Intelligent control method and system for total heat recovery high-temperature heat pump unit

Abstract

The application relates to the technical field of heat pump control, and discloses an intelligent control method and a control system of a total heat recovery high-temperature heat pump unit, wherein the control method comprises the following steps: acquiring a condenser pipeline along-the-way multi-point temperature reading sequence and parameters such as a compressor operating frequency; calculating a temperature gradient value sequence by using a sliding differential method, and identifying a heat exchange section dynamic boundary point; dividing sections based on the dynamic boundary point and calculating heat released by each section; calculating a dynamic section available heat value, and generating a dynamic section available heat value vector; solving an optimal heat distribution matrix by using linear programming; and outputting a regulating valve opening degree and a circulating water pump frequency control signal.

Description

Technical Field

[0001] This invention relates to the field of heat pump control technology, specifically to an intelligent control method and control system for a total heat recovery high-temperature heat pump unit. Background Technology

[0002] High-temperature heat pump units with total heat recovery use variable frequency compressors to drive refrigerant circulation. The condenser transfers heat released from the refrigerant to multiple heat-consuming terminals such as domestic hot water, process heating, and general heating. Existing control methods divide the refrigerant heat release process into three heat exchange sections: superheated, condensing, and subcooled. Based on the temperature and pressure parameters at the ports of each section, a physical property database is consulted to calculate the heat released and average heat release temperature of each section, thereby generating a segmented available heat value vector. Based on this vector, heat is allocated to each heat-consuming terminal using linear programming. However, when the compressor speed is adjusted over a wide frequency range, the refrigerant mass flow rate and exhaust superheat change significantly, causing the boundary positions of each heat exchange section to shift significantly along the condenser piping. The fixed three-segment division assumption cannot reflect these boundary drifts, resulting in a systematic deviation between the segmented available heat value vector and the actual heat release quality distribution. The heat allocation command output by linear programming shows a significant decrease in heat matching accuracy under variable frequency operation, leading to a deterioration in the unit's total heat recovery efficiency. Summary of the Invention

[0003] This invention provides an intelligent control method and control system for a total heat recovery high-temperature heat pump unit, which solves the technical problems in related technologies, such as the dynamic change of heat distribution in each heat exchange section of the condenser with the operating conditions, the mismatch between the heat quality requirements of each heat-using terminal leading to a high total system loss rate, and insufficient real-time heat distribution adjustment.

[0004] This invention discloses an intelligent control method for a total heat recovery high-temperature heat pump unit, comprising the following steps: Acquire the real-time temperature reading sequence of multiple temperature sensors along the condenser piping, the current operating frequency of the compressor, the high and low pressure values ​​of the refrigerant, and the real-time heat load demand and required water supply temperature of each heat-consuming terminal. Based on the temperature reading sequence, the temperature gradient value sequence between adjacent sensors is calculated using the sliding difference method. The position where the temperature gradient value changes from being greater than a preset overheating gradient threshold to approaching zero is identified as the first dynamic boundary point, and the position where the temperature gradient value changes from approaching zero to being greater than a preset undercooling gradient threshold is identified as the second dynamic boundary point. Based on the position coordinates along the path of the first and second dynamic boundary points, the temperature reading sequence is divided into actual superheated section, actual condensing section and actual subcooled section. The enthalpy value at the endpoint of each section is obtained by querying the physical property database, and the heat released in each section is calculated by combining the refrigerant mass flow rate. The logarithmic average heat release temperature is calculated based on the end point temperature of each section, and the dynamic available heat value of each section is calculated in combination with the heat release value of each section, generating a dynamic segmented available heat value vector. Based on the demand vector of each heat-using terminal and the dynamically segmented available demand vector, with the objective function of minimizing the total system loss rate, and with the constraints of the minimum heat load satisfaction rate of each terminal and the constraint that the average heat release temperature of each actual section is higher than the required water supply temperature of the corresponding terminal, the optimal heat distribution matrix is ​​solved using a linear programming solver. Based on the optimal heat distribution matrix, the target circulation flow value of each heat recovery branch is calculated, and the opening control signal of the electric regulating valve of each branch and the frequency control signal of each variable frequency circulating water pump are output to the actuator to complete the heat distribution adjustment of the current control cycle.

[0005] Furthermore, the temperature gradient value The calculation formula is: ; in, For the first Temperature readings from each sensor, For the first Temperature readings from each sensor, and The first The and the first The position coordinates of each sensor along the path, It increases along the direction of refrigerant flow.

[0006] Furthermore, before calculating the temperature gradient value sequence, a sliding mean filter is applied to the temperature reading sequence to smooth out local noise interference. The length of the filter window is determined based on the sensor arrangement spacing and the thermal capacity characteristics of the pipeline.

[0007] Furthermore, the logarithmic average exothermic temperature The calculation formula is: ; in, This refers to the refrigerant temperature at the inlet of this section. This refers to the refrigerant temperature at the outlet of this section, in Kelvin (K). The natural logarithm operator; the dynamically available value The calculation formula is: ; in, This represents the heat released in this section. This is the ambient reference temperature.

[0008] Furthermore, the refrigerant mass flow rate is estimated by combining the compressor's current operating frequency with the compressor's volumetric efficiency characteristic curve; the end temperature of each section is obtained by the temperature readings of the sensors at the boundary of each section or by linear interpolation of the readings of adjacent sensors.

[0009] Furthermore, slack variables are introduced into the constraints. When the heat source value is insufficient, the minimum heat load satisfaction rate constraints of each terminal are allowed to be relaxed in order of priority. The slack amount is incorporated into the objective function in the form of a penalty term.

[0010] Furthermore, the target circulating flow rate is calculated by combining the heat distribution value of each branch with the inlet and outlet water temperature difference and specific heat capacity of each heat-using terminal; the electric regulating valve opening control signal is obtained by mapping the target circulating flow rate to the valve flow characteristic curve; and the variable frequency circulating water pump frequency control signal is obtained by mapping the target circulating flow rate to the pump speed flow characteristic curve.

[0011] Furthermore, after completing the heat distribution adjustment for the current control cycle, the process returns to the step of obtaining the temperature reading sequence to execute the next control cycle, thereby achieving continuous adaptive tracking control for the variable frequency compression operation.

[0012] This invention discloses an intelligent control system for a total heat recovery high-temperature heat pump unit, comprising: The data acquisition module is used to collect real-time temperature readings from multiple temperature sensors along the condenser piping, the current operating frequency of the compressor, the high and low pressure values ​​of the refrigerant, and the real-time heat load demand and required water supply temperature values ​​of each heat-consuming terminal. The boundary identification module is used to calculate the temperature gradient value sequence based on the temperature reading sequence using the sliding difference method, and to identify the first dynamic boundary point between the superheated section and the condensing section and the second dynamic boundary point between the condensing section and the subcooled section along the path coordinates. The heat calculation module is used to divide the actual superheated section, actual condensed section and actual subcooled section based on the position coordinates along the path of the first dynamic boundary point and the second dynamic boundary point, query the physical property database to obtain the enthalpy value of the endpoint of each section and calculate the heat released by each section; The heat value generation module is used to calculate the logarithmic average heat release temperature based on the end temperature of each segment, and to calculate the dynamic available heat value by combining the heat release value of each segment and generate a dynamic segmented available heat value vector. The allocation optimization module is used to solve the optimal heat allocation matrix using a linear programming solver, based on the demand value vector and the dynamically segmented available value vector, under the conditions of satisfying the minimum heat load satisfaction rate and heat release temperature constraints of each terminal. The control output module is used to calculate the target circulation flow value of each heat recovery branch based on the optimal heat distribution matrix, and output the opening control signal of the electric regulating valve of each branch and the frequency control signal of each variable frequency circulating water pump to the actuator.

[0013] Furthermore, before calculating the temperature gradient value sequence, the boundary identification module applies a moving average filter to the temperature reading sequence; the allocation optimization module introduces slack variables into the constraints and incorporates the slack amount into the objective function in the form of a penalty term.

[0014] The beneficial effects of this invention are as follows: This invention addresses the technical problem of decreased refrigerant matching accuracy caused by boundary drift under variable frequency operation when the fixed three-segment division assumption is applied under fixed frequency division conditions. It solves the problem by deploying multiple temperature sensors along the condenser piping and calculating the temperature gradient value sequence between adjacent sensors in real time. Based on the gradient characteristics, the dynamic boundary points between the superheated and condensing segments, and between the condensing and subcooled segments are identified. The invention achieves the following technical effects: the temperature and pressure parameters at the endpoints of each segment always correspond to the actual refrigerant state under the current variable frequency operation; the dynamic segmentation provides a refrigerant value vector that accurately reflects the heat release grade of each heat exchange segment; and the linear programming can output an effective heat distribution matrix based on accurate refrigerant value input and output across the entire operating range. Attached Figure Description

[0015] Figure 1 This is a flowchart of the intelligent control method for a total heat recovery high-temperature heat pump unit provided in an embodiment of the present invention. Detailed Implementation

[0016] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, some features described in the examples may be combined in other examples.

[0017] At least one embodiment of the present invention discloses an intelligent control method for a total heat recovery high-temperature heat pump unit, see [link to relevant documentation]. Figure 1 This includes the following steps: Step 1: Obtain unit operating parameters and heat demand data from end users.

[0018] The system acquires real-time temperature readings from multiple temperature sensors arranged along the condenser piping, the compressor's current operating frequency, refrigerant high and low pressure side pressures, and condenser inlet and outlet refrigerant temperatures; it also acquires real-time heat load demands and required water supply temperatures for each heat-consuming terminal.

[0019] The temperature reading sequence along the condenser piping is composed of temperature readings from sensors numbered sequentially along the condenser piping, arranged in order of location. The coordinates of each sensor are pre-calibrated and stored in the controller. The high and low pressure values ​​of the refrigerant are collected by pressure sensors installed on the compressor's discharge and suction sides, respectively.

[0020] Step 2: Identify the dynamic boundary points of the heat exchange section using temperature gradient calculation.

[0021] Based on the temperature readings along the path, the temperature gradient sequence between adjacent sensors is calculated using the sliding difference method.

[0022] It should be noted that the temperature gradient value mentioned above refers to the ratio of the difference in temperature readings between two adjacent sensors to the difference in their position coordinates along the path. Let the first... The temperature readings of each sensor are Its position coordinates along the route are Then the first To the Temperature gradient between sensors for: ; in, For the first Temperature readings from each sensor, For the first Temperature readings from each sensor, and The first The and the first The position coordinates of each sensor along the path, The sensor index is incremented along the refrigerant flow direction. The sequence of temperature readings along the path contains a total of [number missing]. Sensor readings, The range of values ​​is to ,in This represents the total number of temperature sensors deployed along the route.

[0023] Based on the temperature gradient value sequence, the location where the temperature gradient value changes from being greater than the preset superheating gradient threshold to approaching zero is identified, and this location is determined as the first dynamic boundary point between the superheating section and the condensing section; the location where the temperature gradient value changes from approaching zero to being greater than the preset subcooling gradient threshold is identified, and this location is determined as the second dynamic boundary point between the condensing section and the subcooling section; the coordinates of the two dynamic boundary points along the path are obtained.

[0024] In this embodiment, to improve the robustness of boundary point identification to sensor measurement noise, a moving average filter can be applied to the temperature reading sequence along the pipeline before calculating the temperature gradient value sequence to smooth out local noise interference. The length of the filter window is selected based on the sensor arrangement spacing and the pipeline heat capacity characteristics.

[0025] Step 3: Divide the area into sections based on dynamic boundary points and calculate the heat released in each section.

[0026] Based on the position coordinates along the path of the first and second dynamic boundary points, the temperature reading sequence along the path is divided into three temperature data subsets: the actual superheated section, the actual condensing section, and the actual subcooled section.

[0027] The enthalpy value of each section endpoint is obtained by querying the physical property database using the endpoint temperature and corresponding refrigerant pressure values ​​of each section; based on the endpoint enthalpy value of each section and the refrigerant mass flow rate, the heat release value of each actual section is calculated.

[0028] The end temperature of each section is obtained by taking the temperature reading of the sensor at the boundary of each section, or by linear interpolation of the readings of adjacent sensors; the corresponding refrigerant pressure value is taken as the exhaust pressure value at the end of the superheated section, the suction pressure value at the end of the subcooled section, and the corresponding side pressure value at the end of the condensing section; the refrigerant mass flow rate is estimated by combining the current operating frequency of the compressor with the volumetric efficiency characteristic curve of the compressor.

[0029] Let the endpoint enthalpy values ​​of the actual superheated section, the actual condensing section, and the actual subcooled section be respectively... , , , , , The refrigerant mass flow rate is The heat released in each section , , They are respectively: ; in, and These are the refrigerant enthalpy values ​​at the inlet and outlet of the actual superheated section, respectively. and These are the refrigerant enthalpy values ​​at the inlet and outlet of the actual condensation section, respectively. and These are the refrigerant enthalpy values ​​at the inlet and outlet of the actual subcooled section, respectively. This represents the refrigerant mass flow rate.

[0030] Step 4: Calculate the dynamic available value for each segment and generate a dynamic segmented available value vector.

[0031] Based on the endpoint temperatures of each actual section, the actual average heat release temperature of each section is calculated using the logarithmic average temperature formula. Let the inlet temperature of a certain section be... The outlet temperature is (Unit: K), the logarithmic mean exothermic temperature of this section for: ; in, This refers to the refrigerant temperature at the inlet of this section. This refers to the refrigerant temperature at the outlet of this section. This is the natural logarithm operator. The above formula is applied to the actual superheated section, the actual condensing section, and the actual subcooled section, respectively, to obtain the actual average heat dissipation temperature for each of the three sections. , , .

[0032] By combining the heat release values ​​of each section with the actual average heat release temperature, the dynamic available heat value for each actual section is calculated using the heat release formula. Let the ambient reference temperature be... (Unit: K), the heat released in a certain section is... The actual average heat release temperature is The dynamic available value for this section for: ; in, This represents the heat released in this section. This represents the actual average heat release temperature of this section. The ambient reference temperature is used. Dynamic available values ​​are calculated for the actual superheated, actual condensing, and actual subcooled sections, respectively. , , Arrange the three elements in segment order to generate a dynamically segmented vector of available values. ,in This indicates transpose.

[0033] Step 5: Based on the dynamically segmented available value vector, use linear programming to solve for the optimal heat distribution matrix.

[0034] Based on the real-time demand vector and dynamic segmented available demand vector of each heat-using terminal, the objective function is to minimize the total system loss rate. The constraints are the minimum heat load satisfaction rate of each terminal and the average heat release temperature of each actual section being higher than the required water supply temperature of the corresponding allocated terminal. The optimal heat allocation matrix under the current variable frequency operating condition is calculated using a linear programming solver.

[0035] The real-time demand value of each heat-consuming terminal is obtained from the real-time heat load demand value of each terminal obtained in step 1, combined with the required water supply temperature value and the ambient reference temperature. The calculated values ​​are arranged into a demand value vector. Optimal heat distribution matrix elements Indicates the first The first actual segment is assigned to the first The heat ratio of each heat terminal, of which For actual segment indexes, For hot terminal indexing, the sum of the elements in each row must not exceed 1 and all elements must be non-negative.

[0036] In this embodiment of the application, in order to avoid the situation where linear programming becomes infeasible under extreme variable frequency operating conditions (such as when the heat released in the overheated section during low frequency operation approaches zero), slack variables can be introduced into the constraints. This allows the minimum heat load satisfaction rate constraints of each terminal to be appropriately relaxed in order of priority when the heat source value is insufficient. The slack amount is then incorporated into the objective function and weighted in the form of a penalty term.

[0037] Step 6: Generate and output control signals to complete the heat distribution adjustment for the current control cycle.

[0038] Based on the optimal heat distribution matrix, the target circulating flow rate of each heat recovery branch is calculated; the target circulating flow rate is converted into the opening control signal of the electric regulating valve of each branch and the frequency control signal of each variable frequency circulating water pump, and output to the actuator to complete the heat distribution adjustment of the current control cycle; return to step 1 to execute the next control cycle, so as to realize the continuous adaptive tracking control of the variable frequency compression condition.

[0039] The target circulating flow rate is calculated by combining the heat distribution value of each branch with the temperature difference between the inlet and outlet water of each heat-using terminal and the specific heat capacity of the water; the opening control signal of the electric regulating valve is obtained by mapping the target circulating flow rate with the valve flow characteristic curve; the frequency control signal of the variable frequency circulating water pump is obtained by mapping the target circulating flow rate with the pump speed and flow characteristic curve.

[0040] Existing methods employ a fixed three-segment division assumption, where the boundary positions of each segment do not update with changes in compressor operating frequency. This leads to calculation errors accumulated from boundary drift over a wide frequency range, which are directly transmitted to the available heat value vector, resulting in a decrease in the heat matching accuracy of linear programming. This implementation deploys multiple temperature sensors along the condenser piping to calculate the temperature gradient sequence between adjacent sensors in real time. Based on gradient characteristics, it identifies the dynamic boundary points between the superheated and condensing segments, and between the condensing and subcooled segments. This ensures that the temperature and pressure parameters at the endpoints of each segment always correspond to the actual refrigerant state under the current variable frequency operating conditions, eliminating the segment heat calculation errors introduced by the fixed division assumption. The dynamically segmented available heat value vector generated from the dynamic identification results accurately reflects the heat release grade of each heat exchange segment under the current operating conditions. Even under extreme conditions such as the expansion of the low-frequency subcooling segment and the increase in the high-frequency superheated segment, linear programming can output an effective heat distribution matrix based on accurate heat value input and output, thereby maintaining a high total heat recovery efficiency across the entire operating range.

[0041] On the other hand, the present invention also proposes an intelligent control system for a total heat recovery high-temperature heat pump unit, comprising: The data acquisition module is used to acquire unit operating parameters and heat demand data from multiple temperature sensors along the condenser piping, the current operating frequency of the compressor, the high and low pressure values ​​of the refrigerant, the refrigerant temperature values ​​at the condenser inlet and outlet, and the real-time heat load demand and required water supply temperature values ​​of each heat terminal.

[0042] The boundary identification module is used to identify the dynamic boundary points of the heat exchange section by calculating the temperature gradient. Based on the temperature reading sequence along the flow, it calculates the temperature gradient value sequence using the sliding difference method and identifies the position coordinates along the flow of the first dynamic boundary point between the superheated section and the condensing section, and the second dynamic boundary point between the condensing section and the subcooled section.

[0043] The heat calculation module is used to divide the area into sections based on dynamic boundary points and calculate the heat released by each section. It divides the area into three actual sections based on the coordinates of the first and second dynamic boundary points along the path, queries the physical property database to obtain the enthalpy values ​​of the endpoints of each section, and calculates the heat released by each section.

[0044] The heat value generation module is used to calculate the dynamic available heat value for each segment and generate a dynamic segmented available heat value vector. It calculates the logarithmic average heat release temperature based on the endpoint temperature of each segment, and calculates the dynamic available heat value by combining the heat release value of each segment and generating a dynamic segmented available heat value vector.

[0045] The allocation optimization module is used to solve the optimal heat allocation matrix using linear programming based on the dynamic piecewise available value vector. Based on the demand value vector and the dynamic piecewise available value vector, it solves the optimal heat allocation matrix under the conditions of satisfying the minimum heat load satisfaction rate and heat release temperature constraints of each terminal.

[0046] The control output module is used to generate and output control signals to complete the heat distribution adjustment of the current control cycle. It calculates the target circulation flow value of each branch based on the optimal heat distribution matrix and outputs the opening control signal of the electric regulating valve of each branch and the frequency control signal of each variable frequency circulating water pump to the actuator.

[0047] By way of example, the system proposed in this invention may also include various features and combinations thereof in the method embodiments, which will not be elaborated here.

[0048] The embodiments of the present invention have been described above. However, the embodiments are not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make more equivalent embodiments under the guidance of the present embodiments, and all of them are within the protection scope of the present embodiments.

Claims

1. An intelligent control method for a total heat recovery high-temperature heat pump unit, characterized in that, Includes the following steps: Acquire the real-time temperature reading sequence of multiple temperature sensors along the condenser piping, the current operating frequency of the compressor, the high and low pressure values ​​of the refrigerant, and the real-time heat load demand and required water supply temperature of each heat-consuming terminal. Based on the temperature reading sequence, the temperature gradient value sequence between adjacent sensors is calculated using the sliding difference method. The position where the temperature gradient value changes from being greater than a preset overheating gradient threshold to approaching zero is identified as the first dynamic boundary point, and the position where the temperature gradient value changes from approaching zero to being greater than a preset undercooling gradient threshold is identified as the second dynamic boundary point. Based on the position coordinates along the path of the first and second dynamic boundary points, the temperature reading sequence is divided into actual superheated section, actual condensing section and actual subcooled section. The enthalpy value at the endpoint of each section is obtained by querying the physical property database, and the heat released in each section is calculated by combining the refrigerant mass flow rate. The logarithmic average heat release temperature is calculated based on the end point temperature of each section, and the dynamic available heat value of each section is calculated in combination with the heat release value of each section, generating a dynamic segmented available heat value vector. Based on the demand vector of each heat-using terminal and the dynamically segmented available demand vector, with the objective function of minimizing the total system loss rate, and with the constraints of the minimum heat load satisfaction rate of each terminal and the constraint that the average heat release temperature of each actual section is higher than the required water supply temperature of the corresponding terminal, the optimal heat distribution matrix is ​​solved using a linear programming solver. Based on the optimal heat distribution matrix, the target circulation flow value of each heat recovery branch is calculated, and the opening control signal of the electric regulating valve of each branch and the frequency control signal of each variable frequency circulating water pump are output to the actuator to complete the heat distribution adjustment of the current control cycle.

2. The intelligent control method for a total heat recovery high-temperature heat pump unit according to claim 1, wherein, The temperature gradient value The calculation formula is: ; in, For the first Temperature readings from each sensor, For the first Temperature readings from each sensor, and The first The and the first The position coordinates of each sensor along the path, It increases along the direction of refrigerant flow.

3. The intelligent control method for a total heat recovery high-temperature heat pump unit according to claim 1, wherein, Before calculating the temperature gradient value sequence, a sliding mean filter is applied to the temperature reading sequence to smooth out local noise interference. The length of the filter window is determined based on the sensor arrangement spacing and the thermal capacity characteristics of the pipeline.

4. The intelligent control method for a total heat recovery high-temperature heat pump unit according to claim 1, wherein, The logarithmic mean exothermic temperature The calculation formula is: ; in, This refers to the refrigerant temperature at the inlet of this section. This refers to the refrigerant temperature at the outlet of this section, in Kelvin (K). The natural logarithm operator; the dynamically available value The calculation formula is: ; in, This represents the heat released in this section. This is the ambient reference temperature.

5. The intelligent control method for a total heat recovery high-temperature heat pump unit according to claim 1, wherein, The refrigerant mass flow rate is estimated by combining the compressor's current operating frequency with the compressor's volumetric efficiency characteristic curve; the end temperature of each section is obtained by the temperature readings of the sensors at the boundary of each section or by linear interpolation of the readings of adjacent sensors.

6. The intelligent control method for a total heat recovery high-temperature heat pump unit according to claim 1, wherein, The constraints introduce slack variables, allowing the minimum heat load satisfaction rate constraints of each terminal to be relaxed in order of priority when the heat source value is insufficient. The slack amount is incorporated into the objective function in the form of a penalty term.

7. The intelligent control method for a total heat recovery high-temperature heat pump unit according to claim 1, wherein, The target circulating flow rate is calculated by combining the heat distribution value of each branch with the inlet and outlet water temperature difference and specific heat capacity of each heat-using terminal; the electric regulating valve opening control signal is obtained by mapping the target circulating flow rate to the valve flow characteristic curve; the variable frequency circulating water pump frequency control signal is obtained by mapping the target circulating flow rate to the pump speed and flow characteristic curve.

8. The intelligent control method for a total heat recovery high-temperature heat pump unit according to claim 1, wherein, After completing the heat distribution adjustment for the current control cycle, return to the step of obtaining the temperature reading sequence to execute the next control cycle, thereby achieving continuous adaptive tracking control for the variable frequency compression condition.

9. An intelligent control system for a total heat recovery high-temperature heat pump unit, characterized in that, include: The data acquisition module is used to collect real-time temperature readings from multiple temperature sensors along the condenser piping, the current operating frequency of the compressor, the high and low pressure values ​​of the refrigerant, and the real-time heat load demand and required water supply temperature values ​​of each heat-consuming terminal. The boundary identification module is used to calculate the temperature gradient value sequence based on the temperature reading sequence using the sliding difference method, and to identify the first dynamic boundary point between the superheated section and the condensing section and the second dynamic boundary point between the condensing section and the subcooled section along the path coordinates. The heat calculation module is used to divide the actual superheated section, actual condensed section and actual subcooled section based on the position coordinates along the path of the first dynamic boundary point and the second dynamic boundary point, query the physical property database to obtain the enthalpy value of the endpoint of each section and calculate the heat released by each section; The heat value generation module is used to calculate the logarithmic average heat release temperature based on the end temperature of each segment, and to calculate the dynamic available heat value by combining the heat release value of each segment and generate a dynamic segmented available heat value vector. The allocation optimization module is used to solve the optimal heat allocation matrix using a linear programming solver, based on the demand value vector and the dynamically segmented available value vector, under the conditions of satisfying the minimum heat load satisfaction rate and heat release temperature constraints of each terminal. The control output module is used to calculate the target circulation flow value of each heat recovery branch based on the optimal heat distribution matrix, and output the opening control signal of the electric regulating valve of each branch and the frequency control signal of each variable frequency circulating water pump to the actuator.

10. The intelligent control system for the total heat recovery high-temperature heat pump unit according to claim 9, wherein, Before calculating the temperature gradient value sequence, the boundary identification module applies a moving average filter to the temperature reading sequence; the allocation optimization module introduces relaxation variables into the constraints and incorporates the relaxation amount into the objective function in the form of a penalty term.

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