Air source heat pump control method and device for magnetic levitation refrigeration compressor

Abstract

This invention discloses an air-source heat pump control method and device for a magnetic levitation refrigeration compressor. The method involves constructing a temperature response curve and a load demand curve, performing a correlation assessment to generate a regulation deviation mapping, detecting out-of-tolerance mismatch windows, and extracting load mutation characteristics to determine the regulation cycle. Within the regulation cycle, overlapping sections are located, a refrigerant flow distribution grid is established to identify collaborative constraints, resource conflict identification is performed and conflict locations are marked, a comprehensive weight is generated, and the compressor's operating sequence is reorganized by arranging the weights in descending order into a scheduling table. A preset regulation capacity is fused with the temperature response curve to form an adjustable power range, and insufficient margin areas are identified and margin compensation parameters are generated to establish dynamic regulation limits. Based on these dynamic regulation limits, ambient temperature changes are time-series decomposed to generate rapid and gradual change components, which are then used to generate compressor speed control sequences and heat exchanger regulation sequences, which are then fused to establish control commands. This achieves precise supply-demand matching, resource conflict resolution, and dynamic response optimization.

Description

Technical Field

[0001] This invention relates to the field of air conditioning and refrigeration technology, and in particular to a method and apparatus for controlling an air source heat pump of a magnetic levitation refrigeration compressor. Background Technology

[0002] Air source heat pumps, as highly efficient and clean heating and cooling equipment, have been widely used in building energy conservation. Magnetic levitation compressors, with their frictionless bearing technology, offer significant advantages such as fast speed regulation response and high energy efficiency ratio. However, air source heat pumps face challenges in actual operation, including drastic fluctuations in ambient temperature, rapid changes in indoor load demand, and difficulties in the coordinated adjustment of multiple components. Traditional control methods mainly rely on feedback control of single parameters, lacking in-depth analysis of the patterns of ambient temperature changes, making it difficult to accurately predict the evolution of load demand, and failing to achieve precise coordinated control among multiple components such as compressors, heat exchangers, and water pumps.

[0003] Especially in winter heating scenarios, indoor load demand exhibits a bimodal characteristic during the morning and evening hours, while ambient temperature changes are asynchronous with load demand, making it difficult for traditional control methods to achieve precise supply-demand matching. Furthermore, although magnetic levitation compressors possess rapid adjustment capabilities, their actual acceleration is limited under extreme low-temperature conditions due to heat exchanger capacity constraints and refrigerant flow limitations. Traditional methods lack mechanisms for anticipating and compensating for these dynamic constraints. Therefore, a method is urgently needed to address at least one of the aforementioned problems. Summary of the Invention

[0004] This invention provides a control method and device for an air source heat pump using a magnetic levitation refrigeration compressor. The method aims to perform correlation assessment by constructing temperature response curves and load demand curves, identifying out-of-tolerance mismatch windows and extracting load mutation characteristics, locating overlapping adjustment actions and identifying resource conflicts, establishing a scheduling sequence table and reorganizing the compressor's operating sequence, identifying areas with insufficient margin and generating margin compensation parameters, and implementing time-series decomposition of ambient temperature changes to establish rapid and gradual change components. Finally, these components are integrated to form a multi-component collaborative control command, providing a high-precision, high-efficiency, and high-stability operation control scheme for the magnetic levitation air source heat pump.

[0005] The first aspect of this invention proposes an air-source heat pump control method for a magnetic levitation refrigeration compressor, comprising the following steps:

[0006] Monitor changes in ambient temperature and indoor load demand, construct a temperature response curve from the changes in ambient temperature, construct a load demand curve from the indoor load demand, and perform a correlation assessment on the temperature response curve and the load demand curve to generate a regulation deviation mapping;

[0007] In the adjustment deviation mapping, the out-of-range mismatch window is detected according to the deviation amplitude, the load mutation characteristics are extracted from the out-of-range mismatch window to generate pre-adjustment parameters, and the adjustment period is determined based on the mismatch window and the pre-adjustment parameters.

[0008] In the adjustment cycle, the overlapping section is located, resource conflict identification is performed for the overlapping section to establish conflict location, the conflict location is used to prioritize and form a scheduling sorting table, and the compressor running sequence is reorganized according to the scheduling sorting table.

[0009] According to the compressor's operating sequence, the adjustment capacity is preset in each time sequence. The adjustment capacity is integrated with the temperature response curve to form an adjustable power range. The margin insufficient area is identified from the adjustable power range to generate margin compensation parameters. The dynamic adjustment limit is established based on the margin compensation parameters.

[0010] Based on the dynamic adjustment limit, the ambient temperature change is split into a time sequence to establish abrupt change components and gradual change components. The abrupt change components are associated with the compressor operation sequence to generate a compressor speed control sequence. The gradual change components are adapted with the temperature response curve to generate a heat exchanger adjustment sequence. Control commands are established by fusing the compressor speed control sequence and the heat exchanger adjustment sequence.

[0011] A second aspect of the present invention provides an air source heat pump control device for a magnetic levitation refrigeration compressor, comprising:

[0012] The deviation mapping module is used to monitor changes in ambient temperature and indoor load demand, construct a temperature response curve from the changes in ambient temperature, construct a load demand curve from the indoor load demand, and perform a correlation assessment on the temperature response curve and the load demand curve to generate a regulation deviation mapping.

[0013] The early warning generation module is used to detect the out-of-range mismatch window based on the deviation amplitude in the adjustment deviation mapping, extract the load change characteristics from the out-of-range mismatch window to generate pre-adjustment parameters, and determine the adjustment period based on the mismatch window and the pre-adjustment parameters.

[0014] The conflict handling module is used to locate overlapping segments in the adjustment cycle, perform resource conflict identification to establish conflict locations for the overlapping segments, use the conflict locations to prioritize and arrange them into a scheduling sorting table, and reorganize the compressor running sequence according to the scheduling sorting table.

[0015] The capacity management module is used to preset the adjustment capacity in each time sequence according to the compressor's operating sequence, integrate the adjustment capacity with the temperature response curve to form an adjustable power range, identify the margin insufficient area from the adjustable power range to generate margin compensation parameters, and establish dynamic adjustment limits based on the margin compensation parameters.

[0016] The sequence generation module is used to perform time-series decomposition of the ambient temperature change based on the dynamic adjustment limit to establish abrupt change components and gradual change components, associate the abrupt change components with the compressor running sequence to generate a compressor speed control sequence, adapt the gradual change components with the temperature response curve to generate a heat exchanger adjustment sequence, and establish a control command by fusing the compressor speed control sequence and the heat exchanger adjustment sequence.

[0017] The beneficial effects of this invention are reflected in the following points: 1. By constructing a correlation assessment between the temperature response curve and the load demand curve to generate a regulation deviation mapping, detecting the out-of-tolerance mismatch window and extracting load mutation characteristics to generate pre-regulation parameters, accurate identification of supply-demand mismatch and early prediction of load changes are achieved. By relying on the peak demand moment to perform heat capacity effect compensation and deviation measurement to generate a regulation deviation mapping, and combining high-frequency mismatch demand and advance distribution to formulate an early warning response mode, preheating or auxiliary equipment can be started in advance before the load rises rapidly, improving the prediction accuracy of supply-demand matching and the forward-looking nature of regulation. 2. By locating overlapping sections in the regulation cycle and performing resource conflict identification, using conflict positions to prioritize and arrange the scheduling order table and reorganize the compressor running sequence, the resource competition problem during multi-component coordinated regulation is solved. By establishing a refrigerant flow distribution grid to identify cooperative constraints and constraint duration, conflict levels are determined and conflict locations are marked. The impact range of conflict locations is assessed to generate associated and independent impact domains. A global processing weight is established, and a comprehensive weight scheduling table is generated and arranged in descending order. Conflict locations are processed according to priority based on the scheduling table. Resource conflicts are eliminated by staggering adjustment times or changing adjustment amplitudes, reducing mutual interference when multiple actions are executed simultaneously. 3. Based on the compressor's operating sequence, a preset adjustment capacity is established to form an adjustable power range. Insufficient margin areas are identified, margin compensation parameters are generated, and dynamic adjustment limits are established. Based on the dynamic adjustment limits, ambient temperature changes are time-series decomposed to establish rapid and gradual changes, generating separate adjustment sequences. This achieves dynamic assessment and compensation of adjustment margin and separate response to ambient temperature changes. By conducting speed and acceleration limit tests, acceleration-limited sections are identified, forming areas with insufficient margin. Margin compensation parameters are generated based on the degree of acceleration limitation. Based on the dynamic adjustment limits, sensitivity assessment and decomposition thresholds for rapid and gradual changes are set to filter and separate ambient temperature changes. Rapid changes are handled by the compressor, and slow changes are handled by the heat exchanger, improving the system's response speed and adjustment accuracy to ambient temperature changes. Attached Figure Description

[0018] 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.

[0019] Unless otherwise specified or defined, the same reference numerals in different figures represent the same or similar technical features, and different reference numerals may be used to represent the same or similar technical features.

[0020] Figure 1 This is a schematic flowchart of an air source heat pump control method for a magnetic levitation refrigeration compressor according to the present invention.

[0021] Figure 2 This is a structural block diagram of an air source heat pump control device for a magnetic levitation refrigeration compressor according to the present invention. Detailed Implementation

[0022] 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.

[0023] 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.

[0024] 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.

[0025] The technical solutions of the embodiments of this application are described below.

[0026] like Figure 1 As shown, this embodiment of the invention provides an air source heat pump control method for a magnetic levitation refrigeration compressor, including the following steps S110-S150:

[0027] Step S110: Monitor changes in ambient temperature and indoor load demand, construct a temperature response curve from changes in ambient temperature, construct a load demand curve from indoor load demand, and perform a correlation assessment on the temperature response curve and the load demand curve to generate a regulation deviation mapping.

[0028] Specifically, the system monitors changes in ambient temperature and indoor load demand. An ambient temperature sensor is installed on the outdoor unit of the magnetic levitation refrigeration compressor air source heat pump to monitor the outdoor ambient temperature in real time. The ambient temperature sensor is a high-precision thermistor type with a measurement accuracy of ±0.1℃, and the sampling frequency is set to once every 5 minutes. The real-time ambient temperature value and corresponding timestamp information are recorded, with a continuous monitoring period set to 24 hours. The ambient temperature values ​​at adjacent times are compared to obtain the temperature change. In winter heating mode, the ambient temperature rises from -5℃ to 8℃ in the early morning and drops from 5℃ to -3℃ in the evening, forming a clear temperature change characteristic. The temperature values ​​and temperature changes at each monitoring time are compiled to form an ambient temperature change data sequence. A load monitoring device is installed in the indoor space to monitor indoor load demand in real time. The load monitoring device includes an indoor temperature sensor, a humidity sensor, and a occupant activity sensor. Based on the difference between the actual indoor temperature and the set temperature, the number of people indoors, and the intensity of their activities, the total indoor load demand is comprehensively determined. The indoor load demand value and timestamp at each monitoring time are recorded to form an indoor load demand data sequence.

[0029] A temperature response curve is constructed based on changes in ambient temperature. Temperature values ​​and timestamps are extracted from the ambient temperature change data sequence to establish a correlation between temperature and time. A curve showing the change in ambient temperature over time is plotted with time on the horizontal axis and ambient temperature on the vertical axis. The ambient temperature curve is smoothed to eliminate measurement noise and instantaneous fluctuations using a moving average filtering method with a sliding window width of 3 sampling points. The variation pattern of the smoothed temperature curve is analyzed to identify the temperature rise, steady-state, and fall phases. In magnetic levitation air source heat pump applications, the thermal inertia of buildings delays the response of indoor temperature to changes in ambient temperature; when the ambient temperature rises rapidly in the morning, the indoor temperature response exhibits a lag of 1 to 2 hours. The ambient temperature curve is correlated with the response characteristics of the heat pump to form a temperature response curve. The temperature response curve describes the variation of the heat pump's heating or cooling capacity under different ambient temperature conditions, recording the ambient temperature value and corresponding response capacity at each moment on the temperature response curve.

[0030] A load demand curve is constructed from indoor load demand. Load demand values ​​and timestamp information are extracted from the indoor load demand data sequence to establish a correspondence between load demand and time. Similarly, a curve showing the change in load demand over time is plotted. The load demand curve is validated to identify and remove outlier data points, including those with load demand fluctuations exceeding three times the normal fluctuation range. Missing load demand values ​​are interpolated using linear or spline interpolation methods, estimating the missing values ​​based on adjacent time periods. The fluctuation characteristics of the load demand curve are analyzed to identify peak and trough periods. In typical residential applications of magnetic levitation air source heat pumps, the load demand curve on winter weekdays exhibits a bimodal characteristic, with two load peaks occurring between 7:00 and 9:00 AM and between 6:00 and 10:00 PM, while load demand drops to troughs at noon and late at night. The load demand curve is discretized according to time resolution, with the discretization time interval consistent with the sampling interval of ambient temperature monitoring, forming a discrete sequence of load demand data points.

[0031] In some embodiments, the step of performing a correlation assessment on the temperature response curve and the load demand curve to generate a regulation deviation mapping includes: determining the peak demand time based on the load demand curve; performing heat capacity effect compensation on the temperature response curve for the peak demand time to obtain the compensated response capacity; measuring the deviation between the peak demand time and the compensated response capacity to form a deviation value; and expanding the deviation value along the time dimension to generate a regulation deviation mapping.

[0032] Peak demand times are determined based on the load demand curve. The load demand values ​​at each moment on the load demand curve are analyzed to identify the peak points. The peak point is the moment with the largest value on the load demand curve, corresponding to the period with the highest indoor energy demand. All data points on the load demand curve are traversed, and the magnitude of the load demand values ​​at each moment is compared to find the moment corresponding to the global maximum value. This timestamp is recorded as the primary peak demand moment. Local peaks on the load demand curve are analyzed to identify secondary peak demand times. Local peaks are the moments with the largest load demand values ​​within a certain time window. Although the value is less than the global maximum, it has peak characteristics within a local area. Criteria for determining local peaks are set, requiring that the load demand value of a local peak reaches at least 80% of the global maximum value and is the maximum value within a two-hour time window before and after it. In the residential heating scenario of magnetic levitation air source heat pumps, there are typically two peak demand times on winter weekdays: morning and evening. The morning peak occurs around 7:30 AM, with a load demand of 7.5 kW, and the evening peak occurs around 7:00 PM, with a load demand of 8.2 kW. Extract the time information and corresponding load demand values ​​for each peak demand moment to form a peak demand moment dataset.

[0033] In the temperature response curve, heat capacity effect compensation is applied for the peak demand moment to obtain the compensated response capacity. The response capacity value corresponding to the peak demand moment on the temperature response curve is extracted; this response capacity represents the nominal heating or cooling capacity of the heat pump under the ambient temperature conditions at that moment. The trend of ambient temperature change before and after the peak demand moment is analyzed to identify the impact of temperature change on the building's heat capacity. Buildings exhibit a heat capacity effect; when the ambient temperature changes rapidly, the building envelope stores or releases heat, causing a phase lag between changes in indoor load demand and changes in ambient temperature. During periods of rapid decrease in ambient temperature, the building envelope releases stored heat into the building, partially offsetting the effect of the decrease in ambient temperature, resulting in a slower increase in indoor load demand than the decrease in ambient temperature. The amount of heat capacity effect compensation at peak demand is determined. This compensation amount is based on the rate of change of ambient temperature and the building's thermal coefficient. The formula for calculating the compensation amount is Q_comp = C_thermal × (dT / dt) × Δt, where Q_comp is the heat capacity effect compensation amount, C_thermal is the building's thermal coefficient, dT / dt is the rate of change of ambient temperature at peak demand, and Δt is the time window influencing the heat capacity effect. The heat capacity effect compensation power is then added to the response capacity at peak demand to obtain the compensated response capacity. The formula for calculating the compensated response capacity is P_comp = P_response + Q_comp / Δt, where P_comp is the compensated response capacity, P_response is the original response capacity, and Q_comp / Δt is the heat capacity effect compensation power.

[0034] Deviation values ​​are generated by measuring the difference between peak demand times and compensated response capacity. Load demand and compensated response capacity values ​​are extracted for each peak demand time. The difference between the two values ​​is compared to determine the deviation value, calculated using the formula D = P_comp - P_demand, where D is the deviation value, P_comp is the compensated response capacity, and P_demand is the load demand at the peak demand time. The deviation values ​​for each peak demand time are calculated, creating a deviation value dataset. The positive and negative distribution characteristics of the deviation values ​​are analyzed, and the number of times with positive and negative deviations are counted. Positive deviations indicate surplus heat pump supply capacity, while negative deviations indicate insufficient heat pump supply capacity. The mean and standard deviation of the deviation values ​​are calculated; the mean reflects the overall supply-demand matching degree, and the standard deviation reflects the degree of deviation fluctuation. In a magnetic levitation air source heat pump, the ambient temperature at the peak demand time in the evening is -3℃, the compensated response capacity is 7.5 kW, while the actual load demand reaches 8.2 kW, resulting in a deviation value of -0.7 kW, accounting for 8.5% of the load demand. When the deviation value is negative, it is necessary to increase the compressor speed or start the auxiliary heating device to make up for the insufficient supply.

[0035] The deviation values ​​are expanded along the time dimension to generate a regulation deviation map. Taking peak demand moments as the core, the map extends forward and backward in the time direction to construct a deviation map covering the entire monitoring period. For non-peak demand moments, an interpolation method is used to estimate the deviation values. Cubic spline interpolation is selected, and a smooth deviation change curve is fitted based on the known deviation values ​​at peak demand moments to estimate the deviation values ​​at other moments on the curve. A continuous mapping relationship between deviation values ​​and time is generated, with time as the independent variable and deviation value as the dependent variable. A regulation deviation map is plotted, with time on the horizontal axis and deviation value on the vertical axis, showing the trend of deviation value changes within the monitoring period. The position of peak demand moments on the map is marked to highlight the deviation characteristics of key moments. Deviation threshold lines are set to divide the deviation values ​​into three regions: a supply surplus region, a supply-demand matching region, and a supply shortage region. The supply surplus region corresponds to periods where the deviation value is greater than the positive threshold, the supply shortage region corresponds to periods where the deviation value is less than the negative threshold, and the supply-demand matching region corresponds to periods where the deviation value is between the positive and negative thresholds. The spatial distribution characteristics of the regulation deviation map are analyzed to identify concentrated and dispersed deviation areas.

[0036] Step S120: In the adjustment deviation mapping, the out-of-tolerance mismatch window is detected according to the deviation amplitude. The load change characteristics are extracted from the out-of-tolerance mismatch window to generate the pre-adjustment parameter. The adjustment period is determined based on the mismatch window and the pre-adjustment parameter.

[0037] Specifically, the out-of-tolerance mismatch window is detected based on the deviation amplitude in the adjustment deviation mapping. The deviation values ​​at each time point in the adjustment deviation mapping are extracted, and the amplitude characteristics of the deviation values ​​are analyzed. An allowable range for the deviation amplitude is set, determined based on the heat pump's adjustment capability and user comfort requirements. When the deviation value at a certain time exceeds the allowable range, that time point is marked as a mismatch moment. The number and duration of consecutive mismatch moments are counted. When the duration of consecutive mismatch moments exceeds a set threshold, that time period is marked as an out-of-tolerance mismatch window. An out-of-tolerance mismatch window represents a time interval during which the heat pump's supply capacity and indoor load demand are mismatched for an extended period. In winter residential heating, after family members return home in the evening, indoor heating demand rises rapidly, but the outdoor ambient temperature drops rapidly. The heat pump's heating capacity decreases due to the ambient temperature, resulting in a significant gap between supply capacity and actual demand. This supply-demand imbalance, after lasting for a period, forms an out-of-tolerance mismatch window. The start time, end time, and duration of each out-of-tolerance mismatch window are recorded to form an out-of-tolerance mismatch window dataset. The appearance of an out-of-tolerance mismatch window indicates that the compressor operating parameters need to be adjusted or the operating mode needs to be changed.

[0038] Load mutation characteristics are extracted from the out-of-tolerance mismatch window to generate pre-regulation parameters. The variation patterns of deviation values ​​within each out-of-tolerance mismatch window are analyzed to identify load mutation characteristics. These characteristics include the mutation start time, mutation amplitude, and mutation rate. The mutation start time is the moment the out-of-tolerance mismatch window begins to appear; the mutation amplitude is the change in deviation value from the normal range to the out-of-tolerance state; and the mutation rate is the ratio of the mutation amplitude to the time of mutation occurrence. In a typical residential scenario, during the evening hours, with the turning on of lighting equipment, increased cooking activities, and the use of home entertainment facilities, indoor load demand rises sharply in a short period. The heat pump faces the challenge of rapidly switching from a low-load operating state to a high-load operating state; this rapid load change constitutes a typical mutation characteristic. Pre-regulation parameters are determined based on the load mutation characteristics. These parameters include the compressor speed adjustment amount and the adjustment timing. The compressor speed adjustment amount is determined based on the mutation amplitude; the larger the mutation amplitude, the larger the speed adjustment amount required. The adjustment timing is determined based on the mutation start time; adjustment needs to be initiated a certain time before the mutation occurs to avoid significant indoor temperature fluctuations.

[0039] In some embodiments, determining the adjustment period based on the mismatch window and the pre-adjustment parameter includes: statistically analyzing the frequency of occurrence of the out-of-tolerance mismatch window to form a high-frequency mismatch demand; performing response delay analysis on the pre-adjustment parameter to form an advance distribution; formulating an early warning response mode based on the high-frequency mismatch demand and the advance distribution; and determining the adjustment period based on the early warning response mode.

[0040] Frequency statistics of mismatch windows are used to identify high-frequency mismatch demands. The occurrence frequency of each mismatch window in different time periods is counted, and the temporal distribution pattern of the mismatch windows is analyzed. The 24-hour period is divided into multiple time periods, each one hour long, and the frequency of mismatch windows within each time period is counted. Time periods with higher frequency of occurrence are marked as high-frequency mismatch periods, corresponding to periods where heat pump supply-demand mismatch problems frequently occur. In typical residential heating, the demand for hot water and heating is concentrated in the morning as family members get up, wash, and prepare breakfast. The demand for heating is again concentrated in the evening as people return home from get off work, prepare dinner, and use hot water for showers. Mismatch windows repeatedly occur in these two periods and are marked as high-frequency mismatch periods. The load demand characteristics corresponding to high-frequency mismatch periods are analyzed to identify the causes of high-frequency mismatch. High-frequency mismatch demand includes the time information, frequency of occurrence, and corresponding load demand characteristics of the high-frequency mismatch periods.

[0041] Response delay analysis is performed on pre-adjustment parameters to form an advance distribution. The time interval between the adjustment timing of each pre-adjustment parameter and the actual start time of the mismatch window is analyzed; this time interval is the response delay. The response delay reflects the time required for the heat pump to reach the target state from startup adjustment, and its length directly affects the timeliness and effectiveness of adjustment. The response delay corresponding to different adjustment amounts is statistically analyzed. Larger adjustment amounts usually require longer response delays because large-scale adjustments involve significant changes in compressor speed and refrigerant flow. In magnetic levitation compressor applications, small-scale speed adjustments can complete the response quickly, and the compressor operation transitions smoothly, while large-scale speed jumps require an acceleration process and a system stabilization process, resulting in a relatively long response delay. Based on the statistical results, the advance amount is determined. The advance amount is the pre-start time required to ensure that the heat pump completes adjustment before the start of the mismatch window. The advance amount T_advance is calculated using the formula T_advance = T_delay + T_buffer, where T_delay is the response delay, and T_buffer is the safety buffer time. The buffer time setting considers the uncertainty of the response delay and the error of load forecasting. The distribution characteristics of lead time under different adjustment scenarios are analyzed to form a lead time distribution. The lead time distribution describes the range and probability distribution of lead time corresponding to different adjustment needs. The dispersion of the distribution reflects the stability of the response delay. The more concentrated the distribution, the more stable the response delay and the better the predictability of the adjustment.

[0042] For example, the step of formulating an early warning response mode based on the high-frequency mismatch demand and the lead time distribution includes: determining a mismatch clustering region based on the high-frequency mismatch demand; determining a graded response delay tolerance based on the lead time distribution; matching and mapping the mismatch clustering region with the graded response delay tolerance to generate graded response triggering conditions; and formulating an early warning response mode based on the graded response triggering conditions.

[0043] Mismatch clusters are identified based on high-frequency mismatch demand. The temporal location of each high-frequency mismatch period is analyzed to identify temporally close mismatch periods. Multiple high-frequency mismatch periods with short time intervals are merged into a single mismatch cluster, representing the time range in which mismatch issues occur concentratedly. In typical residential heating, multiple peak heating times in the morning are close together. Heating demand from waking up, washing, preparing breakfast, to leaving home, while slightly different, is concentrated in the morning, forming a morning mismatch cluster. A similar situation occurs in the evening, with concentrated demand for lighting, cooking, hot water, and indoor heating after returning home from get off work, forming an evening mismatch cluster. The total number of mismatch windows and the average mismatch magnitude within each cluster are statistically analyzed. The average mismatch magnitude reflects the severity of the supply-demand mismatch in that area. Mismatch clusters are classified into three levels based on the average mismatch magnitude: mild, moderate, and severe mismatch areas. Mild mismatch areas correspond to areas with small mismatch magnitude and short duration, moderate mismatch areas correspond to areas with moderate mismatch magnitude and medium duration, and severe mismatch areas correspond to areas with large mismatch magnitude and long duration.

[0044] A graded response delay tolerance is determined based on the lead distribution. The numerical range and distribution characteristics of the lead are analyzed to identify the lead intervals corresponding to different adjustment intensities. The lead is divided into three levels according to the adjustment intensity: short lead for small-amplitude adjustments, moderate lead for medium-amplitude adjustments, and long lead for large-amplitude adjustments. A response delay tolerance is set for each level, representing the longest allowable response time for that level of adjustment. The response delay tolerance is set shorter for small-amplitude adjustments, moderate for medium-amplitude adjustments, and longer for large-amplitude adjustments. The setting of the response delay tolerance needs to balance the timeliness of adjustment and the safe operation of the compressor. Too short a tolerance may lead to frequent large-amplitude adjustments, increasing mechanical wear; too long a tolerance may lead to adjustment lag, affecting user comfort. In practical applications, magnetic levitation compressors, due to their frictionless bearing technology, offer more flexible and rapid speed adjustment. Compared to traditional compressors, they can be set with shorter response delay tolerances, improving the heat pump's ability to follow load changes. The three levels of response latency tolerance are stored in a structured manner to form hierarchical response latency tolerance data.

[0045] A tiered response trigger condition is generated by mapping mismatch clusters to tiered response delay tolerances. Mismatch level information and tiered response delay tolerance data for each mismatch cluster are extracted, and a correspondence is established between them. Mild mismatch areas are matched with response delay tolerances for small-amplitude adjustments, moderate mismatch areas with those for medium-amplitude adjustments, and severe mismatch areas with those for large-amplitude adjustments. The response trigger time for each mismatch cluster is determined based on the matching relationship. The trigger time T_trigger is calculated as T_trigger = T_region - T_limit, where T_region is the start time of the mismatch cluster and T_limit is the corresponding response delay tolerance. In a typical residential scenario, mismatch clusters in the evening usually correspond to a moderate mismatch level. The compressor needs to be pre-adjusted some time before family members return home to ensure the indoor temperature reaches a comfortable level upon arrival. Tiered response trigger conditions are established, comprising three elements: mismatch level, trigger time, and adjustment intensity. These three elements together define the activation rules for the warning response. The generation of graded response trigger conditions enables precise matching between mismatch characteristics and regulation strategies. Different levels of mismatch correspond to different intensity of response, avoiding a "one-size-fits-all" regulation mode and improving the targeting and effectiveness of regulation.

[0046] A warning response mode is established based on graded response trigger conditions. The trigger time, mismatch level, and adjustment intensity information from the graded response trigger conditions are extracted to construct the basic framework of the warning response mode. For a mild mismatch level, a mild warning response mode is established, including small-amplitude speed adjustment and short-duration adjustment. For a moderate mismatch level, a moderate warning response mode is established, including moderate-amplitude speed adjustment and moderate-duration adjustment. For a severe mismatch level, a severe warning response mode is established, including large-amplitude speed adjustment and longer-duration adjustment. A warning signal sending mechanism is set in each warning response mode to send a warning signal to the controller when the trigger time arrives, initiating the corresponding adjustment action. The warning signal includes information such as the warning level, adjustment direction, and adjustment amplitude. In a typical residential scenario, the moderate warning response mode is triggered in the evening. After receiving the warning signal, the heat pump controller gradually increases the compressor speed, thereby enhancing the heating capacity. When family members return home, the indoor temperature has already reached the preset comfortable temperature, avoiding a drop in indoor temperature due to a sudden increase in load.

[0047] The adjustment cycle is determined based on the mismatch window and pre-adjustment parameters. The temporal distribution pattern of the mismatch window is analyzed to identify its periodic characteristics. On typical workdays, family members' routines are relatively fixed, with significant load peaks occurring during morning wake-up and evening return home, resulting in a regular daily periodicity for the mismatch window. On rest days, work schedules are more flexible, and the occurrence of the mismatch window is more dispersed, with less pronounced periodicity. Based on the periodicity of the mismatch window and the adjustment timing information in the pre-adjustment parameters, the adjustment cycle is determined. The adjustment cycle represents the time interval and timing arrangement for compressor parameter adjustments; its setting needs to balance adjustment frequency and effect. For scenarios with a clear daily periodicity, a 24-hour adjustment cycle is set, with compressor parameter adjustments performed at key times each day, determined by the adjustment timing in the pre-adjustment parameters. For scenarios with less pronounced periodicity, such as commercial buildings or industrial plants, where personnel activity and equipment use are more random, an adaptive adjustment cycle is adopted. Adjustment is dynamically triggered based on real-time monitored deviation values, and the adjustment action is initiated immediately when the deviation value exceeds a set threshold. The determination of the adjustment cycle combines fixed-cycle adjustment with adaptive adjustment, which not only utilizes the regularity of load changes to improve adjustment efficiency, but also retains the flexibility to deal with sudden load changes.

[0048] Step S130: Locate the overlapping section in the adjustment cycle, perform resource conflict identification for the overlapping section to establish conflict location, use the conflict location to prioritize and arrange to form a scheduling order table, and reorganize the compressor running sequence according to the scheduling order table.

[0049] Specifically, overlapping sections are located within the adjustment cycle. Adjustment action information for each moment within the adjustment cycle is extracted, and the temporal distribution characteristics of these actions are analyzed. Adjustment actions include various control operations such as compressor speed regulation, water pump flow regulation, and electronic expansion valve opening regulation. When the execution times of multiple adjustment actions overlap, overlapping sections are formed. Overlapping sections represent time intervals in which multiple adjustment actions occur simultaneously, requiring various components of the heat pump to respond to multiple control commands concurrently. The causes of overlapping sections are analyzed, primarily including the simultaneous triggering of multiple adjustment demands due to rapid load changes, and the overlapping of adjustment processes due to differences in response delays of different components. In residential heating during winter, hot water demand and heating demand occur simultaneously during the morning wake-up period. The compressor needs to rapidly increase its speed to increase heating capacity, the water pump needs to synchronously increase its flow rate to ensure timely heat delivery to the terminals, and the electronic expansion valve needs to adjust its opening to match the new operating conditions. The execution times of these three adjustment actions overlap. The start and end times of all overlapping sections within the adjustment cycle are identified, and the duration of each overlapping section reflects the length of time multiple adjustment actions coexist. The location results of the overlapping sections clearly show the overlapping distribution of adjustment actions on the time axis.

[0050] In some embodiments, the step of identifying and establishing conflict locations for resource conflict identification in the overlapping section includes: establishing a refrigerant flow distribution grid within the overlapping section; identifying the degree of cooperation and the duration of the constraint between the compressor and the heat exchanger through the refrigerant flow distribution grid; determining the conflict level based on the correlation between the degree of cooperation and the duration of the constraint; and marking the conflict location according to the conflict level.

[0051] A refrigerant flow distribution grid is established within the overlapping section. The time range information of the overlapping section is extracted, and the section is divided into multiple time units according to the time dimension. Each time unit corresponds to a discrete time point, and the granularity of the time unit division is determined based on the response speed of the adjustment action. Spatially, the refrigerant circulation loop of the heat pump is divided into multiple flow nodes, including key locations such as the compressor outlet, condenser inlet, condenser outlet, expansion valve inlet, evaporator inlet, and evaporator outlet. A two-dimensional grid structure of time units and flow nodes is established, where each cell represents the refrigerant flow state at a specific location at a specific time. In magnetic levitation compressor applications, the refrigerant flow distribution grid can clearly display the flow changes at different times and locations within the overlapping section. When compressor speed adjustment and expansion valve opening adjustment are performed simultaneously, the rate and magnitude of flow change at different nodes in the grid differ, reflecting the synergistic characteristics between the adjustment actions. Each cell in the grid stores state parameters such as refrigerant mass flow rate, pressure, and temperature at the corresponding time and location. The rows of the grid represent the time series, and the columns represent the spatial location series. The entire grid forms a complete mapping of refrigerant flow rate in both time and space.

[0052] For example, identifying the degree of cooperation and the duration of the constraint between the compressor and the heat exchanger through the refrigerant flow distribution grid includes: locating flow competition nodes in the refrigerant flow distribution grid; determining the refrigerant flow utilization rate based on the flow competition nodes; performing thermal inertia transfer analysis on the heat exchanger side based on the refrigerant flow utilization rate to generate a thermal inertia influence map; and converging the thermal inertia influence map to the constraint convergence point to form the degree of cooperation and the duration of the constraint.

[0053] Locate flow competition nodes in the refrigerant flow distribution grid. Extract flow data from each flow node in the grid and analyze the flow demand and supply at each node. Flow competition nodes are locations where there is a supply-demand imbalance, with multiple adjustments competing for refrigerant flow. Identifying flow competition nodes involves comparing the upstream flow supply capacity with the downstream flow demand capacity; when the supply capacity cannot meet the demand capacity, the node becomes a flow competition node. In magnetic levitation compressor heat pumps, the node between the compressor outlet and the condenser inlet is often a critical location for flow competition. When the compressor speed increases rapidly, the refrigerant circulation flow increases rapidly, but the condenser's heat exchange area is fixed, and the rate of increase in heat exchange capacity is slower than the rate of increase in flow, making this node a flow competition node. Nodes before and after the expansion valve are also common flow competition locations. Adjusting the expansion valve opening directly affects refrigerant flow distribution; when the expansion valve opening contracts, downstream flow is limited while upstream flow may still be increasing. The location of traffic contention nodes is achieved by traversing all nodes in the grid and calculating the traffic supply-demand difference for each node. Nodes with a positive supply-demand difference are marked as potential contention nodes. The final traffic contention nodes are determined after further analysis of their time duration.

[0054] The refrigerant flow utilization rate is determined based on the flow competition nodes. Flow supply and demand data for the flow competition nodes are extracted to calculate the flow utilization rate. The formula for the flow utilization rate η is η = Q_actual / Q_demand, where Q_actual is the actual supply flow of the node, and Q_demand is the demand flow of the node. The flow utilization rate reflects the degree to which the flow supply meets the demand; a utilization rate of 1 indicates that the supply fully meets the demand, a utilization rate less than 1 indicates insufficient supply, and a utilization rate greater than 1 indicates excessive supply. The trend of the flow utilization rate over time is analyzed; the dynamic changes in the flow utilization rate reflect the evolution of flow competition. Under low-temperature start-up conditions in winter, the flow demand on the evaporator side increases when the compressor first accelerates, but due to the limited heat exchange capacity of the evaporator in the low-temperature environment, the flow utilization rate remains below 1, indicating insufficient refrigerant supply on the evaporator side. The time series curve of the flow utilization rate exhibits obvious dynamic characteristics. In the initial stage of the overlapping section, the flow utilization rate may decrease rapidly, and then gradually recover. The fluctuation amplitude and recovery rate of the curve reflect the intensity and speed of relief of flow competition.

[0055] A thermal inertia influence diagram is generated by analyzing the heat inertia transfer from the refrigerant flow rate utilization rate to the heat exchanger side. Flow rate utilization rate data for each competing flow node is extracted to analyze its impact on the heat transfer process of the heat exchanger. The thermal inertia of a heat exchanger refers to the slowing effect of its heat capacity on the heat transfer response speed; the greater the thermal inertia, the slower the heat exchanger responds to changes in flow rate. A transfer model between flow rate utilization rate and thermal inertia is established, describing how changes in flow rate utilization rate affect the temperature distribution and heat transfer capacity of the heat exchanger through refrigerant flow. When the flow rate utilization rate decreases, the refrigerant flow through the heat exchanger decreases, the internal temperature distribution of the heat exchanger changes, and the heat transfer capacity decreases. This effect gradually spreads to all parts of the heat exchanger over time. A thermal inertia influence diagram is plotted, showing the transfer path and range of influence of changes in flow rate utilization rate within the heat exchanger. In the condenser, a decrease in flow rate utilization rate first affects the heat transfer in the inlet region, and then the effect gradually spreads to the outlet region. The entire transfer process is slowed by the condenser's heat capacity, and the transfer time depends on the size and material properties of the condenser. The thermal inertia effect diagram is presented in the form of a contour plot. The horizontal axis represents the length and position of the heat exchanger, and the vertical axis represents time. The values ​​of the contour lines represent the relative change in heat transfer capacity. The denser the contour lines, the more drastic the change in heat transfer capacity.

[0056] The thermal inertia influence map is converged to the constraint convergence point to form the cooperative constraint degree and constraint duration. Influence intensity data at each location in the thermal inertia influence map are extracted, and the spatial distribution characteristics of the influence are analyzed. The constraint convergence point refers to the location where the thermal inertia influence is most concentrated; at these locations, the impact of flow rate changes on heat transfer capacity is most significant. The method for identifying constraint convergence points is to find the extreme value locations of influence intensity; typically, constraint convergence points are located in the critical heat transfer area of ​​the heat exchanger. The constraint strength at the constraint convergence point is calculated; the constraint strength reflects the degree to which flow rate changes restrict heat transfer capacity. The greater the constraint strength, the greater the difficulty of coordination between the compressor and the heat exchanger. The constraint strength is the cooperative constraint degree, and its numerical value quantifies the difficulty of the compressor and heat exchanger working together. The temporal duration characteristics of the constraint strength at the constraint convergence point are analyzed; the length of time from the initial appearance of the constraint to its disappearance is the constraint duration. During the rapid adjustment process of the magnetic levitation compressor, the refrigerant flow rate increases rapidly after the compressor speed increases. However, due to thermal inertia, the condenser needs some time to upgrade its heat transfer capacity to match the new flow rate. During this period, the cooperative constraint degree at the constraint convergence point remains at a high level, and the constraint duration may reach several minutes. The constraint convergence point is identified by performing gradient analysis on the thermal inertia influence map. The location with the largest influence intensity gradient is the constraint convergence point, and the peak constraint intensity at this point is used as the quantitative value of the cooperative constraint degree.

[0057] Conflict levels are determined based on the correlation between the degree of synergistic constraint and the duration of constraint. The numerical magnitudes of synergistic constraint and the duration of constraint, as well as their interrelationship, are analyzed. High synergistic constraint and long duration correspond to high-level conflicts. Conflict level determination rules are established, based on the combination of synergistic constraint and duration of constraint. Conflict levels are divided into three levels: Level 1 conflict corresponds to high synergistic constraint and long duration of constraint; Level 2 conflict corresponds to medium synergistic constraint or moderate duration of constraint; and Level 3 conflict corresponds to low synergistic constraint and short duration of constraint. Level 1 conflicts have the greatest impact on heat pump operation and require priority handling, while Level 3 conflicts have a smaller impact and can be handled later. In typical heating scenarios, during the rapid load increase in the morning, the compressor needs to significantly increase its speed, but the evaporator cannot keep up due to the low ambient temperature. In this case, the synergistic constraint is high and the duration is long, which is determined to be a Level 1 conflict. The numerical expression of conflict levels uses level coefficients: Level 1 conflict has a level coefficient of 3, Level 2 conflict has a level coefficient of 2, and Level 3 conflict has a level coefficient of 1. The level coefficient directly participates in the weight calculation in the priority arrangement.

[0058] Conflict locations are marked according to their conflict levels. Conflict level information for each conflict is extracted, and the conflict level is associated with its corresponding time and spatial location. The locations of each conflict are marked in the refrigerant flow distribution grid, using different colors or symbols to distinguish different conflict levels. Level 1 conflicts are marked with dark colors, indicating that they require special attention, while Level 3 conflicts are marked with light colors, indicating that their impact is relatively small. The marked conflict locations include time and spatial information. The time information indicates the time and duration of the conflict, and the spatial information indicates the components and resource types involved in the conflict. The marking of conflict locations uses a structured data format, with each conflict location corresponding to a data entry containing a conflict identifier code, time coordinates, spatial coordinates, conflict level, a list of involved components, and a resource type identifier. In the overlapping section of the magnetic levitation compressor heat pump, the distribution of marked conflict locations shows obvious spatiotemporal clustering characteristics. Conflict locations during the morning load rise phase are densely distributed on the flow nodes between the compressor and evaporator. These markings visually demonstrate the areas of severe resource conflict.

[0059] In some embodiments, prioritizing the conflict locations to form a scheduling ranking table includes: assessing the impact range of the conflict locations to generate associated impact domains and independent impact domains; establishing global processing weights based on the associated impact domains; calibrating the independent impact domains using the global processing weights as a benchmark to generate comprehensive weights; and arranging the comprehensive weights in descending order to generate a scheduling ranking table.

[0060] The impact range assessment of conflict locations generates associated and independent impact domains. Conflict characteristic information is extracted from each conflict location, and the impact range of each conflict location on other parts of the heat pump is analyzed. Impact range assessment needs to consider the diffusion characteristics of the conflict in both time and space dimensions. In the time dimension, the conflict may affect the operating status at subsequent times; in the spatial dimension, the conflict may affect the performance of adjacent components. The affected objects of each conflict location are identified, including directly affected and indirectly affected components. When the affected objects of a conflict location overlap with those of other conflict locations, these conflict locations form an association, and their impact range belongs to the associated impact domain. The associated impact domain represents the area where multiple conflicts influence each other and require coordinated handling. When the affected objects of a conflict location are independent of other conflicts, its impact range belongs to the independent impact domain. In typical heat pump regulation processes, conflicts between the compressor and condenser, and conflicts between the compressor and evaporator, both affect the compressor speed setting. The affected objects of these two conflicts overlap, belonging to the associated impact domain. Conflicts between water pump flow regulation and terminal equipment typically only affect the water-side circulation and do not affect the refrigerant-side operation, belonging to the independent impact domain.

[0061] A global processing weight is established based on the associated influence domain. Information on all conflict locations within the associated influence domain is extracted, and the relationships between these conflict locations are analyzed. These relationships include causal relationships and coupling relationships. A causal relationship indicates that handling one conflict directly affects the state of another conflict, while a coupling relationship indicates that multiple conflicts need to be handled simultaneously to achieve the best effect. An influence topology map of the associated influence domain is constructed, showing the associated paths and directions of influence between conflict locations. The overall influence strength of the associated influence domain is calculated, taking into account the degree of influence and correlation of all conflicts within the domain. This overall influence strength is used as the basis for the global processing weight, which reflects the importance of the associated influence domain to the overall operation of the heat pump. A larger global processing weight indicates that the associated influence domain needs to be handled with higher priority. In magnetic levitation compressor heat pumps, multiple conflict locations on the refrigerant side typically constitute an associated influence domain. This domain has a high global processing weight because the refrigerant cycle directly determines the heating or cooling capacity of the heat pump, significantly impacting overall operation. The global processing weight is calculated using a weighted summation method. The conflict level coefficient of each conflict location within the domain is multiplied by the number of objects affected by the conflict, and the sum of all products constitutes the global processing weight value of the associated influence domain.

[0062] The comprehensive weight of independent influence domains is generated by calibrating them using the global processing weight as a benchmark. The influence intensity of each conflict location within an independent influence domain is extracted and compared with the global processing weight. A calibration rule is established: when the influence intensity of an independent influence domain is lower than the minimum global processing weight, its weight is adjusted to a certain proportion of the minimum global processing weight; when the influence intensity is higher than a certain global processing weight, its weight is set to a value between two global processing weights. For conflict locations in associated influence domains, their comprehensive weight directly adopts the global processing weight of that associated influence domain. For conflict locations in independent influence domains, the comprehensive weight W_total = α × W_independent × f(R), where W_independent is the influence intensity of the independent influence domain, α is the calibration coefficient, f(R) is the correlation correction function, and R is the indirect correlation degree between the independent and associated influence domains. The indirect correlation degree reflects the degree of indirect influence of the independent influence domain on the associated influence domain; the higher the indirect correlation degree, the higher the weight of the independent influence domain needs to be. In practical applications, although conflicts in water-side circulation belong to independent influence domains, when their influence intensity reaches a certain level, the calibrated comprehensive weight may be higher than the global processing weight of some associated influence domains. The proportional coefficient in the calibration rules is dynamically adjusted according to the heat pump's control strategy. When prioritizing heating capacity, the refrigerant-side conflict weight coefficient is higher, while when prioritizing energy efficiency, the water-side circulation conflict weight coefficient is appropriately increased.

[0063] A scheduling ranking table is generated by arranging the comprehensive weights in descending order. The comprehensive weight values ​​of all conflict locations are extracted and sorted from largest to smallest. The sorted list of conflict locations forms the basic framework of the scheduling ranking table, with higher-ranked conflict locations having higher processing priority. Key information for each conflict location is marked in the scheduling ranking table, including conflict type, involved components, scope of impact, and recommended handling measures. Handling measures are determined based on the specific characteristics of the conflict and may include delaying the execution time of a regulation action, reducing the magnitude of a regulation action, or implementing a regulation action in stages. During the morning peak load period for residential heating in winter, the top priority in the scheduling ranking table is usually the coordination conflict between the compressor and evaporator, as this conflict directly affects the rate of increase in heating capacity. The next highest priority is the flow distribution conflict between the condenser and the water-side circulation, and finally, the heating distribution conflict between terminal equipment. The scheduling and sorting table is presented in tabular form. The columns of the table include the serial number, conflict location identifier, comprehensive weight value, conflict level, involved components, conflict type and handling measures. The rows of the table are arranged in descending order of comprehensive weight, and each row corresponds to a conflict location. The entire table fully presents the priority order of handling all conflicts within the overlapping section.

[0064] The compressor runtime sequence is reorganized according to the scheduling and sequencing table. Conflict handling sequence information is extracted from the table, and conflicting locations are processed sequentially from highest to lowest priority. For each conflicting location, the execution timing of the relevant component's adjustment actions is adjusted, eliminating resource conflicts by staggering adjustment times or changing adjustment amplitudes. For the highest priority conflicting location, the original adjustment timing remains unchanged, while the timing of other conflicting adjustment actions is adjusted. For lower priority conflicting locations, their adjustment actions are delayed or performed in stages to avoid resource competition with high-priority actions. The reorganized compressor runtime sequence ensures that each adjustment action is performed in an orderly manner, reducing control interference caused by resource conflicts. During the morning hours of winter heating, the reorganized runtime sequence first increases the compressor speed, then adjusts the water pump flow after the refrigerant flow has reached the required level, and finally fine-tunes the electronic expansion valve opening based on the actual heat exchange effect. This orderly adjustment avoids mutual interference when multiple actions are executed simultaneously, resulting in a smoother compressor speed change curve and improved operating efficiency.

[0065] Step S140: According to the compressor's operating sequence, preset the adjustment capacity in each time sequence, integrate the adjustment capacity with the temperature response curve to form an adjustable power range, identify the margin insufficient area from the adjustable power range to generate margin compensation parameters, and establish the dynamic adjustment limit based on the margin compensation parameters.

[0066] Specifically, the adjustment capacity is preset at each time step according to the compressor's operating sequence. The reconstructed compressor operating sequence information is extracted, and the execution time and direction of each adjustment action in the sequence are identified. The adjustment capacity represents the range of heating or cooling capacity adjustment that the compressor can achieve at a specific time. The size of the adjustment capacity depends on the compressor's speed adjustment range and the heat transfer capacity of the heat exchanger. For each adjustment time in the sequence, a preset adjustment capacity value is set based on the operating conditions at that time. Operating conditions include ambient temperature, indoor load demand, and current compressor speed, among other state parameters. In winter heating scenarios, the compressor needs to quickly increase from low to high speed during the morning load increase period, requiring a larger preset adjustment capacity due to the large load fluctuations and time constraints. During the relatively stable midday load period, the compressor only needs minor speed adjustments, resulting in a smaller preset adjustment capacity. The feasibility of the adjustment capacity at each time step is analyzed, ensuring that the adjustment capacity does not exceed the physical limits of the compressor and the heat transfer limits of the heat exchanger. Magnetic levitation compressors, due to their frictionless bearing technology, have a fast speed adjustment response and can achieve a larger adjustment capacity, but motor power limitations and refrigerant flow limitations still need to be considered. Assess the matching relationship between regulation capacity and system inertia. Rapid changes in regulation capacity require overcoming the inertia of refrigerant circulation and water-side circulation. The greater the inertia, the more limited the actual achievability of the regulation capacity.

[0067] The adjustable power range is constructed by fusing the regulating capacity and temperature response curve. The temperature response curve describes the impact of ambient temperature changes on the compressor's heating or cooling capacity, while the regulating capacity describes the compressor's active adjustment capability. The regulating capacity is superimposed on the temperature response curve, taking into account both positive and negative directions. When the regulating capacity is positive, it indicates that the compressor can further increase its heating capacity based on the temperature response curve; when the regulating capacity is negative, it indicates that the compressor can decrease its heating capacity based on the temperature response curve. The fused curve forms the upper and lower boundaries of the adjustable power range, with the upper boundary corresponding to the maximum heating capacity and the lower boundary corresponding to the minimum heating capacity. The adjustable power range represents the power adjustment range that the compressor can achieve at any given time, considering both the influence of ambient temperature and the active adjustment capability. In low-temperature winter conditions, the decrease in ambient temperature causes the temperature response curve to shift downwards overall. However, the magnetic levitation compressor can partially compensate for the adverse effects of ambient temperature by increasing its speed. Although the fused adjustable power range is generally low, it still maintains a certain adjustment range. Analyzing the temporal evolution characteristics of the adjustable power range identifies periods of range expansion and contraction. Range expansion indicates enhanced adjustment capability, while range contraction indicates limited adjustment capability.

[0068] In some embodiments, the step of identifying a margin-deficient region from the adjustable power range and generating margin compensation parameters includes: mapping the adjustable power range to a magnetic levitation compressor speed adjustment domain; performing a speed acceleration limit test within the speed adjustment domain to generate an acceleration constraint curve; identifying acceleration-limited sections from the acceleration constraint curve to form a margin-deficient region; and generating margin compensation parameters based on the degree of acceleration limitation in the margin-deficient region.

[0069] The adjustable power range is mapped to the speed regulation domain of the magnetic levitation compressor. The upper and lower boundary data of the adjustable power range are extracted, and the correspondence between power and compressor speed is analyzed. The compressor's heating or cooling capacity is approximately linearly related to its speed; the higher the speed, the greater the heating capacity. A mapping function from power to speed is established, determined based on the compressor's performance curve. The upper boundary of the adjustable power range is mapped to the upper speed limit curve, and the lower boundary to the lower speed limit curve. The area between the upper and lower limit curves is the speed regulation domain. The speed regulation domain describes the allowable speed regulation range of the compressor at various times; any speed value within the domain represents a feasible operating state. In winter heating scenarios, the upper boundary of the adjustable power range is higher during the morning load increase phase, and the corresponding upper speed limit curve is also higher, allowing the compressor to reach near its maximum speed. During the midday load stabilization phase, the adjustable power range narrows, and the speed regulation domain narrows accordingly, with the compressor adjusting within a medium speed range. The speed regulation domain is represented graphically in two dimensions, with time on the horizontal axis and speed on the vertical axis. The regulation domain is marked by a shaded area, and the width of the shaded area reflects the degree of freedom in speed regulation at various times. The mapping process also needs to consider the nonlinear correction of speed and power. In the extremely low and extremely high speed ranges, the compressor efficiency decreases, and the relationship between power and speed deviates from linearity. The mapping function needs to introduce a correction coefficient for correction. The establishment of the speed adjustment domain provides a clear test range for acceleration testing. Test activities are limited to the adjustment domain to ensure the rationality and representativeness of the test conditions.

[0070] Acceleration constraint curves are generated through speed acceleration limit tests within the speed regulation domain. Boundary information of the speed regulation domain is extracted to simulate the compressor's speed change process within the domain. Speed ​​acceleration represents the rate of change of compressor speed over time; the greater the acceleration, the faster the speed increases. The speed acceleration of a magnetic levitation compressor is limited by factors such as motor drive capability, magnetic bearing control stability, and refrigerant flow rate change rate. Speed ​​acceleration limit tests are conducted, selecting test points at different locations within the speed regulation domain. A maximum acceleration command is applied to each test point, and the compressor's actual acceleration response is observed. Under conditions of low ambient temperature and low evaporation pressure, the compressor's acceleration capability is limited by the rate of increase in refrigerant mass flow rate, resulting in an actual acceleration lower than under high-temperature conditions. At higher operating speeds, the motor power approaches its rated power, reducing the available acceleration power reserve and consequently decreasing the acceleration. Acceleration limit data from each test point are summarized to plot acceleration constraint curves. These curves describe the maximum allowable acceleration of the compressor at different speeds and operating conditions. The curves vary with speed and ambient temperature, with tighter acceleration constraints in the low-temperature, low-speed region and relatively looser constraints in the high-temperature, medium-speed region. The acceleration constraint curve is generated using a piecewise fitting method. The speed regulation domain is divided into multiple sub-regions, and the acceleration constraint curve is fitted separately in each sub-region. Finally, the curves of each sub-region are smoothly connected to form a complete constraint curve.

[0071] Acceleration-limited sections are identified from acceleration constraint curves to form insufficient margin regions. Data from the acceleration constraint curves is extracted, and the acceleration values ​​at each point on the curve are analyzed. An acceleration demand threshold is set, determined based on the load change rate and user comfort requirements. When the acceleration value of a certain segment on the acceleration constraint curve is lower than the acceleration demand threshold, that segment is marked as an acceleration-limited segment. An acceleration-limited segment indicates that the compressor cannot achieve the desired rapid speed increase, resulting in insufficient adjustment response speed. During the rapid load increase period in winter mornings, users expect the indoor temperature to rise quickly, corresponding to a higher acceleration demand threshold. However, at this time, the ambient temperature is very low, and the compressor's acceleration constraint curve is at a low level, with the acceleration constraint value lower than the demand threshold, forming an acceleration-limited segment. The range of the acceleration-limited segment on the time and speed axes is mapped back to the adjustable power range; the mapped area is the insufficient margin region. The insufficient margin region in the adjustable power range manifests as time periods and power segments with significantly insufficient adjustment capability. Identifying these regions provides clear targets for formulating compensation measures. The identification of acceleration-limited sections also needs to consider the duration factor. Only limited sections whose duration exceeds a set threshold are identified as true margin-deficient regions. Temporary acceleration limitations can be temporarily addressed through other adjustment methods without activating the compensation mechanism. The boundaries of the margin-deficient region are clearly defined in both time and power dimensions, and the boundary information fully describes the specific range and degree of insufficient adjustment capability.

[0072] Margin compensation parameters are generated based on the degree of acceleration limitation in the margin-deficient area. The acceleration limitation characteristics of the margin-deficient area are extracted, and the degree of acceleration limitation, D, is calculated using the formula D = (a_demand - a_actual) / a_demand, where a_demand is the acceleration demand threshold and a_actual is the actual acceleration constraint value. The larger the acceleration limitation value, the greater the gap in the compressor's regulation capacity, and the greater the required compensation magnitude. The compensation magnitude of the margin compensation parameters is determined based on the degree of acceleration limitation, and the compensation magnitude is directly proportional to the degree of acceleration limitation. The temporal distribution of the margin-deficient area is analyzed to identify the start time and duration of the margin-deficient area. The compensation timing is set to an advance amount before the start time of the margin-deficient area, and the advance amount is determined based on the start delay of the compensation measures. In typical residential heating, the degree of acceleration limitation in the margin-deficient area is higher in the early morning. The generated margin compensation parameters indicate that auxiliary heating equipment needs to be started before users get up, or the compressor needs to be started for preheating half an hour in advance. These compensation measures reduce the gap between the actual regulation capacity and demand. The margin compensation parameters also include a compensation method identifier, which indicates whether compensation is made through auxiliary equipment, early start compensation, or load distribution adjustment. Different compensation methods correspond to different execution strategies.

[0073] Dynamic adjustment limits are established based on margin compensation parameters. The compensation amplitude and timing information from these parameters are extracted to analyze their constraint effect on compressor operation. A large compensation amplitude indicates a severe deficiency in the compressor's adjustment capacity, requiring stricter dynamic adjustment limits to prevent overload operation. An early compensation timing indicates an impending margin deficiency, necessitating advance adjustment of the dynamic adjustment limits. The dynamic adjustment limits include an upper and lower speed adjustment limit, dynamically adjusted based on the margin compensation parameters. In areas with sufficient margin, the dynamic adjustment limits are set more leniently, allowing the compressor to adjust its speed within a wider range. In areas with insufficient margin, the dynamic adjustment limits narrow, limiting the compressor's speed adjustment amplitude to prevent operational instability due to over-adjustment. The established dynamic adjustment limits are expressed as a time series, with each data point corresponding to a specific moment and its corresponding upper and lower speed limits. In magnetic levitation compressor applications, real-time adjustment of the dynamic adjustment limits maximizes the compressor's adjustment capacity while ensuring operational safety. When the ambient temperature rises or load demand decreases, the margin deficiency disappears, the dynamic adjustment limits automatically widen, and the compressor returns to its normal adjustment range.

[0074] Step S150: Based on the dynamic adjustment limit, the ambient temperature change is split into a time sequence to establish a rapid change component and a gradual change component. The rapid change component is associated with the compressor running sequence to generate a compressor speed control sequence. The gradual change component is adapted with the temperature response curve to generate a heat exchanger adjustment sequence. The control command is established by merging the compressor speed control sequence and the heat exchanger adjustment sequence.

[0075] In some embodiments, the step of performing time-series decomposition on the ambient temperature change based on the dynamic adjustment limit to establish abrupt change components and gradual change components includes: evaluating the decomposition sensitivity based on the dynamic adjustment limit to determine a decomposition strategy, wherein the decomposition sensitivity includes the limit response rate, the rate of change of disturbance amplitude, and frequency stability; setting an abrupt-gradual separation threshold according to the decomposition strategy; and using the abrupt-gradual separation threshold to filter and separate the ambient temperature change to generate abrupt change components and gradual change components.

[0076] The splitting sensitivity is assessed and the splitting strategy is determined based on the dynamic adjustment limit. Time series data of the dynamic adjustment limit are extracted, and the temporal variation characteristics of the limit value are analyzed. The limit response rate represents the speed at which the dynamic adjustment limit adjusts with changes in operating conditions; a faster response rate indicates greater sensitivity to changes in operating conditions. The limit response rate is calculated by dividing the difference between limit values ​​at adjacent times by the time interval. During periods of sufficient margin, the dynamic adjustment limit changes slowly, resulting in a lower limit response rate; during periods of insufficient margin, the dynamic adjustment limit contracts significantly, resulting in a higher limit response rate. Time series data of ambient temperature changes are extracted, and the rate of change of disturbance amplitude is calculated. The rate of change of disturbance amplitude represents the speed of change in ambient temperature disturbance; a larger rate indicates more severe temperature fluctuations. The spectral characteristics of ambient temperature changes are analyzed, and frequency stability is assessed. Frequency stability indicates whether the dominant frequency components of temperature changes are stable; high stability indicates regular periodicity in temperature changes, while low stability indicates strong randomness in temperature changes. The splitting sensitivity is assessed by combining the limit response rate, the rate of change of disturbance amplitude, and frequency stability. Decomposition sensitivity reflects the matching relationship between the characteristics of ambient temperature changes and the response capability of the dynamic adjustment limit. High sensitivity indicates that temperature changes have a significant impact on the operation of the heat pump, requiring a refined decomposition strategy. Low sensitivity indicates that temperature changes have a smaller impact, allowing for a simplified decomposition strategy. The decomposition strategy is determined based on the decomposition sensitivity value, including parameter settings such as decomposition frequency, decomposition method, and decomposition accuracy. Under extreme winter weather conditions, with drastic ambient temperature fluctuations and a high dynamic adjustment limit response rate, the decomposition sensitivity assessment result is high, and a high-frequency decomposition and high-precision decomposition method are selected as the decomposition strategy.

[0077] Set rapid-gradient separation thresholds according to the splitting strategy. Extract the splitting frequency and splitting accuracy information from the splitting strategy to establish rules for setting the rapid-gradient separation thresholds. The rapid-gradient separation threshold is the critical criterion for distinguishing between rapid and gradual change components. The threshold setting needs to comprehensively consider the compressor's adjustment capability and the heat exchanger's response characteristics. Analyze the compressor's maximum speed change rate, which reflects the upper limit of the rate of change that the compressor can respond quickly. Analyze the heat exchanger's thermal inertia time constant, which reflects the heat exchanger's response delay characteristics to temperature changes. Compare the rate of change of ambient temperature with the compressor's maximum speed change rate. When the rate of change of temperature exceeds the range that the compressor can smoothly follow, the corresponding change is classified as a rapid change component. Compare the frequency of ambient temperature change with the reciprocal of the heat exchanger's thermal inertia time constant. When the frequency of temperature change is higher than the reciprocal of the time constant, the heat exchanger cannot follow the change in time, and the corresponding change is classified as a rapid change component. Set the values ​​for the rapid-gradient separation thresholds, which include two dimensions: the rate of change threshold and the frequency threshold. In magnetic levitation compressor heat pumps, the rate of change threshold is usually set to trigger a sudden change judgment when the rate of change of ambient temperature exceeds a certain critical value, and the frequency threshold is set to trigger a sudden change judgment when the temperature change period is shorter than a certain critical period.

[0078] A rapid-change and gradual-change components are generated by using a rapid-change / gradual-change separation threshold. Time-series data of ambient temperature changes and rapid-change / gradual-change separation threshold information are extracted, and the temperature change data is filtered point by point. The rate of change of ambient temperature at each time moment is calculated as the ratio of the temperature difference between adjacent time moments to the time interval. The rate of change at each time moment is compared with the rate of change threshold; data points with rates of change exceeding the threshold are marked as rapid-change points, and those below the threshold are marked as gradual-change points. Frequency domain analysis is performed on the ambient temperature change data to extract the frequency components of the temperature changes. Each frequency component is compared with a frequency threshold; components with frequencies above the threshold are classified as rapid-change bands, and those with frequencies below the threshold are classified as gradual-change bands. Filters are applied to separate the ambient temperature change data: a high-pass filter extracts the signal from the rapid-change band to form a rapid-change component, and a low-pass filter extracts the signal from the gradual-change band to form a gradual-change component. In the context of winter heating, the rapid temperature drop caused by the rapid intrusion of cold air in the early morning is selected as the abrupt change component, and the time series of this component shows a rapid decrease. On the other hand, the slow warming process from night to day is selected as the gradual change component, and the time series of this component shows a gradual increase.

[0079] This paper correlates rapid change components with compressor operating sequence to generate a compressor speed control sequence. Time series data of the rapid change components and compressor operating sequence information are extracted to analyze the matching relationship between the changing characteristics of the rapid change components and the compressor's adjustment capability. The rapid changes in rapid change components require the compressor to have rapid speed adjustment capability. The frictionless characteristics of the magnetic levitation compressor enable it to respond quickly to speed commands, making it suitable for handling rapid change components. Peak and trough moments in the rapid change components are identified; peaks correspond to moments when the ambient temperature rises rapidly, and troughs correspond to moments when the ambient temperature falls rapidly. In winter heating conditions, the evaporator's heat exchange capacity decreases when the ambient temperature drops rapidly, requiring the compressor to increase its speed and refrigerant circulation to compensate for the loss of heat exchange capacity. Based on the amplitude and rate of change of the rapid change components, the compressor speed adjustment amplitude and timing are determined. A mapping relationship between rapid change components and speed adjustment is established, considering the compressor's dynamic response characteristics and the physical constraints of speed adjustment. A compressor speed control sequence is generated, describing the trajectory of compressor speed change over time. Each data point in the sequence corresponds to a specific moment and the target speed at that moment. In a typical heating scenario, when cold air rapidly intrudes in the early morning, the abrupt change factor increases rapidly. The compressor speed control sequence generates a rapid speed-up command accordingly, and the compressor speed increases from medium speed to high speed within a few minutes. The heating capacity is rapidly enhanced to offset the adverse effects of the sudden drop in ambient temperature.

[0080] A heat exchanger regulation sequence is generated by adapting the gradual change component to the temperature response curve. Time-series data of the gradual change component and temperature response curve data are extracted to analyze the degree of matching between the variation pattern of the gradual change component and the temperature response curve. The slow variation characteristic of the gradual change component allows the heat exchanger to gradually adapt to temperature changes by adjusting heat exchange parameters, without requiring frequent compressor speed adjustments. The temperature response curve describes the relationship between ambient temperature and the heat pump's heat exchange capacity, changing with ambient temperature. Superimposing the gradual change component onto the temperature response curve reveals a curve reflecting the evolution trend of heat exchange capacity considering slow temperature changes. The regulation requirements of the heat exchanger are determined based on the superimposed curve, including heat exchanger fan speed regulation, water pump flow rate regulation, and electronic expansion valve opening regulation. In winter heating, during the slow cooling process from midnight to dawn, the heat exchange temperature difference on the evaporator side gradually increases. Appropriately increasing the evaporator fan speed can enhance convective heat exchange on the air side, partially offsetting the impact of the decrease in ambient temperature. A heat exchanger control sequence is generated, which describes the time-varying plan of various control parameters of the heat exchanger. The sequence includes multiple control parameters such as fan speed setpoint, water pump frequency setpoint, and expansion valve opening setpoint.

[0081] Control commands are established by fusing compressor speed control sequences and heat exchanger regulation sequences. Data from both sequences are extracted, and their temporal coordination is analyzed. The compressor speed control sequence primarily addresses rapid changes in ambient temperature, while the heat exchanger regulation sequence addresses slow changes; the two sequences are functionally complementary. Sequence fusion rules are established to ensure that compressor and heat exchanger regulation are coordinated in time, avoiding mutual interference. When the compressor speed control sequence indicates a rapid increase in speed, the heat exchanger regulation sequence synchronously adjusts the fan speed and water pump flow rate to ensure that the heat exchanger can promptly transfer the increased heat after the refrigerant flow increases. When the compressor speed control sequence indicates a stable speed, the heat exchanger regulation sequence can be finely adjusted to optimize heat exchange efficiency. A fused control command set is generated, containing multiple control variables such as compressor speed command, fan speed command, water pump frequency command, and expansion valve opening command. In magnetic levitation compressor heat pump applications, during the early morning load surge, the integrated control commands first activate the compressor to rapidly accelerate and cope with the sudden drop in ambient temperature. Simultaneously, the evaporator fan speed is increased to enhance heat exchange. Subsequently, the water pump frequency is gradually increased to ensure timely heat output from the condenser side. Throughout the entire control process, all components operate in a coordinated and orderly manner. The control commands are arranged chronologically to form a complete control sequence, and the controller executes the control commands at each moment in sequence, achieving comprehensive response and precise regulation of the heat pump to changes in ambient temperature.

[0082] To implement the above-described method embodiment, a magnetic levitation refrigeration compressor air source heat pump control method is provided to achieve the corresponding functions and technical effects. See also... Figure 2 , Figure 2 This diagram illustrates a structural block diagram of an air source heat pump control device 200 for a magnetic levitation refrigeration compressor according to an embodiment of this application. For ease of explanation, only the parts relevant to this embodiment are shown. The air source heat pump control device 200 for a magnetic levitation refrigeration compressor according to an embodiment of this application includes:

[0083] The deviation mapping module 201 is used to monitor changes in ambient temperature and indoor load demand, construct a temperature response curve from the changes in ambient temperature, construct a load demand curve from the indoor load demand, and perform a correlation assessment on the temperature response curve and the load demand curve to generate a regulation deviation mapping.

[0084] The early warning generation module 202 is used to detect the out-of-range mismatch window according to the deviation amplitude in the adjustment deviation mapping, extract the load change characteristics from the out-of-range mismatch window to generate pre-adjustment parameters, and determine the adjustment period based on the mismatch window and the pre-adjustment parameters;

[0085] The conflict handling module 203 is used to locate overlapping sections in the adjustment cycle, perform resource conflict identification to establish conflict locations for the overlapping sections, use the conflict locations to perform priority arrangement to form a scheduling sorting table, and reorganize the compressor running sequence according to the scheduling sorting table.

[0086] The capacity management module 204 is used to preset the adjustment capacity in each time sequence according to the compressor's operating sequence, integrate the adjustment capacity with the temperature response curve to form an adjustable power range, identify the margin insufficient area from the adjustable power range to generate margin compensation parameters, and establish dynamic adjustment limits based on the margin compensation parameters.

[0087] The sequence generation module 205 is used to perform time-series decomposition of the ambient temperature change based on the dynamic adjustment limit to establish abrupt change components and gradual change components, associate the abrupt change components with the compressor running sequence to generate a compressor speed control sequence, adapt the gradual change components with the temperature response curve to generate a heat exchanger adjustment sequence, and establish a control command by fusing the compressor speed control sequence and the heat exchanger adjustment sequence.

[0088] The air source heat pump control device 200 for a magnetic levitation refrigeration compressor described above can implement the air source heat pump control method for a magnetic levitation refrigeration compressor described in 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 the embodiments of this application can be referred to the contents of the above method embodiments, and will not be repeated in this embodiment. The purpose of the above embodiments is to exemplarily reproduce and derive the technical solution of the present invention, and to completely describe the technical solution, purpose, and effects 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 is not intended to limit the scope of protection of the present invention.

Claims

1. A method for controlling an air-source heat pump in a magnetic levitation refrigeration compressor, characterized in that, include: Monitor changes in ambient temperature and indoor load demand, construct a temperature response curve from the changes in ambient temperature, construct a load demand curve from the indoor load demand, and perform a correlation assessment on the temperature response curve and the load demand curve to generate a regulation deviation mapping, including: determining the peak demand time based on the load demand curve; and performing heat capacity effect compensation on the temperature response curve for the peak demand time to obtain the compensated response capacity. The deviation between the peak demand time and the compensated response capacity is measured to form a deviation value; the deviation value is expanded along the time dimension to generate an adjustment deviation mapping. In the adjustment deviation mapping, the out-of-range mismatch window is detected according to the deviation amplitude, the load mutation characteristics are extracted from the out-of-range mismatch window to generate pre-adjustment parameters, and the adjustment period is determined based on the out-of-range mismatch window and the pre-adjustment parameters. In the adjustment cycle, the overlapping section is located, resource conflict identification is performed for the overlapping section to establish conflict location, the conflict location is used to prioritize and form a scheduling sorting table, and the compressor running sequence is reorganized according to the scheduling sorting table. According to the compressor's operating sequence, preset adjustment capacities are set at each time step. The adjustment capacities are then integrated with the temperature response curve to form an adjustable power range. From the adjustable power range, regions with insufficient margin are identified to generate margin compensation parameters. This includes: mapping the adjustable power range to a magnetic levitation compressor speed adjustment domain; performing a speed acceleration limit test within the speed adjustment domain to generate an acceleration constraint curve; and identifying acceleration-limited sections from the acceleration constraint curve to form regions with insufficient margin. Based on the degree of acceleration limitation in the margin-deficient region, a margin compensation parameter is generated, and a dynamic adjustment limit is established based on the margin compensation parameter. Based on the dynamic adjustment limit, the ambient temperature change is split into a time sequence to establish abrupt change components and gradual change components. The abrupt change components are associated with the compressor operation sequence to generate a compressor speed control sequence. The gradual change components are adapted with the temperature response curve to generate a heat exchanger adjustment sequence. Control commands are established by fusing the compressor speed control sequence and the heat exchanger adjustment sequence.

2. The method according to claim 1, characterized in that, The step of determining the adjustment period based on the mismatch window and the pre-adjustment parameter includes: The frequency of occurrence of the out-of-tolerance mismatch window is statistically analyzed to form high-frequency mismatch requirements; Response delay analysis is performed on the pre-adjustment parameters to generate an advance distribution; A warning response model is formulated based on the high-frequency mismatch demand and the advance distribution. The adjustment cycle is determined based on the aforementioned early warning response mode.

3. The method according to claim 1, characterized in that, The step of performing resource conflict identification and establishing conflict locations for the overlapping sections includes: A refrigerant flow distribution grid is established within the overlapping section; The degree of coordination constraint and the duration of constraint between the compressor and the heat exchanger are identified through the refrigerant flow distribution grid. The conflict level is determined based on the correlation between the degree of collaborative constraint and the duration of the constraint; The conflict location is marked according to the conflict level.

4. The method according to claim 1, characterized in that, The step of using the conflict locations to prioritize and construct a scheduling order table includes: An impact range assessment of the conflict locations generates associated and independent impact domains. A global processing weight is established based on the aforementioned related influence domain; The independent influence domains are calibrated using the global processing weights as a benchmark to generate comprehensive weights; The comprehensive weights are arranged in descending order to generate a scheduling sorting table.

5. The method according to claim 1, characterized in that, The step of establishing abrupt and gradual change components by performing time-series decomposition on the environmental temperature change based on the dynamic adjustment limit includes: The splitting strategy is determined by evaluating the splitting sensitivity based on the dynamically adjusted limits, wherein the splitting sensitivity includes the limit response rate, the rate of change of disturbance amplitude, and frequency stability; Set the rapid and slow separation thresholds according to the aforementioned splitting strategy; The rapid-gradient separation threshold is used to filter and separate the environmental temperature changes to generate rapid-gradient components and slow-gradient components.

6. The method according to claim 2, characterized in that, The method for formulating an early warning response mode based on the high-frequency mismatch demand and the advance distribution includes: Based on the aforementioned high-frequency mismatch requirements, the mismatch clustering region is determined; The graded response delay tolerance is determined based on the aforementioned lead distribution; The mismatch clustering region is matched and mapped with the graded response delay tolerance to generate graded response triggering conditions; An early warning response mode is formulated based on the aforementioned graded response triggering conditions.

7. The method according to claim 3, characterized in that, The identification of the cooperative constraint degree and constraint duration of the compressor and heat exchanger through the refrigerant flow distribution grid includes: Locate flow competition nodes in the refrigerant flow distribution grid; The refrigerant flow utilization rate is determined based on the aforementioned flow competition nodes; Based on the refrigerant flow rate occupancy rate, a thermal inertia transfer analysis is performed on the heat exchanger side to generate a thermal inertia influence diagram; The thermal inertia influence diagram is converged to the constraint convergence point to form a cooperative constraint degree and constraint duration.

8. An air-source heat pump control device for a magnetic levitation refrigeration compressor, characterized in that, The method described in any one of claims 1-7 includes: The deviation mapping module is used to monitor changes in ambient temperature and indoor load demand, construct a temperature response curve from the changes in ambient temperature, construct a load demand curve from the indoor load demand, and perform a correlation assessment on the temperature response curve and the load demand curve to generate a regulation deviation mapping, including: determining the peak demand time based on the load demand curve; and performing heat capacity effect compensation on the temperature response curve for the peak demand time to obtain the compensated response capacity. The deviation between the peak demand time and the compensated response capacity is measured to form a deviation value; the deviation value is expanded along the time dimension to generate an adjustment deviation mapping. The early warning generation module is used to detect the out-of-range mismatch window based on the deviation amplitude in the adjustment deviation mapping, extract the load change characteristics from the out-of-range mismatch window to generate pre-adjustment parameters, and determine the adjustment period based on the out-of-range mismatch window and the pre-adjustment parameters. The conflict handling module is used to locate overlapping segments in the adjustment cycle, perform resource conflict identification to establish conflict locations for the overlapping segments, use the conflict locations to prioritize and arrange them into a scheduling sorting table, and reorganize the compressor running sequence according to the scheduling sorting table. The capacity management module is used to preset the adjustment capacity at each time sequence according to the compressor's operating sequence, integrate the adjustment capacity with the temperature response curve to form an adjustable power range, and identify the margin-deficient area from the adjustable power range to generate margin compensation parameters. This includes: mapping the adjustable power range to a magnetic levitation compressor speed adjustment domain; performing a speed acceleration limit test within the speed adjustment domain to generate an acceleration constraint curve; and identifying acceleration-limited sections from the acceleration constraint curve to form a margin-deficient area. Based on the degree of acceleration limitation in the margin-deficient region, a margin compensation parameter is generated, and a dynamic adjustment limit is established based on the margin compensation parameter. The sequence generation module is used to perform time-series decomposition of the ambient temperature change based on the dynamic adjustment limit to establish abrupt change components and gradual change components, associate the abrupt change components with the compressor running sequence to generate a compressor speed control sequence, adapt the gradual change components with the temperature response curve to generate a heat exchanger adjustment sequence, and establish a control command by fusing the compressor speed control sequence and the heat exchanger adjustment sequence.

Images