A method and system for coupling a graphene-enhanced CO2 heat pump with a boiler for heating.

Abstract

The application discloses a graphene reinforced CO2 heat pump and boiler coupling heat supply method and system, constructs a heat source feature library by collecting water supply temperature and return water temperature for graphene reinforced heat exchange characteristic correlation analysis; identifies a heat pump-boiler coupling heat supply interval, generates a temperature compensation coefficient to form a dynamic coupling heat supply interval; performs heat source matching on the dynamic coupling heat supply interval, obtains a heat supply load response time to generate a hierarchical heat source distribution parameter; performs start-stop time delay analysis on the hierarchical heat source distribution parameter to determine a priority start heat source, obtains graphene rapid heat exchange response characteristics to form a heat supply action sequence; and combines the heat source distribution result and the heat supply action sequence to construct a collaborative heat supply strategy and output hot water centralized heat supply instructions. The method realizes accurate evaluation of heat source performance, reasonable configuration of heat supply load and optimization and coordination of heat sources, and improves the energy efficiency and stability of the CO2 heat pump and boiler coupling heat supply system.

Description

Technical Field

[0001] This invention relates to the field of heat pump centralized heating technology, and in particular to a graphene-enhanced CO2 heat pump coupled with a boiler for heating and a system thereof. Background Technology

[0002] CO2 heat pumps, as an environmentally friendly and efficient heating device, have broad application prospects in urban district heating. However, in actual operation, CO2 heat pumps generally suffer from insufficient heating capacity under high-temperature conditions and decreased energy efficiency ratio under low-temperature conditions, making it difficult for a single heat pump to meet heating demands across all operating conditions. While multi-heat source heating methods that couple heat pumps with boilers can leverage the advantages of each heat source, existing coupled heating methods still have many shortcomings in terms of heat source configuration strategies, operating parameter optimization, and coordinated control.

[0003] Traditional multi-source heating control methods rely primarily on simple temperature feedback regulation, lacking in-depth analysis of heat source operating characteristics and failing to accurately identify the optimal heat source combination under different load conditions. Existing methods often employ fixed timing strategies when handling heat source switching, which neither fully utilizes the rapid response capability of heat pumps nor adequately addresses heating instability caused by differences in start-up and shutdown delays. Current control schemes generally neglect the impact of heat exchanger performance on overall system efficiency, exhibiting limited capabilities in improving heat exchange efficiency and shortening response time, thus restricting the energy efficiency and heating quality of multi-source heating systems. Summary of the Invention

[0004] This invention provides a graphene-enhanced CO2 heat pump coupled heating method and system with a boiler. The aim is to achieve accurate evaluation of heat source performance by constructing a heat source characteristic library, dynamically adjust the coupled heating range by temperature compensation coefficient, optimize heat source allocation strategy by load response level, coordinate heat source switching process by start-stop delay analysis, and achieve coordinated operation of heat pump and boiler by flow allocation scheme, ultimately improving the energy efficiency and heating quality of the CO2 heat pump coupled heating system with a boiler.

[0005] The first aspect of this invention proposes a method for coupling a graphene-enhanced CO2 heat pump with a boiler for heating, comprising the following steps:

[0006] Collect the supply and return water temperatures of the centralized hot water heating network, and construct a heat source feature library based on the correlation analysis of the graphene-enhanced heat exchange characteristics on the CO2 heat pump side based on the supply and return water temperatures. Based on the heat source feature library, the heat pump-boiler coupled heating zone is identified. The graphene enhancement effect evaluation parameters are extracted from the supply water temperature and the return water temperature to generate a temperature compensation coefficient. The heating load of the heat pump-boiler coupled heating zone is adjusted using the temperature compensation coefficient to form a dynamic coupled heating zone. Heat source matching is performed on the dynamically coupled heating zone to form a heat source allocation result. Based on the heat source allocation result, the dominant heat source type is identified. The graphene-enhanced heat exchange performance of the dominant heat source type is mapped to obtain the heating load response time. The load response level is evaluated based on the heating load response time to generate graded heat source allocation parameters. The start-stop delay analysis of the graded heat source allocation parameters is used to determine the priority heat source to be started. The switching interval time of the priority heat source is detected. Based on the switching interval time, the graphene rapid heat exchange response characteristics are obtained to adjust the heat source priority and form a heating action sequence. A heat pump-boiler coordinated heating strategy is constructed by combining the heat source allocation results with the heating action sequence, and a hot water centralized heating command is output based on the heat pump-boiler coordinated heating strategy.

[0007] A second aspect of this invention provides a graphene-enhanced CO2 heat pump coupled with a boiler for heating, comprising: The data acquisition module is used to collect the supply water temperature and return water temperature of the hot water centralized heating network, and to construct a heat source feature library based on the correlation analysis of the graphene-enhanced heat exchange characteristics on the CO2 heat pump side based on the supply water temperature and the return water temperature. The interval identification module is used to identify the heat pump-boiler coupled heating interval based on the heat source feature library, extract the graphene enhancement effect evaluation parameters from the supply water temperature and the return water temperature to generate a temperature compensation coefficient, and use the temperature compensation coefficient to adjust the heating load of the heat pump-boiler coupled heating interval to form a dynamic coupled heating interval. The heat source allocation module is used to perform heat source matching on the dynamically coupled heating zone to form a heat source allocation result, identify the dominant heat source type based on the heat source allocation result, perform graphene-enhanced heat exchange performance mapping on the dominant heat source type to obtain the heating load response time, and evaluate the load response level based on the heating load response time to generate graded heat source allocation parameters. The action management module is used to perform start-stop delay analysis on the graded heat source allocation parameters to determine the priority heat source to start, detect the switching interval time of the priority heat source to start, and adjust the heat source priority based on the graphene rapid heat exchange response characteristics obtained from the switching interval time to form a heating action sequence. The strategy execution module is used to construct a heat pump-boiler coordinated heating strategy by combining the heat source allocation results with the heating action sequence, and output a hot water centralized heating command based on the heat pump-boiler coordinated heating strategy.

[0008] The beneficial effects of this invention are reflected in the following aspects: First, a heat source feature library is constructed by identifying the transcritical temperature glide of CO2 and correlating it with the characteristics of graphene-enhanced heat transfer. The correlated feature parameters quantify the degree of improvement of temperature glide by the graphene coating. The temperature compensation coefficient dynamically adjusts the boundary of the coupled heating interval based on the distribution density of the abrupt change points of the enhancement effect, improving the rationality of the interval configuration. Second, the heating load response time is obtained by mapping the graphene-enhanced heat transfer performance. The load response level is divided into three levels: fast, medium, and slow, based on the percentile of the response time. The differentiated configuration of the start-up sequence and power ratio of the graded heat source allocation parameters avoids problems such as improper power configuration and slow response, improving the accuracy of heat source configuration. Finally, by evaluating the uniformity of the start-up and shutdown delay gradient sequence, insufficient coordination links are identified and the delay configuration is optimized. The rapid heat transfer response characteristics of graphene shorten the heat source switching transition time. The heat pump-boiler collaborative heating strategy achieves coordinated operation of multiple heat sources through flow regulation, temperature control, and pressure balance commands, improving the adaptability of the heating system to load fluctuations and ensuring the stability of centralized hot water heating. Attached Figure Description

[0009] The accompanying drawings illustrate specific examples of the technical solutions described in this invention and, together with the detailed embodiments, form part of the specification, serving to explain the technical solutions, principles, and effects of this invention.

[0010] Figure 1 This is a schematic flowchart of a graphene-enhanced CO2 heat pump coupled with a boiler for heating according to the present invention.

[0011] Figure 2 This is a schematic diagram of the physical layout of the graphene-enhanced CO2 heat pump coupled with a boiler heating system according to the present invention.

[0012] Figure 3 This is a structural block diagram of a graphene-enhanced CO2 heat pump coupled with a boiler for heating, according to the present invention.

[0013] Among them: 1-CO2 heat pump; 2-graphene-enhanced condensing heat exchanger; 3-gas boiler; 4-mixer; 5-main heating network; 6-main return water network; 7-supply water temperature sensor; 8-return water temperature sensor; 9-heat pump circulation pump; 10-boiler circulation pump; 11-control system; 12-user end. Detailed Implementation

[0014] In the following description, specific details such as particular system architectures and techniques are set forth for illustrative purposes and not for limitation, in order to provide a thorough understanding of the embodiments of this application. However, those skilled in the art will understand that this application may also be implemented in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, apparatuses, circuits, and methods have been omitted so as not to obscure the description of this application with unnecessary detail.

[0015] It should be understood that, when used in this application specification and the appended claims, the term "comprising" indicates the presence of the described features, integrals, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or a collection thereof.

[0016] References to "one embodiment" or "some embodiments" as described in this specification mean that one or more embodiments of this application include a specific feature, structure, or characteristic described in connection with that embodiment. Therefore, the phrases "in one embodiment," "in some embodiments," "in other embodiments," "in still other embodiments," etc., appearing in different parts of this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "comprising," "including," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0017] The technical solutions of the embodiments of this application will be described below.

[0018] like Figure 1 As shown, this embodiment of the invention provides a method for coupling a graphene-enhanced CO2 heat pump with a boiler for heating, including the following steps S110-S150: Step S110: Collect the supply water temperature and return water temperature of the hot water centralized heating network, and construct a heat source feature library based on the correlation analysis of the graphene-enhanced heat exchange characteristics on the CO2 heat pump side using the supply water temperature and return water temperature.

[0019] Specifically, the supply and return water temperatures of the centralized hot water heating network are collected. For example... Figure 2As shown, CO2 heat pump 1 heats hot water through graphene-enhanced condenser-side heat exchanger 2, and gas boiler 3 heats hot water through its own heat exchanger. The two hot water streams are combined via mixing valve 4 and then transported to user terminal 12 through main heating network 5. The low-temperature hot water after heat exchange at user terminal 12 is returned to the system for recycling via return water network 6. Graphene-enhanced condenser-side heat exchanger 2 uses graphene coating or graphene-copper composite fins to enhance the heat exchange between CO2 working fluid and hot water. The graphene coating is uniformly distributed on the heat exchange surface with a nanometer-level thickness to improve thermal conductivity, and the graphene-copper composite fins increase the heat exchange area through the composite structure of graphene layer and copper substrate. In the heating conditions of severely cold northern regions, the heat transfer coefficient of the graphene-enhanced CO2 heat pump condenser is increased from 800W / (m²·K) of the traditional heat exchanger to 1200W / (m²·K), enabling the heat pump to still output a supply water temperature of 75℃ when the outdoor temperature is -15℃, meeting the high-temperature hot water demand of building heating. A supply water temperature sensor 7 is located at the outlet of the main heating network 5, and a return water temperature sensor 8 is located at the inlet of the main return water network 6. The supply water temperature sensor 7 measures the temperature of the high-temperature hot water output by the heat pump or boiler, and the return water temperature sensor 8 measures the temperature of the low-temperature hot water returning from the user side after heat exchange. The sampling frequency for both supply and return water temperatures is set to 10Hz, with a sampling duration of 60 seconds, resulting in 600 temperature sampling points. The supply water temperature fluctuates between 50℃ and 90℃, and the return water temperature fluctuates between 30℃ and 60℃. During urban heating, the supply water temperature is maintained between 75℃ and 85℃, and the return water temperature is maintained between 45℃ and 55℃, with a temperature difference of approximately 30℃ to ensure sufficient heating. The heat pump circulation pump 9 controls the supply water flow rate on the heat pump side, and the boiler circulation pump 10 controls the supply water flow rate on the boiler side. Each supply branch is equipped with a regulating valve to precisely regulate the branch flow rate and pressure. The control system 11 collects the supply and return water temperature data and outputs control commands to each actuator.

[0020] In some embodiments, the step of constructing a heat source feature library based on the correlation analysis of graphene-enhanced heat transfer characteristics on the CO2 heat pump side using the supply water temperature and the return water temperature includes: extracting heat source feature parameters by extracting temperature curve features from the supply water temperature and the return water temperature; performing CO2 transcritical cycle temperature glide identification based on the heat source feature parameters to obtain temperature glide; performing graphene-enhanced heat transfer feature correlation analysis based on the temperature glide to form correlation feature parameters; and constructing a heat source feature library using the correlation feature parameters.

[0021] Temperature curve feature extraction was performed on the supply and return water temperatures to obtain heat source characteristic parameters. Temperature curves were generated from 600 sampling points of the supply and return water temperatures. ， The curve presents three stages: a heating phase, a stabilizing phase, and a cooling phase. ， The heating phase corresponds to the process of temperature gradually increasing after the heat source is activated. ，The stable phase corresponds to the process of continuous operation after the heat source reaches the target temperature. ， The cooling phase corresponds to the natural temperature drop after the heat source is unloaded or shut down. Temperature curve feature extraction involves identifying four characteristic parameters of the heat source by recognizing the curve's peaks, troughs, rising slope, and falling slope. ， The peak value corresponds to the highest temperature when the heat source is operating at full load. ， The valley value corresponds to the lowest temperature when the heat source is shut down or operating at low load. The peak value of the water supply temperature curve is approximately 85℃. ， The valley temperature was approximately 65℃ ， The peak-to-valley difference of 20℃ reflects the adjustment range of the heat source. The peak value of the return water temperature curve is approximately 55℃. ， The valley temperature is approximately 40℃ ， The peak-to-valley difference is 15℃ ， The return water temperature fluctuates less than the supply water temperature. The slope of the temperature curve is defined as the rate of temperature increase per unit time. ， The CO2 heat pump's water supply temperature rise rate is approximately 2°C / minute. ， The boiler's water supply temperature rise rate is approximately 5℃ / minute. ， The difference in slope reflects the heating rate of different heat sources. The descending slope is defined as the rate at which the temperature decreases per unit time. ， The CO2 heat pump's water supply temperature decreases at a rate of approximately 1.5℃ / minute. ， The boiler's water supply temperature decreases at a rate of approximately 3°C per minute. ， The fact that the downward slope is less than the upward slope indicates that the heat storage effect of the heat exchanger and piping network delays the temperature drop. The peak and trough values ​​of the heat source characteristic parameters characterize the temperature regulation range of the heat source. ， The rising and falling slopes of the heat source characteristic parameters characterize the dynamic response speed of the heat source.

[0022] Temperature slip in the CO2 transcritical cycle is identified based on heat source characteristic parameters. The rising and falling slopes of these parameters are used to identify the temperature slip, which refers to the deviation of the CO2 working fluid's temperature change process in the heat exchanger from the ideal isothermal process. Excessive temperature slip leads to a decrease in the heat transfer temperature difference at the heat exchanger outlet, affecting heating capacity. In the CO2 transcritical cycle, the working fluid's temperature continuously decreases as it releases heat on the high-pressure side, exhibiting a non-linear temperature slip curve. The amount of slip is obtained by calculating the average temperature deviation between the actual temperature curve and the isothermal baseline along the heat transfer length. Where L is the effective length of the heat exchanger in meters, T_actual(x) is the actual working fluid temperature distribution along the length of the heat exchanger in °C, T_iso is the theoretical temperature of the isothermal condensation process in °C, and ΔT_glide is the temperature glide in °C. The greater the slope of the supply water temperature rise in the heat source characteristic parameters, the more significant the temperature glide of the CO2 working fluid. An increased glide indicates uneven heat transfer temperature distribution during the heat exchange process. In centralized heating applications, the temperature glide of a CO2 heat pump during the process of increasing the supply water temperature from 50 °C to 80 °C is approximately 8 °C to 12 °C. The magnitude of the glide affects the heating efficiency of the heat pump. Temperature glide identification is achieved by comparing the actual slope of the supply water temperature curve with the theoretical slope corresponding to the isothermal condensation process of CO2. The theoretical slope is calculated based on the rated temperature distribution under the design operating conditions of the heat exchanger. The area where the actual slope deviates from the theoretical slope corresponds to a heat exchange section with significant temperature glide. The peak and trough values ​​of the heat source characteristic parameters define the start and end temperature range of the temperature glide, and the average glide amount within the start and end range of the heat source characteristic parameters is defined as the temperature glide characteristic value.

[0023] Correlation analysis of graphene-enhanced heat transfer characteristics was conducted based on temperature glide to identify correlation parameters. A correlation exists between temperature glide and graphene-enhanced heat transfer performance. ， Graphene-enhanced heat exchangers reduce temperature slip by increasing the heat transfer coefficient. In actual operation of district heating stations... ， The heating load fluctuates drastically during peak winter heating periods. ， After adopting graphene-enhanced heat exchangers ， The heat pump's response time to load changes has been reduced from 60 seconds to 45 seconds. ， Large fluctuations in water supply temperature were avoided. ， This improved heating quality. A correlation analysis of graphene-enhanced heat transfer characteristics established a mapping model between temperature glide and the heat transfer coefficient of the heat exchanger. The mapping function is η = η0 + k × ΔT_glide, where η is the dimensionless enhancement factor of the heat transfer coefficient, η0 is the baseline enhancement factor (valued at 1.2), k is the glide sensitivity coefficient (valued at 0.03 / ℃), and ΔT_glide is the characteristic value of temperature glide in ℃. When the temperature glide is 10℃, the heat transfer coefficient of the traditional heat exchanger is approximately 800 W / (m²·K). ， The heat transfer coefficient of the graphene-enhanced heat exchanger has been increased to 1200 W / (m²·K). ， The enhancement factor is 1.5 times. A heat transfer coefficient enhancement factor of 1.5 means that under the same heat transfer area... ， The heat transfer power of the graphene-enhanced heat exchanger is 1.5 times that of the conventional heat exchanger. Correlation characteristic parameters quantify the degree to which the graphene enhancement effect improves temperature glide. ， The associated characteristic parameters include three fields: heat transfer coefficient enhancement factor, temperature glide reduction, and heat exchange efficiency improvement ratio. During building heating, graphene-enhanced heat exchange reduces the temperature glide of the CO2 working fluid from 12℃ to 8℃. ，The decrease in slip was 4°C. ， Heat exchange efficiency improved by approximately 15%. ， The three values ​​correspond to the three field values ​​of the associated feature parameters.

[0024] A heat source feature library is constructed by associating characteristic parameters. The heat transfer coefficient enhancement factor, temperature glide reduction, and heat exchange efficiency improvement ratio of the associated characteristic parameters serve as the core data items in the heat source feature library. These data items are stored according to heat source type and operating conditions. The heat source feature library is organized in a structured database format, containing three tables: a heat source type table, an operating condition table, and an enhancement effect table. The heat source type table records the basic parameters of two types of heat sources: CO2 heat pumps and gas boilers. These basic parameters include rated power, rated heating capacity, and rated supply and return water temperatures. The operating condition table records the heat source characteristic parameters under different load rates, divided into four levels: 30%, 50%, 70%, and 100%. The heat source characteristic parameters are stored according to load rate, with each level corresponding to a set of heat source characteristic parameters. The enhancement effect table records the associated characteristic parameters of the graphene-enhanced heat exchanger under different operating conditions, establishing a correlation between these associated characteristic parameters and the heat source type and operating conditions. When the heating system load increases from 300kW to 500kW, the control system queries the heat source characteristic database to obtain the enhancement effect parameters under 100% load conditions. It then automatically adjusts the heat pump compressor frequency and boiler combustion power to achieve a smooth load switch, avoiding drastic fluctuations in supply water temperature that could affect the heating quality on the user side. The heat source characteristic database quickly retrieves corresponding related characteristic parameters by querying the heat source type and operating conditions. The search results are used for heat source performance evaluation and optimized configuration. The three data tables in the heat source characteristic database establish a query relationship based on heat source type and operating conditions. The search results cover information in four dimensions: timestamp, heat source type, operating condition parameters, and enhancement effect. During the operation of the district heating system, the heat source characteristic database stores nearly one month's worth of historical data, totaling approximately 100,000 records.

[0025] Step S120: Identify the heat pump-boiler coupled heating zone based on the heat source feature library, extract graphene enhancement effect evaluation parameters from the supply water temperature and return water temperature to generate a temperature compensation coefficient, and use the temperature compensation coefficient to adjust the heating load of the heat pump-boiler coupled heating zone to form a dynamic coupled heating zone.

[0026] Specifically, the heat pump-boiler coupled heating range is identified based on a heat source characteristic library. The heat transfer coefficient enhancement factor, temperature glide reduction, and heat exchange efficiency improvement ratio from the heat source characteristic library serve as the basis for heat source performance evaluation. The evaluation results are used to determine the applicable operating conditions for different heat sources. The heat pump-boiler coupled heating range is defined as the heating load range within which the CO2 heat pump and gas boiler can operate simultaneously or alternately. The boundary of the range is determined by the maximum heating capacity of the heat pump and the minimum stable operating load of the boiler. The operating condition table in the heat source characteristic library provides heating capacity data for the CO2 heat pump at different load rates. The heat pump provides approximately 500kW of heat at 100% load and approximately 150kW of heat at 30% load. The minimum stable operating load of the gas boiler is approximately 20% of the rated load, corresponding to a heating capacity of approximately 200kW. Below this load, boiler combustion is unstable. The heat pump-boiler coupled heating range is defined within a heating load range of 200kW to 500kW, within which the heat pump can operate independently or in conjunction with the boiler. In typical heating applications, the coupled heating zone covers most of the system's operating conditions. The heat pump handles the base load, while the boiler handles peak loads and emergency backup. This configuration ensures both heating reliability and fully leverages the energy efficiency advantages of the heat pump. The enhancement effect table in the heat source characteristic library records the improvement effect of graphene-enhanced heat exchangers on the heat pump's heating capacity. Graphene-enhanced heat exchange technology increases the maximum heating capacity of the heat pump at the physical level from the design value of 500kW to 600kW by improving the heat transfer coefficient.

[0027] In some embodiments, the step of extracting graphene enhancement effect evaluation parameters from the supply water temperature and the return water temperature to generate a temperature compensation coefficient includes: performing CO2 supercritical state identification on the supply water temperature and the return water temperature to obtain a state identifier sequence; using the state identifier sequence to extract supercritical operating state feature parameters to obtain a state offset; performing graphene enhancement effect mapping on the state offset to identify enhancement effect mutation points; and generating a temperature compensation coefficient based on the distribution density of the enhancement effect mutation points.

[0028] A state identifier sequence was obtained by identifying the supercritical state of CO2 based on the supply and return water temperatures. The working fluid temperature and pressure on the high-pressure and low-pressure sides of the CO2 heat pump were calculated from the supply and return water temperature data. The working fluid state was determined by comparing it with the CO2 critical parameters (critical temperature 31.1℃, critical pressure 7.38MPa). For CO2 supercritical state identification, the working fluid temperature and pressure at each sampling time were compared with the critical parameters. When both the working fluid temperature and pressure were higher than the critical temperature and critical pressure, it was determined to be in a supercritical state; when either the working fluid temperature or pressure was lower than the critical parameters, it was determined to be in a two-phase state. When the supply water temperature is between 75℃ and 85℃, the working fluid on the high-pressure side is in a supercritical state, with a pressure of approximately 9MPa to 11MPa, higher than the critical pressure of 7.38MPa. When the return water temperature is between 45℃ and 55℃, the working fluid on the low-pressure side is in a gas-liquid two-phase state, with a pressure of approximately 3MPa to 4MPa, lower than the critical pressure. The state identifier sequence records the state identifiers of the CO2 working fluid at each sampling time, categorized into supercritical state and two-phase state. The results of CO2 supercritical state identification reflect the stability of the heat pump's operating conditions. Frequent switching in supercritical state identification indicates drastic fluctuations in operating conditions, while prolonged stability in supercritical state identification indicates smooth system operation. The periodic variation characteristics of the state identifier sequence are related to the start-up and shutdown cycle of the heat pump compressor. After the compressor starts, the high-pressure side working fluid rapidly enters the supercritical state, and after the compressor stops, the working fluid gradually returns to the two-phase state.

[0029] For example, the step of extracting supercritical operating state feature parameters using the state identifier sequence to obtain the state offset includes: obtaining supercritical state key points in the state identifier sequence; detecting state fluctuation features at the supercritical state key points to generate state tracking parameters; using the state tracking parameters to perform temperature-related positioning to generate an offset candidate set; and generating a state offset based on the offset candidate set and the state tracking parameters.

[0030] Key points of the supercritical state are obtained from the state identifier sequence. The state identifier sequence is segmented to identify the start and end positions of continuous supercritical state intervals. The start position is defined as the starting point of the supercritical state key point, and the end position is defined as the ending point. Extraction of supercritical state key points is achieved through abrupt change detection of the state identifiers. The start point is marked when the state identifier switches from a two-phase state to a supercritical state, and the end point is marked when the state identifier switches back from a supercritical state to a two-phase state. Multiple supercritical state intervals are typically identified in the state identifier sequence, each containing dozens of sampling points. The duration of each interval depends on the operating mode and load adjustment frequency of the heat pump compressor. The timestamps of the supercritical state key points and their corresponding supply water temperature values ​​are recorded in the key point dataset, which includes three fields: key point location index, temperature value, and state type. The distribution density of supercritical state key points reflects the frequency of CO2 working fluid state transitions; dense key point distribution indicates frequent state switching, while sparse key point distribution indicates long-term state stability. During periods of drastic fluctuation in heating load, the distribution density of key points in the supercritical state increases significantly, indicating rapid changes in the heat pump's operating conditions. At this time, the graphene-enhanced heat exchanger's effect on improving heating stability is more pronounced. The initial supply water temperature is typically higher than the average temperature of the interval because the heat exchanger outlet temperature has not yet stabilized when the working fluid first enters the supercritical state. The final supply water temperature is lower than the average temperature of the interval because the heat pump load decreases as the working fluid is about to exit the supercritical state.

[0031] State tracking parameters are generated by detecting state fluctuation characteristics at key points in the supercritical state. The temperature fluctuation amplitude at these key points is used to detect state stability. State stability is quantified by calculating the temperature fluctuation amplitude at sampling points in the neighborhood of the key point; the fluctuation amplitude is characterized by the temperature standard deviation. State fluctuation characteristics are represented by the temperature standard deviation of sampling points in the neighborhood of the key point, and the temperature fluctuation amplitude reflects the temperature stability at the key point location. The temperature fluctuation amplitude at the starting point is usually large because the heat transfer process is in a transition phase when the working fluid first enters the supercritical state, and the drastic change in the heat transfer coefficient leads to temperature fluctuations. The temperature fluctuation amplitude at the ending point is also large because the state transition process is more drastic when the working fluid exits the supercritical state. The state tracking parameters are normalized to map the fluctuation amplitude to the 0-1 interval. The normalization process divides the temperature standard deviation of the sampling points in the neighborhood of the key point by the difference between the maximum and minimum temperature values ​​in that neighborhood to obtain a dimensionless value in the 0-1 interval. When the temperature change range is too small (less than 0.1℃), it is considered a completely steady state, and the state tracking parameters are set to 0. Smaller state tracking parameter values ​​indicate stable supercritical conditions, minimal fluctuations in heat pump supply water temperature, and good heating quality. Larger values ​​indicate severe state fluctuations, with significant fluctuations in supply water temperature, potentially requiring the activation of an auxiliary heat source to mitigate these fluctuations. In extremely cold weather or under conditions of sudden load changes, state tracking parameters are typically larger. In such cases, the rapid response characteristics of the graphene-enhanced heat exchanger can shorten the temperature stabilization time, reducing the impact of supply water temperature fluctuations on the user side. The state tracking parameter sequence records the fluctuation characteristics of each key point in the supercritical state. The changing trends of the parameter values ​​in the sequence reflect the evolution of the heat pump's operational stability. The key points in the supercritical state and the state tracking parameters together constitute the characteristic parameters of the supercritical operating state, comprehensively characterizing the state transition characteristics of the CO2 working fluid.

[0032] Temperature correlation positioning is used to generate a candidate set of offsets by applying state tracking parameters. There is a correlation between state tracking parameters and the degree to which the supply water temperature deviates from the design value; key points with drastic state fluctuations often correspond to large temperature deviations. Temperature correlation positioning is achieved by establishing a correlation analysis between state tracking parameters and supply water temperature deviations, using linear regression to fit the quantitative relationship between the two. Temperature correlation positioning identifies key points that simultaneously meet two conditions: state tracking parameters exceeding a threshold and supply water temperature deviation exceeding a limit. These key points constitute the candidate set of offsets. The selection threshold is set based on the statistical distribution of historical operating data; the state tracking parameter threshold is typically set to 0.2, and the supply water temperature deviation limit is typically set to 5℃. Key points meeting both conditions indicate significant deviations in operating conditions at that location. The temporal distribution of key points in the candidate set of offsets exhibits a non-uniform characteristic, with dense distribution in some periods and sparse distribution in others. Dense distribution periods typically correspond to operating conditions with rapid load changes or drastic fluctuations in ambient temperature. In actual operation of the heating system, the key points in the candidate set of offsets are mainly concentrated in the load adjustment phase. The performance of the graphene-enhanced heat exchanger in these phases directly affects the heating response speed and temperature control accuracy.

[0033] State offsets are generated based on the offset candidate set and state tracking parameters. The temperature deviation of each key point in the offset candidate set is calculated by the difference from the design temperature, with the absolute value of the deviation representing the degree of deviation. State tracking parameters are used as weighting coefficients to perform a weighted summation of the deviations of each key point. This weighted summation involves multiplying the temperature deviation of each key point by its corresponding state tracking parameter, summing the results, and then dividing by the sum of the state tracking parameters for all key points to obtain the weighted average offset. When the state tracking parameters for all key points are less than a set threshold, the state offset is directly taken as the arithmetic mean of the temperature deviations. The calculated state offset reflects the overall deviation level of each key point in the offset candidate set. The weighted averaging eliminates the influence of individual outliers, resulting in a more robust offset estimate. The magnitude of the state offset reflects the degree to which the actual operating conditions deviate from the design conditions; a larger offset indicates a more significant deviation, and the temperature compensation strategy needs to be adjusted accordingly for the graphene reinforcement effect evaluation parameters. In heating scenarios with seasonal changes or large diurnal temperature differences, the dynamic range of state deviation is large. Graphene-enhanced heat exchangers can improve the heat transfer coefficient, enabling the system to maintain stable operation over a wide range of operating conditions.

[0034] A mapping study was conducted to identify abrupt changes in the graphene enhancement effect based on state offsets. A nonlinear mapping relationship exists between state offsets and the graphene-enhanced heat transfer effect, describing the degree of improvement in heat transfer performance by the graphene coating at different offsets. The graphene enhancement effect mapping was achieved by establishing a correspondence table between state offsets and the enhancement factor of the heat transfer coefficient. This table was constructed based on experimental data and simulation results, and piecewise linear interpolation was used to estimate the enhancement factor between known data points. At a state offset of 0℃, the heat transfer coefficient enhancement factor was approximately 1.4 times; as the state offset increased to 6℃, the enhancement factor increased to 1.6 times; and as the offset further increased to 10℃, the enhancement factor reached 1.8 times. Abrupt changes in the enhancement effect were defined as the state offset positions where the heat transfer coefficient enhancement factor changed rapidly. These abrupt changes corresponded to a new operating range for the heat transfer performance of the graphene coating. The graphene enhancement effect evaluation parameters quantified the performance characteristics of the graphene coating based on the identification results of these abrupt changes. The distribution of these abrupt change positions reflected the sensitivity of the graphene-enhanced heat exchanger to changes in operating conditions. The graphene-enhanced effect mapping curve exhibits piecewise linear characteristics, with a rapid increase in the enhancement factor within the state offset range of 3℃ to 7℃, corresponding to the sensitive area of ​​the enhancement effect. The abrupt change point in the enhancement effect is obtained by identifying the node with the largest slope change among adjacent segments in the piecewise linear interpolation curve; the larger the slope change, the more significant the abrupt change in the enhancement effect. Under operating conditions with frequent adjustments to the heating load, the state offset often falls within the sensitive area. The performance advantage of the graphene-enhanced heat exchanger in this range can effectively shorten the system's response time to load changes and improve the quality of heating control. Identifying the abrupt change point in the enhancement effect provides a key threshold for generating the temperature compensation coefficient. Different compensation strategies are employed before and after the abrupt change point to optimize the coupled operation of the heat pump-boiler.

[0035] The temperature compensation coefficient is generated based on the distribution density of abrupt change points in the enhancement effect. The distribution density of these abrupt change points within the state offset range reflects the variation law of graphene-enhanced heat transfer performance. High density indicates that the heat transfer performance is sensitive to state offset, while low density indicates that the heat transfer performance is relatively stable. The distribution density is calculated by statistically analyzing the number of abrupt change points in each offset interval. The state offset range of 0℃ to 10℃ is divided into three representative intervals: low offset, sensitive, and high offset. The distribution density of abrupt change points is highest in the sensitive interval of 3℃ to 5℃ (approximately 0.8 points / ℃), approximately 0.3 points / ℃ in the low offset interval of 0℃ to 3℃, and approximately 0.4 points / ℃ in the high offset interval of 7℃ to 10℃. The temperature compensation coefficient is generated based on the distribution density of the abrupt change points in the enhancement effect. The compensation coefficient is larger in intervals with higher density and smaller in intervals with lower density, showing a positive correlation between the compensation coefficient and the distribution density. The temperature compensation coefficient is calculated using the formula K_comp = 1 + α × ρ_mutation, where α is a proportionality coefficient with a value of 0.2, and ρ_mutation is a normalized dimensionless index obtained by dividing the distribution density of each interval by the maximum distribution density among the three intervals. The value of ρ_mutation ranges from 0 to 1. When the distribution density is 0.8 particles / ℃, the maximum value among the three intervals is normalized to ρ_mutation of 1.0, and the temperature compensation coefficient is 1.20. When the distribution density is 0.3 particles / ℃, the normalized ρ_mutation is 0.375, and the temperature compensation coefficient is 1.075. When the distribution density is 0.4 particles / ℃, the normalized ρ_mutation is 0.5, and the temperature compensation coefficient is 1.10. The difference in the compensation coefficient reflects the difference in the graphene strengthening effect within different offset intervals.

[0036] A dynamic coupled heating zone is formed by adjusting the heating load of the heat pump-boiler coupled heating zone using a temperature compensation coefficient. The temperature compensation coefficient adjusts the upper and lower boundaries of the heat pump-boiler coupled heating zone. The upper boundary is adjusted using the temperature compensation coefficient corresponding to the sensitive zone, and the lower boundary is adjusted using the temperature compensation coefficient corresponding to the low offset zone. The adjustment range is equal to the original boundary value multiplied by the compensation coefficient minus 1. The original upper boundary of the heat pump-boiler coupled heating zone was 500kW; with a temperature compensation coefficient of 1.20, the adjusted upper boundary is 600kW, an increase of 100kW. The original lower boundary was 200kW; with a compensation coefficient of 1.075, the adjusted lower boundary is 215kW, an increase of 15kW. The dynamic coupled heating zone ranges from 215kW to 600kW, with the zone width expanding from the original 300kW to 385kW. The better the graphene reinforcement effect, the wider the coupled zone. The heating load adjustment process considers the matching relationship between actual heating demand and the dynamically coupled heating zone. When the demand falls within the zone, heat pump heating is used first; when the demand exceeds the upper boundary, boiler auxiliary heating is activated. The dynamically coupled heating zone is dynamically adjusted according to the temperature compensation coefficient. When the compensation coefficient increases, the zone expands; when the compensation coefficient decreases, the zone shrinks. This dynamic adjustment adapts to the changes in graphene enhancement effect with operating conditions.

[0037] Step S130: Perform heat source matching on the dynamically coupled heating zone to form heat source allocation results; identify the dominant heat source type based on the heat source allocation results; perform graphene-enhanced heat exchange performance mapping on the dominant heat source type to obtain the heating load response time; evaluate the load response level based on the heating load response time to generate graded heat source allocation parameters.

[0038] Specifically, heat source matching is performed on the dynamically coupled heating zone to determine the heat source allocation result. The upper and lower boundaries of the dynamically coupled heating zone define the load range within which the heat pump and boiler can work collaboratively. Heat source matching allocates heat sources based on the actual heating load's location within this zone. The heat source matching strategy divides the dynamically coupled heating zone into three sub-zones: a heat pump priority zone, a collaborative heating zone, and a boiler priority zone. The heat pump priority zone corresponds to low-load conditions where the load is close to the lower boundary; the collaborative heating zone corresponds to medium-load conditions where the load is in the middle of the zone; and the boiler priority zone corresponds to high-load conditions where the load is close to the upper boundary. When the dynamically coupled heating zone is 215kW to 600kW, the heat pump priority zone is defined as 215kW to 350kW, the collaborative heating zone as 350kW to 480kW, and the boiler priority zone as 480kW to 600kW. When the actual heating load is 300kW and falls within the heat pump priority zone, the heat source allocation result is that the heat pump operates alone, outputting 300kW of heat to meet the load demand. When the actual heating load of 420kW falls within the coordinated heating zone, the heat source allocation result is a heat pump output of 350kW and a boiler output of 70kW, with both working together to meet the total demand. The heat source matching strategy ensures the system maintains optimal operation across the entire load range, prioritizing the use of heat pumps to maximize energy efficiency during low-load periods and activating the boiler during high-load periods to guarantee heating capacity. The heat source allocation result includes three fields: heat pump allocated power, boiler allocated power, and operating mode identifier. The operating mode identifier is an enumerated value indicating whether the heat pump operates alone, the boiler operates alone, or they operate in coordination.

[0039] The dominant heat source type is identified based on the heat source allocation results. The dominant heat source is determined by calculating the power ratio of the heat pump and boiler allocation results. Heat sources with a power ratio of at least 60% are identified as dominant heat sources. There are three types of dominant heat sources: heat pump dominant, boiler dominant, and balanced configuration. Heat pump dominant indicates that the heat pump undertakes the main heating task; boiler dominant indicates that the boiler undertakes the main heating task; and balanced configuration indicates that the power ratios of the heat pump and boiler are close. The identification of the dominant heat source type guides the optimization direction of graphene-enhanced heat transfer performance. When heat pump is dominant, the system focuses on monitoring the temperature glide characteristics of CO2 transcritical cycles; when boiler is dominant, the stability of the boiler outlet temperature is monitored; and in a balanced configuration, the switching timing of the two is coordinated to avoid fluctuations in the supply water temperature. Different dominant types correspond to different control strategies and performance evaluation indicators. When the heat source allocation result is 350kW heat pump and 70kW boiler, the heat pump power ratio exceeds the threshold, and the dominant heat source type is identified as heat pump dominant. When the heat source allocation result is 200kW heat pump and 300kW boiler, the boiler power ratio reaches the threshold, and the dominant heat source type is identified as boiler dominant. In long-term operational statistics of the heating system, heat pumps dominate during low-load periods to fully leverage energy efficiency advantages, while boilers take the lead during peak high-load periods to ensure heating capacity. A balanced configuration mode is adopted during transitional load periods to balance economy and reliability. The identification results of the dominant heat source type guide the key directions for mapping graphene-enhanced heat transfer performance: when heat pumps dominate, the focus is on analyzing the graphene enhancement effect on the heat pump side; when boilers dominate, the focus is on the heat transfer performance on the boiler side.

[0040] The graphene-enhanced heat transfer performance was mapped to the dominant heat source type to obtain the heating load response time. Different dominant heat source types correspond to different graphene-enhanced heat transfer performance characteristics. When the heat pump is dominant, the graphene coating mainly enhances the heat exchange process between CO2 and hot water; when the boiler is dominant, the graphene coating mainly enhances the heat exchange process between flue gas and hot water. The graphene-enhanced heat transfer performance mapping establishes a correspondence between the dominant heat source type and the heat transfer response time. The mapping relationship is constructed based on the enhancement effect table of the heat source feature library and real-time operating data. When the heat pump is dominant, the heat transfer coefficient of the graphene-enhanced heat exchanger is approximately 1200 W / (m²·K), corresponding to a heating load response time of approximately 45 seconds. When the boiler is dominant, the heat transfer coefficient is approximately 900 W / (m²·K), corresponding to a response time of approximately 30 seconds. In a balanced configuration, the heat pump and boiler jointly undertake the load regulation task. The heating load response time is taken as the weighted average of the heat pump response time and the boiler response time, with the weights allocated according to their respective power proportions. The heating load response time corresponding to the balanced configuration is approximately 38 seconds. The graphene-enhanced heat exchange performance mapping comprehensively considers the inherent load regulation mechanisms of each heat source and the graphene enhancement effect. The shorter response time of a boiler compared to a heat pump is mainly due to the rapid nature of combustion regulation; gas valve opening adjustments can be completed within seconds, while heat pumps require compressor frequency adjustment and refrigerant state changes to achieve load regulation, a relatively slower process. The heating load response time is defined as the time interval from when the heat source receives a load change command to when the supply water temperature stabilizes at the target value. A shorter response time indicates a stronger dynamic regulation capability of the heat source. Graphene-enhanced heat exchangers reduce the heat pump response time from the traditional 60 seconds to 45 seconds, improving heating quality through faster response. The graphene-enhanced heat exchange performance mapping also considers the magnitude of heating load changes; the larger the load change, the longer the response time. When the change magnitude increases from 50kW to 150kW, the response time increases from 35 seconds to 55 seconds.

[0041] In some embodiments, the step of generating graded heat source allocation parameters by evaluating the load response level based on the heating load response time includes: performing historical statistics on the heating load response time to obtain a response time change sequence; performing load response rate analysis based on the response time change sequence to obtain a response rate level; performing level mapping on the response rate level to form a load response level; and allocating graded weights based on the load response level to generate graded heat source allocation parameters.

[0042] Historical statistics on heating load response time were performed to obtain a response time variation sequence. Historical data on heating load response time was extracted from the heating system operation log, which recorded the response time value corresponding to each load change event. The historical statistical period was set to the past 30 days, during which approximately 200 load change events occurred. For each event, the response time, load change amplitude, and dominant heat source type were recorded. The response time variation sequence consists of 200 response time values ​​arranged chronologically. The sequence is a discrete time series, with uneven time intervals depending on the frequency of load change events. The numerical range of the response time variation sequence fluctuates between 25 and 75 seconds, with a mean of approximately 48 seconds and a standard deviation of approximately 12 seconds. The standard deviation reflects the dispersion of the response time. The fluctuation characteristics of the response time variation sequence reflect the stability of the heating system operation. Large fluctuations in the sequence value within a certain period indicate frequent load changes or unstable operating conditions during that period, while small fluctuations indicate stable operation during that period. Anomalies with sudden increases in response time were identified in the response time variation sequence. These anomalies corresponded to moments of heating system malfunction or heat source performance degradation. Response times exceeding 65 seconds at these anomalies were significantly higher than the sequence mean. The trend of the response time variation sequence was calculated through historical statistical analysis. This trend was obtained by fitting the sequence data with linear regression. A positive regression slope indicated a gradually increasing response time, while a negative slope indicated a gradually decreasing response time. After the graphene-enhanced heat exchanger was put into use, the regression slope changed from positive to negative, indicating improved response performance.

[0043] Load response rate analysis is performed based on response time variation sequences to obtain response rate levels. The numerical distribution of the response time variation sequences is used for quantitative analysis of load response rate, and the distribution characteristics are obtained by constructing a frequency histogram of response times. The frequency histogram shows that the response times are mainly distributed in the 35 to 45-second range, accounting for the highest proportion (approximately 35%), indicating that the response times of most load change events are concentrated in this range, corresponding to a medium level. Load response rate analysis identifies the central tendency and dispersion trend of response times. The central tendency is determined by the peak position of the frequency histogram, and the dispersion trend is determined by the width of the histogram. The peak position of the response time variation sequence is in the 35 to 45-second range, indicating that the response times of most load change events are concentrated in this range, and the response rate corresponding to this range is a medium level. Response rate levels are divided according to the percentiles of response times, with the 25th percentile corresponding to the upper limit of the fast response level and the 75th percentile corresponding to the lower limit of the slow response level. The percentile classification method avoids the subjectivity of absolute threshold setting, allowing the classification to adapt to the historical operating characteristics of the system. As the overall system response performance improves, the thresholds for each level automatically increase, maintaining the relativity and fairness of the evaluation criteria. This dynamic threshold mechanism enables the graded heat source allocation parameters to optimize along with the evolution of system performance. The 25th percentile of response time is approximately 38 seconds, and the 75th percentile is approximately 58 seconds. The middle 50% of response times fall within the 38-58 second range, corresponding to the medium-speed response level. The response speed level also records the coefficient of variation of the response time variation sequence as additional information on level stability. The coefficient of variation is the ratio of the standard deviation to the mean, and it is passed to the level mapping stage for confidence assessment.

[0044] A load response level is formed by mapping response speed levels. The three categories of fast, medium, and slow response speed levels are converted into numerical representations of load response levels through mapping. The mapping rule maps a fast response level to a level value of 3, a medium response level to a level value of 2, and a slow response level to a level value of 1. The mapping process considers the confidence level of the response speed level. The confidence level is determined by combining the proportion of data points in the response time sequence within the corresponding level interval and the coefficient of variation carried by the response speed level. The higher the proportion of data points and the smaller the coefficient of variation, the higher the confidence level. The proportion of data points reflects the concentration of level affiliation, and the coefficient of variation reflects the dispersion of response time. A higher proportion of data points in the response time sequence falling within the 35-45 second interval leads to a higher confidence level for the medium response speed level. The load response level value and confidence level together describe the response performance of the heat source. Heat sources with high level values ​​and high confidence levels have the best response performance, while heat sources with low level values ​​and low confidence levels have poor response performance. In practical applications of heating systems, gas-fired boilers outperform CO2 heat pumps in load response speed due to their rapid combustion adjustment. While CO2 heat pumps have a longer absolute response time than boilers, graphene-enhanced heat exchange technology reduces the response time from the traditional 60 seconds to 45 seconds, significantly narrowing the response gap with boilers. A load response level mapping establishes a correspondence between load response levels and heat source priorities. Heat sources with higher level values ​​have higher priority in the tiered heat source allocation, and higher-priority heat sources are started first and allocated more power. The numerical representation of load response levels facilitates weight allocation and optimization calculations, and numerical processing avoids the ambiguity of qualitative descriptions.

[0045] The hierarchical heat source allocation parameters are generated based on the load response level and its assigned weights. The load response level value is used for weight allocation, following the principle that higher-level values ​​have higher weights. Weight values ​​are normalized to ensure the sum of all weights is 1. A gas-fired boiler has a load response level of 3 corresponding to a weight of 0.6, and a CO2 heat pump has a load response level of 2 corresponding to a weight of 0.4. The weight ratios are consistent with the level value ratios. A boiler weight of 0.6 indicates that the boiler undertakes 60% of the load response task during the initial startup phase, while a heat pump weight of 0.4 indicates that the heat pump undertakes 40% of the load response task. The allocation of hierarchical weights considers the confidence level of the load response level. Heat sources with high confidence levels have their weights increased appropriately, while those with low confidence levels have their weights decreased appropriately. Confidence level reflects the stability of the heat source response time. When the response time fluctuation is small and consistent with expectations, the confidence level is high, and the system increases its weight allocation; when the response time fluctuation is large, the confidence level decreases, and the weight is correspondingly reduced. When the confidence level of the heat pump is above average, the weight is adjusted from 0.4 to 0.42; when the confidence level of the boiler is close to average, the weight is adjusted to 0.58. The adjusted weights are written into the tiered heat source allocation parameters. The tiered heat source allocation parameters integrate the tiered weights with the heat source start-up order and power allocation ratio. In the tiered heat source allocation parameters, the start-up order is arranged from largest to smallest weight, with the boiler (weight 0.58) starting first and the heat pump (weight 0.42) starting second. The power allocation ratio is proportional to the weight.

[0046] Step S140: Perform start-stop delay analysis on the graded heat source allocation parameters to determine the priority heat source to start, detect the switching interval time of the priority heat source to start, and adjust the heat source priority based on the graphene rapid heat exchange response characteristics obtained from the switching interval time to form a heating action sequence.

[0047] In some embodiments, the step of performing start-stop delay analysis on the graded heat source allocation parameters to determine the priority heat source to start includes: extracting delay differences from the graded heat source allocation parameters to obtain a delay gradient sequence; evaluating delay uniformity based on the delay gradient sequence to obtain a coordination degree; performing anomaly detection on the coordination degree to identify insufficient coordination links of the heat source; and optimizing the delay configuration through the insufficient coordination links of the heat source to determine the priority heat source to start.

[0048] Delay gradient sequences are obtained by extracting time delay differences from the tiered heat source allocation parameters. These parameters include the startup sequence and startup delay information for each heat source. The startup delay represents the time interval from receiving the startup command to actually starting heating. The startup delay comprises three components: equipment preheating time, system detection time, and safety confirmation time. The equipment preheating time depends on the heat exchanger's heat capacity and heating power. The system detection time verifies the equipment status, and the safety confirmation time ensures that startup conditions are met. In a typical district heating station configuration, the heating system includes four heat source units: heat pump 1, heat pump 2, boiler 1, and boiler 2. The startup delays recorded in the tiered heat source allocation parameters are 25 seconds, 50 seconds, 85 seconds, and 95 seconds, respectively. Delay difference extraction calculates the difference in startup delays between adjacent heat sources in the tiered heat source allocation parameters. The delay difference between heat pump 1 and heat pump 2 is 25 seconds, between heat pump 2 and boiler 1 is 35 seconds, and between boiler 1 and boiler 2 is 10 seconds. The magnitude of the time delay difference reflects the degree of difference in the start-up capabilities of adjacent heat sources. A large difference indicates a significant difference in the start-up mechanisms of the two heat sources. The time delay gradient sequence consists of the start-up time delay difference between adjacent heat sources, reflecting the rate of increase in start-up time delay. A uniform gradient in the time delay gradient sequence indicates a reasonable start-up sequence of heat sources, resulting in a smooth transition in water supply temperature when each heat source starts sequentially. Abrupt gradient changes in the time delay gradient sequence indicate an excessively large start-up interval between two heat sources, which may lead to brief fluctuations in water supply temperature or heating interruptions. These abrupt gradient changes often correspond to switching points between different types of heat sources and require special attention. The time delay gradient sequence contains three gradient values, corresponding to the gradients between three pairs of adjacent heat sources, and the sequence is a discrete numerical sequence.

[0049] The coordination degree is obtained by evaluating the uniformity of time delay based on the time delay gradient sequence. The uniformity of the time delay gradient sequence reflects the rationality of the heat source start-up time delay configuration. High uniformity indicates that the start-up time delay increments between heat sources are similar, while low uniformity indicates that the time delay increments between some heat source pairs are abnormal. Time delay uniformity is a key performance indicator for the coordinated start-up of multiple heat sources. Poor uniformity can lead to some heat sources starting up too early, resulting in energy waste, or starting up too late, resulting in insufficient heating. The time delay uniformity assessment is achieved by calculating the standard deviation and mean of the time delay gradient sequence. The standard deviation reflects the dispersion of the gradient values, and the mean reflects the average level of the gradient. The smaller the standard deviation, the closer each gradient value is to the mean, and the more uniform the start-up time delay configuration. The larger the standard deviation, the more significantly some gradient values ​​deviate from the mean, indicating the existence of unreasonable start-up time delay configurations. The coordination degree is defined as a normalized uniformity index, with the formula C=1-σ_g / μ_g, where σ_g is the standard deviation of the time delay gradient sequence, and μ_g is the mean of the time delay gradient sequence. When the average gradient sequence value is less than the threshold of 1 second, the latency configuration is considered too dense, and the coordination degree is set to 0. The coordination degree value ranges from 0 to 1; the closer the value is to 1, the more coordinated the latency configuration, and the closer the value is to 0, the less coordinated the latency configuration. When the coordination degree is higher than 0.7, the start-up of each heat source is smooth, and the water supply temperature fluctuation during the switching of multiple heat sources is less than 2℃, which is not noticeable to the user. When the coordination degree is lower than 0.5, the start-up sequence of heat sources is chaotic, which may result in two heat sources starting simultaneously and competing for resources, or the heat source start-up interval being too long, causing heating interruption and affecting the heating quality. The threshold for coordination degree is set between 0.6 and 0.7; a coordination degree higher than the threshold indicates that the latency configuration is reasonable, and a coordination degree lower than the threshold indicates that optimization is needed.

[0050] Anomaly detection is performed on the coordination degree to identify insufficient heat source coordination. Anomaly detection is achieved by comparing the coordination degree value with a threshold; a coordination degree below the threshold triggers the anomaly detection process. The anomaly detection process identifies outliers in the time delay gradient sequence, defined as gradient values ​​exceeding 1.5 times the average gradient value. Outlier identification is achieved by calculating the ratio of each gradient value to the average value; points with gradient values ​​exceeding 1.5 times the average value are considered significant anomalies. In the time delay gradient sequence, the position with a gradient value of 35 seconds corresponds to the switching from heat pump 2 to boiler 1. The average gradient value is approximately 23.3 seconds. 35 seconds exceeds 1.5 times the average value, meeting the anomaly judgment criteria and being identified as an insufficient coordination link. An insufficient heat source coordination link is defined as an adjacent heat source pair containing an abnormal gradient value; the switching from heat pump 2 to boiler 1 is identified as an insufficient coordination link. The existence of an insufficient coordination link indicates a time delay configuration problem in the heat source switching at that location, which may lead to heating interruptions or temperature fluctuations during the switching process. The number of insufficient heat source coordination links reflects the severity of the time delay configuration problem; the more links, the more unreasonable the configuration.

[0051] The optimization of the startup delay configuration in cases of insufficient heat source coordination determines the priority heat source to start. Delay configuration optimization is achieved by adjusting the startup order of heat sources or modifying startup delay parameters. Optimization strategies include inserting intermediate heat sources, adjusting delay intervals, and reordering. The choice of optimization strategy depends on the characteristics of the insufficient coordination links. For links with excessively large gradients, methods such as shortening delays or inserting intermediate heat sources are used; for links with excessively small gradients, methods such as extending delays or adjusting the order are used. The insufficient coordination link between heat pump 2 and boiler 1 was optimized by shortening the startup delay of boiler 1 from 85 seconds to 65 seconds. After optimization, the delay gradient decreased from 35 seconds to 15 seconds. The optimization process needs to consider the physical constraints of the equipment; the boiler startup delay cannot be shortened indefinitely, and sufficient boiler preheating and stable combustion must be ensured. The optimized coordination degree was recalculated, increasing from 0.56 to 0.73. A value above the threshold of 0.7 indicates effective optimization. The priority heat source to start is determined based on the optimized startup order and delay configuration. The heat source with the highest priority is designated as the priority startup heat source and undertakes the basic heating load. The selection of priority heat sources takes into account three factors: short start-up delay, fast response speed, and high energy efficiency ratio. Heat sources that meet these conditions have the highest priority. The optimized start-up order of heat sources is heat pump 1, heat pump 2, boiler 1, and boiler 2, with heat pump 1 as the priority heat source.

[0052] The switching interval for priority-starting heat sources is detected. Defined as the time interval between switching from one heat source to another during alternating or sequential start-stop operations, this interval encompasses the entire process of stopping the current heat source and starting the next. The switching process includes four stages: issuing a stop command, cooling the current heat source, issuing a start command for the next heat source, and heating up the next heat source. The switching interval covers the total time of all stages. The switching interval is obtained by monitoring the operating status signals of the priority-starting heat sources. These signals include start and stop signals, recording the start and stop times of the heat source. The signal monitoring system uses high-precision timestamps to record each start and stop event, with millisecond-level accuracy ensuring the accuracy of the switching interval measurement. The switching interval is the time difference between two consecutive start signals minus the duration of the previous heat source's operation. This calculation method excludes the normal operating period of the heat source, retaining only the transition period. The switching interval time sequence records the time intervals of all switching events within a heating cycle, with each value in the sequence corresponding to one switching event. The switching interval ranges from 10 to 300 seconds, with shorter intervals corresponding to frequent switching conditions and longer intervals corresponding to stable operation conditions. The switching interval between heat pumps is approximately 120 seconds, the switching interval between boilers is approximately 180 seconds, and the switching interval from heat pump to boiler is approximately 90 seconds.

[0053] In some embodiments, the step of adjusting the heat source priority based on the switching interval time to form a heating action sequence includes: obtaining the switching frequency distribution characteristics of the switching interval time; extracting the graphene rapid heat exchange response characteristics based on the switching frequency distribution characteristics to obtain a response rate coefficient; performing a threshold determination on the response rate coefficient to form a heat source availability determination result; and adjusting the heat source priority based on the heat source availability determination result to form a heating action sequence.

[0054] The switching frequency distribution characteristics of the switching interval time were obtained. The switching interval time series includes all switching events within a heating cycle, and the sequence length depends on the number of switching events. The switching frequency is defined as the number of switching events per unit time. A high switching frequency indicates frequent heat source switching, while a low switching frequency indicates stable heat source operation. The switching frequency is an important parameter for evaluating the dynamic characteristics of the system. An excessively high frequency indicates frequent system adjustments, which may affect equipment lifespan; an excessively low frequency indicates a sluggish system response, which may affect heating quality. The switching frequency distribution characteristics were obtained by statistically analyzing the number of switching events in different interval time segments: 0 to 30 seconds, 30 to 60 seconds, 60 to 90 seconds, 90 to 120 seconds, and over 120 seconds. The number of switching events in each time segment reflects the density of switching events in that time segment, and the statistical results are presented in the form of a frequency distribution graph. The switching frequency distribution characteristics exhibit a bimodal distribution. The first peak is located around 40 seconds, corresponding to rapid switching conditions, and the second peak is located around 120 seconds, corresponding to normal switching conditions. Rapid switching conditions typically occur when the load changes drastically, and the rapid response capability of the graphene-enhanced heat exchanger plays an important role in this condition. Normal switching conditions correspond to a slow load adjustment process, with a relatively long switching interval, allowing the heat source sufficient time to complete the start-up and shutdown process. The peak position and width of the switching frequency distribution characteristics reflect the system's operating mode; concentrated peaks indicate a single operating mode, while dispersed peaks indicate diverse operating modes.

[0055] The rapid heat exchange response characteristics of graphene were extracted based on the switching frequency distribution features to obtain the response rate coefficient. The peak interval time of the switching frequency distribution features served as the benchmark for extracting the rapid heat exchange response characteristics of graphene; short-interval peaks corresponded to the rapid response conditions of the graphene-enhanced heat exchanger. The rapid heat exchange response characteristics of graphene were obtained by measuring the temperature rise rate and fall rate of the graphene heat exchanger during the switching process. The rise rate reflects the heating capacity, and the fall rate reflects the cooling capacity. The core indicator of the rapid heat exchange response characteristics of graphene is the temperature change rate; a higher rate indicates a faster response. Under short-interval switching conditions, when the temperature rise rate of the graphene-enhanced heat exchanger reaches 5℃ / second, the heat source can heat the heat exchanger from the standby temperature to the operating temperature within 10 seconds after receiving the start command. Compared with the 50-second preheating time of traditional heat exchangers, this significantly shortens the cold start time of the heat source, enabling the system to respond quickly to sudden load changes. The temperature rise rate of the graphene heat exchanger is approximately 5 times that of traditional heat exchangers, and the temperature fall rate is approximately 4 times that of traditional heat exchangers. The response rate coefficient is defined as the ratio of the actual temperature rise rate to the reference rate, where the reference rate is the average temperature rise rate of a conventional heat exchanger (1°C / second). The response rate coefficient for a standard graphene heat exchanger is approximately 5, while that for a high-performance graphene heat exchanger can reach 8. A response rate coefficient less than 2 indicates limited heat exchanger response capability, suitable for steady-state conditions with slow load changes. A coefficient between 2 and 5 indicates moderate heat exchanger response capability, sufficient for routine load adjustments. A coefficient greater than 5 indicates excellent heat exchanger response capability, capable of handling rapid load changes and frequent start-stop conditions. Graphene's rapid heat exchange response characteristics enable the heat source to complete temperature regulation in a short time, shortening the switching interval and improving the system's dynamic performance. The response rate coefficient quantifies the response capability of a graphene heat exchanger; a higher coefficient value indicates a stronger response capability.

[0056] For example, the step of thresholding the response rate coefficient to form a heat source availability determination result includes: performing feature intensity quantization on the response rate coefficient to obtain a response intensity value; dividing the response intensity value into threshold intervals to determine the response level; performing time-series stability analysis on the response level to form a response stability feature; and performing availability confidence assessment on the response stability feature and the response intensity value to form a heat source availability determination result.

[0057] The response rate coefficient is subjected to characteristic intensity quantization to obtain the response intensity value. The numerical range of the response rate coefficient is wide, and the coefficient values ​​differ significantly between different heat sources, requiring normalization to convert them into intensity values ​​with uniform dimensions. Normalization maps response rate coefficients of different orders of magnitude to a standardized 0-1 interval, eliminating the influence of numerical range differences on the comparison results. Normalization of the response intensity values ​​eliminates the influence of different dimensions, facilitating the comparison of response capabilities between different heat sources. Through normalization, the wide range of response rate coefficients from 0.8 to 8 is mapped to the standard interval of 0-1, enabling the evaluation and ranking of response capabilities of old cast iron heat exchangers, conventional copper tube heat exchangers, standard graphene heat exchangers, and high-performance graphene heat exchangers on the same scale. The response intensity value I = (R - R_min) / (R_max - R_min), where I is the response intensity value, R is the response rate coefficient, R_max is the maximum value of the response rate coefficient within the system, and R_min is the minimum value of the response rate coefficient within the system. The extreme values ​​of the system's response rate coefficients were obtained through historical data statistics. R_max is approximately 8, corresponding to a high-performance graphene heat exchanger, while R_min is approximately 0.8, corresponding to an older, traditional heat exchanger. The selection of extreme parameters needs to cover all possible heat source types within the system to ensure the validity of the normalization results. The response intensity value ranges from 0 to 1; the closer the value is to 1, the stronger the response capability, and the closer the value is to 0, the weaker the response capability.

[0058] Response levels are determined by dividing response intensity values ​​into threshold intervals. The continuous distribution of response intensity values ​​is converted into discrete response levels through threshold interval division, which facilitates heat source classification and management. Discretized level representation simplifies the description of heat source performance, enabling operators to quickly identify the response capability level of a heat source. Threshold interval division divides response intensity values ​​into four levels: low response, medium response, high response, and extremely high response. Low response corresponds to an intensity value less than 0.3, medium response to an intensity value between 0.3 and 0.6, high response to an intensity value between 0.6 and 0.85, and extremely high response to an intensity value greater than 0.85. The interval boundaries are set based on system operating experience and heat source performance distribution characteristics, ensuring that each level has clear physical meaning in practical applications. The thresholds for dividing response levels are determined based on the actual needs of the heating system and the heat source performance distribution, taking into account the response capability requirements under different load conditions. Heat sources with low response levels are suitable for stable operating conditions, heat sources with medium response levels are suitable for routine load regulation, heat sources with high response levels are suitable for rapid load changes, and heat sources with extremely high response levels are suitable for emergency conditions and extreme load changes.

[0059] A time-series stability analysis is performed on the response level to determine its stability characteristics. The response level may change during operation. ，Grade changes reflect fluctuations or degradation in heat source performance. Causes of grade changes include equipment aging, heat exchanger fouling, control system deviations, and fluctuations in the external environment. ， Temporal stability analysis can identify these performance changes. It identifies the frequency and magnitude of level changes by monitoring the time series of response levels. ， High frequency indicates unstable performance ， A low frequency indicates stable performance. The time series of response levels records the level values ​​at each moment within a runtime cycle. ， The sequence length depends on the sampling frequency. The sampling frequency for the time series is set to once per hour. ， This ensures that significant changes in response levels can be captured without being affected by short-term fluctuations. Time-series stability analysis calculates the frequency and amplitude of fluctuations in response levels. ， Fluctuation frequency is the number of times the level changes. ， The fluctuation amplitude represents the difference in the order of the level changes. The stability characteristics of the response are quantified using a stability coefficient. ， The stability coefficient S is defined as S = 1 - N_change / N_total ， Where N_change represents the number of level changes. ， N_total represents the total number of samples. The stability coefficient of the response stability feature ranges from 0 to 1. ， The closer the value is to 1, the more stable the response level. ， A value closer to 0 indicates a more unstable response level. A stability coefficient higher than 0.9 indicates stable and reliable heat source performance. ， A stability coefficient below 0.7 indicates that the heat source has performance fluctuations that require attention.

[0060] Availability confidence assessment is performed on the response stability characteristics and response intensity values ​​to determine the availability of the heat source. The heat source availability determination comprehensively considers both the response intensity value and the response stability characteristics. ， The response strength value reflects the current response capability. ， The stability of the response reflects performance reliability. Relying solely on the response strength value may overlook the risk of performance fluctuations. ， Relying solely on response stability characteristics may overlook the current inadequacy of response capability. ， A comprehensive assessment can fully reflect the availability level of a heat source. Availability confidence assessment is calculated through weighted summation. ， The formula is P = α × I + β × S ， Where P is the availability confidence level. ， I represents the response intensity value. ， S is the stability coefficient of the response stability characteristic. ， α and β are weighting coefficients. The allocation of weighting coefficients α=0.6 and β=0.4 reflects a control strategy that prioritizes responsiveness over stability.， When the heating system is dealing with an emergency load ， Rapid response capability is more important than long-term stability; however, for base load heat sources operating continuously around the clock... ， The stability weight β can be appropriately increased to 0.5. ， Ensure reliable system operation. Heat sources with a confidence level higher than 0.7 are considered usable. ， In the heat source availability assessment results, heat sources with a confidence level below 0.7 are classified as unusable or underperforming. The heat source availability assessment results include two fields: availability status and confidence level value. ， Availability status is a boolean value indicating whether it is available or unavailable. ， The confidence level is a continuous rating.

[0061] The heating action sequence is formed by adjusting the priority of heat sources based on their availability assessment results. The availability assessment identifies available and unavailable heat sources within the system. Available heat sources enter the priority ranking process, while unavailable heat sources are excluded. Excluding unavailable heat sources prevents insufficiently performing heat sources from participating in heating scheduling, thus preventing a decline in heating quality due to poor heat source response capabilities. Heat source priority adjustment is based on availability confidence levels; heat sources with higher confidence levels have higher priority, and those with lower confidence levels have lower priority. Available heat sources are ranked from highest to lowest confidence level, and the ranking result determines the position of each heat source in the heating action sequence. The priority ranking directly affects the start-up order and load allocation ratio of heat sources; higher-priority heat sources start first and bear more load. The heating action sequence defines the start-up and stop-up order of heat sources, with heat sources at the beginning of the sequence starting first and those at the end starting last. The heating action sequence includes three fields: start-up time, stop-up time, and operating mode. The start-up time is the time when the heat source receives the start-up command, the stop-up time is the time when the heat source receives the stop-up command, and the operating mode is independent operation or collaborative operation. The heating action sequence is dynamically adjusted according to the actual load demand. When the load increases, the heat sources in the sequence are started in sequence, and when the load decreases, the heat sources in the sequence are stopped in sequence.

[0062] Step S150: Combine the heat source allocation results with the heating action sequence to construct a heat pump-boiler coordinated heating strategy, and output a hot water centralized heating command based on the heat pump-boiler coordinated heating strategy.

[0063] In some embodiments, constructing a heat pump-boiler coordinated heating strategy by combining the heat source allocation results with the heating action sequence includes: identifying the parallel operation mode of the heat source allocation results to obtain a parallel heating identifier; performing flow demand analysis on the heating action sequence based on the parallel heating identifier to determine the heat pump flow allocation ratio and the boiler flow allocation ratio; performing dynamic matching analysis on the heat pump flow allocation ratio and the boiler flow allocation ratio to form a flow allocation scheme; and constructing a heat pump-boiler coordinated heating strategy based on the flow allocation scheme.

[0064] The parallel operation mode identification of the heat source allocation results is performed to obtain the parallel heating identifier. The operation mode identifier of the heat source allocation results includes three modes: heat pump alone, boiler alone, and coordinated operation. ， The parallel operation mode identification further subdivides the cooperative operation mode in the heat source allocation results into two connection methods: parallel and series. Parallel operation mode refers to the heat pump and boiler independently heating their respective water supply branches. ， The two hot water streams converge at the mixing valve and are then fed into the heating network. ， In series operation mode, the preheated hot water from the heat pump enters the boiler for further heating to the target temperature. In parallel operation mode, the heat pump and boiler can independently adjust their respective outlet temperatures and flow rates. ， When one heat source fails, another heat source can continue to operate independently to ensure basic heating. ， The mixer adjusts the mixing ratio of the two hot water sources to achieve precise control of the water supply temperature. Parallel heating is indicated by comparing the heat pump outlet temperature and the boiler outlet temperature. ， The heat pump outlet temperature and boiler outlet temperature are detected and obtained by outlet temperature sensors on the heat pump side and boiler side, respectively. When the temperature difference between the two does not exceed 15℃, the parallel heating indicator determines that it is in parallel operation mode. ， When the temperature difference exceeds 15℃, the parallel heating system is identified as operating in series mode. ， In series operation, the boiler reheats the preheated hot water from the heat pump. When the heat source distribution is 350kW for the heat pump and 70kW for the boiler, with the heat pump outlet temperature at 75℃ and the boiler outlet temperature at 80℃, a temperature difference of 5℃ (less than 15℃) indicates parallel operation. Under typical operating conditions in a centralized heating system, the temperature difference between the heat pump and boiler outlets is usually less than 15℃, making parallel operation the primary operating mode.

[0065] Based on the parallel heating identification, flow demand analysis is performed on the heating action sequence to determine the heat pump flow allocation ratio and the boiler flow allocation ratio. After confirming the parallel heating identification, the flow demand analysis calculates the water supply flow demand of each heat source. The heating action sequence provides the start-up time and operating mode information of each heat source. The flow demand analysis calculates the flow demand of each heat source based on the heat source in operation in the heating action sequence, its power allocation, and the supply and return water temperature difference. Flow demand analysis is a key step in determining the actual operating parameters of each heat source, and the analysis results directly affect the accuracy of the flow allocation ratio. The flow demand is calculated by dividing the allocated power of the heat source by the product of the supply and return water temperature difference and the specific heat capacity of water at constant pressure. When the heat pump allocated power is 350kW and the supply and return water temperature difference is 30℃, the heat pump flow demand is approximately 10 tons / hour; when the boiler allocated power is 70kW and the temperature difference is 35℃, the boiler flow demand is approximately 1.7 tons / hour. The heat pump flow allocation ratio is defined as the percentage of the heat pump flow demand to the total flow demand, and the boiler flow allocation ratio is defined as the percentage of the boiler flow demand to the total flow demand. Flow demand analysis considers pipeline pressure loss and circulating pump head limitations. Excessive flow demand can lead to insufficient pipeline pressure, while insufficient flow can limit heating capacity. In building heating, the heat pump flow allocation ratio is approximately 85%, and the boiler flow allocation ratio is approximately 15%, with the ratio consistent with the power allocation.

[0066] A flow allocation scheme is formed by dynamically matching the heat pump flow allocation ratio with the boiler flow allocation ratio. The actual flow allocation is optimized through dynamic matching analysis, which considers the flow regulation capability and speed of each heat source. The dynamic matching analysis establishes a correlation model between the flow allocation ratio and the supply and return water temperatures of the pipe network. The model input is the flow allocation ratio, and the output is the predicted supply water temperature. When the heat pump flow allocation ratio is 85% and the boiler flow allocation ratio is 15%, the predicted supply water temperature is approximately 76℃. The actual supply water temperature is 78℃, indicating a 2℃ deviation that requires flow allocation adjustment. The adjustment strategy of the flow allocation scheme is determined based on the direction of the temperature deviation. When the actual supply water temperature is higher than the predicted value, the flow allocation of high-temperature heat sources is reduced and the flow allocation of low-temperature heat sources is increased. When the actual supply water temperature is lower than the predicted value, the opposite adjustment is made. The adjustment range is determined based on the magnitude of the temperature deviation and the flow response sensitivity. The heat pump flow regulation sensitivity is approximately 0.5℃ / ton·hour, and the boiler flow regulation sensitivity is approximately 2℃ / ton·hour. The boiler flow regulation has a more significant impact on the supply water temperature. The boiler flow rate regulation sensitivity is four times that of the heat pump, indicating that boiler flow rate changes have a more significant impact on water supply temperature. Prioritizing heat pump flow rate adjustment during load fluctuations ensures a smooth transition and avoids drastic fluctuations in water supply temperature, while maintaining relatively stable boiler flow rate serves as a fine-tuning tool for temperature regulation. The flow allocation scheme includes three fields: heat pump flow rate setpoint, boiler flow rate setpoint, and flow rate regulation priority. The flow rate regulation priority indicates which heat source's flow rate is prioritized for adjustment during load fluctuations. In district heating, the flow allocation scheme prioritizes heat pump flow rate adjustment, while maintaining relatively stable boiler flow rate as the primary heat source. Flexible adjustment of the heat pump flow rate addresses load fluctuations.

[0067] A heat pump-boiler coordinated heating strategy is constructed based on a flow distribution scheme. The flow setpoint and flow regulation priority of the flow distribution scheme are converted into control commands for the heat pump-boiler coordinated heating strategy. These control commands include flow regulation commands, temperature control commands, and pressure balancing commands. Flow regulation commands control the circulation pump speed or valve opening of each heat source to achieve precise tracking of the flow setpoint. Temperature control commands adjust the heat pump compressor frequency and boiler combustion power to maintain the outlet temperature of each heat source within the target range. Pressure balancing commands coordinate the water supply pressure of each heat source to prevent backflow from the high-pressure side heat source to the low-pressure side during parallel operation. If the heat pump water supply pressure is 0.6 MPa and the boiler water supply pressure is 0.4 MPa during parallel operation, the hot water from the high-pressure side heat pump will flow back to the low-pressure side boiler through the mixer, causing heat loss and flow turbulence. The pressure balancing command controls the water supply pressure difference within 0.05 MPa by adjusting the circulation pump speed of each heat source, ensuring that the two hot water sources merge normally without backflow. The control logic of the heat pump-boiler coordinated heating strategy is executed according to the flow distribution scheme. When one heat source fails, the system automatically switches to another heat source to operate independently to ensure basic heating. For urban heating, the temperature control accuracy of the coordinated heating strategy must be within ±1℃, and the flow control accuracy must be within ±5%. Control accuracy directly affects heating quality and user satisfaction. A temperature control accuracy of ±1℃ ensures that the room temperature fluctuation on the user side is less than 0.5℃, meeting comfort requirements. A flow control accuracy of ±5% ensures that the flow distribution deviation of each heating branch is less than the design value, avoiding insufficient or excessive heating in some areas. Improving control accuracy depends on the response speed of the actuators and the accuracy of the feedback signals.

[0068] The system outputs centralized hot water heating commands based on a heat pump-boiler coordinated heating strategy. The control commands of the heat pump-boiler coordinated heating strategy are converted into centralized hot water heating commands, which include the action commands and operating parameter setpoints of each actuator. These commands are distributed to each actuator according to the control logic of the coordinated heating strategy, ensuring that each heat source operates collaboratively according to the set flow ratio and temperature requirements. The actuators for the centralized hot water heating commands include the heat pump compressor, boiler burner, circulating pump, regulating valve, and mixer. The action commands of each actuator are transmitted using a standardized communication protocol. The heat pump compressor commands include start / stop commands and frequency setpoints. The start / stop commands are binary signals, and the frequency setpoints are continuous values ​​from 0 to 100 Hz. The boiler burner commands include ignition commands, gas valve opening, and fan speed. The gas valve opening controls the boiler's thermal power, and the fan speed controls combustion efficiency. The circulating pump commands include start / stop commands and speed setpoints. The speed setpoints control the water supply flow rate, with a speed range of 30% to 100% of the rated speed. The mixing valve command includes the valve opening setpoint, which controls the mixing ratio of hot water from the heat pump and boiler. The centralized hot water heating command is issued at a frequency of 1Hz, ensuring a rapid response to the coordinated heating strategy. During operation, heating commands are sent to each actuator via a fieldbus network, with a transmission delay of less than 50 milliseconds. This delay control ensures that all actuators execute commands synchronously.

[0069] To implement the above-described method embodiment, a graphene-enhanced CO2 heat pump coupled with a boiler is proposed for heating, in order to achieve the corresponding functions and technical effects. See also... Figure 3 , Figure 3 This paper illustrates a structural block diagram of a graphene-enhanced CO2 heat pump coupled with a boiler heating system 300 according to an embodiment of this application, comprising: The data acquisition module 301 is used to acquire the supply water temperature and return water temperature of the hot water centralized heating network, and to construct a heat source feature library based on the correlation analysis of the graphene-enhanced heat exchange characteristics on the CO2 heat pump side based on the supply water temperature and the return water temperature. The interval identification module 302 is used to identify the heat pump-boiler coupled heating interval based on the heat source feature library, extract the graphene enhancement effect evaluation parameters from the supply water temperature and the return water temperature to generate a temperature compensation coefficient, and use the temperature compensation coefficient to adjust the heating load of the heat pump-boiler coupled heating interval to form a dynamic coupled heating interval. The heat source allocation module 303 is used to perform heat source matching on the dynamically coupled heating zone to form a heat source allocation result, identify the dominant heat source type based on the heat source allocation result, perform graphene-enhanced heat exchange performance mapping on the dominant heat source type to obtain the heating load response time, and evaluate the load response level based on the heating load response time to generate graded heat source allocation parameters. The action management module 304 is used to perform start-stop delay analysis on the graded heat source allocation parameters to determine the priority heat source to start, detect the switching interval time of the priority heat source to start, and adjust the heat source priority based on the graphene rapid heat exchange response characteristics obtained from the switching interval time to form a heating action sequence. The strategy execution module 305 is used to construct a heat pump-boiler coordinated heating strategy by combining the heat source allocation result with the heating action sequence, and output a hot water centralized heating command based on the heat pump-boiler coordinated heating strategy.

[0070] The graphene-enhanced CO2 heat pump coupled with a boiler heating system 300 described above can implement a graphene-enhanced CO2 heat pump coupled with a boiler heating method according to the above method embodiments. The options in the above method embodiments are also applicable to this embodiment, and will not be detailed here. The remaining contents of this application embodiment can be referred to the contents of the above method embodiments, and will not be repeated in this embodiment.

[0071] The purpose of the above embodiments is to reproduce and derive the technical solution of the present invention by way of example, and to fully describe the technical solution, purpose and effect of the present invention. The purpose is to enable the public to have a more thorough and comprehensive understanding of the disclosure of the present invention, and not to limit the scope of protection of the present invention.

[0072] The above embodiments are not an exhaustive list based on the present invention, and there may be many other embodiments not listed. Any substitutions and improvements made without departing from the concept of the present invention are within the protection scope of the present invention.

Claims

1. A method for coupling a graphene-enhanced CO2 heat pump with a boiler for heating, characterized in that, include: Collect the supply and return water temperatures of the centralized hot water heating network, and construct a heat source feature library based on the correlation analysis of the graphene-enhanced heat exchange characteristics on the CO2 heat pump side based on the supply and return water temperatures. Based on the heat source feature library, the heat pump-boiler coupled heating zone is identified. The graphene enhancement effect evaluation parameters are extracted from the supply water temperature and the return water temperature to generate a temperature compensation coefficient. The heating load of the heat pump-boiler coupled heating zone is adjusted using the temperature compensation coefficient to form a dynamic coupled heating zone. Heat source matching is performed on the dynamically coupled heating zone to form a heat source allocation result. Based on the heat source allocation result, the dominant heat source type is identified. The graphene-enhanced heat exchange performance of the dominant heat source type is mapped to obtain the heating load response time. The load response level is evaluated based on the heating load response time to generate graded heat source allocation parameters. The start-stop delay analysis of the graded heat source allocation parameters is used to determine the priority heat source to be started. The switching interval time of the priority heat source is detected. Based on the switching interval time, the graphene rapid heat exchange response characteristics are obtained to adjust the heat source priority and form a heating action sequence. A heat pump-boiler coordinated heating strategy is constructed by combining the heat source allocation results with the heating action sequence, and a hot water centralized heating command is output based on the heat pump-boiler coordinated heating strategy.

2. The method according to claim 1, characterized in that, The method of constructing a heat source feature library based on the correlation analysis of the graphene-enhanced heat transfer characteristics on the CO2 heat pump side using the supply water temperature and the return water temperature includes: Heat source characteristic parameters are obtained by extracting temperature curve features from the supply water temperature and the return water temperature; Based on the heat source characteristic parameters, CO2 transcritical cycle temperature slip identification is performed to obtain the temperature slip. Correlation analysis of graphene-enhanced heat transfer characteristics is performed based on the temperature slip to form correlation characteristic parameters; A heat source feature library is constructed using the associated feature parameters.

3. The method according to claim 1, characterized in that, The step of extracting graphene enhancement effect evaluation parameters from the supply water temperature and the return water temperature to generate a temperature compensation coefficient includes: The supply water temperature and the return water temperature are used to identify the supercritical state of CO2 and obtain a state identifier sequence. The state offset is obtained by extracting supercritical operating state feature parameters from the state identifier sequence. The graphene enhancement effect is mapped and the abrupt change point of the enhancement effect is identified based on the state offset. A temperature compensation coefficient is generated based on the distribution density of the abrupt change points in the enhancement effect.

4. The method according to claim 1, characterized in that, The process of generating graded heat source allocation parameters based on the load response level assessment using the heating load response time includes: Historical statistics were performed on the heating load response time to obtain the response time change sequence; Based on the response time change sequence, perform load response rate analysis to obtain the response rate level; The response speed levels are mapped to form load response levels; Based on the load response level, a graded heat source allocation parameter is generated by assigning graded weights.

5. The method according to claim 1, characterized in that, The step of performing start-stop delay analysis on the graded heat source allocation parameters to determine the priority heat source to start includes: The time delay difference is extracted from the graded heat source allocation parameters to obtain the time delay gradient sequence; The coordination degree is obtained by evaluating the time delay uniformity based on the time delay gradient sequence. Anomaly detection is performed on the coordination degree to identify insufficient coordination of heat sources; The delay configuration is optimized by addressing the insufficient coordination of heat sources to determine the priority for starting the heat source.

6. The method according to claim 1, characterized in that, The step of adjusting the heat source priority to form a heating action sequence based on the graphene rapid heat exchange response characteristics obtained from the switching interval time includes: Obtain the switching frequency distribution characteristics of the switching interval time; Based on the switching frequency distribution characteristics, the rapid heat exchange response characteristics of graphene are extracted to obtain the response rate coefficient. The response rate coefficient is thresholded to determine the availability of the heat source. Based on the heat source availability determination results, the heat source priority is adjusted to form a heating action sequence.

7. The method according to claim 1, characterized in that, The method of constructing a heat pump-boiler coordinated heating strategy by combining the heat source allocation results with the heating action sequence includes: The parallel operation mode is identified by performing parallel operation mode identification on the heat source allocation results to obtain the parallel heating identifier; Based on the parallel heating identifier, a flow demand analysis is performed on the heating action sequence to determine the heat pump flow allocation ratio and the boiler flow allocation ratio; A flow allocation scheme is formed by performing dynamic matching analysis between the heat pump flow allocation ratio and the boiler flow allocation ratio. A heat pump-boiler coordinated heating strategy is constructed based on the aforementioned flow distribution scheme.

8. The method according to claim 3, characterized in that, The step of extracting supercritical operating state feature parameters and obtaining state offset using the state identifier sequence includes: Obtain the key points of the supercritical state in the state identifier sequence; State fluctuation characteristics are detected at key points in the supercritical state to generate state tracking parameters. The aforementioned state tracking parameters are used to perform temperature-related positioning to generate an offset candidate set; A state offset is generated based on the offset candidate set and the state tracking parameters.

9. The method according to claim 6, characterized in that, The step of thresholding the response rate coefficient to form a heat source availability determination result includes: Perform feature intensity quantization on the response rate coefficients to obtain response intensity values; The response level is determined by dividing the threshold interval based on the response intensity value; Temporal stability analysis is performed on the response levels to generate response stability characteristics; Availability confidence assessment is performed on the stability characteristics of the response and the response intensity value to form a heat source availability determination result.

10. A graphene-enhanced CO2 heat pump coupled with a boiler for heating, characterized in that, include: The data acquisition module is used to collect the supply water temperature and return water temperature of the hot water centralized heating network, and to construct a heat source feature library based on the correlation analysis of the graphene-enhanced heat exchange characteristics on the CO2 heat pump side based on the supply water temperature and the return water temperature. The interval identification module is used to identify the heat pump-boiler coupled heating interval based on the heat source feature library, extract the graphene enhancement effect evaluation parameters from the supply water temperature and the return water temperature to generate a temperature compensation coefficient, and use the temperature compensation coefficient to adjust the heating load of the heat pump-boiler coupled heating interval to form a dynamic coupled heating interval. The heat source allocation module is used to perform heat source matching on the dynamically coupled heating zone to form a heat source allocation result, identify the dominant heat source type based on the heat source allocation result, perform graphene-enhanced heat exchange performance mapping on the dominant heat source type to obtain the heating load response time, and evaluate the load response level based on the heating load response time to generate graded heat source allocation parameters. The action management module is used to perform start-stop delay analysis on the graded heat source allocation parameters to determine the priority heat source to start, detect the switching interval time of the priority heat source to start, and adjust the heat source priority based on the graphene rapid heat exchange response characteristics obtained from the switching interval time to form a heating action sequence. The strategy execution module is used to construct a heat pump-boiler coordinated heating strategy by combining the heat source allocation results with the heating action sequence, and output a hot water centralized heating command based on the heat pump-boiler coordinated heating strategy.

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