications or changes to the above embodiments without departing from the spirit of the present invention. Therefore, the scope of protection of the present invention is not limited to the above embodiments, but should be the maximum scope that conforms to the innovative features mentioned in the claims.

Claims

1. An embedded control system for efficient waste heat recovery management, characterized in that, Including: The data acquisition module includes a temperature sensor, a flow sensor, and a pressure sensor, used to monitor the temperature, flow rate, and pressure parameters of the waste heat recovery equipment in real time. An embedded control module, connected to the data acquisition module, employs a high-performance microprocessor and incorporates waste heat analysis algorithms and control strategies to process the acquired data and generate control commands. A communication module, connected to the embedded control module, supports wired and wireless communication protocols, and is used to enable interconnection between the system and a host computer or other devices. The actuator, connected to the embedded control module, includes an electric regulating valve, a variable frequency pump, and a waste heat generator set, used to perform corresponding operations according to control commands to realize the recovery and reuse of waste heat; The thermal energy processing module is used to further process the recovered high-efficiency waste heat, including a thermal energy storage unit and a thermal energy distribution unit. The human-machine interaction module, connected to the embedded control module, provides a visual interface to display waste heat parameters, system operating status, and control command execution results, and supports manual intervention and parameter adjustment.

2. An embedded control system for efficient waste heat reuse management based on claim 1, characterized in that, The embedded control module's built-in waste heat analysis algorithms include a waste heat quality assessment algorithm based on waste heat temperature, flow rate, and pressure data; a waste heat utilization optimization algorithm based on historical and real-time data; and a feedback control algorithm that dynamically adjusts the control strategy. The waste heat quality assessment algorithm calculates the waste heat energy value by inputting waste heat temperature T, waste heat flow rate Q, and waste heat pressure P, using the following formula: E=ρ·Q·c p ·(T-T env ); Where ρ is the density of the fluid, c p T is the specific heat capacity of the fluid. env Using the current ambient temperature data, after obtaining the waste heat energy value, the difference between the waste heat temperature and the current ambient temperature data is compared with the difference between the maximum waste heat temperature and the current ambient temperature data to obtain the waste heat quality coefficient. The closer the waste heat quality coefficient is to 1, the higher the waste heat temperature and the stronger the availability. The closer the waste heat quality coefficient is to 0, the lower the waste heat temperature and the weaker the availability. The scale of waste heat resources and the availability of waste heat are assessed based on the waste heat energy value and waste heat quality coefficient, providing data support for energy allocation and optimizing waste heat utilization methods. The waste heat utilization optimization algorithm based on historical and real-time data needs to first calculate the waste heat recovery efficiency. The objective function for the waste heat recovery efficiency is set as f(x), and its expression is: Where η is the heat exchanger efficiency, ranging from 0 to 1, Q is the heat medium flow rate, and c p Let T1 be the specific heat capacity of the heat medium, T2 be the inlet temperature of the heat medium, and E1 be the total input heat energy. After obtaining the waste heat recovery efficiency, the system will autonomously optimize the waste heat recovery and utilization method based on historical and real-time data to maximize the waste heat recovery efficiency. The calculation steps are as follows: First, input the waste heat temperature, flow rate, pressure, and utilization efficiency for the past hour, as well as the current waste heat temperature, flow rate, and pressure. Then, calculate using the formula. Constraints must be placed on the waste heat temperature, flow rate, and pressure; that is, the waste heat temperature, flow rate, and pressure must be maintained between the system's maximum and minimum values. The formula is as follows: Where, η k Let w be the efficiency of the k-th waste heat utilization method, i.e., the efficiency of power generation, heating, and cooling. i Let be the weighting coefficient for the i-th utilization method. This weighting coefficient needs to be dynamically adjusted based on historical data and real-time requirements. The adaptive adjustment formula for the weighting coefficient is: Among them, s i Data is provided for each sensor, i = 1, 2, 3…n, F is an exponential function, a is an adjustment parameter used to control the sensitivity of the weights to the variance of the sensor data, and V is a variance function. The weight contribution of the i-th sensor is calculated based on the data variance of the i-th sensor and the preset adjustment parameter. This contribution is then compared with the sum of the weight contributions of all sensors to obtain the proportion data. Based on the calculated maximum waste heat recovery efficiency, the system automatically matches the optimal waste heat recovery method. Relying on the feedback control algorithm of the dynamic adjustment control strategy, the system operation is automatically adjusted and controlled to ensure stable operation and maximize waste heat recovery efficiency. The feedback control algorithm uses a PID control algorithm, and the calculation formula is as follows: Where u(t) represents the control output data, indicating the control command generated by the system at time t, i.e., valve opening, pump speed, and power generation; e(t) represents the error signal value, i.e., the difference between the waste heat temperature, flow rate, or pressure and the target value; and K... p K i K d These are the proportional, integral, and differential coefficients, respectively. This is the integral term of the error signal, representing the cumulative error from system startup to the current time t. If the waste heat temperature has remained consistently low over a period of time, the integral term will gradually increase, prompting the system to adjust valve openings or pump speeds. The derivative of the error signal represents the rate of change of the error over time. That is, if the waste heat temperature is rising rapidly, the derivative term will reduce the valve opening in advance to prevent the temperature from exceeding the target value.

3. An embedded control system for efficient waste heat reuse management based on claim 1, characterized in that, The thermal energy storage unit uses phase change materials or high-temperature heat storage devices to store recovered waste heat.

4. An embedded control system for efficient waste heat reuse management based on claim 1, characterized in that, The heat energy distribution unit distributes the stored heat energy to the heating system, cooling system, or industrial production line according to actual needs.

5. An embedded control system for efficient waste heat reuse management based on claim 1, characterized in that, The embedded control module also has a built-in environment adaptation algorithm, which is used to dynamically adjust the waste heat recovery and utilization strategy according to external environmental parameters.

6. An embedded control system for efficient waste heat reuse management based on claim 5, characterized in that, The environmental adaptation algorithm includes a multi-objective optimization algorithm based on ambient temperature, humidity, and load demand. It dynamically adjusts waste heat recovery efficiency, thermal energy storage ratio, and allocation priority by analyzing environmental parameters and system operating status in real time. The calculation formula for environmental status data is as follows: Where E represents environmental condition data, namely ambient temperature, humidity, and load demand, l(s) i The formula for calculating the sum of nonlinear functions is used to process data from a single sensor. The formula for calculating the sum of nonlinear functions is: Where a, b, and c are the coefficients of the polynomial, x i h(s) is the i-th input value, n is the total number of inputs, and h(s) is the ith input value. i This is used for preprocessing sensor data, and the specific formula is as follows: Where μ is the mean of the sensor data and σ is the standard deviation of the sensor data, after obtaining the environmental state data, the logic rules are adjusted according to the waste heat temperature, flow rate, pressure and current thermal energy storage capacity to match the current waste heat recovery efficiency.

7. An embedded control system for efficient waste heat reuse management based on claim 6, characterized in that, The logical rules of the environment adaptation algorithm are as follows: In low-temperature environments, the priority of the heating system is increased, waste heat is allocated to the heating system first, the proportion of heat energy storage is increased, and more heat energy is reserved for the heating system. In high-temperature environments, the priority of the refrigeration system is increased, and waste heat is allocated to the refrigeration system first, reducing the proportion of heat energy storage. When load demand fluctuates, priorities and storage ratios are dynamically adjusted to ensure supply and demand balance and stable heat supply.

8. An embedded control system for efficient waste heat reuse management based on claim 2, characterized in that, The error signal is calculated as follows: e(t) = r(t) - y(t), where r(t) is the target value and y(t) is the actual output value.

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