Intelligent air conditioner energy-saving analysis and control method

Abstract

The invention discloses an energy-saving analysis and control method for an intelligent air conditioner, relates to the technical field of intelligent air conditioners, and solves the problem that energy consumption adjustment of a traditional air conditioner lacks a quantitative basis and a dynamic optimization mechanism. High-frequency common energy consumption abnormity time periods are locked, energy consumption characteristics are bound, mismatching of energy-saving resources is avoided, and it is ensured that the time periods to be regulated and controlled are energy consumption abnormity scenes occurring at high frequency in a historical period through quantitative verification that the cross proportion ZB is larger than or equal to 0.5, and accidental fluctuation is avoided. According to the precise positioning mode, invalid regulation and control over normal energy consumption time periods are avoided, omission of key abnormal time periods is also prevented, clear action targets are provided for subsequent energy saving measures, and the problems of energy consumption waste or temperature control failure caused by blind regulation and control are reduced from the source.

Description

Technical Field

[0001] This invention relates to the field of intelligent air conditioning technology, specifically to an intelligent air conditioning energy-saving analysis and control method. Background Technology

[0002] As building energy consumption continues to rise as a proportion of total social energy consumption, air conditioning systems, as one of the main energy-consuming devices in buildings, directly impact overall energy efficiency. Currently, whether in residential homes, commercial offices, or public buildings, air conditioning systems generally face a contradiction between "extensive energy consumption management" and "precise temperature control requirements." Traditional air conditioning control methods are no longer adequate for modern energy-saving and comfort needs. Specific technical challenges can be discussed from the following aspects: Traditional air conditioning control relies heavily on fixed parameters or manual intervention, lacking the ability to accurately identify periods of abnormal energy consumption. Existing air conditioning systems often employ control logic based on a "24-hour unified mode" or "simple timed on / off," failing to incorporate historical operating data to uncover energy consumption patterns. For example, during certain periods, due to factors such as fluctuations in ambient temperature and changes in human activity, abnormal scenarios may occur, such as excessively high energy consumption (e.g., operating at high power even when the room temperature is far below the set value) or insufficient energy consumption (e.g., operating at low power even when the room temperature is far above the set value). However, traditional methods cannot identify these high-frequency abnormal periods through multi-cycle cross-validation of historical data, resulting in a lack of targeted control, leading to energy waste and difficulty in ensuring stable temperature control.

[0003] Traditional air conditioning energy consumption regulation lacks quantitative basis and dynamic optimization mechanism. In terms of regulation strategy, existing methods mostly adopt "fixed ratio regulation" or "empirical regulation", without using the standard energy consumption corresponding to the preset temperature as a benchmark, nor combining the actual energy consumption data of the period to be regulated to calculate the precise regulation ratio. This easily leads to problems such as "over-regulation" (such as drastically reducing power to save energy, resulting in room temperature exceeding the standard) or "under-regulation" (such as incomplete energy consumption optimization and the existence of redundant losses). During operation, when the room temperature reaches a stable state, traditional methods cannot conduct fine-grained energy consumption analysis of core actuators such as refrigeration units and cooling water pumps. They usually maintain fixed power operation, failing to make full use of the energy consumption optimization space during the room temperature stabilization period, further reducing energy utilization efficiency.

[0004] Against this backdrop, there is an urgent need for an intelligent air conditioning energy-saving analysis and control method that can accurately pinpoint periods of abnormal energy consumption based on historical data, generate on-demand control strategies by combining quantitative logic, and deeply explore energy-saving potential through dynamic fine-tuning. This method aims to solve problems such as extensive control, energy waste, and poor adaptability to various scenarios in traditional technologies, and achieve a synergistic unity of "precise temperature control" and "high-efficiency energy saving". Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides an intelligent air conditioning energy-saving analysis and control method, which solves the problem that traditional air conditioning energy consumption regulation lacks quantitative basis and dynamic optimization mechanism.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent air conditioning energy-saving analysis and control method, comprising the following steps: Step 1: Confirm the historical process data past the current reference time, and identify the abnormal energy consumption periods associated with a single time period from the historical process data. Then, perform overlap verification on the abnormal energy consumption periods associated with different time periods to lock in the period to be regulated. The specific method is as follows: Using the current time as the baseline time, the historical process data associated with the historical period of the baseline time is confirmed from the historical data, and the historical period is a preset period. The confirmed historical process data is divided into several single-cycle periods, each of which is a preset period. Feature verification is performed on the periodic data associated with each single cycle. Temperature data for the intelligent air conditioning temperature control scenario is identified from the periodic data, and different temperature data associated with different times are labeled as W. i and the confirmed temperature data W i Real-time comparison with the preset temperature Wb: If |W i If -Wb|≥Y1, then the corresponding time is recorded as an abnormal time, where Y1 and Wb are preset values. Otherwise, no marking is made. The consecutive marked abnormal times within a single cycle are recorded as abnormal energy consumption periods, and the energy consumption characteristics associated with these abnormal energy consumption periods are identified: several sets of temperature data W associated with the abnormal energy consumption periods are... i Perform averaging to confirm the average temperature. If the average temperature is less than Wb, assign the corresponding abnormal energy consumption period a high energy consumption characteristic; if the average temperature is greater than Wb, assign the corresponding abnormal energy consumption period a low energy consumption characteristic. In step one, the specific method for locking in the period to be regulated is as follows: Cross-validation is performed on the abnormal energy consumption periods associated with each single cycle. The multiple sets of abnormal energy consumption periods cross-validated are all specific periods associated with the same energy consumption characteristics. The cross-periods generated by multiple sets of abnormal energy consumption periods are identified, and the cross-period ratio associated with the cross-periods is recorded. The number of associations G between the cross-periods and different abnormal energy consumption periods is identified, and the total number Z of multiple sets of abnormal energy consumption periods that are cross-processed is identified. The cross-period ratio ZB associated with the corresponding cross-periods is confirmed by the formula: G ÷ Z = ZB. It is then determined whether the cross-period ratio ZB satisfies: ZB ≥ 0.5. If it satisfies this condition, the corresponding cross-period is recorded as a period to be controlled, and the energy consumption characteristics associated with the corresponding period to be controlled are confirmed. If it does not satisfy this condition, no marking is required. Step 2: Based on the energy consumption characteristics and historical data of the period to be regulated, confirm the energy consumption adjustment direction associated with the period to be regulated, and determine the adjustment ratio based on the confirmed energy consumption adjustment direction. Generate the adjustment logic and directly implement energy consumption regulation. Specific methods include: If the energy consumption characteristic associated with the period to be regulated is high energy consumption: extract the unit energy consumption data associated with the preset temperature Wb from the historical process data, and average the extracted unit energy consumption data to confirm the average energy consumption. Record the confirmed average energy consumption as the standard energy consumption associated with the preset temperature Wb. Then, mark the average temperature associated with the period to be regulated as Wj, and confirm the unit energy consumption data associated with Wj from the historical process data. Average the confirmed unit energy consumption data to confirm the average energy consumption. Record the confirmed average energy consumption as the period energy consumption associated with the period to be regulated. Use the formula: (period energy consumption - standard energy consumption) ÷ period energy consumption = adjustment ratio. Mark the adjustment direction of the energy consumption associated with this period to be regulated as the downward adjustment direction. During the period to be regulated, reduce the power, and the reduction ratio is consistent with the adjustment ratio. If the energy consumption characteristic associated with the period to be regulated is low energy consumption: confirm the standard energy consumption associated with the preset temperature Wb, then confirm the time period energy consumption associated with the corresponding period to be regulated, and use: (standard energy consumption - time period energy consumption) ÷ time period energy consumption = adjustment ratio, and mark the energy consumption to be regulated associated with this period as the upward adjustment direction. During the period to be regulated, the power is increased, and the upward adjustment ratio is consistent with the adjustment ratio. Step 3: During the operation of the smart air conditioner, the room temperature is monitored in real time. When the room temperature tends to stabilize and meet the standard, energy-saving fine-tuning measures are implemented to reduce air conditioner losses. Specifically, this includes: The room temperature is monitored in real time, and the monitored room temperature is calibrated as SW. k Where k represents the corresponding time, if the monitored room temperature SW k Satisfy: |SW k When -26|≤2, a stable signal is generated and the duration of the stable signal is continuously monitored. If the duration exceeds 3 minutes, it means that the room temperature is approaching a stable and compliant state, and a fine-tuning signal is generated directly. If it does not exceed 3 minutes, it is continuously monitored. The system identifies energy-consuming actuators within the smart air conditioner and marks them as pending processes. From these pending processes, a random set is selected for energy consumption reduction by 5%. After reduction, the room temperature is checked to see if it stabilizes and meets the target. If so, other pending processes are selected and their energy consumption reduced. If not, the selected pending process is marked as unselectable, and energy consumption reduction is continued for other pending processes. This process is repeated for different pending processes to fully optimize the energy consumption associated with the smart air conditioner.

[0007] This invention provides an intelligent air conditioning energy-saving analysis and control method. Compared with the prior art, it has the following advantages: This invention eliminates occasional anomalies by screening for temperature deviations and performing cross-validation based on similar characteristics, pinpointing high-frequency, common energy consumption anomaly periods and binding them to energy consumption characteristics. This avoids misallocation of energy-saving resources. Quantitative verification using a cross-validation ratio of ZB ≥ 0.5 ensures that the period to be regulated is a high-frequency energy consumption anomaly scenario within a historical cycle, rather than a random fluctuation. This precise positioning method avoids ineffective regulation of normal energy consumption periods and prevents the omission of key anomaly periods, providing a clear "target" for subsequent energy-saving measures and reducing energy waste or temperature control failure caused by blind regulation from the source. By calculating the adjustment ratio using a quantitative formula, a "customized on-demand" control logic is achieved: for high-energy-consumption periods, the power reduction ratio is determined by "(period energy consumption - standard energy consumption) ÷ period energy consumption" to precisely reduce redundant energy consumption; for low-energy-consumption periods, the power increase ratio is determined by "(standard energy consumption - period energy consumption) ÷ period energy consumption" to avoid room temperature deviating from the target range due to excessively low energy consumption. This control method, "anchored by standard energy consumption and based on temperature difference requirements," eliminates the extreme situation of "sacrificing temperature control for energy saving" and avoids the problem of "excessive energy consumption for temperature control," achieving a two-way balance between "constant temperature protection" and "energy consumption optimization," ensuring that the air conditioner meets the space's temperature control requirements while minimizing energy loss. Energy consumption optimization is performed on actuators such as refrigeration units and cooling water pumps: randomly select actuators and reduce their energy consumption by 5%. If the room temperature remains stable, the reduction range is expanded. If the room temperature fluctuates, the actuator is marked as "unselectable". This "trial reduction + real-time feedback" mode can deeply explore the energy-saving potential of each actuator without disrupting room temperature stability. It avoids excessive reduction of critical actuators that may lead to temperature control failure, and does not overlook the energy reduction potential of non-critical actuators. It achieves "refined squeezing" of the overall energy consumption of air conditioning, further reduces energy waste during stable operation, and improves the energy utilization efficiency of air conditioning operation. Attached Figure Description

[0008] Figure 1 This is a schematic diagram of the method flow of the present invention. Detailed Implementation

[0009] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0010] First Embodiment Please see Figure 1 This application provides a smart air conditioner energy-saving analysis and control method, including the following steps: Step 1: Confirm the historical process data past the current reference time, and identify the abnormal energy consumption periods associated with a single time period from the historical process data. Then, verify the overlap of the abnormal energy consumption periods associated with different time periods to lock in the period to be regulated. Specifically, take 24 hours as a processing time period. In the corresponding temperature control scenario, analyze and process the different energy consumption situations associated with different time lines in the past. From the analysis and verification process, confirm the specific time period of abnormal energy consumption. Then, based on the comprehensive verification process of the corresponding abnormal time period, the cross-time period associated with the corresponding abnormal time period can be confirmed, thereby effectively identifying the associated period to be regulated. The specific method for locking in the period to be regulated is as follows: Using the current time as the base time, the historical process data associated with the historical period of the base time is confirmed from the historical data. The historical period is a preset period, generally taken as 720 hours, which is the historical data associated with the past month of the current time. The confirmed historical process data is divided into several single-cycle periods, each of which is a preset period, typically 24 hours. Feature verification is performed on the periodic data associated with each single cycle: temperature data for the intelligent air conditioning temperature control scenario is identified from the periodic data, and different temperature data associated with different times are labeled as W. i (Its temperature data is collected in real time by the corresponding sensor, and its temperature control scenario is the specific space being cooled, where the temperature is collected in real time by the corresponding sensor), and the confirmed temperature data W i Real-time comparison with the preset temperature Wb: If |W iIf -Wb|≥Y1, then the corresponding time is recorded as an abnormal time, where Y1 is a preset value, generally 6℃, and Wb is a preset value, generally 26℃. Conversely, if -Wb|≥Y1, then no marking is made. The consecutive marked abnormal times within a single cycle are recorded as abnormal energy consumption periods, and the energy consumption characteristics associated with these abnormal energy consumption periods are identified: several sets of temperature data W associated with the abnormal energy consumption periods are... i Perform averaging to confirm the average temperature. If the average temperature is less than Wb, assign the corresponding abnormal energy consumption period a high energy consumption characteristic (energy consumption reduction processing needs to be carried out in the subsequent same period). If the average temperature is greater than Wb, assign the corresponding abnormal energy consumption period a low energy consumption characteristic. Cross-validation is performed on the abnormal energy consumption periods associated with each single cycle. All the abnormal energy consumption periods cross-validated are specific periods associated with the same energy consumption characteristics (that is, cross-validation is performed on periods with the same energy consumption characteristics, and no cross-validation is required for periods that do not belong to the same energy consumption characteristics). The cross periods generated by multiple abnormal energy consumption periods are identified, and the cross ratio associated with the cross periods is recorded. The number of associations G between the cross periods and different abnormal energy consumption periods is identified (that is, the cross periods completely belong to the corresponding abnormal energy consumption periods). The total number Z of multiple abnormal energy consumption periods that are cross-processed is identified. The cross ratio ZB associated with the corresponding cross periods is confirmed by using: G÷Z=ZB. It is determined whether the cross ratio ZB satisfies: ZB≥0.5. If it satisfies, the corresponding cross period is recorded as the period to be controlled, and the energy consumption characteristics associated with the corresponding period to be controlled are confirmed. If it does not satisfy, no marking processing is required. Specifically, in the actual processing, there are different overlaps between different energy consumption periods, that is, different overlap periods. After the overlap period is confirmed, there is an overlap ratio associated with the corresponding overlap period. When there are abnormal energy consumption periods within ten cycles, and there is an overlap between five of the ten abnormal energy consumption periods, the corresponding overlap period is 5, and the associated ratio is (5÷10). Thus, the corresponding period to be controlled can be quickly identified. Step 2: Based on the energy consumption characteristics of the period to be regulated and historical process data, confirm the energy consumption adjustment direction associated with the period to be regulated, and based on the confirmed energy consumption adjustment direction, confirm the adjustment ratio and generate the adjustment logic. Subsequently, based on this adjustment logic, when the corresponding period arrives, execute the corresponding energy consumption adjustment measures to achieve the energy consumption reduction process. The specific generation process of the logic to be adjusted includes: If the energy consumption characteristic associated with the period to be regulated is high energy consumption (indicating that the actual energy consumption is too high and needs to be reduced): Extract the unit energy consumption data associated with the preset temperature Wb from the historical process data, and average the extracted unit energy consumption data to confirm the average energy consumption. Record the confirmed average energy consumption as the standard energy consumption associated with the preset temperature Wb (its energy consumption is the power characteristic associated with the smart air conditioner during actual operation; the higher the power, the higher the energy consumption, and the lower the power, the lower the associated energy consumption; here, power and energy consumption are equivalent). The average temperature associated with the period to be regulated is then calibrated as Wj, and the unit energy consumption data associated with Wj is confirmed from the historical process data. The confirmed unit energy consumption data is then averaged to confirm the average energy consumption. The confirmed average energy consumption is recorded as the period energy consumption associated with the period to be regulated. The adjustment ratio is calculated as: (period energy consumption - standard energy consumption) ÷ period energy consumption = adjustment ratio. The adjustment direction of the energy consumption associated with this period to be regulated is marked as the downward adjustment direction. When the corresponding period to be regulated arrives, the power is reduced based on the original energy consumption. The reduction ratio is consistent with the adjustment ratio. If the energy consumption characteristic associated with the period to be regulated is low energy consumption (meaning that the actual energy consumption is too low and energy consumption needs to be increased): confirm the standard energy consumption associated with the preset temperature Wb, and then confirm the time period energy consumption associated with the corresponding period to be regulated. Use: (standard energy consumption - time period energy consumption) ÷ time period energy consumption = adjustment ratio, and mark the energy consumption adjustment direction associated with this period to be regulated as the upward adjustment direction. When the corresponding period to be regulated arrives, increase the power based on the original energy consumption. The increase ratio is consistent with the adjustment ratio. Its adjustment logic is that when the corresponding time period arrives, the associated energy consumption is adjusted according to the corresponding adjustment ratio, and then the associated energy consumption is adjusted according to the set adjustment method to ensure that energy can be effectively saved during the corresponding time period. At the same time, it can ensure effective control of energy consumption. During the temperature control process, the smart air conditioner controls the temperature of the area according to the preset standard temperature (26℃). When the temperature is too low, the energy consumption can be appropriately reduced to ensure that the temperature of the corresponding area rebounds. When the corresponding temperature is too high, the energy consumption can be appropriately increased to ensure that the temperature of the corresponding area decreases. Thus, it can not only effectively ensure that the corresponding area is in a constant temperature state, but also effectively realize the energy consumption management process to ensure that the energy consumption of the corresponding area is in an optimized state, and achieve effective control process on the basis of energy saving. Step 3: During the operation of the smart air conditioner, the room temperature is monitored in real time. When the room temperature tends to stabilize and meet the standard, energy-saving fine-tuning measures are implemented to reduce air conditioner losses. Its energy-saving fine-tuning measures specifically include: The room temperature is monitored in real time, and the monitored room temperature is calibrated as SW.k Where k represents the corresponding time, if the monitored room temperature SW k Satisfy: |SW k When -26|≤2, a stable signal is generated and the duration of the stable signal is continuously monitored. If the duration exceeds 3 minutes (including 3 minutes), it means that the room temperature is approaching a stable and compliant state, and a fine-tuning signal is generated directly. If the duration does not exceed 3 minutes, monitoring continues. Identify the energy-consuming components (refrigeration unit, cooling water pump, etc.) within the smart air conditioner and mark them as pending tasks. From the multiple sets of pending tasks identified, randomly select one set for energy consumption reduction by 5%. After the reduction, check if the room temperature stabilizes and meets the standard. If so, continue selecting other pending tasks and reducing their energy consumption. If not, mark the selected pending task as unselectable and continue reducing the energy consumption of other pending tasks. This process is repeated for different pending tasks to confirm energy consumption reduction, thus fully optimizing the energy consumption associated with the smart air conditioner and effectively reducing its overall energy consumption. The associated components to be processed are designated as A, B, or C. Prioritize reducing the energy consumption of A. If the room temperature stabilizes after reducing A, then reduce the energy consumption of B. Identify whether the room temperature stabilizes after reducing B. If not, B is marked as unselectable, and C is processed using the same energy consumption reduction method. If it stabilizes, C is directly adjusted, and the corresponding adjustment process is executed. This process continues to optimize the energy consumption associated with the smart air conditioner, achieving a significant reduction in overall energy consumption.

[0011] Some of the data in the above formulas are numerical calculations with dimensions removed, and the contents not described in detail in this specification are all prior art known to those skilled in the art.

[0012] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. A method for intelligent air conditioning energy-saving analysis and control, characterized in that, Includes the following steps: Step 1: Confirm the historical process data past the current reference time, and identify the abnormal energy consumption periods associated with a single time period from the historical process data. Then, verify the overlap of the abnormal energy consumption periods associated with different time periods to lock in the period to be regulated. Step 2: Based on the energy consumption characteristics of the period to be regulated and historical process data, confirm the energy consumption adjustment direction associated with the period to be regulated, and based on the confirmed energy consumption adjustment direction, confirm the adjustment ratio, generate the adjustment logic, and directly perform energy consumption regulation. Step 3: When the smart air conditioner is running, monitor the room temperature in real time. When the room temperature tends to be stable and meets the standard, implement energy-saving fine-tuning measures to reduce air conditioner losses.

2. The intelligent air conditioning energy-saving analysis and control method according to claim 1, characterized in that, In step one, the specific method for identifying abnormal energy consumption periods is as follows: Using the current time as the baseline time, the historical process data associated with the historical period of the baseline time is confirmed from the historical data, and the historical period is a preset period. The confirmed historical process data is divided into several single-cycle periods, each of which is a preset period. Feature verification is performed on the periodic data associated with each single cycle. Temperature data for the intelligent air conditioning temperature control scenario is identified from the periodic data, and different temperature data associated with different times are labeled as W. i and the confirmed temperature data W i Real-time comparison with the preset temperature Wb: If |W i If -Wb|≥Y1, then the corresponding time is recorded as an abnormal time, where Y1 and Wb are preset values. Otherwise, no marking is made. The consecutive marked abnormal times within a single cycle are recorded as abnormal energy consumption periods, and the energy consumption characteristics associated with these abnormal energy consumption periods are identified: several sets of temperature data W associated with the abnormal energy consumption periods are... i Perform averaging to confirm the average temperature. If the average temperature is less than Wb, assign the high energy consumption characteristic to the corresponding abnormal energy consumption period.

3. The intelligent air conditioning energy-saving analysis and control method according to claim 2, characterized in that, If the average temperature is greater than Wb, then the corresponding abnormal energy consumption period is assigned a low energy consumption characteristic.

4. The intelligent air conditioning energy-saving analysis and control method according to claim 2, characterized in that, In step one, the specific method for locking the time period to be regulated is as follows: Cross-validation is performed on the abnormal energy consumption periods associated with each single cycle. The multiple sets of abnormal energy consumption periods cross-validated are all specific periods associated with the same energy consumption characteristics. The cross-periods generated by multiple sets of abnormal energy consumption periods are identified, and the cross-period ratio associated with the cross-periods is recorded. The number of associations G between the cross-periods and different abnormal energy consumption periods is identified, and the total number Z of multiple sets of abnormal energy consumption periods that are cross-processed is identified. The cross-period ratio ZB associated with the corresponding cross-period is confirmed by the formula: G ÷ Z = ZB. It is then determined whether the cross-period ratio ZB satisfies: ZB ≥ 0.

5. If it satisfies this condition, the corresponding cross-period is recorded as a period to be controlled, and the energy consumption characteristics associated with the corresponding period to be controlled are confirmed. If it does not satisfy this condition, no marking is required.

5. The intelligent air conditioning energy-saving analysis and control method according to claim 1, characterized in that, In step two, the specific methods for generating the logic to be adjusted include: If the energy consumption characteristic associated with the period to be regulated is high energy consumption: extract the unit energy consumption data associated with the preset temperature Wb from the historical process data, and average the extracted unit energy consumption data to confirm the average energy consumption. Record the confirmed average energy consumption as the standard energy consumption associated with the preset temperature Wb. Then, mark the average temperature associated with the period to be regulated as Wj, and confirm the unit energy consumption data associated with Wj from the historical process data. Average the confirmed unit energy consumption data to confirm the average energy consumption, and record the confirmed average energy consumption as the period energy consumption associated with the period to be regulated. Use the formula: (period energy consumption - standard energy consumption) ÷ period energy consumption = regulation ratio. Mark the direction of energy consumption to be regulated associated with this period as the downward direction. During the period to be regulated, reduce the power, and the reduction ratio is consistent with the regulation ratio.

6. The intelligent air conditioning energy-saving analysis and control method according to claim 5, characterized in that, If the energy consumption characteristic associated with the period to be regulated is low energy consumption: Confirm the standard energy consumption associated with the preset temperature Wb, and then confirm the time period energy consumption associated with the corresponding time period to be controlled. Use the formula: (standard energy consumption - time period energy consumption) ÷ time period energy consumption = adjustment ratio. Mark the energy consumption to be adjusted associated with this time period as the upward adjustment direction. When the time period to be controlled arrives, increase the power, and the upward adjustment ratio is consistent with the adjustment ratio.

7. The intelligent air conditioning energy-saving analysis and control method according to claim 1, characterized in that, The energy-saving fine-tuning measures implemented in step three specifically include: The room temperature is monitored in real time, and the monitored room temperature is calibrated as SW. k Where k represents the corresponding time, if the monitored room temperature SW k Satisfy: |SW k When -26|≤2, a stable signal is generated and the duration of the stable signal is continuously monitored. If the duration exceeds 3 minutes, it means that the room temperature is approaching a stable and compliant state, and a fine-tuning signal is generated directly. The system identifies energy-consuming actuators within the smart air conditioner and marks them as pending processes. From these pending processes, a random set is selected for energy consumption reduction by 5%. After reduction, the room temperature is checked to see if it stabilizes and meets the target. If so, other pending processes are selected and their energy consumption reduced. If not, the selected pending process is marked as unselectable, and energy consumption reduction is continued for other pending processes. This process is repeated for different pending processes to fully optimize the energy consumption associated with the smart air conditioner.

8. The intelligent air conditioning energy-saving analysis and control method according to claim 7, characterized in that, If the duration does not exceed 3 minutes, continue monitoring.

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