An intelligent refrigeration equipment adaptive energy-saving control method and system

Abstract

The application relates to the technical field of refrigeration control, and discloses an intelligent refrigeration equipment adaptive energy-saving control method and system, which comprises the following steps: acquiring real-time environment parameters outside a controlled environment as first real-time environment parameters, and determining variation characteristic values of the external environment parameters based on the first real-time environment parameters at different sampling moments; comparing the variation characteristic values with preset fluctuation threshold values, and dynamically adjusting a feedback period for effect evaluation of the controlled environment according to a comparison result; acquiring real-time state parameters of the controlled environment as second real-time environment parameters at a starting moment of the feedback period, and determining a deviation of the second real-time environment parameters from a preset target value; and adjusting operation control parameters of refrigeration equipment according to the deviation and the length of the current feedback period. The application fundamentally solves the hysteresis problem of traditional fixed period control, and significantly improves the adaptive capacity, temperature control precision and energy-saving effect of the system.

Description

Technical Field

[0001] This application relates to the field of refrigeration control technology, and more specifically, to an adaptive energy-saving control method and system for intelligent refrigeration equipment. Background Technology

[0002] In cold chain logistics centers and other environments requiring precise temperature control, refrigeration equipment typically employs a control system to maintain a stable ambient temperature. The conventional control method involves the system checking the internal temperature at fixed intervals, such as every 15 minutes, and comparing it to a set target temperature. If the temperature is too high, the compressor power is increased; if the temperature is too low, the power is reduced. This method is effective when the external environment is relatively stable.

[0003] However, in actual operation, changes in the external environment are often drastic and unpredictable. For example, a sudden thunderstorm on a summer afternoon can cause the outdoor temperature to drop by more than ten degrees Celsius in a short period of time, which can greatly improve the heat dissipation efficiency of refrigeration equipment. However, because the control system still responds and adjusts according to the original fixed cycle, it cannot detect this beneficial change in time, causing the system to continue to operate at excessive power, resulting in unnecessary energy waste and excessively low storage temperature. Conversely, if the outdoor temperature rises sharply due to sunlight, the fixed and long feedback cycle will cause the system to respond slowly, unable to quickly increase the cooling output to counteract the increased heat load, thus causing the storage temperature to exceed the standard and affecting the quality of stored products. This time difference between the control strategy and the actual load change is the core reason why the existing technology has poor temperature control performance and high energy consumption in dynamic environments.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this application discloses an adaptive energy-saving control method and system for intelligent refrigeration equipment, aiming to solve the problems of control lag, poor temperature control accuracy, and high energy consumption caused by the use of fixed feedback cycles in existing technologies.

[0006] In a first aspect, this application discloses an adaptive energy-saving control method for intelligent refrigeration equipment, used to control the refrigeration equipment to cool a controlled environment. The method includes the following steps: The real-time environmental parameters outside the controlled environment are obtained as the first real-time environmental parameters, and the change characteristic values ​​of the external environmental parameters are determined based on the first real-time environmental parameters at different sampling times. The changing characteristic values ​​are compared with the preset fluctuation threshold, and the feedback cycle for evaluating the effect on the controlled environment is dynamically adjusted based on the comparison results. At the beginning of the feedback cycle, the real-time state parameters of the controlled environment are acquired as the second real-time environment parameters, and the deviation between the second real-time environment parameters and the preset target value is determined. Based on the deviation and the length of the current feedback cycle, adjust the operating control parameters of the refrigeration equipment so that the cooling capacity output of the refrigeration equipment responds to changes in the load of the external environment.

[0007] This technical solution incorporates the degree of change in the external environment into the control logic, enabling the system to dynamically adjust its response speed according to external changes. When the external environment changes drastically, the feedback cycle is shortened to achieve a rapid response, while when the external environment is stable, the feedback cycle is extended to save energy. This fundamentally solves the lag problem of traditional fixed-cycle control and significantly improves the system's adaptability, temperature control accuracy, and energy-saving effect.

[0008] Furthermore, the steps of acquiring real-time environmental parameters outside the controlled environment as the first real-time environmental parameter, and determining the change characteristic values ​​of the external environmental parameters based on the first real-time environmental parameter at different sampling times, include: The first real-time environmental parameter is acquired at a preset first sampling interval and stored in the historical parameter sequence; Read the first real-time environmental parameter at the current moment and the first real-time environmental parameter from the previous moment in the historical parameter sequence; Based on the difference between the first real-time environmental parameter at the current time and the previous time and the first sampling interval, the first rate of change of the first real-time environmental parameter is calculated, and the first rate of change is used as the change characteristic value.

[0009] This technical solution provides a specific and quantifiable way to calculate the characteristic values ​​of changes in the external environment. Specifically, it characterizes the severity of changes by calculating the rate of change of parameters, so that the judgment of changes is no longer a vague qualitative description, but a precise numerical value, providing a reliable input basis for subsequent dynamic adjustments.

[0010] Furthermore, the fluctuation threshold includes a first threshold and a second threshold, wherein the second threshold is greater than the first threshold; The steps for dynamically adjusting the feedback cycle for evaluating the effectiveness of the controlled environment based on the comparison results include: If the changing characteristic value is less than or equal to the first threshold, the feedback period remains the standard period. If the change characteristic value is greater than the first threshold and less than or equal to the second threshold, the feedback period will be adjusted from the standard period to the first shortened period. If the change characteristic value is greater than the second threshold, the feedback period will be adjusted to the second shortened period. The second shortening period is smaller than the first shortening period, and the first shortening period is smaller than the standard period.

[0011] This technical solution establishes a multi-level feedback cycle adjustment strategy, which can match different feedback cycles of varying lengths according to the different degrees of intensity of external environmental changes (stable, moderate fluctuations, and severe fluctuations). This enables hierarchical management of control response sensitivity, making control behavior more refined. It can respond quickly to drastic changes while avoiding over-responding to minor fluctuations, thereby improving the stability and rationality of control.

[0012] Furthermore, the steps for adjusting the operating control parameters of the refrigeration equipment based on the deviation and the length of the current feedback cycle include: When the deviation exceeds the preset allowable fluctuation range, the adjustment step size of the refrigeration equipment's actuator is determined according to the positive or negative direction and magnitude of the deviation. Based on the length of the current feedback cycle, the adjustment step size is corrected to obtain the final adjustment step size; where the longer the feedback cycle, the larger the final adjustment step size. Adjust the operating control parameters of the refrigeration equipment according to the final adjustment step size; the operating control parameters include the operating frequency of the compressor and / or the speed of the blower in the refrigeration equipment.

[0013] This technical solution links the length of the feedback cycle with the magnitude of the adjustment action, forming a coordinated adjustment mechanism. In stable environments with long feedback cycles, the system can make larger adjustments to quickly correct deviations; while in turbulent environments with short feedback cycles, it performs small, rapid fine-tuning. This design effectively suppresses overshoot and system oscillations that may occur during rapid response, further enhancing the stability of the control process.

[0014] Furthermore, the method also includes: Based on the current operating control parameters and the first real-time environmental parameters, combined with the preset cooling output model, the first expected temperature curve of the controlled environment in the future period is calculated. The second real-time environmental parameters of the controlled environment are obtained at a preset second sampling interval, and the second actual change trend of the second real-time environmental parameters over time is determined. The second actual trend is compared with the first expected temperature curve to determine the degree of trend deviation.

[0015] This technical solution introduces a forward-looking prediction-verification mechanism. Instead of passively waiting for the temperature to deviate from the target value, the system actively predicts the future trend of the temperature and, by comparing it with the actual trend, can detect early signs of discrepancies between the control effect and expectations. This represents a shift from post-event correction to in-event intervention, significantly improving the foresight and proactiveness of the control.

[0016] Furthermore, the method also includes: When the trend deviation exceeds the preset deviation threshold, a priority response command is generated to forcibly interrupt the timing of the current feedback cycle.

[0017] This technical solution establishes a circuit breaker mechanism. When the forecast deviates significantly from the actual situation, indicating a potential emergency that conventional adjustments cannot handle, the system can decisively interrupt the current waiting period and immediately enter the response process. This provides a rapid channel for dealing with emergency or unexpected load shocks, greatly enhancing the system's robustness and security.

[0018] Furthermore, after the priority response command is triggered, the feedback cycle is switched to a preset emergency verification cycle until the second actual change trend recovers to the allowable range corresponding to the first expected temperature curve. The emergency verification period is less than or equal to the second shortened period.

[0019] This technical solution clarifies the specific response measures after the circuit breaker mechanism is triggered, namely, switching to the shortest emergency verification cycle and forcing the system to monitor and adjust at the highest frequency until the system state returns to control. This ensures that the system can recover stability as quickly as possible after an abnormal event occurs, minimizing the amplitude and duration of temperature fluctuations.

[0020] Furthermore, the method also includes: The response lag time after adjusting the operating control parameters of the refrigeration equipment, which is the time when the second real-time environmental parameter of the controlled environment begins to change in a trend. Determine the response coordination coefficient by comparing the response lag time with the length of the current feedback cycle; The feedback period after the next adjustment is corrected using the response coordination coefficient so that the corrected feedback period is greater than or equal to the response lag time.

[0021] This technical solution adds a self-reflection and optimization capability to the control system. By measuring the system's response lag time, the system can assess whether the current feedback cycle is set too short. If the effect of the adjustment action has not yet manifested before the system begins the next evaluation and adjustment, it can easily lead to oscillations. This solution ensures that the feedback cycle setting fully considers the system's physical inertia, avoiding instability caused by premature adjustment.

[0022] Furthermore, the steps to determine the response coordination coefficient by comparing the response lag time with the length of the current feedback cycle include: Determine if the response lag time is greater than the current feedback cycle; If the response lag time is greater than the current feedback cycle, it is determined that there is a risk of premature oscillation in the current adjustment, and the response coordination coefficient is set to a compensation gain greater than 1; the compensation gain is used to extend the duration of the next feedback cycle. If the response lag time is less than or equal to the current feedback period, the current adjustment is determined to be in a controlled convergence state, and the response coordination coefficient is set to 1.

[0023] This technical solution provides the specific logic for achieving the self-optimization described in the previous technical solution. By judging the relationship between response lag time and feedback period, the system can autonomously diagnose potential oscillation risks and automatically extend the next feedback period with a compensation gain greater than 1 to actively suppress them. This allows the entire adaptive control system to form a self-correcting closed loop, further ensuring its stability during long-term operation.

[0024] Secondly, this application also discloses an intelligent refrigeration equipment adaptive energy-saving control system for controlling the refrigeration equipment to cool a controlled environment, including: The change characteristic determination module is used to acquire real-time environmental parameters outside the controlled environment as the first real-time environmental parameter, and determine the change characteristic value of the external environmental parameter based on the first real-time environmental parameter at different sampling times. The feedback cycle adjustment module is used to compare the changing characteristic value with the preset fluctuation threshold and dynamically adjust the feedback cycle for evaluating the effect of the controlled environment based on the comparison result. The state deviation determination module is used to acquire the real-time state parameters of the controlled environment at the beginning of the feedback cycle, as the second real-time environment parameter, and determine the deviation between the second real-time environment parameter and the preset target value. The operation control and adjustment module is used to adjust the operation control parameters of the refrigeration equipment according to the deviation and the length of the current feedback cycle, so that the cooling capacity output of the refrigeration equipment responds to the load changes of the external environment.

[0025] This technical solution provides a physical device capable of executing the above method, materializing each functional step in the method into specific hardware or software functional modules, and providing a clear system architecture for the actual deployment and application of this energy-saving control method.

[0026] In summary, the intelligent refrigeration equipment adaptive energy-saving control method and system provided in this application dynamically and intelligently adjusts the feedback evaluation cycle of the internal control system by monitoring the drastic changes in external environmental parameters in real time. When the external environment is stable, the system uses a longer standard cycle for evaluation and adjustment, avoiding unnecessary frequent actions and thus saving energy. When the external environment changes drastically, such as a sudden rise or fall in outdoor temperature, the system can quickly shorten the feedback cycle, increasing the frequency of control adjustment. This allows the refrigeration capacity output of the refrigeration equipment to closely follow changes in the external load, enabling the system to achieve energy savings under stable operating conditions and ensure timely and accurate temperature control under dynamic operating conditions. This effectively avoids large fluctuations in storage temperature and energy waste caused by control lag, achieving a dual improvement in temperature control accuracy and operational energy efficiency. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating an adaptive energy-saving control method for intelligent refrigeration equipment provided in an embodiment of this application.

[0028] Figure 2 This is a schematic diagram of the structure of an adaptive energy-saving control system for an intelligent refrigeration device provided in an embodiment of this application.

[0029] Labeling Explanation: 210, Change Characteristics Determination Module; 220, Feedback Cycle Adjustment Module; 230, State Deviation Determination Module; 240, Operation Control Adjustment Module. Detailed Implementation

[0030] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0031] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0032] In a large fresh food cold chain logistics center, the cold storage warehouses need to maintain an internal temperature of -18 degrees Celsius year-round to ensure the quality of frozen meat products. This logistics center is located in a region with variable climate, often experiencing thunderstorms in the summer afternoons. Under traditional control methods, the refrigeration system's control unit typically checks and adjusts the internal temperature at fixed intervals, such as every 30 minutes. On a sunny summer afternoon, with outdoor temperatures reaching 38 degrees Celsius, the refrigeration system operates at high power to combat the enormous heat load.

[0033] However, a sudden thunderstorm caused the outdoor temperature to plummet to 22 degrees Celsius within 15 minutes. Because the control system still needed to wait for the next 30-minute detection cycle, it couldn't detect the rapid improvement in external heat dissipation in time, causing the refrigeration compressor and condenser fan to continue operating at unnecessarily high power. This not only resulted in significant energy waste but could also lead to excessive temperature drops inside the storage room, a phenomenon known as "overcooling," affecting the quality of the goods. Conversely, when the rain stopped and the sun shone brightly, causing the outdoor temperature to rise rapidly, the fixed long-cycle feedback would also make the system sluggish, unable to increase the cooling capacity in time, causing the storage room temperature to exceed the -18 degree Celsius upper limit for a period of time, posing a food safety hazard. This response delay between the control strategy and actual load changes is the fundamental reason why existing technologies struggle to balance temperature control accuracy and energy efficiency in dynamic environments.

[0034] In this regard, firstly, referring to Figure 1 This application provides an adaptive energy-saving control method for intelligent refrigeration equipment. This method is used to control the refrigeration equipment to cool a controlled environment, and its steps include: S1. Obtain the real-time environmental parameters outside the controlled environment as the first real-time environmental parameter, and determine the change characteristic value of the external environmental parameter based on the first real-time environmental parameter at different sampling times. S2. Compare the changing characteristic values ​​with the preset fluctuation threshold, and dynamically adjust the feedback cycle for evaluating the effect of the controlled environment based on the comparison results. S3. At the beginning of the feedback cycle, obtain the real-time state parameters of the controlled environment as the second real-time environment parameters, and determine the deviation between the second real-time environment parameters and the preset target value. S4. Adjust the operating control parameters of the refrigeration equipment according to the deviation and the length of the current feedback cycle so that the cooling capacity output of the refrigeration equipment responds to the load changes of the external environment.

[0035] In the context of this application, a controlled environment specifically refers to a physical space that requires temperature control, such as the cold storage warehouse in a cold chain logistics center, the refrigerated display case in a fresh food supermarket, or the cool storage room for medicines.

[0036] The first real-time environmental parameter refers to a physical quantity located outside the controlled environment that can directly or indirectly affect the heat load of the controlled environment. This is not a single parameter, but a set of parameters. In one specific implementation, this parameter can be the outdoor air temperature measured near the outdoor unit or condenser of the refrigeration system, as this temperature directly determines the heat dissipation efficiency of the refrigeration system. In another implementation, this parameter can also be the radiative flux value measured by a solar radiation intensity sensor installed on the building's roof or exterior wall, because direct sunlight significantly increases the building's heat gain. In more complex systems, the first real-time environmental parameter can also include outdoor air humidity, wind speed, etc., which together constitute a comprehensive description of the external load on the refrigeration system.

[0037] The second real-time environmental parameter refers to a physical quantity that characterizes the current state inside the controlled environment. The most crucial parameter is the air temperature within the controlled environment, which is typically obtained by averaging or weighted averaging multiple temperature sensors placed at different locations within the space to reflect the overall temperature level. In addition, depending on the application requirements, the second real-time environmental parameter may also include internal air humidity, the surface temperature of the object being cooled, etc.

[0038] The feedback period is a core variable parameter in this technical solution. It defines the time interval between the start of one state assessment and control adjustment action of the control system and the start of the next state assessment and control adjustment action. In conventional technologies, this is a fixed value, while in this application, it is a variable that is dynamically adjusted according to changes in the external environment.

[0039] The change characteristic value is a numerical value used to quantify the degree of change of a first real-time environmental parameter over a period of time. The larger the value, the faster and more drastic the change in the external environment; the smaller the value, the more stable the external environment.

[0040] Operating control parameters refer to the command signals or set values ​​output by the control system to each actuator in the refrigeration equipment. These parameters directly determine the cooling capacity output of the refrigeration equipment. For example, for a system using a variable frequency compressor, the operating control parameter could be the compressor's target operating frequency. For an air-cooled system, the operating control parameters could include the speed of the evaporator-side blower. For more complex systems, these parameters might also include the opening degree of the electronic expansion valve, the state of the four-way reversing valve, etc.

[0041] The overall process of the adaptive energy-saving control method for this intelligent refrigeration equipment is described in detail below.

[0042] The core idea of ​​this method is to enable the refrigeration control system to actively match the rate of change of the external environment.

[0043] First, the system needs to continuously sense changes in the external environment. This is achieved by deploying sensors outside the controlled environment. Taking a cold chain warehouse as an example, one or more temperature sensors, such as industrial-grade Pt100 platinum resistance thermometers, are installed outdoors in well-ventilated locations away from direct sunlight to measure the outdoor ambient temperature. These sensors are connected to a central controller (such as a programmable logic controller (PLC) or industrial computer (IPC) via a 4-20 mA current loop or an RS485 bus. The controller continuously reads the sensor values ​​at a fixed, very short hardware sampling period (e.g., once per second) to obtain a continuous first real-time environmental parameter time series.

[0044] Next, based on the acquired first real-time environmental parameter sequence, the controller calculates a change characteristic value that can characterize its recent trend. A basic implementation is that the controller can calculate the difference between the maximum and minimum values ​​of the parameter over a recent period, such as the past 5 minutes. For example, if the outdoor temperature rose from 25 degrees Celsius to 26 degrees Celsius in the past 5 minutes, then this difference is 1 degree Celsius, and this 1 can be used as the change characteristic value. Although this method is simple, it can initially reflect the fluctuation range of the parameter.

[0045] The controller then compares the calculated change characteristic value with a preset fluctuation threshold. This threshold, set based on experience or historical data analysis, represents the boundary between stability and fluctuation as perceived by the system administrator. For example, the fluctuation threshold could be set to 0.5 degrees Celsius. If the calculated change characteristic value (temperature difference over the past 5 minutes) is less than or equal to 0.5 degrees Celsius, the controller determines that the current external environment is in a relatively stable state. If it is greater than 0.5 degrees Celsius, it is determined to be in a fluctuating state. Based on this determination, the controller dynamically sets the feedback cycle for the next internal state assessment. If the environment is determined to be stable, the feedback cycle is set to a longer standard value, such as 30 minutes; if the environment is determined to be fluctuating, a shorter emergency value is set, such as 5 minutes.

[0046] At the start of each feedback cycle, whether the cycle is 30 minutes or 5 minutes, the controller performs an assessment of the internal state of the controlled environment. It reads multiple temperature sensors installed inside the cold storage (i.e., the second real-time environmental parameters) and calculates the current average temperature inside the storage. Subsequently, this average temperature is compared with a preset target value (e.g., -18 degrees Celsius) to obtain a temperature deviation value.

[0047] Finally, the controller uses this temperature deviation value and the current feedback cycle length to jointly determine how to adjust the operation of the refrigeration equipment. For example, when the deviation is +0.5 degrees Celsius (i.e., the storage temperature is too high), if the current feedback cycle is 30 minutes, the controller might consider this a slowly accumulating deviation and make a relatively strong adjustment, such as increasing the compressor frequency by 5 Hz, in order to bring the temperature back down within one cycle. However, if the current feedback cycle is 5 minutes, the controller will take a more conservative approach, perhaps only increasing the compressor frequency by 1 Hz. This is because a short cycle means the system will be evaluated again quickly, and fine-tuning can avoid system oscillations caused by overreaction. This strategy of linking the adjustment magnitude to the feedback cycle length is key to ensuring the system maintains stability while responding quickly.

[0048] Returning to the initial application scenario using the above method, when a thunderstorm causes a sudden drop in outdoor temperature, the controller quickly calculates a significant change in characteristic value and immediately shortens the feedback cycle from 30 minutes to 5 minutes. After 5 minutes, the system performs an internal assessment. Even if the storage temperature is only just beginning to drop and hasn't deviated too much from the target value, the system will react in advance based on this trend, reducing compressor power. This effectively avoids the severe overcooling and energy waste caused by response lag in traditional methods.

[0049] Based on the above scheme, in order to more accurately quantify changes in the external environment, a specific method for determining the characteristic values ​​of changes in external environmental parameters includes: The first real-time environmental parameter is acquired at a preset first sampling interval and stored in the historical parameter sequence; Read the first real-time environmental parameter at the current moment and the first real-time environmental parameter from the previous moment in the historical parameter sequence; Based on the difference between the first real-time environmental parameter at the current time and the previous time and the first sampling interval, the first rate of change of the first real-time environmental parameter is calculated, and the first rate of change is used as the change characteristic value.

[0050] This step concretizes and scientizes the vague concept of calculating characteristic values ​​of change. Simply using the difference over a period of time as the characteristic value cannot distinguish the rate of change. For example, a change of 1 degree Celsius in 5 minutes is completely different from a change of 1 degree Celsius in 1 minute. Therefore, introducing the concept of rate of change is crucial.

[0051] In practice, the controller sets a first sampling interval, such as 1 minute, for calculating the rate of change. Internally, the controller maintains a storage queue to store environmental parameter values ​​from several past sampling moments. At the current moment T, the controller reads an outdoor temperature of 28 degrees Celsius. Simultaneously, it retrieves the temperature value from the storage queue, T minus 1 minute, let's say 27.8 degrees Celsius. Therefore, the first rate of change can be calculated as (28 - 27.8) degrees Celsius / 1 minute = 0.2 degrees Celsius / minute. This 0.2 is the characteristic value of change at the current moment, accurately representing the instantaneous rate of change of the external temperature. Compared to using only the difference, the rate of change is a more stable and physically meaningful indicator, providing a more reliable basis for subsequent feedback cycle adjustments.

[0052] To make the adjustment of the feedback cycle more precise and to match different levels of external environmental fluctuations, the fluctuation threshold may include a first threshold and a second threshold, wherein the second threshold is greater than the first threshold. The steps for dynamically adjusting the feedback cycle for evaluating the effectiveness of the controlled environment based on the comparison results include: If the changing characteristic value is less than or equal to the first threshold, the feedback period remains the standard period. If the change characteristic value is greater than the first threshold and less than or equal to the second threshold, the feedback period will be adjusted from the standard period to the first shortened period. If the change characteristic value is greater than the second threshold, the feedback period will be adjusted to the second shortened period. The second shortening period is smaller than the first shortening period, and the first shortening period is smaller than the standard period.

[0053] This step upgrades the feedback cycle adjustment strategy from a simple binary choice (stable / volatile) to a multi-level, sophisticated response mechanism. In the real world, environmental changes are not black and white, but rather exist on a continuous spectrum from calm to stormy. By employing multi-level thresholds, the response sensitivity of the control system can be matched to the severity of external disturbances.

[0054] In one specific embodiment, the following configuration can be made: The standard cycle is set at 30 minutes, which is the lowest frequency assessment used when the external environment is extremely stable (such as late at night) to maximize energy savings.

[0055] The first shortened cycle is set at 10 minutes to deal with moderate fluctuations.

[0056] The second shortened cycle is set to 3 minutes to respond to drastic and urgent changes in the external environment.

[0057] Correspondingly, the first threshold is set to 0.1 degrees Celsius per minute, and the second threshold is set to 0.5 degrees Celsius per minute.

[0058] Now, the controller's decision logic becomes: When the calculated rate of change of outdoor temperature is less than or equal to 0.1 degrees Celsius per minute, the system is determined to be in a stable state and a standard cycle of 30 minutes is adopted.

[0059] When the rate of change is between 0.1 and 0.5 degrees Celsius per minute, the system determines it as a moderate fluctuation, switches to the first shortened period of 10 minutes, and raises alert levels.

[0060] When the rate of change exceeds 0.5 degrees Celsius per minute, the system determines it to be a violent fluctuation, immediately switches to a second shortened cycle of 3 minutes, enters a state of high alert, and responds as quickly as possible.

[0061] This graded response mechanism enables the control system to find the optimal balance between safety and economy under various operating conditions.

[0062] After determining that adjustment is needed, in order to match the magnitude of the adjustment action with the current response rhythm and avoid overshoot or undershoot, the steps for adjusting the operating control parameters of the refrigeration equipment, based on the deviation and the length of the current feedback cycle, may include: When the deviation exceeds the preset allowable fluctuation range, the adjustment step size of the refrigeration equipment's actuator is determined according to the positive or negative direction and magnitude of the deviation. Based on the length of the current feedback cycle, the adjustment step size is corrected to obtain the final adjustment step size; where the longer the feedback cycle, the larger the final adjustment step size. Adjust the operating control parameters of the refrigeration equipment according to the final adjustment step size; the operating control parameters include the operating frequency of the compressor and / or the speed of the blower in the refrigeration equipment.

[0063] This step further refines the details of control adjustment by introducing a linkage mechanism between the adjustment step size and the feedback cycle length. The underlying physical logic is that the system's response has inertia, and the effect of an adjustment action requires time to fully manifest. With a very short feedback cycle, if large adjustments are made each time, the next adjustment may begin before the effect of the previous adjustment has been fully realized. This can easily cause the system to oscillate around the target value, a phenomenon known as "overshoot."

[0064] In practice, the controller's adjustment algorithm can be divided into two steps.

[0065] The first step is to determine a basic adjustment step size based on the current temperature deviation. For example, a simple proportional control logic can be used: basic adjustment step size (e.g., the increment of compressor frequency) = proportional coefficient K * temperature deviation. Assuming K = 10 (Hz / degree Celsius), and the current storage temperature is 0.3 degrees Celsius higher than the target, then the basic adjustment step size is 10 * 0.3 = 3 Hz.

[0066] The second step is to adjust this base step size using the current feedback cycle length. The adjustment formula can be designed as: Final adjustment step size = Base adjustment step size * (Current feedback cycle / Standard cycle). Here, the standard cycle (e.g., 30 minutes) serves as a benchmark.

[0067] The specific scenarios are as follows: Scenario 1: The external environment is stable, and the system is in a standard 30-minute cycle. In this case, the final adjustment step size = 3 Hz * (30 minutes / 30 minutes) = 3 Hz. The controller will directly increase the compressor frequency by 3 Hz. This is a complete and standard adjustment action.

[0068] Scenario 2: The external environment fluctuates drastically, and the system is in its second shortening cycle of 3 minutes. At this point, the final adjustment step size = 3 Hz * (3 minutes / 30 minutes) = 0.3 Hz. The controller will only fine-tune the compressor frequency by 0.3 Hz. This is a very cautious, tentative adjustment. The system will re-evaluate after 3 minutes; if the deviation persists, another fine-tuning will be performed.

[0069] In this way, the system ensures that the control process is convergent and stable under any external environment, avoiding increased energy consumption and frequent equipment start-ups and shutdowns caused by inappropriate adjustment. The adjustment command is ultimately sent to the frequency setpoint port of the frequency converter or the control port of the fan speed controller through a digital or analog output module to complete the physical control execution.

[0070] Building upon the aforementioned adaptive adjustment feedback cycle, this application also provides a model-predictive control optimization method to further enhance the forward-looking nature of the control system and avoid correction only after actual temperature deviations occur. Traditional control methods are essentially reactive, waiting for temperature deviations to actually occur before taking action. However, in some extremely temperature-sensitive scenarios, even brief deviations can cause losses. Therefore, a better strategy is to predict future temperature trends and intervene proactively when a deviation is anticipated.

[0071] Therefore, the method may also include: Based on the current operating control parameters and the first real-time environmental parameters, combined with the preset cooling output model, the first expected temperature curve of the controlled environment in the future period is calculated. The second real-time environmental parameters of the controlled environment are obtained at a preset second sampling interval, and the second actual change trend of the second real-time environmental parameters over time is determined. The second actual trend is compared with the first expected temperature curve to determine the degree of trend deviation.

[0072] Trend deviation is a dimensionless quantitative indicator used to quantify the difference between the actual temperature change trend of a controlled environment and the temperature change trend predicted by the model. It is used to determine whether the current control effect meets expectations and whether the system is in a normal controlled state. This indicator provides a forward-looking judgment basis for the control system. In this application, trend deviation is the overall deviation value obtained by comparing the second actual temperature change trend with the first expected temperature curve in a time-synchronized manner.

[0073] The core of this step is the cooling output model. This model is a mathematical function or algorithm whose inputs are the current system operating state (such as compressor frequency and fan speed) and external environmental conditions (such as outdoor temperature), and whose output is a prediction of the controlled environmental temperature change over a future period of time.

[0074] This model can be implemented in several ways. One is a physical model based on thermodynamic principles, which calculates based on physical laws such as the enthalpy-humidity diagram of the refrigerant cycle, the heat transfer coefficient of the heat exchanger, and the heat transfer of the building envelope. Its advantage is strong interpretability, but the modeling is complex. Another is a machine learning model based on historical data, such as Long Short-Term Memory (LSTM) networks or Gated Recurrent Units (GRUs). By collecting a large amount of operating data from the system under various operating conditions (including operating parameters, internal and external environmental parameters, and corresponding temperature changes), a model capable of accurately predicting the dynamic temperature response can be trained.

[0075] In one specific embodiment, the controller incorporates a pre-trained LSTM model. After each adjustment action is executed, the controller immediately takes the current compressor frequency (e.g., 45 Hz), the blower speed (e.g., 1200 rpm), and the current outdoor temperature (e.g., 28 degrees Celsius) as inputs and calls the LSTM model. The model outputs a temperature prediction sequence for the next 15 minutes, for example, [t+1 minute: -18.1°C, t+2 minutes: -18.2°C, ..., t+15 minutes: -18.5°C], which constitutes the first expected temperature curve.

[0076] Meanwhile, the controller continuously reads the actual temperature in the library at a second sampling interval (e.g., every 10 seconds) that is much shorter than the feedback cycle, forming a second actual temperature trend. By comparing the actual temperature point every 10 seconds with the predicted value at the corresponding time point on the expected temperature curve, the controller can calculate a trend deviation. This deviation can be the root mean square error (RMSE) or the maximum absolute error between the actual and predicted values ​​over the past minute.

[0077] This mechanism of comparing predictions with actual results allows the system to detect anomalies that are difficult to detect using conventional methods. For example, if a small door in a cold storage room is not closed tightly, this slight increase in heat load may not immediately cause a significant rise in the storage temperature, but it will lead to a slower actual cooling rate than the model predicts. This deviation in trend will be detected by the system in advance, thus enabling more timely intervention.

[0078] When predictions deviate significantly from reality, it indicates that unforeseen circumstances may have arisen in the system that the model had not considered. In such cases, continuing to wait for the current feedback cycle to end is unwise. To address this, the method also includes: When the trend deviation exceeds the preset deviation threshold, a priority response command is generated to forcibly interrupt the timing of the current feedback cycle.

[0079] After the priority response command is triggered, the feedback cycle is switched to the preset emergency verification cycle until the second actual change trend recovers to the allowable range corresponding to the first expected temperature curve. The emergency verification period is less than or equal to the second shortened period.

[0080] These two steps together constitute an emergency response mechanism. When the trend deviation calculated by the controller exceeds a preset deviation threshold (for example, the actual temperature has been more than 0.3 degrees Celsius higher than the predicted value in the past minute), the system determines that the current state has deviated from the normal track and that an emergency event may have occurred, such as the warehouse door being wide open, goods being stored in a concentrated manner, or the fan malfunctioning.

[0081] At this point, the system will immediately generate a priority response command. This command forcibly interrupts the currently timing feedback cycle, regardless of how much time remains in the cycle. This is equivalent to pressing the emergency stop button on the control system and immediately switching to the emergency handling procedure.

[0082] The core of the emergency response process is to forcibly switch the feedback cycle to a preset, extremely short emergency verification cycle. This cycle is typically the shortest evaluation interval the system can support, such as 1 minute, and it is less than or equal to the aforementioned second shortened cycle (e.g., 3 minutes). In this emergency mode, the system evaluates and adjusts the warehouse status at the highest frequency, taking the most decisive control actions (e.g., maximizing compressor power) to quickly bring the runaway temperature trend back under control. This emergency mode will continue until the monitored second actual temperature trend re-aligns substantially with the model-predicted first expected temperature curve, meaning the trend deviation falls back within acceptable limits. Only then will the system exit the emergency mode and revert to the regular mode, which dynamically adjusts the feedback cycle based on the rate of change in the external environment.

[0083] This mechanism greatly enhances the system's robustness, enabling it to adapt not only to predictable changes in the external environment but also to respond quickly and effectively to unpredictable internal emergencies.

[0084] In addition to responding to external changes and internal anomalies, a good control system should also be able to recognize and adapt to its own physical characteristics, especially response delay. Any control action takes time from initiation to effect. If the system's adjustment pace is faster than its physical response pace, instability will inevitably result. To address this issue, the method may also include: The response lag time after adjusting the operating control parameters of the refrigeration equipment, which is the time when the second real-time environmental parameter of the controlled environment begins to change in a trend. Determine the response coordination coefficient by comparing the response lag time with the length of the current feedback cycle; The feedback period after the next adjustment is corrected using the response coordination coefficient so that the corrected feedback period is greater than or equal to the response lag time.

[0085] This step introduces self-reflection and calibration capabilities to the control system. The key here is measuring the response lag time. Specifically, after the controller performs a regulation action (e.g., increasing the compressor frequency from 40 Hz to 45 Hz), it starts an internal timer and continuously monitors the temperature change trend inside the refrigeration unit at high frequency (e.g., the first derivative of the temperature). Before regulation, the temperature may be slowly rising (the derivative is positive). The purpose of the regulation is to lower it. The system precisely records the time elapsed from the moment the command is issued until the temperature change rate first turns negative or significantly decreases (e.g., the decrease exceeds a preset threshold). This time is the response lag time of this regulation. It reflects the thermal inertia of the entire refrigeration system.

[0086] Once this lag time is obtained, the system compares it with the current feedback cycle. If the feedback cycle is shorter than the response lag time, it means the system started the next operation before seeing the effect of the previous operation. This is the root cause of control oscillations. To avoid this, the system calculates a response coordination coefficient and uses it to correct (usually extend) the next feedback cycle, ensuring that the pace of evaluation and decision-making does not outpace the system's physical response capability.

[0087] To clarify how to perform the above comparisons and corrections, the steps to determine the response coordination coefficient by comparing the response lag time with the length of the current feedback cycle may include: Determine if the response lag time is greater than the current feedback cycle; If the response lag time is greater than the current feedback cycle, it is determined that there is a risk of premature oscillation in the current adjustment, and the response coordination coefficient is set to a compensation gain greater than 1; the compensation gain is used to extend the duration of the next feedback cycle. If the response lag time is less than or equal to the current feedback period, the current adjustment is determined to be in a controlled convergence state, and the response coordination coefficient is set to 1.

[0088] This step provides the specific logic for calculating the response coordination coefficient.

[0089] In one embodiment, suppose the system measures a response lag of 4 minutes for a certain frequency ramp adjustment. At that time, due to drastic fluctuations in the external environment, the system was in the second shortening cycle of 3 minutes.

[0090] The controller determines the response lag time (4 minutes) > current feedback cycle (3 minutes). The result is yes.

[0091] This implies a risk of premature oscillation. The controller then calculates a compensation gain greater than 1 as the response coordination coefficient. A simple calculation is: Compensation gain = Response lag time / Current feedback period = 4 / 3 ≈ 1.33.

[0092] When determining the feedback cycle in the next iteration, assuming that the basic feedback cycle calculated based on the rate of change of the external environment is still 3 minutes, the final feedback cycle will be corrected to: Corrected cycle = Basic cycle * Compensation gain = 3 minutes * 1.33 = 4 minutes.

[0093] In this way, the system actively slows down its adjustment rhythm to 4 minutes, which matches the physical inertia, thereby effectively suppressing oscillations.

[0094] Conversely, if the measured response lag time is 2.5 minutes and the current feedback cycle is 3 minutes, the controller determines that the lag time is not greater than the feedback cycle, and therefore judges that the current adjustment is in a controlled convergence state, and the response coordination coefficient is set to 1. The next feedback cycle will not be affected by this mechanism and will be directly determined by the rate of change of the external environment.

[0095] This adaptive hysteresis compensation mechanism enables the control system to learn and adapt to its own dynamic characteristics online, forming a deeper adaptive closed loop that fundamentally guarantees the stability and reliability of long-term operation.

[0096] Secondly, referring to Figure 2 To implement the aforementioned intelligent refrigeration equipment adaptive energy-saving control method, this application also provides an intelligent refrigeration equipment adaptive energy-saving control system. This system is the physical carrier of the aforementioned method and can be a software system integrated into the main controller of the refrigeration equipment, or a separate hardware box connected to the refrigeration equipment via a communication bus. This system is used to control the refrigeration equipment to cool the controlled environment, including: The change feature determination module 210 is used to acquire real-time environmental parameters outside the controlled environment as the first real-time environmental parameters, and determine the change feature values ​​of the external environmental parameters based on the first real-time environmental parameters at different sampling times. The feedback cycle adjustment module 220 is used to compare the changing characteristic value with the preset fluctuation threshold and dynamically adjust the feedback cycle for evaluating the effect of the controlled environment based on the comparison result. The state deviation determination module 230 is used to acquire the real-time state parameters of the controlled environment at the beginning of the feedback cycle, as the second real-time environment parameter, and determine the deviation between the second real-time environment parameter and the preset target value. The operation control and adjustment module 240 is used to adjust the operation control parameters of the refrigeration equipment according to the deviation and the length of the current feedback cycle, so that the cooling capacity output of the refrigeration equipment responds to the load changes of the external environment.

[0097] Through modular design, the system clearly divides complex control logic into different functional units, providing a clear architecture for the engineering implementation of the method.

[0098] The change characteristic determination module 210 can, in hardware, correspond to one or more external sensors (such as temperature, humidity, and light sensors) connected to the system's analog or digital input interfaces, and a piece of program code in the processor specifically for processing this sensor data. This code is responsible for acquiring sensor data at a high frequency, executing the aforementioned rate of change calculation algorithm, and finally outputting a quantified change characteristic value.

[0099] The feedback cycle adjustment module 220 receives the output from the change characteristic determination module 210 and determines the most suitable feedback cycle length based on preset multi-level threshold logic. The core of this module is a state machine or a set of logical judgment rules, which manages the timing of the system's main control loop.

[0100] The state deviation determination module 230 is activated at the beginning of each feedback cycle. It obtains the current state of the controlled environment by reading the values ​​of internal sensors and compares them with the target setpoint stored in non-volatile memory to calculate the deviation.

[0101] The operation control and adjustment module 240 receives the deviation value calculated by the state deviation determination module 230 and the current cycle length set by the feedback cycle adjustment module 220. It then executes the aforementioned adjustment algorithm that correlates the adjustment step size with the feedback cycle length to calculate the final control output value. These output values ​​are subsequently converted into actual control signals for the compressor inverter, fan speed controller, or electronic expansion valve driver through the system's digital output (DO) or analog output (AO) channels.

[0102] These modules work together to form a complete, adaptive, closed-loop control system that transforms all the technical concepts in the aforementioned methods into executable physical operations.

[0103] The above are merely embodiments of this application and are not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An adaptive energy-saving control method for intelligent refrigeration equipment, used to control the refrigeration equipment to cool a controlled environment, characterized in that, The steps of this method include: The real-time environmental parameters outside the controlled environment are obtained as the first real-time environmental parameters, and the change characteristic values ​​of the external environmental parameters are determined based on the first real-time environmental parameters at different sampling times. The change characteristic value is compared with a preset fluctuation threshold, and the feedback cycle for evaluating the effect of the controlled environment is dynamically adjusted based on the comparison result. At the start of the feedback cycle, the real-time state parameters of the controlled environment are acquired as the second real-time environment parameters, and the deviation between the second real-time environment parameters and the preset target value is determined. Based on the deviation and the current length of the feedback cycle, the operating control parameters of the refrigeration equipment are adjusted so that the cooling capacity output of the refrigeration equipment responds to the load changes of the external environment.

2. The adaptive energy-saving control method for intelligent refrigeration equipment according to claim 1, characterized in that, The step of acquiring real-time environmental parameters outside the controlled environment as first real-time environmental parameters, and determining the change characteristic values ​​of the external environmental parameters based on the first real-time environmental parameters at different sampling times, includes: The first real-time environmental parameter is acquired at a preset first sampling interval and stored in a historical parameter sequence; Read the first real-time environmental parameters at the current moment and the first real-time environmental parameters from the previous moment in the historical parameter sequence; Based on the difference between the first real-time environmental parameter at the current time and the previous time and the first sampling interval, the first rate of change of the first real-time environmental parameter is calculated, and the first rate of change is used as the change feature value.

3. The adaptive energy-saving control method for intelligent refrigeration equipment according to claim 1, characterized in that, The fluctuation threshold includes a first threshold and a second threshold, wherein the second threshold is greater than the first threshold; The step of dynamically adjusting the feedback cycle for evaluating the effect of the controlled environment based on the comparison results specifically includes: If the change characteristic value is less than or equal to the first threshold, then the feedback period is kept as a standard period. If the change characteristic value is greater than the first threshold and less than or equal to the second threshold, then the feedback period is adjusted from the standard period to the first shortened period. If the change characteristic value is greater than the second threshold, then the feedback period is adjusted to the second shortened period; Wherein, the second shortening period is smaller than the first shortening period, and the first shortening period is smaller than the standard period.

4. The adaptive energy-saving control method for intelligent refrigeration equipment according to claim 1, characterized in that, The step of adjusting the operating control parameters of the refrigeration equipment based on the deviation and the current length of the feedback cycle includes: When the deviation exceeds the preset allowable fluctuation range, the adjustment step size of the actuator of the refrigeration equipment is determined according to the positive and negative direction and the magnitude of the deviation. Based on the current length of the feedback cycle, the adjustment step size is corrected to obtain the final adjustment step size; wherein, the longer the feedback cycle, the larger the final adjustment step size. The operating control parameters of the refrigeration equipment are adjusted according to the final adjustment step size; wherein the operating control parameters include the operating frequency of the compressor and / or the speed of the blower in the refrigeration equipment.

5. The adaptive energy-saving control method for intelligent refrigeration equipment according to claim 3, characterized in that, The method also includes: Based on the current operating control parameters and the first real-time environmental parameters, combined with the preset cooling output model, the first expected temperature curve of the controlled environment in the future period is calculated. The second real-time environmental parameters of the controlled environment are obtained at a preset second sampling interval, and the second actual change trend of the second real-time environmental parameters over time is determined. The second actual change trend is compared with the first expected temperature curve to determine the degree of trend deviation.

6. The adaptive energy-saving control method for intelligent refrigeration equipment according to claim 5, characterized in that, The method also includes: When the trend deviation exceeds a preset deviation threshold, a priority response command is generated to forcibly interrupt the timing of the current feedback cycle.

7. The adaptive energy-saving control method for intelligent refrigeration equipment according to claim 6, characterized in that, After the priority response command is triggered, the feedback cycle is switched to a preset emergency verification cycle until the second actual change trend recovers to the allowable range corresponding to the first expected temperature curve. The emergency verification period is less than or equal to the second shortened period.

8. The adaptive energy-saving control method for intelligent refrigeration equipment according to claim 1, characterized in that, The method also includes: The response lag time after adjusting the operating control parameters of the refrigeration equipment to monitor the second real-time environmental parameter of the controlled environment to detect the trend change. The response coordination coefficient is determined by comparing the response lag time with the length of the current feedback cycle. The feedback period after the next adjustment is corrected using the response coordination coefficient, so that the corrected feedback period is greater than or equal to the response lag time.

9. The adaptive energy-saving control method for intelligent refrigeration equipment according to claim 8, characterized in that, The step of determining the response coordination coefficient by comparing the response lag time with the length of the current feedback period includes: Determine whether the response lag time is greater than the current feedback period; If the response lag time is greater than the current feedback cycle, it is determined that there is a risk of premature oscillation in the current adjustment, and the response coordination coefficient is set to a compensation gain greater than 1; the compensation gain is used to extend the duration of the next feedback cycle. If the response lag time is less than or equal to the current feedback period, it is determined that the current adjustment is in a controlled convergence state, and the response coordination coefficient is set to 1.

10. An intelligent refrigeration equipment adaptive energy-saving control system, used to control the refrigeration equipment to refrigerate a controlled environment, characterized in that, include: The change feature determination module is used to acquire real-time environmental parameters outside the controlled environment as the first real-time environmental parameter, and determine the change feature value of the external environmental parameter based on the first real-time environmental parameter at different sampling times. The feedback cycle adjustment module is used to compare the changing characteristic value with a preset fluctuation threshold and dynamically adjust the feedback cycle for evaluating the effect of the controlled environment based on the comparison result. The state deviation determination module is used to acquire the real-time state parameters of the controlled environment at the beginning of the feedback cycle as the second real-time environment parameters, and determine the deviation between the second real-time environment parameters and the preset target value. The operation control and adjustment module is used to adjust the operation control parameters of the refrigeration equipment according to the deviation and the current length of the feedback cycle, so that the cooling capacity output of the refrigeration equipment responds to the load changes of the external environment.

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