Refrigerator main transformer integrated control algorithm with self-learning energy consumption level
By using a self-learning energy efficiency rating integrated control algorithm for refrigerators, the compressor speed and cooling demand are precisely matched, solving the problems of long development cycles and inconsistencies in energy efficiency rating testing of inverter refrigerators, and improving the test pass rate and hardware compatibility.
Patent Information
- Application Number
- CN202511906522.2
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-17
- Publication Date
- 2026-03-27
AI Technical Summary
In existing technologies, variable frequency refrigerators face challenges in energy efficiency rating testing, including long development cycles, significant differences between machines, and high testing and debugging risks. This results in a large consumption of manpower and resources in testing and hinders the improvement of the pass rate.
The refrigerator adopts a self-learning energy consumption level integrated control algorithm, which achieves precise matching between compressor speed and cooling demand through adaptive speed adjustment and energy consumption optimization logic. This includes a true energy consumption learning stage and a false energy consumption operation stage, dynamically adjusting the compressor speed to adapt to different environments and usage scenarios.
It improved the pass rate of energy efficiency tests, reduced the risk of testing and debugging, reduced the difficulty of hardware adaptation, achieved energy consumption optimization under different environments and usage habits, and improved hardware compatibility and testing efficiency.
Smart Images
Figure CN121739700A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of variable frequency refrigerator technology, and particularly relates to a self-learning energy consumption level integrated control algorithm for refrigerator main and variable frequency drives. Background Technology
[0002] The refrigerator energy consumption rating system is a system that assesses and classifies the power consumption of refrigerators according to relevant national standards. It aims to promote energy conservation and emission reduction and guide consumers to purchase energy-saving products.
[0003] Refrigerator energy efficiency ratings are divided into 5 levels, with lower numbers indicating greater energy efficiency. The energy consumption of the 5 levels is as follows:
[0004] Level 1: Most energy-efficient, representing the international leading level;
[0005] Level 2: Entry-level energy-saving products, highly recommended for consumers;
[0006] Levels 3 and 4: Represent the average market level;
[0007] Level 5: Entry threshold. Products that do not meet the entry threshold cannot be sold on the market.
[0008] In existing technologies, after selecting a suitable performance compressor, the entire unit is tested and debugged in a constant temperature and humidity chamber according to the standard: the power consumption limit and energy efficiency rating of household refrigerators. According to the standard, it needs to be tested in two ambient temperature environments, with a standard test cycle of 32 days. If the energy consumption does not meet the requirements, the control system software needs to be adjusted to adjust the compressor target setting and start / stop points under the two ambient temperatures, and the test is repeated to see if it passes. The testing and debugging process consumes a lot of manpower and resources. Moreover, due to the differences in refrigeration systems, the energy efficiency test may fail due to market spot checks, resulting in a high risk of testing and debugging, which is not conducive to the testing work. Summary of the Invention
[0009] This invention provides a self-learning energy consumption level integrated control algorithm for refrigerator main transformer, which aims to solve the problems of long development cycle and differences between machines caused by traditional algorithms for variable frequency refrigerators.
[0010] This invention is implemented as follows: a self-learning energy consumption level integrated control algorithm for refrigerator main transformer, comprising the following operation steps:
[0011] S1. The software obtains the compressor start-up point Tfk and stop-down point Tft based on different ambient temperatures, design modes, and set temperatures.
[0012] S2. Determine the initial and maximum compressor settings based on the ambient temperature and compartment requirements of different modes. Refer to the table below to obtain the target setting Gi and maximum setting Gmax when the compressor starts, based on the ambient temperature range, refrigerator operating status, and set freezing temperature.
[0013] S3. Based on the temperature difference adjustment, calculate the temperature difference from the current temperature of the freezer compartment and the difference between the start-up point obtained in the first step;
[0014] S4. Find the target compressor speed from the table: Gt = Gi + ΔG. If the obtained Gt is greater than Gmax, then operate at the highest gear: Gt = Gmax. Find the compressor's target operating speed St from the table.
[0015] S5. Determine the energy consumption entering and exiting:
[0016] 5.1 The software determines whether the compressor can enter the energy consumption mode based on certain conditions. If any condition is not met, the compressor will not enter the energy consumption mode.
[0017] 5.2 The software continuously checks whether the conditions are met during execution. Once one condition is met, it exits the energy consumption mode.
[0018] S6. According to the preset compressor state under different ambient temperature and energy consumption conditions, the compressor runs the first cycle. The compressor runs at the gear Gt, which corresponds to the speed St. After the compressor starts, there is a soft start. It runs at gear 1 first, and increases the gear every 30 seconds until the target gear is reached. Note that the algorithm starts timing the compressor start-up working time Tcomp when the compressor starts.
[0019] S7, Adaptive speed regulation and energy consumption optimization logic:
[0020] 7.1 True energy consumption stage: Based on the dynamic speed adaptation of the compressor's operating cycle, after the algorithm enters the energy consumption state, the first operating cycle of the compressor is used as the "true energy consumption" judgment benchmark, and the core execution logic is as follows;
[0021] 7.2 Learning Iteration and Determination of Optimal Speed: Through the loop of "adjust-stop-record" and "adjust-stop-record", the algorithm will complete adaptive learning within 3-5 compressor operating cycles. After each iteration, the deviation between the current operating time Tcomp and the theoretical operating time Tth is compared, and the deviation range is gradually reduced. When the deviation between the compressor operating time and the theoretical time is less than 5% in two consecutive cycles, and the speed is not significantly adjusted, the current speed is determined to be the optimal speed for the refrigeration system under the current energy consumption mode.
[0022] 7.3 False Energy Consumption Stage: Based on the stable operation of the optimal speed, after the optimal speed is determined, the subsequent operation cycle of the press will enter the "false energy consumption operation" mode. The press will directly operate at the learned optimal speed without repeated triggering of adaptive adjustment, thus reducing the system's computing power consumption.
[0023] 7.4 Initial gear optimization, a supplementary strategy to shorten the learning cycle: To avoid excessively long learning time due to a large deviation between the initial gear and the optimal speed, the algorithm sets up a dynamic correction mechanism for the initial gear.
[0024] Preferably, in step S1, after dividing the ambient temperature into zones, the start-up and stop points of the compressor are obtained by looking up a table based on the set temperature of the freezer compartment and whether it is in quick-freeze mode.
[0025] Preferably, in step S3, the temperature Tf is set, the start-up point Tfk is obtained, the temperature difference ΔT = Tf - Tfk is obtained, and the freezing temperature difference upgrade level ΔG is obtained by looking up the table.
[0026] Preferably, in section 5.1, the ambient temperature and humidity conditions are: ambient temperature between 13 and 19 degrees Celsius and between 29 and 35 degrees Celsius, humidity ≤ 75%, for 12 consecutive hours; the mode conditions are: no special modes such as intelligent, quick-freeze, quick-cool, extreme-freeze, and whole-machine refrigeration are set; the fault conditions are: no sensor fault and no communication fault; the door closing condition is: the door is continuously closed for 12 hours; and the gear setting adjustment is: no gear adjustment action for 12 consecutive hours and no compartment closure is set.
[0027] Preferably, in section 5.2, the ambient temperature and humidity conditions are: ambient temperature exceeding 13–19 degrees Celsius and 29–35 degrees Celsius, humidity >75%, for 20 minutes; the mode conditions are: setting intelligent, quick-freeze, quick-cool, extreme-freeze, and whole-machine refrigeration special modes; the fault conditions are: sensor fault and communication fault; the compartment overload condition is: any compartment overload; the compressor forced stop condition is: forced stop ≥1 time; the door opening condition is: door opening >1 time; the gear setting condition is: gear adjustment action and set compartment closed; the automatic exit condition is: running for 3 defrosting cycles under energy consumption.
[0028] Preferably, in section 7.1, the running time calculation and shutdown condition judgment are as follows: the running time Tcomp of the compressor at the current speed is statistically analyzed in real time, and the key parameters of the refrigeration system are monitored to see if they reach the shutdown threshold.
[0029] Adaptive speed increase mechanism: If the compressor runs continuously for 30 minutes and does not reach the stop point, the algorithm will trigger a step-by-step speed increase. Each increase must be combined with the hardware parameters of the refrigeration system until the system reaches the stop condition. Record the speed after this increase and the corresponding running time.
[0030] Adaptive speed reduction mechanism: If the actual running time of the compressor is less than 30 minutes of the theoretically calculated working time in the first true energy consumption cycle, it means that the current speed is too high and causes excessive cooling. The algorithm will trigger a step-by-step speed reduction until the shutdown condition is reached. Record the speed and running time after this reduction.
[0031] Preferably, in section 7.3, during operation, the algorithm will still monitor the room temperature and ambient temperature parameters in real time. If a sudden change in heat load occurs, it will switch back to the "true energy consumption stage" and start a new round of adaptive learning to ensure a dynamic balance between energy consumption and cooling efficiency.
[0032] Preferably, in section 7.4, when the actual operating gear of the compressor deviates from the theoretically calculated initial gear by more than 3 gears during the first true energy consumption cycle, the algorithm will directly modify the initial value of the "speed-gear" lookup table inside the software, and update the initial gear to the gear corresponding to the speed after the first adaptive adjustment; the corrected initial gear will be used as the default gear for the next start-up and energy consumption mode switching, reducing the number of subsequent learning iterations and enabling the system to quickly enter the optimal operating state.
[0033] Beneficial effects
[0034] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention provides a self-learning energy consumption level integrated control algorithm for refrigerators. Through precise energy consumption control using a dual-stage mode of "true energy consumption learning and false energy consumption operation," it avoids the problems of "insufficient cooling" or "excessive energy consumption" caused by traditional fixed speed control. This allows for precise matching of compressor speed with cooling demand, adaptive scene adaptation, and can cope with different ambient temperatures, such as high temperatures in summer and low temperatures in winter, and different usage habits, such as energy consumption optimization under frequent door opening and long standby periods. No manual parameter adjustment is required, hardware compatibility is improved, and the initial gear correction mechanism reduces the difficulty of adapting to different specifications of compressor hardware. The same algorithm is compatible with multiple models of integrated refrigerator control systems, improving the pass rate of energy efficiency tests, reducing testing and debugging risks, and thus facilitating testing work. Attached Figure Description
[0035] Figure 1 This is a flowchart of the self-learning energy consumption level refrigerator main transformer integrated control algorithm of the present invention;
[0036] Figure 2 This is a block diagram of the self-learning energy consumption level refrigerator main transformer integrated control algorithm of the present invention;
[0037] Figure 3 This is the timing logic diagram for controlling the refrigerator compressor in this invention;
[0038] Figure 4 This is a diagram illustrating the refrigeration request determination for the freezer compartment in this invention.
[0039] Figure 5 This is a schematic diagram of the start-up and shutdown points between the ambient temperature zones in this invention. Detailed Implementation
[0040] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.
[0041] Please see Figure 1-4 This invention provides a technical solution: a self-learning energy consumption level integrated control algorithm for refrigerator main transformer, comprising the following operation steps:
[0042] S1. The software obtains the compressor start-up point Tfk and stop-down point Tft based on different ambient temperatures, design modes, and set temperatures.
[0043] S2. Determine the initial and maximum compressor settings based on the ambient temperature and compartment requirements of different modes. Refer to the table below to obtain the target setting Gi and maximum setting Gmax when the compressor starts, based on the ambient temperature range, refrigerator operating status, and set freezing temperature.
[0044] S3. Based on the temperature difference adjustment, calculate the temperature difference from the current temperature of the freezer compartment and the difference between the start-up point obtained in the first step;
[0045] S4. Find the target compressor speed from the table: Gt = Gi + ΔG. If the obtained Gt is greater than Gmax, then operate at the highest gear: Gt = Gmax. Find the compressor's target operating speed St from the table.
[0046] S5. Determine the energy consumption entering and exiting:
[0047] 5.1 The software determines whether the compressor can enter the energy consumption mode based on certain conditions. If any condition is not met, the compressor will not enter the energy consumption mode.
[0048] 5.2 The software continuously checks whether the conditions are met during execution. Once one condition is met, it exits the energy consumption mode.
[0049] S6. According to the preset compressor state under different ambient temperature and energy consumption conditions, the compressor runs the first cycle. The compressor runs at the gear Gt, which corresponds to the speed St. After the compressor starts, there is a soft start. It runs at gear 1 first, and increases the gear every 30 seconds until the target gear is reached. Note that the algorithm starts timing the compressor start-up working time Tcomp when the compressor starts.
[0050] S7, Adaptive speed regulation and energy consumption optimization logic:
[0051] 7.1 True energy consumption stage: Based on the dynamic speed adaptation of the compressor's operating cycle, after the algorithm enters the energy consumption state, the first operating cycle of the compressor is used as the "true energy consumption" judgment benchmark, and the core execution logic is as follows;
[0052] 7.2 Learning Iteration and Determination of Optimal Speed: Through the loop of "adjust-stop-record" and "adjust-stop-record", the algorithm will complete adaptive learning within 3-5 compressor operation cycles (the specific number of cycles can be adjusted according to the system stability requirements). After each iteration, the deviation between the current speed-corresponding running time Tcomp and the theoretical working time Tth is compared, and the deviation range is gradually reduced. When the deviation between the compressor running time and the theoretical time is less than 5% (the deviation threshold can be customized) for two consecutive cycles, and the speed is not significantly adjusted, the current speed is determined to be the optimal speed for the refrigeration system under the current energy consumption mode.
[0053] 7.3 False Energy Consumption Stage: Based on the stable operation of the optimal speed, after the optimal speed is determined, the subsequent operation cycle of the press will enter the "false energy consumption operation" mode. The press will directly operate at the learned optimal speed without repeated triggering of adaptive adjustment, thus reducing the system's computing power consumption.
[0054] 7.4 Initial gear optimization, a supplementary strategy to shorten the learning cycle: To avoid excessively long learning time due to a large deviation between the initial gear and the optimal speed, the algorithm sets up a dynamic correction mechanism for the initial gear.
[0055] Please see Figure 5 In S1, the ambient temperature is divided into intervals: (the threshold is only for the interpretation algorithm and may be adjusted in practice):
[0056] Ambient temperature less than or equal to 8℃ is considered interval one;
[0057] Ambient temperature less than or equal to 13℃ is classified as interval two.
[0058] Ambient temperature less than or equal to 19℃ is classified as interval three.
[0059] Ambient temperature less than or equal to 23℃ is classified as interval four;
[0060] Ambient temperature less than or equal to 28℃ is classified as interval five.
[0061] Ambient temperature less than or equal to 35℃ is classified as interval six.
[0062] Ambient temperature less than or equal to 40℃ is classified as interval seven.
[0063] After dividing the ambient temperature into eight intervals (40℃ or higher), the start and stop points of the compressor can be determined by referring to a table based on the set temperature of the freezer compartment and whether it is in quick-freeze mode.
[0064] Furthermore, in S3, the temperature is set, for example: Tf = 10℃, the start-up point Tfk = -16℃, and the temperature difference ΔT = Tf - Tfk is obtained. The freezing temperature difference upgrade level ΔG is obtained by looking up the table.
[0065] Furthermore, in section 5.1, the ambient temperature and humidity conditions are: ambient temperature between 13 and 19 degrees Celsius and between 29 and 35 degrees Celsius, humidity ≤ 75%, for 12 consecutive hours; the mode conditions are: no special modes such as intelligent, quick-freeze, quick-cool, extreme-freeze, and whole-machine refrigeration are set; the fault conditions are: no sensor fault and no communication fault; the door closing condition is: the door is continuously closed for 12 hours; and the gear setting adjustment is: no gear adjustment action for 12 consecutive hours and no compartment closure is set.
[0066] Furthermore, in section 5.2, the ambient temperature and humidity conditions are: ambient temperature exceeding 13–19 degrees Celsius and 29–35 degrees Celsius, humidity >75%, for 20 minutes; the mode conditions are: setting intelligent, quick-freeze, quick-cool, extreme-freeze, and whole-machine refrigeration special modes; the fault conditions are: sensor fault and communication fault; the compartment overload condition is: any compartment overload; the compressor forced stop condition is: forced stop ≥1 time; the door opening condition is: door opening >1 time; the gear setting condition is: gear adjustment action and set compartment closed; the automatic exit condition is: running 3 defrost cycles under energy consumption (4 cycles in total, the first is normal defrosting, and the defrost exit temperature is the energy consumption mode exit temperature).
[0067] Furthermore, in section 7.1, the running time calculation and shutdown condition judgment are as follows: the running time Tcomp of the compressor at the current speed is statistically analyzed in real time, and the key parameters of the refrigeration system (such as evaporator temperature, target temperature difference between compartments, etc.) are monitored to see if they reach the shutdown threshold.
[0068] Adaptive speed increase mechanism: If the compressor runs continuously for 30 minutes (the theoretical time is pre-calculated based on parameters such as ambient temperature, room set temperature, and food storage volume) and has not reached the shutdown point (i.e., the current cooling efficiency is insufficient and cannot meet the room cooling requirements), the algorithm will trigger a step-by-step speed increase. Each increase must be combined with the hardware parameters of the refrigeration system (such as the compressor's maximum setting and the room's heat load) until the system reaches the shutdown condition. The speed after this increase and the corresponding running time are recorded.
[0069] Adaptive speed reduction mechanism: If the actual running time of the compressor is less than 30 minutes of the theoretically calculated working time in the first true energy consumption cycle (the theoretical time is pre-calculated based on parameters such as ambient temperature, room set temperature, and food storage amount), it means that the current speed is too high, resulting in excessive cooling. The algorithm will trigger a step-by-step reduction of the speed until the shutdown condition is reached, and record the speed and running time after this reduction.
[0070] Furthermore, in section 7.3, during operation, the algorithm will still monitor the room temperature and ambient temperature parameters in real time. If a sudden change in heat load occurs (such as frequent door opening or the addition of a large amount of high-temperature food), it will switch back to the "true energy consumption stage" and start a new round of adaptive learning to ensure a dynamic balance between energy consumption and cooling efficiency.
[0071] Furthermore, in section 7.4, when the actual operating gear of the compressor deviates from the theoretically calculated initial gear by more than 3 gears during the first true energy consumption cycle, the algorithm will directly modify the initial value of the "speed-gear" lookup table inside the software, updating the initial gear to the gear corresponding to the speed after the first adaptive adjustment; the corrected initial gear will be used as the default gear for the next startup and energy consumption mode switching, reducing the number of subsequent learning iterations and enabling the system to quickly enter the optimal operating state.
[0072] The working principle and usage process of this invention: The software obtains the compressor start-up point Tfk and stop point Tft based on different ambient temperatures, design modes, and set temperatures. It determines the compressor's initial and maximum speed based on different ambient temperatures and compartment requests. From the ambient temperature range, refrigerator operating status, and set freezing temperature, it looks up the table to obtain the compressor's target speed Gi and maximum speed Gmax at startup. The speed is increased based on the temperature difference. The temperature difference is calculated from the current freezing compartment temperature and the start-up point obtained in the first step. The target compressor speed is then obtained from the table: Gt = Gi + ΔG. When the obtained Gt is greater than Gmax, the highest speed is applied: Gt = Gm. The software uses the algorithm to look up the target compressor speed St from a table, then determines whether to enter or exit energy consumption mode. Based on certain conditions, the software checks if the compressor can enter energy consumption mode. If any condition is not met, it does not enter energy consumption mode. During execution, the software continuously checks if the conditions are met; if one condition is met, it exits energy consumption mode. The first cycle of the compressor runs according to the preset energy consumption states at different ambient temperatures. The compressor operates at gear Gt, corresponding to speed St. After startup, the compressor has a soft start, initially running at gear 1, increasing by one gear every 30 seconds until the target gear is reached. Note that the algorithm starts timing when the compressor starts working. Inter-Tcomp, adaptive speed adjustment and energy consumption optimization logic: In the true energy consumption stage, the speed is dynamically adapted based on the compressor's operating cycle. After the algorithm enters the energy consumption state, the first operating cycle of the compressor is used as the "true energy consumption" judgment benchmark, which is the core execution logic; learning iteration and optimal speed determination: through the loop of "adjust-stop-record" and "adjust-stop-record", the algorithm will complete adaptive learning within 3-5 compressor operating cycles (the specific number of cycles can be adjusted according to the system stability requirements). After each iteration, the deviation between the current speed-corresponding operating time Tcomp and the theoretical working time Tth is compared, and the deviation range is gradually reduced; when two consecutive Within a cycle, if the deviation between the compressor's running time and the theoretical time is less than 5% (the deviation threshold can be customized) and the speed is not significantly adjusted, the current speed is determined to be the optimal speed for the refrigeration system under the current energy consumption mode. In the "false energy consumption" phase: based on the stable operation at the optimal speed, after determining the optimal speed, subsequent compressor running cycles will enter the "false energy consumption operation" mode. The compressor will directly operate at the learned optimal speed without repeatedly triggering adaptive adjustment, reducing system computing power consumption. This is a supplementary strategy for initial speed optimization and shortening the learning cycle. To avoid excessively long learning times due to large deviations between the initial speed and the optimal speed, the algorithm sets a dynamic correction mechanism for the initial speed.
[0073] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.
Claims
1. A self-learning energy consumption level integrated control algorithm for refrigerator main transformer, characterized in that: The following steps are included: S1. The software obtains the compressor start-up point Tfk and stop-down point Tft based on different ambient temperatures, design modes, and set temperatures. S2. Determine the initial and maximum compressor settings based on the ambient temperature and compartment requirements of different modes. Refer to the table below to obtain the target setting Gi and maximum setting Gmax when the compressor starts, based on the ambient temperature range, refrigerator operating status, and set freezing temperature. S3. Based on the temperature difference adjustment, calculate the temperature difference from the current temperature of the freezer compartment and the difference between the start-up point obtained in the first step; S4. Find the target compressor speed from the table: Gt = Gi + ΔG. If the obtained Gt is greater than Gmax, then operate at the highest gear: Gt = Gmax. Find the compressor's target operating speed St from the table. S5. Determine the energy consumption entering and exiting: 5.1 The software determines whether the compressor can enter the energy consumption mode based on certain conditions. If any condition is not met, the compressor will not enter the energy consumption mode. 5.2 The software continuously checks whether the conditions are met during execution. Once one condition is met, it exits the energy consumption mode. S6. According to the preset compressor state under different ambient temperature and energy consumption conditions, the compressor runs the first cycle. The compressor runs at the gear Gt, which corresponds to the speed St. After the compressor starts, there is a soft start. It runs at gear 1 first, and increases the gear every 30 seconds until the target gear is reached. Note that the algorithm starts timing the compressor start-up working time Tcomp when the compressor starts. S7, Adaptive speed regulation and energy consumption optimization logic: 7.1 True energy consumption stage: Based on the dynamic speed adaptation of the compressor's operating cycle, after the algorithm enters the energy consumption state, the first operating cycle of the compressor is used as the "true energy consumption" judgment benchmark, and the core execution logic is as follows; 7.2 Learning Iteration and Determination of Optimal Speed: Through the loop of "adjust-stop-record" and "adjust-stop-record", the algorithm will complete adaptive learning within 3-5 compressor operation cycles. After each iteration, the deviation between the current speed-corresponding running time Tcomp and the theoretical working time Tth is compared, and the deviation range is gradually reduced. When the deviation between the compressor running time and the theoretical time is less than 5% in two consecutive cycles, and the speed is not significantly adjusted, the current speed is determined to be the optimal speed for the refrigeration system under the current energy consumption mode. 7.3 False Energy Consumption Stage: Based on the stable operation of the optimal speed, after the optimal speed is determined, the subsequent operation cycle of the press will enter the "false energy consumption operation" mode. The press will directly operate at the learned optimal speed without repeated triggering of adaptive adjustment, thus reducing the system's computing power consumption. 7.4 Initial gear optimization, a supplementary strategy to shorten the learning cycle: To avoid excessively long learning time due to a large deviation between the initial gear and the optimal speed, the algorithm sets up a dynamic correction mechanism for the initial gear.
2. The self-learning energy consumption level refrigerator main transformer integrated control algorithm as described in claim 1, characterized in that: In S1, after dividing the ambient temperature into zones, the start and stop points of the compressor are obtained by looking up a table based on the set temperature of the freezer compartment and whether it is in quick-freeze mode.
3. The self-learning energy consumption level refrigerator main transformer integrated control algorithm as described in claim 1, characterized in that: In step S3, the temperature Tf and the start-up point Tfk are set to obtain the temperature difference ΔT = Tf - Tfk. The freezing temperature difference upgrade level ΔG is obtained by looking up the table.
4. The self-learning energy consumption level refrigerator main transformer integrated control algorithm as described in claim 1, characterized in that: In section 5.1, the ambient temperature and humidity conditions are: ambient temperature between 13 and 19 degrees Celsius and between 29 and 35 degrees Celsius, humidity ≤ 75%, for a continuous period of 12 hours; the mode conditions are: no special modes such as intelligent, quick-freeze, quick-cool, extreme-freeze, and whole-machine refrigeration are set; and the fault conditions are: no sensor faults and no communication faults. The door closing condition is that the door is continuously closed for 12 hours; the gear setting adjustment is based on 12 consecutive hours without gear adjustment and no room being set to close.
5. The self-learning energy consumption level refrigerator main transformer integrated control algorithm as described in claim 1, characterized in that: In section 5.2, the ambient temperature and humidity conditions are: ambient temperature exceeding 13–19 degrees Celsius and 29–35 degrees Celsius, humidity >75%, for 20 minutes; the mode conditions are: setting intelligent, quick-freeze, quick-cool, extreme-freeze, and whole-machine refrigeration special modes; the fault conditions are: sensor fault and communication fault; the compartment overload condition is: any compartment overload; the compressor forced stop condition is: forced stop ≥1 time; the door opening condition is: door opening >1 time; the gear setting condition is: gear adjustment action and set compartment closed; the automatic exit condition is: running for 3 defrost cycles under energy consumption.
6. The self-learning energy consumption level refrigerator main transformer integrated control algorithm as described in claim 1, characterized in that: In section 7.1, the running time calculation and shutdown condition judgment are as follows: the running time Tcomp of the compressor at the current speed is statistically analyzed in real time, and the key parameters of the refrigeration system are monitored to see if they reach the shutdown threshold. Adaptive speed increase mechanism: If the compressor runs continuously for 30 minutes and does not reach the stop point, the algorithm will trigger a step-by-step speed increase. Each increase must be combined with the hardware parameters of the refrigeration system until the system reaches the stop condition. Record the speed after this increase and the corresponding running time. Adaptive speed reduction mechanism: If the actual running time of the compressor is less than 30 minutes of the theoretically calculated working time in the first true energy consumption cycle, it means that the current speed is too high and causes excessive cooling. The algorithm will trigger a step-by-step speed reduction until the shutdown condition is reached. Record the speed and running time after this reduction.
7. The self-learning energy consumption level refrigerator main transformer integrated control algorithm as described in claim 1, characterized in that: In section 7.3, during operation, the algorithm will continue to monitor the room temperature and ambient temperature parameters in real time. If a sudden change in heat load occurs, it will switch back to the "true energy consumption stage" and start a new round of adaptive learning to ensure a dynamic balance between energy consumption and cooling efficiency.
8. The self-learning energy consumption level refrigerator main transformer integrated control algorithm as described in claim 1, characterized in that: In section 7.4, when the actual operating gear of the compressor deviates from the theoretically calculated initial gear by more than 3 gears during the first true energy consumption cycle, the algorithm will directly modify the initial value of the "speed-gear" lookup table inside the software, and update the initial gear to the gear corresponding to the speed after the first adaptive adjustment; the corrected initial gear will be used as the default gear for the next start-up and switching of energy consumption mode, reducing the number of subsequent learning iterations and enabling the system to quickly enter the optimal operating state.