Direct flue gas heat exchange heat pump control method and system

Abstract

The application provides a direct flue gas heat exchange heat pump control method and system, and relates to the technical field of flue gas waste heat recovery. The method comprises the following steps: according to external working condition parameters, dividing running parameters into multiple running working condition sections, and storing the historical highest performance coefficient of each running working condition section and the internal running parameter combination; according to the change trend of each running working condition section, updating the dynamic baseline performance coefficient of each running working condition section; calculating the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient; and determining the adjustment value of the internal running parameter according to the deviation and controlling the system to work. The method aims to solve the problem of COP optimal running point drift of the direct flue gas heat exchange heat pump system caused by component performance degradation in long-term operation, can adaptively determine the real optimal running point of the system under the current degradation state, so that the system can continuously maintain the optimal economy during operation, and effectively avoid accelerating component wear caused by long-term suboptimal operation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of flue gas waste heat recovery, in particular to a direct flue gas heat exchange heat pump control method and system. BACKGROUND

[0002] In a large industrial park, in order to realize the cascade utilization of energy and the reduction of carbon emissions, a direct flue gas heat exchange heat pump system is deployed. The system is responsible for recovering the waste heat of flue gas discharged by industrial boilers, and using the recovered heat for low-temperature hot water supply in a production workshop in the park. The heat pump system mainly consists of an evaporator directly connected with the flue gas channel, a variable frequency compressor, a condenser and an electronic expansion valve. In order to maximize energy utilization efficiency, the system is configured with a coefficient of performance (COP) optimal operating point search method based on real-time energy consumption data. The method continuously monitors the temperature, pressure, flow rate and compressor power consumption at each key point of the system, calculates the current COP value in real time, and dynamically adjusts the core operating parameters such as compressor speed and electronic expansion valve opening degree according to the trend of COP, aiming to continuously track and maintain the highest COP of the system.

[0003] However, as the heat pump system is running for a long time, high intensity and continuously, its internal physical properties and performance are experiencing slow and irreversible degradation. This constitutes the first challenge for system optimization. For example, as the core power component, the variable frequency compressor, its internal key moving parts such as bearings, piston rings or impellers will produce micro wear under long time friction, causing the internal clearance to gradually increase, thus slowly reducing the isentropic efficiency and volumetric efficiency of the compressor. This decrease in efficiency is not a sudden failure, but a gradual one, which is difficult to immediately identify through conventional fault indicator lights or simple alarm thresholds. Similarly, as an accurate regulator of refrigerant flow, the electronic expansion valve may have slight mechanical fatigue or wear of its stepping motor or valve core due to long-term frequent operation, causing slight deviation in the accuracy and response speed of its opening control, and failing to accurately control the refrigerant superheat within the optimal range as the new device. In addition, as key heat exchange equipment, the condenser and evaporator may have a thin layer of scale or oil film layer formed on the water side and refrigerant side of the tube wall, which is not easy to be detected by the naked eye, even after water treatment. These deposits can significantly increase the heat transfer resistance, causing the heat transfer coefficient of the heat exchanger to continuously and slowly decrease, thus affecting the overall heat exchange efficiency. The slow degradation of these component performances collectively causes changes in the overall operating characteristics of the system, so that the optimal operating parameter combination point of the heat pump under the same external working conditions is no longer the theoretical optimal value determined during initial commissioning.

[0004] Secondly, due to the continuous and slow degradation of the performance of the aforementioned components, the overall operating characteristic curves of the heat pump system, such as the efficiency characteristic curve of the compressor, the flow characteristic curve of the electronic expansion valve, and the heat transfer characteristic curve of the heat exchanger, will experience subtle and continuous drift. This means that, under given external operating conditions such as flue gas inlet temperature and flow rate, and hot water outlet temperature and flow rate, the combination of parameters required for the system to achieve the highest COP under the current state, including compressor speed and electronic expansion valve opening, has deviated from the "optimal" parameter combination determined in the initial system design or based on the new equipment model. For example, previously, under a specific operating condition, the system COP could reach its maximum value when the compressor speed was set to X and the electronic expansion valve opening was Y. However, now, due to the decrease in the compressor's isentropic efficiency, it may be necessary to adjust the compressor speed to X' and the electronic expansion valve opening to Y' to maximize the COP under the current degraded state, even if this maximum COP value itself may be lower than the theoretical maximum COP under the new equipment. This drift of the system characteristic curves poses a challenge to any COP optimization method based on a fixed model or static empirical rules.

[0005] Furthermore, a highly complex nonlinear coupling relationship exists among multiple operating parameters within a heat pump system, and this coupling becomes even more complex due to component performance degradation. Adjusting the compressor speed directly affects not only the refrigerant circulation flow rate, suction pressure, and discharge pressure, but also indirectly the heat transfer temperature difference between the evaporator and condenser. Simultaneously, the opening of the electronic expansion valve directly controls the superheat and subcooling of the refrigerant, thus decisively influencing the thermodynamic efficiency of the entire refrigeration cycle. Auxiliary parameters such as cooling water flow rate and flue gas bypass valve opening are also interconnected with the core refrigeration cycle parameters. When system component performance degrades, this complex coupling relationship subtly changes. For example, when compressor efficiency decreases, simply increasing the compressor speed to compensate for insufficient heat output may cause the compressor to operate in an even less efficient region, actually increasing energy consumption. In this case, it may be necessary to simultaneously fine-tune the opening of the electronic expansion valve, and even consider optimizing other auxiliary parameters such as cooling water flow rate, to find a new balance point and optimal combination under the current degraded state. Traditional optimization methods based on single-parameter perturbation or simple iteration often fail to converge quickly and efficiently to the true optimal operating point when faced with complex systems with multiple parameters, nonlinearity, and where the underlying physical relationships have drifted. They may even linger near local optima for a long time without being able to escape.

[0006] Further, due to the slow degradation of system component performance, the real-time energy consumption data collected by the system during operation, even if the sensors themselves are accurate, no longer reflects the system's "health" status and actual energy efficiency performance as designed or modeled based on new equipment. For example, even if the compressor power meter reading is accurate, due to the internal efficiency of the compressor, the same power input may only produce relatively less heat output, resulting in a lower COP value than expected for new equipment. This gradual deviation between "energy consumption data" and "true physical performance" makes the optimization objective function on which the COP optimal operating point search algorithm based on "real-time energy consumption data" itself based on a gradually distorted system model. The algorithm may be constantly trying to optimize a "hypothetical" optimal point, but it may not actually reach the real optimal operating point under the current degraded system.

[0007] Finally, under the combined effects of the above multiple factors, i.e., slow degradation of component performance, drift of system characteristic curve, complex coupling of control parameters, and deviation of real-time energy consumption data from true performance, the optimization efficiency of traditional COP optimal operating point search methods, such as simple perturbation observation or hill climbing algorithm, will be significantly reduced, or even fail to optimize. After each parameter adjustment, due to changes in the underlying physical characteristics of the system, the original empirical rules or fixed models are no longer applicable, resulting in the system taking longer to reach a new stable state or failing to converge to the true optimum. The algorithm may make multiple iterations in the "wrong" direction or oscillate around the local optimal solution, resulting in the system running in a suboptimal state for a long time, consuming additional electrical energy. This inefficient optimization process not only increases the operating cost of the system, but also accelerates the further wear and tear of the components, ultimately deviating from the original design goal of maximizing energy efficiency. For example, when the compressor efficiency decreases slightly, the system may try to compensate for the lack of heat output by increasing the speed, but this compensatory operation may cause the compressor to run in a lower efficiency region, further increasing power consumption, and failing to find the true optimal operating point under the current efficiency, thus keeping the system in a low efficiency state for a long time. SUMMARY

[0008] The present application aims to provide a direct flue gas heat exchange heat pump control method and system, which aims to solve the problem of COP optimal operating point drift caused by component performance degradation in the long-term operation of direct flue gas heat exchange heat pump systems, and can adaptively perceive and accurately track the true optimal operating point of the system under the current degraded state, so that the system can continuously maintain optimal economy under dynamic changes in component performance, and effectively avoid accelerated component wear due to long-term suboptimal operation.

[0009] In a first aspect, the present application provides a direct flue gas heat exchange heat pump control method, applied to a direct flue gas heat exchange heat pump control system, comprising the following steps:

[0010] Obtaining the operating parameters of the direct flue gas heat exchange heat pump control system; the operating parameters include external working condition parameters and internal operating parameters;

[0011] According to the external working condition parameters, the operating parameters are divided into multiple operating condition sections, and the historical maximum performance coefficient of each operating condition section and the internal operating parameter combination for achieving the historical maximum performance coefficient are stored;

[0012] According to the change trend of the historical maximum performance coefficient of each operating condition section over time, the dynamic baseline performance coefficient of each operating condition section is updated by excluding abnormal low performance coefficient data points;

[0013] Obtaining the real-time performance coefficient of the direct flue gas heat exchange heat pump control system, and calculating the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient according to the real-time performance coefficient and the dynamic baseline performance coefficient;

[0014] Based on the internal operating parameter combination corresponding to the dynamic baseline performance coefficient, the deviation is used to adjust the disturbance amplitude and disturbance direction of the internal operating parameters, and the change trend of the operating state parameters of the key components in the direct flue gas heat exchange heat pump control system is combined to determine the adjustment value of the internal operating parameters, wherein the key components in the direct flue gas heat exchange heat pump control system include: variable frequency compressor, electronic expansion valve, evaporator and condenser, and the operating state parameters of the key components in the direct flue gas heat exchange heat pump control system include: temperature, pressure, flow, and power consumption;

[0015] According to the determined internal operating parameter adjustment value, the direct flue gas heat exchange heat pump control system is controlled to work.

[0016] The direct flue gas heat exchange heat pump control method provided by the present application adjusts the parameter disturbance strategy of the optimization algorithm adaptively by monitoring the deviation between the real-time COP and the dynamic baseline, so as to accurately track and maintain the optimal economic operation point of the system under the current state in the environment with continuous changes in component performance. This intelligent adjustment enables the system to quickly and stably converge to the highest COP>4 that can be actually reached at present, instead of wandering around the old and unattainable target, so as to continuously maintain the optimal economic operation of the system under the current degraded state.

[0017] In a second aspect, the present application provides a direct flue gas heat exchange heat pump control system, comprising:

[0018] The first obtaining module is configured to obtain the operating parameters of the direct flue gas heat exchange heat pump control system; the operating parameters include external working condition parameters and internal operating parameters;

[0019] The dividing module is configured to divide the operation parameter into a plurality of operation condition segments according to external working condition parameters, and store a historical maximum performance coefficient of each operation condition segment and an internal operation parameter combination for achieving the historical maximum performance coefficient;

[0020] The updating module is configured to update a dynamic baseline performance coefficient of each operation condition segment according to a change trend of the historical maximum performance coefficient of each operation condition segment over time;

[0021] The second obtaining module is configured to obtain a real-time performance coefficient of the direct flue gas heat exchange heat pump control system, and calculate a deviation between the real-time performance coefficient and the dynamic baseline performance coefficient according to the real-time performance coefficient and the dynamic baseline performance coefficient;

[0022] The adjustment determining module is configured to adjust a disturbance amplitude and a disturbance direction of the internal operation parameter according to the deviation based on the internal operation parameter combination corresponding to the dynamic baseline performance coefficient, and determine an adjustment value of the internal operation parameter in combination with a change trend of an operation state parameter of a key component in the direct flue gas heat exchange heat pump control system;

[0023] The control module is configured to control the direct flue gas heat exchange heat pump control system to work according to the determined adjustment value of the internal operation parameter.

[0024] As can be seen from the above, the direct flue gas heat exchange heat pump control method provided by the application continuously senses the actual degradation trend of the system component performance, dynamically adjusts the reference baseline of the COP optimal operation, and adaptively adjusts the optimization strategy of the operation parameter based on the deviation between the real-time COP and the dynamic baseline. This method enables the heat pump system to overcome the characteristic drift caused by the degradation of the component performance and always maintain the optimal economic operation state under the current state in the long-term operation. Specifically, it solves the problems of slow degradation of the component performance and drift of the system characteristic curve, adapts the optimization target to the actual performance of the system; solves the problem of dynamic change of the nonlinear coupling relationship between the control parameters, and intelligently optimizes by combining the component operation state parameters; solves the problem of deviation between the real-time energy consumption data and the real physical performance, and ensures that the optimization target is based on the actual achievable performance. Finally, the application effectively avoids the accelerated wear of the system due to long-term operation in the suboptimal state, and significantly improves the long-term operation efficiency and economy of the system.

[0025] Other features and advantages of the present application will be set forth in the following description, and in part will become apparent from the description, or can be learned by practice of the present application according to the embodiments. The objects and other advantages of the present application can be achieved and obtained by the structure particularly pointed out in the written description and the accompanying drawings. BRIEF DESCRIPTION OF DRAWINGS

[0026] Figure 1This is a flowchart of a direct flue gas heat exchange heat pump control method provided in an embodiment of the present invention.

[0027] Figure 2 The COP value is the simulated heat pump system value after closed-loop regulation in this embodiment of the invention.

[0028] Figure 3 The COP value of the heat pump system after closed-loop regulation in this embodiment of the invention is the actual measured value.

[0029] Figure 4 This is a schematic diagram of a direct flue gas heat exchange heat pump control system provided in an embodiment of the present invention.

[0030] Label Explanation:

[0031] 100. First Acquisition Module; 200. Division Module; 300. Update Module; 400. Second Acquisition Module; 500. Adjustment Confirmation Module; 600. Control Module. Detailed Implementation

[0032] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0033] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this invention, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0034] Reference Appendix Figure 1 This invention provides a control method for a direct flue gas heat exchange heat pump, applied to a direct flue gas heat exchange heat pump control system, comprising the following steps:

[0035] Obtain the operating parameters of the direct flue gas heat exchange heat pump control system; the operating parameters include external operating condition parameters and internal operating parameters;

[0036] Based on external operating parameters, the operating parameters are divided into multiple operating condition segments, and the historical highest performance coefficient of each operating condition segment and the combination of internal operating parameters that achieve the historical highest performance coefficient are stored.

[0037] According to the change trend of the historical maximum performance coefficient of each operating condition section over time, the dynamic baseline performance coefficient of each operating condition section is updated; the updating operation includes excluding abnormal low performance coefficient data points;

[0038] The real-time performance coefficient of the direct flue gas heat exchange heat pump control system is obtained, and according to the real-time performance coefficient and the dynamic baseline performance coefficient, the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient is calculated;

[0039] Based on the internal operating parameter combination corresponding to the dynamic baseline performance coefficient, according to the deviation, the disturbance amplitude and disturbance direction of the internal operating parameter are adjusted, and combined with the change trend of the operating state parameter of the key component in the direct flue gas heat exchange heat pump control system, the adjustment value of the internal operating parameter is determined; the change trend of the operating state parameter reflects the gradual degradation of the corresponding key component performance or the drift of the operating characteristics;

[0040] According to the determined internal operating parameter adjustment value, the direct flue gas heat exchange heat pump control system is controlled to work.

[0041] The operation parameter division is divided into multiple operation condition sections, which refers to the classification of system operation data according to external environmental conditions, which can be realized by interval division based on temperature, load, flow and other external parameters, for example, the flue gas inlet temperature is divided into multiple ranges, or the hot water load is divided into multiple levels, which is mainly to analyze and compare the performance of the system under different external conditions; the highest performance coefficient and the internal operation parameter combination for realizing the highest performance coefficient refer to the highest energy efficiency performance of the system in a specific operation condition section and the corresponding internal control parameter setting at that time, which can be realized by data recording and retrieval technology, for example, storing the performance coefficient and its corresponding compressor frequency, expansion valve opening degree and the like of each operation in the database, which is mainly to provide reference data for the subsequent performance baseline establishment; the dynamic baseline performance coefficient refers to the system energy efficiency reference standard adjusted over time, which can be realized by time series analysis, regression analysis or machine learning model, for example, by trend fitting of historical performance coefficient data, which is mainly to reflect the actual change of system performance in long-term operation and serve as the basis for real-time performance evaluation; the change trend of the operation state parameters of the system components refers to the regular change of the operation state parameters (such as temperature, pressure, flow, and power consumption) of the main components (such as compressor, expansion valve, and heat exchanger) in the system over time, which can be realized by sensor data acquisition and trend analysis technology, for example, monitoring the long-term rising trend of the compressor discharge temperature, which is mainly to reflect the gradual degradation of component performance or the drift of operation characteristics; the disturbance amplitude and direction of adjusting the internal operation parameters refers to the degree and direction of adjusting the internal control parameters (such as compressor frequency and expansion valve opening degree) according to the deviation of system real-time performance and dynamic baseline, and the change of component state, which can be realized by adaptive control algorithm or fuzzy logic control, for example, the disturbance step size is determined according to the performance coefficient deviation, which is mainly to guide the system to converge to the current actual optimal operation state.

[0042] The core idea of the present application is to establish and continuously update a "dynamic operation baseline", which represents the highest performance coefficient (COP) value that the heat pump system can achieve under the current component degradation state and under specific external operating condition. The system adjusts the parameter disturbance strategy of the COP optimization algorithm adaptively by monitoring the deviation between the actual COP and the dynamic baseline in real time, so as to accurately track and maintain the optimal economic operation point of the system under the current state in the environment of continuous change of component performance.

[0043] The innovation of the present application lies in combining the dynamic baseline performance coefficient updated according to the historical performance trend with the system component operating state parameter change trend reflecting the gradual degradation of component performance, thereby solving the problem that the traditional optimization method is difficult to effectively track and maintain the system operating state due to component performance degradation and system characteristic drift in the long-term operation of the direct flue gas heat exchange heat pump system, and achieving the effect of continuously maintaining high system operating efficiency.

[0044] Specifically, the present method aims to solve the problem that the traditional fixed model or simple real-time optimization method is difficult to effectively maintain the system operating state due to the progressive degradation of internal component performance and the drift of system characteristic curve in the long-term operation of the direct flue gas heat exchange heat pump system. The core of the present method lies in establishing and dynamically updating the performance baseline of the system under different operating conditions, and combining the operating state change trend of the main components to adaptively adjust the internal operating parameters to adapt to the actual degradation state of the system, thereby realizing continuous and efficient operation.

[0045] Firstly, the system obtains the operating parameters of the direct flue gas heat exchange heat pump control system, including external operating condition parameters and internal operating parameters. This data collection step provides necessary data support for subsequent performance evaluation, operating condition division and parameter adjustment.

[0046] Next, according to the external operating condition parameters, the operating parameters are divided into multiple operating condition sections. In each operating condition section, the system stores its historical highest performance coefficient and the internal operating parameter combination that achieves this performance coefficient. This operating condition division enables meaningful comparison and analysis of system performance under similar external conditions, and provides historical reference for the establishment of dynamic baseline.

[0047] Subsequently, the system updates the dynamic baseline performance coefficient of each operating condition section according to the change trend of the historical highest performance coefficient of each operating condition section over time. This updating process excludes abnormal low performance coefficient data points to ensure the accuracy of the baseline. The dynamic baseline reflects the actual performance level that the system can achieve under the current degradation state, rather than the fixed and unchanging theoretical optimum, thereby adapting to the performance degradation after long-term operation of the system.

[0048] Then, the system obtains the real-time performance coefficient of the direct flue gas heat exchange heat pump control system and compares it with the dynamic baseline performance coefficient to calculate the deviation between them. This deviation value quantifies the gap between the current operating state of the system and the current actual optimal state, providing a clear optimization direction for subsequent parameter adjustment.

[0049] Furthermore, based on the combination of internal operating parameters corresponding to the dynamic baseline performance coefficient, the system adjusts the disturbance amplitude and direction of the internal operating parameters according to the aforementioned deviations. Simultaneously, this method combines the changing trends of the operating state parameters of the main components in the direct flue gas heat pump control system to determine the adjustment values ​​of the internal operating parameters. The changing trends of the operating state parameters reflect the gradual degradation of the corresponding component performance or the drift of operating characteristics. This adjustment, combined with component state adjustments, makes the optimization process more adaptive and robust, enabling it to find the actual optimal solution under the current degradation state.

[0050] Ultimately, the system controls the operation of the direct flue gas heat pump control system based on the determined internal operating parameter adjustment values. These adjustment values ​​are obtained by comprehensively considering real-time performance deviations, dynamic baselines, and the degradation trends of major components, thus achieving closed-loop regulation of the system operating parameters and guiding the system towards its current optimal operating state. (See attached reference.) Figure 2 and attached Figure 3 , attached Figure 2 The COP value of the heat pump system obtained after closed-loop regulation (i.e., dynamic operating baseline) is attached. Figure 3 The measured COP value of the heat pump system after closed-loop regulation shows that the deviation between the two values ​​is less than 10% and the COP value is stably maintained above 4.2. This proves that deviation correction through dynamic operating baseline can accurately control system performance and help the control system reach the optimal economic operating point.

[0051] In some embodiments, the step of updating the dynamic baseline performance coefficient of each operating condition segment by excluding abnormally low performance coefficient data points, based on the historical highest performance coefficient variation trend of each operating condition segment over time, includes:

[0052] Based on the historical highest performance coefficient of each operating condition segment and its changing trend over time, the overall performance degradation characteristics of the direct flue gas heat exchange heat pump control system are obtained by analyzing the overall performance degradation law.

[0053] Based on the global performance degradation characteristics, the dynamic baseline performance coefficients for each operating condition segment are adjusted; during the adjustment process, abnormally low performance coefficient data points are excluded.

[0054] For each operating condition segment, the unique local performance degradation characteristics of that operating condition segment are identified by analyzing the difference between the historical highest performance coefficient changing trend over time and the global performance degradation characteristics.

[0055] The dynamic baseline performance coefficients for each operating condition segment are updated by combining the adjusted dynamic baseline performance coefficients and local performance degradation characteristics; abnormally low performance coefficient data points are excluded during the update process.

[0056] The "global performance degradation feature" refers to the general downward trend in performance of the direct flue gas heat exchange heat pump control system over a long period of operation due to factors that are common to the system as a whole. It reflects the performance degradation law of the system as a whole at the macro level, and is not limited to a specific operating condition segment. This feature can be obtained by aggregating and analyzing the historical highest performance coefficient data of all operating condition segments, for example, using time series analysis methods or machine learning models to fit the performance degradation curve or model of the system as a whole. This feature can be represented as a decay rate, a decay function, or decay model parameters. The introduction of this feature aims to capture the general degradation of system performance, providing a unified and system-level reference for subsequent dynamic baseline adjustment, avoiding the deviation that may be caused by relying on local data alone, and thus improving the accuracy of baseline updating. The "local performance degradation feature" refers to the performance degradation pattern exhibited by each specific operating condition segment based on the global performance degradation. This pattern is unique and may be caused by factors such as wear and tear, fouling, or drift of operating parameters of specific components in that operating condition segment, resulting in differences in performance degradation from the general degradation of the system as a whole. This feature can be identified by comparing the residual, deviation, or difference pattern between the historical highest performance coefficient of each operating condition segment over time and the global performance degradation feature that has been obtained. For example, the mean square error, correlation coefficient, or clustering analysis can be used to distinguish the unique decay pattern of different operating condition segments. This feature can be represented as an additional decay rate, offset, or correction factor for a specific operating condition segment. The introduction of this feature aims to fine-tune and individualize the adjustment of the dynamic baseline, capturing the differentiated performance in specific operating conditions, making the baseline more accurate, and thus improving the precision of baseline updating. In addition, "adjusting the dynamic baseline performance coefficients for each operating condition segment" refers to using the global performance degradation feature to make a preliminary and system-level correction to the dynamic baseline performance coefficients when they are initially determined or updated. This adjustment can be achieved by applying the global performance degradation feature to the initial dynamic baseline performance coefficients of each operating condition segment. During the adjustment process, abnormal low performance coefficient data points can continue to be excluded to ensure the robustness of the adjustment. This adjustment ensures that the dynamic baseline of each operating condition segment reflects its own historical trend while also being consistent with the overall degradation trend of the system, making the baseline reasonable and predictive. Finally, "updating the dynamic baseline performance coefficients for each operating condition segment by combining the adjusted dynamic baseline performance coefficients and the local performance degradation feature" refers to fusing the dynamic baseline adjusted by the global feature with the identified local performance degradation feature to obtain dynamic baseline performance coefficients with higher accuracy. This fusion can be achieved through weighted averaging, superposition, or more complex fusion algorithms. During the fusion process, abnormal low performance coefficient data points can continue to be excluded to ensure the accuracy and stability of the updated results.

[0057] The method introduces a dynamic baseline updating mechanism in two stages of global and local to ensure that the dynamic baseline can more accurately reflect the actual performance capability of the system in the current degradation state. Specifically, the method first obtains the global performance degradation characteristic by analyzing the performance attenuation law of the direct flue gas heat pump control system as a whole according to the historical highest performance coefficient of each operating condition section over time. The contribution of this step is that it no longer only considers the performance change of each operating condition section in isolation, but starts from the perspective of the system as a whole to identify the macro performance degradation trend that runs through all operating condition sections. The extraction of this global performance degradation characteristic can more comprehensively reflect the general decline in performance caused by long-term operation of the system, providing a universal reference for subsequent dynamic baseline adjustment and avoiding the deviation that may be caused by relying solely on local data. Second, the dynamic baseline performance coefficient of each operating condition section is adjusted according to the global performance degradation characteristic, and abnormal low performance coefficient data points are excluded during the adjustment process. This step uses the global performance degradation characteristic obtained earlier to correct the preliminary dynamic baseline performance coefficient. By introducing global characteristics for adjustment, it can ensure that the dynamic baseline of each operating condition section not only reflects its own historical trend, but also maintains consistency with the overall degradation trend of the system, thereby making the baseline reasonable and predictive. Excluding abnormal low performance coefficient data points ensures the robustness of the adjustment process and avoids interference from occasional low performance data on the accuracy of the baseline. Furthermore, for each operating condition section, the unique local performance degradation characteristic is identified by analyzing the difference between the historical highest performance coefficient and the global performance degradation characteristic. This step is one of the innovations of the invention, which further explores the unique mode of performance degradation for each operating condition section based on the consideration of global degradation. The identification of this local performance degradation characteristic can capture the differentiated performance caused by factors such as component wear and fouling under specific operating conditions, enabling the dynamic baseline to be updated with fine and personalized processing, thereby reflecting the actual performance state of the operating condition section with higher accuracy. Finally, the dynamic baseline performance coefficient of each operating condition section is updated by combining the adjusted dynamic baseline performance coefficient and the local performance degradation characteristic, and abnormal low performance coefficient data points are excluded during the updating process. This step is a comprehensive application of the previous analysis, which integrates the baseline adjusted by the global characteristic with the identified local characteristic. Through this integration, the final dynamic baseline performance coefficient not only considers the general degradation of the system as a whole, but also takes into account the special degradation of each operating condition section, thereby making the baseline more comprehensive, accurate, and fine in reflecting the complex performance degradation law of the direct flue gas heat pump control system in long-term operation. Excluding abnormal low performance coefficient data points further ensures the accuracy and stability of the updating result. This hierarchical and fine-grained baseline updating method ensures that the dynamic baseline can more accurately reflect the actual performance capability of the system in the current degradation state.The method refines the updating of the dynamic baseline performance coefficient, so that the baseline can more accurately reflect the actual performance capability of the system in the current degradation state, thereby providing a more reliable basis for subsequent performance deviation calculation and parameter adjustment. This improvement in baseline updating enables the system to more effectively identify the actual performance deviation when facing component performance degradation, thereby guiding the adjustment of internal operating parameters and ensuring that the system can continuously operate near the current optimal performance point. Therefore, based on the original dynamic baseline updating, the method introduces analysis and fusion of global and local performance decay characteristics, making the baseline updating process more accurate and adaptive, thereby improving the ability of the entire control method to maintain system performance over a long period of operation.

[0058] In some embodiments, the step of updating the dynamic baseline performance coefficient of each operating condition section in combination with the adjusted dynamic baseline performance coefficient and the local performance decay characteristic includes:

[0059] Obtaining the change trend of the operating state parameters of the key components in the direct flue gas heat exchange heat pump control system, and determining the performance degradation degree of the key components by analyzing the change trend of the operating state parameters;

[0060] According to the performance degradation degree of the key components, determine the relative influence weight of the adjusted dynamic baseline performance coefficient and the local performance decay characteristic when updating the dynamic baseline performance coefficient;

[0061] Using the relative influence weight, the adjusted dynamic baseline performance coefficient and the local performance decay characteristic are weighted and fused to obtain the updated dynamic baseline performance coefficient of each operating condition section.

[0062] The change trend of the operating state parameters of the key components refers to the regularity of the change of the operating parameters such as temperature, pressure, flow rate, and power consumption of the core components such as the compressor, the expansion valve, the evaporator, or the condenser in the direct flue gas heat exchange heat pump control system over time, which can be obtained and characterized by historical data recording, trend analysis algorithm, or statistical model. The performance degradation degree of the key components refers to the degree of efficiency decline, wear aggravation, or performance attenuation of the key components in long-term operation, which can be quantitatively evaluated by analyzing the change trend of the operating state parameters, and can be determined by using a threshold comparison-based, slope analysis-based, or machine learning model. The relative influence weight refers to the relative importance coefficient assigned to the adjusted dynamic baseline performance coefficient and the local performance degradation feature when updating the dynamic baseline performance coefficient, which can be generated by using a linear function, a nonlinear mapping, or a lookup table method. The weighted fusion refers to the combination of the adjusted dynamic baseline performance coefficient and the local performance degradation feature according to their respective relative influence weights to obtain the final updated dynamic baseline performance coefficient, which can be realized by using a weighted average, a weighted sum, or a fuzzy logic-based fusion algorithm.

[0063] The present application introduces a dynamic weight adjustment mechanism based on the performance degradation degree of the components, which ensures that the dynamic baseline can optimally reflect the actual performance capability of the system at different degradation stages. Specifically, first, the system obtains the change trend of the operating state parameters of the key components in the direct flue gas heat exchange heat pump control system, and analyzes these trends in depth, thereby determining the performance degradation degree of each key component. This step is the basis of the scheme, which solves the limitation of inferring the system degradation degree only from the system overall performance coefficient or the working condition segment performance degradation feature. By directly monitoring and analyzing the change trend of the operating parameters such as temperature, pressure, flow rate, and power consumption of the key components such as the compressor, the expansion valve, and the heat exchanger, the specific degradation conditions such as wear, efficiency decline, or heat transfer performance attenuation of the components can be quantified from the physical level. This degradation degree evaluation based on the component state provides a more fundamental and reliable basis for the accurate updating of the subsequent dynamic baseline performance coefficient.

[0064] On this basis, according to the determined performance degradation degree of the key components, the system intelligently determines the relative influence weight of the adjusted dynamic baseline performance coefficient and the local performance attenuation feature in updating the dynamic baseline performance coefficient. This link solves the problem of how to reasonably integrate the adjusted baseline reflecting the overall performance attenuation of the system and the local performance attenuation feature reflecting the performance attenuation under a specific working condition when updating the dynamic baseline. By taking the actual degradation degree of the components as the basis for weight determination, the system can intelligently allocate the importance of the two in the final update. For example, if a key component shows significant degradation under a specific working condition, the local performance attenuation feature of this working condition can be given a higher weight in updating the baseline, so that the updated baseline more accurately reflects the actual performance upper limit of the system under this working condition due to component degradation.

[0065] Finally, the system uses these relative influence weights to weight and integrate the adjusted dynamic baseline performance coefficient and the local performance attenuation feature, thereby obtaining the updated dynamic baseline performance coefficient of each operating condition section. This weighting and integration process ensures that the final dynamic baseline performance coefficient not only considers the overall performance attenuation trend of the system, but also fully integrates the unique local attenuation mode of each operating condition section, and this integration is intelligently adjusted based on the actual degradation degree of the key components. This makes the updated dynamic baseline performance coefficient more accurately and more adaptively reflect the performance drift and degradation of the heat pump system under different operating conditions due to long-term operation of the components.

[0066] Through the synergistic effect of the above steps, the present application further introduces direct consideration of the performance degradation degree of the key components on the basis of the previous scheme (i.e., adjusting the dynamic baseline by analyzing the global performance attenuation feature and identifying the local performance attenuation feature). This consideration makes the updating of the dynamic baseline no longer a simple combination of global and local, but an intelligent and adaptive adjustment based on the internal physical state of the system. When the system components degrade to different degrees, this dynamic weight mechanism can ensure that the dynamic baseline performance coefficient can more accurately track the current real performance boundary of the system, avoiding the problem of inaccurate baseline updating caused by fixed weight or single combination method. This deep insight and quantitative application of the internal physical degradation mechanism of the system enables the dynamic baseline to more accurately reflect the actual performance capability of the system at different degradation stages, thereby providing a more reliable reference benchmark for subsequent real-time performance evaluation and fine-tuned adjustment of operating parameters, and thus helping the system to continuously approach its optimal operating efficiency under the current state in the long-term operation.

[0067] In some embodiments, based on the internal operating parameter combination corresponding to the dynamic baseline performance coefficient, the disturbance amplitude and disturbance direction of the internal operating parameters are adjusted according to the deviation, and the adjustment value of the internal operating parameters is determined in combination with the change trend of the operating state parameters of the key components in the direct flue gas heat exchange heat pump control system. The step includes:

[0068] Obtain the change trend of the operating state parameters of the key components in the direct flue gas heat exchange heat pump control system, and determine the operating stress characteristics of the key components by analyzing the change trend of the operating state parameters;

[0069] According to the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient, and the operating stress characteristics of the key components, the initial range of the disturbance amplitude and disturbance direction of the internal operating parameters is determined;

[0070] Based on the internal operating parameter combination corresponding to the dynamic baseline performance coefficient, a plurality of candidate adjustment values of the internal operating parameters are generated within the initial range;

[0071] For the candidate adjustment values of the plurality of internal operating parameters, the influence on the operating stress characteristics of the key components is analyzed, and according to the analysis result, the candidate adjustment values are screened to obtain an adjustment value set that meets the preset operating stress limit;

[0072] From the adjustment value set, the adjustment value that can minimize the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient is selected as the final adjustment value of the internal operating parameters.

[0073] The operating stress characteristic of a key component refers to the physical state that affects the service life and reliability of the key component, such as mechanical load, thermal load, chemical corrosion or fatigue degree, which is borne by the key component during operation. The system can evaluate and quantify the stress by monitoring the change trend of the operating state parameters such as vibration frequency, surface temperature, current fluctuation, pressure pulsation, wear particle analysis or acoustic characteristics of the key component, combining the material properties, structural design and historical operation data of the component, and establishing a stress model or machine learning algorithm. The initial range of disturbance amplitude and disturbance direction refers to the upper limit, lower limit and parameter increase or decrease trend allowed when adjusting the internal operating parameters, which can be determined by preset empirical rules, statistical analysis based on historical operation data, or combined with real-time system model prediction. The purpose is to ensure the optimization of system performance while avoiding excessive parameter adjustment, which may cause system instability or damage to the components. The preset operating stress limit refers to the threshold or safety interval that the operating stress characteristic of the key component cannot exceed in order to ensure the long-term stable operation and prolong the service life of the key component. It can be determined according to the design life of the component, material fatigue limit, manufacturer's recommended operating range, or through historical failure data analysis and reliability engineering evaluation, for example, the upper limit of the allowed vibration amplitude of the compressor bearing, the upper limit of the allowed fouling thickness of the condenser tube wall, or the maximum allowed working current of the stepping motor of the electronic expansion valve.

[0074] The application refines and improves the determination process of the internal operating parameter adjustment value under the dynamic baseline performance optimization framework provided by the basic scheme. First, the system obtains the change trend of the operating state parameters of the key components in the direct flue gas heat exchange heat pump control system, and analyzes these trends in depth, thereby determining the operating stress characteristic of the key components. This process enables the system to quantitatively evaluate the load and stress borne by each component in actual operation, providing a data basis for subsequent parameter adjustment.

[0075] Secondly, the system determines the initial range of disturbance amplitude and disturbance direction of the internal operating parameters according to the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient, as well as the operating stress characteristic of the key components that has been determined. This means that when exploring the parameter adjustment space, the system not only considers the gap between the current performance and the ideal baseline, but more importantly, takes into account the health status of the key components. If the component stress is high, even if the performance deviation is large, the system will limit the amplitude or direction of parameter adjustment to avoid further increasing the component stress; on the contrary, if the component stress is low, a larger adjustment space can be allowed to quickly optimize the performance. This combination of stress characteristics to set the initial range can effectively avoid blindly pursuing performance and causing damage to the components, and set a safe and effective boundary for subsequent parameter search.

[0076] On this basis, the system generates multiple candidate adjustment values of the internal operating parameters within the initial range based on the internal operating parameter combination corresponding to the dynamic baseline performance coefficient. This ensures that the parameter adjustment is performed on a benchmark with known superior performance and within a safe range, improving the efficiency and safety of the optimization.

[0077] Subsequently, the system further analyzes the potential impact of the generated multiple candidate adjustment values of the internal operating parameters on the operating stress characteristics of the key components. Through this impact analysis and comparison with the preset operating stress limit, the system can eliminate adjustment values that may cause damage to the components, thereby obtaining an adjustment value set that can optimize performance while ensuring component health. This screening process reflects the importance of long-term reliability and component life of the system.

[0078] Finally, the system selects the adjustment value that minimizes the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient from the adjustment value set that meets the preset operating stress limit as the final adjustment value of the internal operating parameters. This step-by-step screening and optimization strategy enables the system to maximize energy efficiency while effectively avoiding excessive wear and tear on key components.

[0079] It is precisely due to this fine consideration of component operating stress characteristics and multi-stage screening mechanism that the system can not only continuously approach optimal performance in the long run, but also effectively avoid accelerated wear and tear and shortened life of components due to excessive optimization, thereby achieving a balance between performance improvement and healthy operation of components, and solving the problem that traditional methods may accelerate component degradation and shorten service life due to excessive optimization, which is contrary to the long-term optimal economic goal of the system.

[0080] In a specific embodiment, the direct flue gas heat exchange heat pump control system can include a main controller, such as an industrial PC or an embedded controller, which integrates data acquisition, data processing, and control output functions.

[0081] During the process of determining the adjustment value of the internal operating parameters, the main controller first acquires the operating state parameters of key components such as the variable frequency compressor, electronic expansion valve, evaporator, and condenser, such as compressor vibration sensor data, motor current, discharge temperature, electronic expansion valve opening feedback, valve before and after pressure, and evaporator and condenser inlet and outlet temperature, pressure, and flow. The main controller can use these real-time data to analyze the trends of these parameters over time through methods such as moving average, exponential smoothing, or Kalman filtering, and then determine the operating stress characteristics of the key components. For example, a sustained upward trend in compressor vibration amplitude can indicate an increase in bearing wear stress, and a sustained increase in heat exchanger inlet and outlet temperature difference under the same load can indicate an increase in fouling stress. These stress characteristics can be quantified as stress indices.

[0082] Subsequently, the main controller determines an initial range of perturbation magnitude and perturbation direction for the internal operating parameters based on the deviation between the current real-time performance coefficient and the dynamic baseline performance coefficient, as well as the determined operating stress characteristics of the critical components. For example, if the real-time performance coefficient is significantly lower than the dynamic baseline, and the stress indices of all critical components are at low levels, the system can allow a large adjustment range for the compressor speed and the electronic expansion valve opening, for example, the compressor speed can be allowed to float 500 RPM above and below the current value, and the electronic expansion valve opening can be allowed to float 10% above and below the current value. Conversely, if the real-time performance coefficient deviation is not large, but the stress index of a certain critical component has approached the preset limit, the system will strictly limit the adjustment range of that parameter, or even only allow adjustment in the direction of reducing stress, for example, the compressor speed can only be adjusted downward, or the adjustment range is limited to within 100 RPM.

[0083] Within this initial range, the main controller can generate a plurality of candidate adjustment values for the internal operating parameters based on the internal operating parameter combination corresponding to the dynamic baseline performance coefficient. For example, if the optimal combination corresponding to the dynamic baseline is a compressor speed of 3000 RPM and an electronic expansion valve opening of 50%, and the initial range allows the speed to be between 2800-3200 RPM and the opening to be between 45%-55%, the system can generate a series of discrete combinations, such as [2850 RPM, 46%], [2900 RPM, 48%], [3000 RPM, 50%], [3100 RPM, 52%], [3150 RPM, 54%], etc., as candidate adjustment values.

[0084] For these candidate adjustment values, the main controller can use a built-in system simulation model or a prediction model trained based on historical data to analyze the potential impact of each candidate value on the operating stress characteristics of the critical components. For example, it is predicted whether the vibration stress index of the compressor will exceed the preset 0.8 safety threshold when the compressor speed is adjusted to 3150 RPM, or whether the condenser fouling stress will be accelerated due to high load operation. By comparing the stress characteristics predicted for each candidate value with the preset operating stress limits, for example, the preset upper limit of the compressor vibration stress index is 0.8, the upper limit of the electronic expansion valve response time deviation is 5%, and the upper limit of the heat transfer coefficient decay rate of the heat exchanger is 10%, the system can screen out all adjustment values that meet these limits to form a set of adjustment values.

[0085] Finally, the main controller selects the adjustment value from the set of adjustment values that satisfies the preset operating stress limit, which can minimize the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient, as the final adjustment value of the internal operating parameter. For example, in the screened set, if the (3050 RPM, 51%) combination predicts the performance coefficient closest to the dynamic baseline, and all component stresses are within the safe range, the combination is selected as the final adjustment value, and is sent to the actuator to control the operation of the heat pump system.

[0086] The present application effectively solves the problem that the key components of the direct flue gas heat exchange heat pump system may be operated in a non-design condition or a high stress region due to the pursuit of performance optimization, thereby accelerating wear and shortening the service life, by introducing the consideration of the operating stress characteristics of the key components when determining the adjustment value of the internal operating parameter. Specifically, by obtaining and analyzing the operating stress characteristics of the key components, the system can quantitatively evaluate the health status of the components and avoid blindly pursuing performance to damage the components. When determining the initial range of parameter adjustment, the performance deviation and the component stress characteristics are combined, so that the exploration space of parameter adjustment can meet the demand of performance optimization and ensure that the components are operated within a safe range. Further, by stress impact analysis and screening of the candidate adjustment values, the system can eliminate adjustment values that may damage the components, thereby obtaining an adjustment value set that can optimize performance and ensure component health. Finally, the optimal adjustment value is selected from the safe set, which ensures that the system improves performance while effectively prolonging the service life of the key components, thereby improving the long-term economy of the system.

[0087] In some embodiments, the step of determining the initial range of the perturbation amplitude and the perturbation direction of the internal operating parameter according to the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient, and the operating stress characteristics of the key components comprises:

[0088] obtaining the current external working condition parameters; the external working condition parameters include the flue gas inlet temperature, the hot water load, and the ambient temperature;

[0089] According to the external working condition parameters, the potential risk level of the external working condition on the operating stress response of the key components in the direct flue gas heat exchange heat pump control system is analyzed to obtain the external working condition risk level;

[0090] According to the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient, the reference range of the perturbation amplitude and the perturbation direction of the internal operating parameter is preliminarily determined;

[0091] The reference range is corrected in combination with the operating stress characteristics of the key components and the external working condition risk level to obtain the initial range of the perturbation amplitude and the perturbation direction of the internal operating parameter.

[0092] The external working condition risk level refers to a quantitative evaluation of potential risks that the current external operating environment may cause to the operating stress of key components in the direct flue gas heat exchange heat pump control system. It can be realized by using a risk evaluation model established based on historical operating data, a preset risk threshold rule, or a risk classifier trained by a machine learning algorithm. The reference range refers to the adjustment interval of the internal operating parameter disturbance amplitude and disturbance direction preliminarily determined according to the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient. It can be generated based on a preset lookup table, an empirical formula, or a simple proportional relationship. The initial range refers to the final parameter disturbance amplitude and disturbance direction available interval obtained by correcting the reference range based on the operating stress characteristics of the key components and the external working condition risk level. It can be obtained by scaling, shifting, or boundary limiting the reference range.

[0093] The present application first acquires the current external operating condition parameters, including flue gas inlet temperature, hot water load, and ambient temperature, when determining the initial range of disturbance amplitude and disturbance direction of internal operating parameters. These parameters can fully grasp the external operating environment of the heat pump system, as they directly affect the load demand, heat source and heat sink conditions of the system, and are important factors affecting the system operating state and component stress, providing basic data for subsequent risk assessment. Based on these external operating condition parameters, the system further analyzes the potential risk level of the external operating condition on the stress response of the key components in the direct flue gas heat exchange heat pump control system, thereby obtaining the external operating condition risk level. This analysis can identify the additional stress or potential risks that the key components of the system may face under the current external environment, such as greater pressure, temperature or wear that the components may experience under extreme temperature or load conditions. By quantifying this risk, important safety considerations are provided for subsequent parameter adjustment, avoiding aggressive parameter adjustment in unsafe operating conditions. On this basis, the system preliminarily determines the reference range of disturbance amplitude and disturbance direction of internal operating parameters based on the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient. This step is based on the difference between the current performance and the ideal performance of the system to determine the preliminary adjustment direction and intensity, aiming to guide the system to develop towards better performance, which is the core driving force of performance optimization. Finally, the present application combines the operating stress characteristics of key components and the external operating condition risk level to modify the preliminarily determined reference range, thereby obtaining the initial range of disturbance amplitude and disturbance direction of internal operating parameters. By introducing the operating stress characteristics of key components, it can be ensured that parameter adjustment will not cause excessive wear or damage to components, ensuring the long-term healthy operation of components; and in combination with the external operating condition risk level, the running safety in different external environments is further ensured, avoiding aggressive parameter disturbance in potentially high-risk operating conditions. This correction mechanism makes the determined initial range not only consider performance optimization, but also take into account the long-term stable operation of the system and the health status of the components. Through the above steps, the present application further enhances the robustness and safety of the initial range determination of parameter disturbance on the basis of the prior art solution. The previous solution has been able to determine the initial range based on performance deviation and component stress characteristics, but has not fully considered the dynamic impact of external operating conditions. The present application dynamically adapts to changes in external environment by real-time sensing of external operating conditions and assessing their potential risks to component stress. This means that when generating candidate adjustment values for internal operating parameters and performing screening, the system can operate based on a more accurate and safe initial range, thereby ensuring that the finally selected adjustment values not only effectively improve performance, but also effectively avoid component stress risks in various complex and variable external operating conditions, ensuring the long-term stable operation of the system and the health status of the components, thereby achieving the goal of overall optimal economy of the system.

[0094] In some embodiments, based on the internal operating parameter combination corresponding to the dynamic baseline performance coefficient, within the initial range, the step of generating candidate adjustment values of the plurality of internal operating parameters comprises:

[0095] Obtaining the change trend of the internal operating parameter combination corresponding to the historical maximum performance coefficient of each operating condition section over time, and obtaining the drift pattern of the internal operating parameter combination by analyzing the change trend of the internal operating parameter combination over time;

[0096] By analyzing the operating state parameters of the key components in the direct flue gas heat exchange heat pump control system and the real-time performance coefficient, the response characteristics of the current internal operating parameters to the real-time performance coefficient and the operating stress characteristics of the key components are obtained;

[0097] Based on the internal operating parameter combination corresponding to the dynamic baseline performance coefficient, combined with the drift pattern and response characteristics of the internal operating parameter combination, within the initial range, a plurality of candidate adjustment values of the internal operating parameters are generated.

[0098] The "drift pattern of the internal operating parameter combination" refers to the law of how the internal parameter combination for achieving performance changes over time during long-term operation of the system. It can be realized by historical data analysis, trend prediction model or machine learning algorithm. The "response law of the current internal operating parameter to the performance coefficient and the component operating stress characteristics" refers to the immediate influence and correlation of the adjustment of the internal operating parameter to the system performance (performance coefficient) and the component health condition (operating stress characteristics) under the current system state. It can be realized by real-time data acquisition, parameter perturbation experiment or simulation analysis based on physical model.

[0099] The application aims to solve the problem that in the long-term operation of a direct flue gas heat exchange heat pump system, due to the continuous degradation of component performance, the system operating point constantly drifts, and when simply generating internal operating parameter candidate adjustment values within the preset starting range, a large number of candidate values may fall in the current non-optimal or useless area, thereby reducing the efficiency of the optimization process, and even missing the current operating point optimization area. The scheme introduces the perception of the internal operating parameter combination drift rule and the system response rule, adjusts the generation strategy of the candidate value, focuses on the current optimization area that may exist, and avoids useless exploration. First, by obtaining the change trend of the internal operating parameter combination corresponding to the historical performance coefficient high point of each operating condition section over time, and by analyzing the change trend of the internal operating parameter combination over time, the drift rule of the internal operating parameter combination is obtained. This step identifies how the internal parameter combination that achieves performance changes over time during the long-term operation of the system. Due to factors such as component wear and scale formation, the parameter combination required for system performance is not fixed, but slowly drifts. By analyzing this historical drift rule, the possible area of the future parameter combination can be predicted, thereby providing a time-varying, history-based guidance direction for the generation of candidate values, and avoiding searching in useless or inefficient areas. Secondly, by analyzing the operating state parameters and performance coefficients of the components in the direct flue gas heat exchange heat pump control system, the response rule of the current internal operating parameters to the performance coefficient and the component operating stress characteristics is obtained. This step real-time evaluates how the performance (performance coefficient) and component health status (operating stress characteristics) of the current system respond under different internal parameter settings. This reflects the actual physical characteristics and parameter coupling relationship of the current system at this moment. For example, a small adjustment of a certain parameter may affect the performance, or cause excessive operating stress to a certain component. By obtaining this real-time response rule, the "sensitivity" and "tolerance" of the current system under different parameter combinations can be understood, so that when generating candidate values later, those parameter combinations that can effectively improve performance and avoid excessive stress on components can be more accurately selected. Finally, based on the baseline performance coefficient changing internal operating parameter combination, combined with the drift rule and response rule of the internal operating parameter combination, a number of candidate adjustment values of the internal operating parameters are generated within the starting range. This core step integrates the historical drift rule and real-time response rule obtained in the foregoing. The baseline performance coefficient changing internal operating parameter combination represents the parameter starting point corresponding to the historical performance of the system under the current operating condition. On this basis, combined with the drift rule, it can be predicted that the current operating point may have deviated from the historical starting point, thereby guiding the generation direction of the candidate value to the area where the optimization solution is more likely to exist. At the same time, combined with the response rule, it can be ensured that the generated candidate value not only considers the potential of performance improvement, but also takes into account the influence on the component operating stress, thereby avoiding generating parameter combinations that may cause system instability or accelerated degradation of components.The comprehensive generation manner makes the candidate adjustment value more targeted and effective, focuses on the current possible optimization area, avoids useless exploration, significantly improves the efficiency and success rate of the optimization process, and enables the system to quickly and accurately find and maintain the operating point in the current degradation state, thereby continuously maximizing energy efficiency.

[0100] Reference is made to the accompanying drawings Figure 4 The application provides a direct flue gas heat exchange heat pump control system, comprising:

[0101] The first acquisition module 100 is configured to acquire operating parameters of the direct flue gas heat exchange heat pump control system, wherein the operating parameters include external working condition parameters and internal operating parameters.

[0102] The division module 200 is configured to divide the operating parameters into a plurality of operating condition sections according to the external working condition parameters, and store the historical maximum performance coefficients of the operating condition sections and the internal operating parameter combinations for achieving the historical maximum performance coefficients.

[0103] The update module 300 is configured to update the dynamic baseline performance coefficients of the operating condition sections according to the change trends of the historical maximum performance coefficients of the operating condition sections over time.

[0104] The second acquisition module 400 is configured to acquire a real-time performance coefficient of the direct flue gas heat exchange heat pump control system, and calculate the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient according to the real-time performance coefficient and the dynamic baseline performance coefficient.

[0105] The adjustment determination module 500 is configured to adjust the disturbance amplitude and disturbance direction of the internal operating parameters according to the deviation based on the internal operating parameter combinations corresponding to the dynamic baseline performance coefficients, and determine the adjustment value of the internal operating parameters in combination with the change trends of the operating state parameters of key components in the direct flue gas heat exchange heat pump control system.

[0106] The control module 600 is configured to control the direct flue gas heat exchange heat pump control system to work according to the determined adjustment value of the internal operating parameters.

[0107] In this document, relational terms such as first and second and the like can be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions.

[0108] The above merely illustrates the embodiments of the application, and is not used to limit the protection scope of the application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the application shall be included in the protection scope of the application.

Claims

1. A direct flue gas heat exchange heat pump control method applied to a direct flue gas heat exchange heat pump control system, characterized in that, The method comprises the following steps: obtaining the operating parameters of the direct flue gas heat exchange heat pump control system, the operating parameters including external working condition parameters and internal operating parameters; dividing the operating parameters into multiple operating condition sections according to the external working condition parameters, and storing the historical highest performance coefficients of each operating condition section and the internal operating parameter combinations for achieving the historical highest performance coefficients; updating the dynamic baseline performance coefficients of each operating condition section by excluding abnormal low performance coefficient data points according to the change trend of the historical highest performance coefficients of each operating condition section over time; obtaining the real-time performance coefficient of the direct flue gas heat exchange heat pump control system, and calculating the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient according to the real-time performance coefficient and the dynamic baseline performance coefficient; based on the internal operating parameter combinations corresponding to the dynamic baseline performance coefficients, adjusting the disturbance amplitude and disturbance direction of the internal operating parameters according to the deviation, and determining the adjustment value of the internal operating parameters in combination with the change trend of the operating state parameters of the key components in the direct flue gas heat exchange heat pump control system, wherein the key components in the direct flue gas heat exchange heat pump control system include a variable frequency compressor, an electronic expansion valve, an evaporator and a condenser, and the operating state parameters of the key components in the direct flue gas heat exchange heat pump control system include temperature, pressure, flow, and electric energy consumption; controlling the direct flue gas heat exchange heat pump control system to work according to the determined adjustment value of the internal operating parameters.

2. The direct flue gas heat exchange heat pump control method according to claim 1, characterized in that, The step of updating the dynamic baseline performance coefficients of each operating condition section according to the change trend of the historical highest performance coefficients of each operating condition section over time by excluding abnormal low performance coefficient data points comprises: obtaining the global performance degradation characteristics by analyzing the performance degradation law of the direct flue gas heat exchange heat pump control system as a whole according to the change trend of the historical highest performance coefficients of each operating condition section over time; adjusting the dynamic baseline performance coefficients of each operating condition section according to the global performance degradation characteristics, and excluding abnormal low performance coefficient data points in the adjustment process; identifying the local performance degradation characteristics specific to each operating condition section by analyzing the difference between the change trend of the historical highest performance coefficients of the operating condition section over time and the global performance degradation characteristics; updating the dynamic baseline performance coefficients of each operating condition section in combination with the adjusted dynamic baseline performance coefficients and the local performance degradation characteristics, and excluding abnormal low performance coefficient data points in the updating process.

3. The direct flue gas heat exchange heat pump control method according to claim 2, characterized in that, The step of updating the dynamic baseline performance coefficients of each operating condition section in combination with the adjusted dynamic baseline performance coefficients and the local performance degradation characteristics comprises: obtaining the change trend of the operating state parameters of the key components in the direct flue gas heat exchange heat pump control system, and determining the performance degradation degree of the key components by analyzing the change trend of the operating state parameters; determining the relative influence weights of the adjusted dynamic baseline performance coefficients and the local performance degradation characteristics in updating the dynamic baseline performance coefficients according to the performance degradation degree of the key components; performing weighted fusion on the adjusted dynamic baseline performance coefficients and the local performance degradation characteristics by using the relative influence weights, to obtain the updated dynamic baseline performance coefficients of each operating condition section.

4. The direct flue gas heat exchange heat pump control method of claim 1, wherein, The steps of adjusting the disturbance amplitude and disturbance direction of the internal operating parameters according to the deviation, and determining the adjustment value of the internal operating parameters in combination with the change trend of the operating state parameters of the key components in the direct flue gas heat exchange heat pump control system, include: Obtaining the change trend of the operating state parameters of the key components in the direct flue gas heat exchange heat pump control system, and determining the operating stress characteristics of the key components by analyzing the change trend of the operating state parameters; According to the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient, and the operating stress characteristics of the key components, the initial range of the disturbance amplitude and disturbance direction of the internal operating parameters is determined; Based on the internal operating parameter combination corresponding to the dynamic baseline performance coefficient, a plurality of candidate adjustment values of the internal operating parameters are generated within the initial range; For the candidate adjustment values of the internal operating parameters, the influence on the operating stress characteristics of the key components is analyzed, and according to the influence analysis result, the adjustment value set satisfying the preset operating stress limit is obtained by screening the candidate adjustment values; From the adjustment value set, the adjustment value that can minimize the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient is selected as the final adjustment value of the internal operating parameters.

5. The direct flue gas heat exchange heat pump control method according to claim 4, characterized in that, The steps of determining the initial range of the disturbance amplitude and disturbance direction of the internal operating parameters according to the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient, and the operating stress characteristics of the key components, include: Obtaining the current external working condition parameters; According to the external working condition parameters, the external working condition risk level is obtained by analyzing the potential risk level of the external working condition on the operating stress response of the key components in the direct flue gas heat exchange heat pump control system; According to the deviation between the real-time performance coefficient and the dynamic baseline performance coefficient, the reference range of the disturbance amplitude and disturbance direction of the internal operating parameters is preliminarily determined; In combination with the operating stress characteristics of the key components and the external working condition risk level, the reference range is corrected to obtain the initial range of the disturbance amplitude and disturbance direction of the internal operating parameters.

6. The direct flue gas heat exchange heat pump control method according to claim 4, wherein, The steps of generating a plurality of candidate adjustment values of the internal operating parameters within the initial range based on the internal operating parameter combination corresponding to the dynamic baseline performance coefficient, include: Obtaining the change trend of the internal operating parameter combination corresponding to the historical highest performance coefficient of each operating condition section over time, and obtaining the drift mode of the internal operating parameter combination by analyzing the change trend of the internal operating parameter combination over time; By analyzing the operating state parameters of the key components in the direct flue gas heat exchange heat pump control system and the real-time performance coefficient, the response characteristics of the current internal operating parameters on the real-time performance coefficient and the operating stress characteristics of the key components are obtained; Based on the internal operating parameter combination corresponding to the dynamic baseline performance coefficient, in combination with the drift mode and response characteristics of the internal operating parameter combination, a plurality of candidate adjustment values of the internal operating parameters are generated within the initial range.

7. A direct flue gas heat exchange heat pump control system employing the direct flue gas heat exchange heat pump control method according to any one of claims 1 to 6, characterized by, It includes: The first acquisition module is used for acquiring the operating parameters of the direct flue gas heat exchange heat pump control system; the operating parameters include external working condition parameters and internal operating parameters; The dividing module is configured to divide the operation parameter into a plurality of operation condition sections according to external working condition parameters, and store a historical maximum performance coefficient of each operation condition section and an internal operation parameter combination for achieving the historical maximum performance coefficient; The updating module is configured to update a dynamic baseline performance coefficient of each operation condition section according to a change trend of the historical maximum performance coefficient of each operation condition section over time; The second obtaining module is configured to obtain a real-time performance coefficient of the direct flue gas heat exchange heat pump control system, and calculate a deviation between the real-time performance coefficient and the dynamic baseline performance coefficient according to the real-time performance coefficient and the dynamic baseline performance coefficient; The adjustment determining module is configured to adjust a disturbance amplitude and a disturbance direction of the internal operation parameter according to the deviation based on the internal operation parameter combination corresponding to the dynamic baseline performance coefficient, and determine an adjustment value of the internal operation parameter in combination with a change trend of an operation state parameter of a key component in the direct flue gas heat exchange heat pump control system; The control module is configured to control the direct flue gas heat exchange heat pump control system to work according to the determined adjustment value of the internal operation parameter.

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