Intelligent energy-saving control method and system for refrigerated display cabinet

Abstract

The application relates to the technical field of intelligent control of refrigeration equipment, and discloses an intelligent energy-saving control method and system for a refrigeration display cabinet. The method comprises the following steps: collecting temperature distribution and passenger flow data in the refrigeration display cabinet through a multi-parameter sensor network, inputting the data into an ARIMA model after sliding average filtering processing, and predicting a refrigeration load fluctuation trend; a dynamic energy efficiency ratio indicator is established based on the ratio of compressor power to the predicted value of the load, and the energy-saving effect is quantified; the compressor frequency and the fan rotating speed are adjusted in combination with a nonlinear prediction control algorithm, and a cooperative control instruction is generated; the influence of the instruction on the system load is analyzed through a refrigerant flow tendency function, the defrosting cycle is optimized synchronously, an energy-saving control strategy is formed, and the strategy is executed. The application improves the predictability and cooperativeness of energy efficiency management of the refrigeration display cabinet.

Description

Technical Field

[0001] This application relates to the field of intelligent control technology for refrigeration equipment, and in particular to an intelligent energy-saving control method and system for refrigerated display cases. Background Technology

[0002] Existing intelligent energy-saving control methods for refrigerated display cases mainly employ a temperature feedback-based PID control strategy, adjusting the operation of the compressor and fan by monitoring temperature changes within the display case. These traditional control methods typically utilize temperature and humidity sensors to collect environmental parameters. When a temperature deviation from the set value is detected, the control system adjusts the operating frequency of the refrigeration equipment accordingly. Some advanced systems also integrate timed defrosting functions and simple energy consumption monitoring modules, enabling them to initiate defrosting procedures at preset time intervals and record basic energy consumption data.

[0003] However, traditional single-variable temperature control cannot effectively handle the combined effects of multiple factors such as changes in pedestrian traffic and fluctuations in commodity load on cooling demand, resulting in a lack of foresight in the control strategy. Secondly, the existing PID control method is a passive response control, which can only adjust after a temperature deviation occurs and cannot predict future load change trends, often resulting in control lag and energy waste. Thirdly, the fixed defrosting cycle setting ignores the dynamic changes in the actual defrosting rate with environmental conditions, causing problems of over-defrosting or untimely defrosting.

[0004] Because it is impossible to establish a predictive relationship between environmental parameters and cooling load, the system cannot adjust its control strategy in advance to adapt to load changes. At the same time, due to the lack of real-time correlation analysis between compressor power and cooling effect, the system cannot dynamically evaluate and optimize operating efficiency. Furthermore, due to the lack of correlation analysis between refrigerant flow changes and system response, the system cannot achieve coordinated optimization control among multiple devices and intelligent adjustment of defrosting cycles. Summary of the Invention

[0005] This application provides an intelligent energy-saving control method and system for refrigerated display cases, which improves the predictability and synergy of energy efficiency management for refrigerated display cases.

[0006] Firstly, this application provides an intelligent energy-saving control method for a refrigerated display case, the intelligent energy-saving control method for the refrigerated display case comprising:

[0007] Step S1: Collect temperature distribution data and people flow detection data inside the refrigerated display case through a multi-parameter sensor network, and obtain a set of preprocessed environmental parameters after processing by a moving average filtering algorithm;

[0008] Step S2: Input the preprocessed environmental parameter set into the ARIMA time series model, calculate the fluctuation trend of the cooling load in the preset time domain, and output the load change prediction data;

[0009] Step S3: Based on the ratio between the real-time power of the compressor and the predicted load change data, establish a dynamic energy efficiency ratio indicator to quantify the energy-saving effect of the current operating state;

[0010] Step S4: Based on the numerical range of the dynamic energy efficiency ratio indicator, a nonlinear predictive control algorithm is used to adjust the compressor frequency and fan speed to generate multi-machine collaborative control commands;

[0011] Step S5: Analyze the impact of the multi-machine collaborative control command on the system load through the refrigerant flow tendency function, synchronously adjust the defrost cycle parameters, obtain the energy-saving control strategy, and execute it.

[0012] Secondly, this application provides an intelligent energy-saving control system for a refrigerated display case, the intelligent energy-saving control system for the refrigerated display case comprising:

[0013] The data acquisition module is used to collect temperature distribution data and people flow detection data inside the refrigerated display case through a multi-parameter sensor network, and obtain a set of preprocessed environmental parameters after processing by a moving average filtering algorithm.

[0014] The prediction module is used to input the preprocessed environmental parameter set into the ARIMA time series model, calculate the fluctuation trend of the cooling load in the preset time domain, and output load change prediction data.

[0015] The evaluation module is used to establish a dynamic energy efficiency ratio indicator based on the ratio of the compressor's real-time power to the load change prediction data, and to quantify the energy-saving effect of the current operating state.

[0016] The optimization module is used to adjust the compressor frequency and fan speed according to the numerical range of the dynamic energy efficiency ratio indicator, and generate multi-machine collaborative control commands by using a nonlinear predictive control algorithm.

[0017] The execution module is used to analyze the impact of the multi-machine collaborative control command on the system load through the refrigerant flow tendency function, synchronously adjust the defrost cycle parameters, obtain the energy-saving control strategy, and execute it.

[0018] Thirdly, an intelligent energy-saving control device for a refrigerated display case is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor invokes the instructions in the memory to cause the intelligent energy-saving control device for the refrigerated display case to execute the aforementioned intelligent energy-saving control method for the refrigerated display case.

[0019] Fourthly, a computer-readable storage medium is provided, wherein instructions are stored therein, which, when executed on a computer, cause the computer to perform the aforementioned intelligent energy-saving control method for refrigerated display cases.

[0020] The technical solution provided in this application collects temperature distribution data and pedestrian flow detection data inside the refrigerated display case through a multi-parameter sensor network, and processes the data using a moving average filtering algorithm to obtain a pre-processed environmental parameter set. This solves the problem of incomplete environmental information caused by single temperature parameter control in existing technologies, enabling the system to comprehensively consider the impact of temperature changes and personnel activities on the cooling load. The pre-processed environmental parameter set is input into an ARIMA time series model to calculate the cooling load fluctuation trend and output load change prediction data, overcoming the lag of traditional passive response control and giving the system a forward-looking control capability. A dynamic energy efficiency ratio indicator is established based on the ratio of real-time compressor power to load change prediction data, realizing a quantitative assessment of the energy-saving effect of the current operating state and providing a scientific basis for subsequent control decisions. Based on the numerical range of the dynamic energy efficiency ratio indicator, a nonlinear predictive control algorithm is used to adjust the compressor frequency and fan speed to generate multi-machine collaborative control commands, breaking through the limitations of traditional single-device independent control and realizing unified coordination and optimized configuration among multiple devices. By analyzing the impact of multi-machine collaborative control commands on system load through refrigerant flow tendency function analysis and synchronously adjusting defrost cycle parameters, the rigid mode of fixed defrost cycle is changed, and intelligent linkage between defrost control and refrigeration control is realized.

[0021] In particular, the application of the ARIMA time series model in predicting the refrigeration load of refrigerated display cases fully considers the temporal characteristics and periodic patterns of load changes in a commercial environment. Its second-order difference and moving average characteristics can effectively capture the trend and random components of load fluctuations, providing a reliable data foundation for predictive control. The nonlinear predictive control algorithm is specifically optimized for the multivariable coupling characteristics and nonlinear dynamic response of the refrigeration system of the refrigerated display case. By constructing a multivariable control state-space model and a multi-objective optimization function, a balance between temperature control accuracy and energy-saving effect is achieved. The refrigerant flow tendency function, as a bridge connecting equipment control commands and system response, fully reflects the impact mechanism of flow changes on overall performance in the refrigeration cycle system. This ensures that defrosting cycle adjustments are synchronized with changes in refrigeration load, avoiding mutual interference between control strategies and significantly improving the overall intelligent control level and energy-saving effect of the refrigerated display case. Attached Figure Description

[0022] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1This is a schematic diagram of one embodiment of the intelligent energy-saving control method for refrigerated display cases in this application;

[0024] Figure 2 This is a comparison diagram of the filtering effects of temperature and pedestrian flow data in the embodiments of this application;

[0025] Figure 3 This is a comprehensive diagram of load analysis and prediction for the refrigeration system of the fresh food display case in the embodiments of this application;

[0026] Figure 4 This is a closed-loop optimization control diagram for the energy efficiency of the refrigeration equipment in the embodiments of this application;

[0027] Figure 5 This is a schematic diagram of one embodiment of the intelligent energy-saving control system for the refrigerated display case in this application.

[0028] Figure 6 This is a schematic block diagram of the intelligent energy-saving control device for the refrigerated display case in an embodiment of the present invention. Detailed Implementation

[0029] This application provides an intelligent energy-saving control method and system for a refrigerated display case. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data used can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0030] For ease of understanding, the specific process of the embodiments of this application is described below. Please refer to [link / reference]. Figure 1 One embodiment of the intelligent energy-saving control method for refrigerated display cases in this application includes:

[0031] Step S1: Collect temperature distribution data and people flow detection data inside the refrigerated display case through a multi-parameter sensor network, and obtain a set of preprocessed environmental parameters after processing by a moving average filtering algorithm.

[0032] Specifically, to accurately obtain the environmental parameters of the refrigerated display case, multiple sensors are deployed at different locations inside the case to collect real-time data on temperature distribution and customer traffic. The sensors are distributed across different levels of the display case to ensure comprehensive capture of temperature changes within the internal environment. Simultaneously, a customer traffic detection module monitors customer activity in front of the display case, including the number of customers and their dwell time. Since raw data is often affected by external environmental factors and equipment operating conditions, a moving average filtering algorithm is used for data preprocessing. This algorithm smooths out short-term fluctuations and noise in the data by setting a fixed window length, making the data more stable and reliable. This processing removes errors caused by instantaneous changes, ensuring more stable data. After filtering, the resulting set of environmental parameters accurately reflects temperature and customer traffic changes within the display case, providing a reliable basis for subsequent load forecasting and energy-saving control.

[0033] Step S2: Input the set of pre-processed environmental parameters into the ARIMA time series model, calculate the fluctuation trend of cooling load in the preset time domain, and output load change prediction data.

[0034] Specifically, to predict the refrigeration load changes of refrigerated display cases over a future period, a pre-processed set of environmental parameters is input into the ARIMA time series model. The ARIMA model captures the temporal characteristics of the refrigeration load by analyzing trends, seasonality, and random fluctuations in historical data, thus effectively predicting load fluctuations. The ARIMA model performs differencing on the input data to eliminate non-stationarity, ensuring that the model can capture the true patterns of load changes. The model uses autoregression and moving average calculations, combined with historical time series data, to predict the trend of refrigeration load fluctuations over a future period. Through this prediction, the system can understand the load changes of the refrigerated display cases in advance and output predicted load change data. This data provides an important basis for subsequent energy-saving control, enabling the system to adjust before load fluctuations occur, thereby optimizing the operating efficiency of the refrigeration equipment and avoiding unnecessary energy waste.

[0035] Step S3: Based on the ratio of real-time compressor power to load change prediction data, establish a dynamic energy efficiency ratio indicator to quantify the energy-saving effect of the current operating state.

[0036] Specifically, to quantify the energy-saving effect of the refrigerated display case under its current operating state, a dynamic energy efficiency ratio (EER) indicator is established based on the ratio between the compressor's real-time power data and the predicted load change data. This indicator reflects the relationship between the power consumed by the compressor during actual operation and the predicted load. In this process, the system monitors the compressor's power consumption in real time and calculates the ratio between the two, combining this with the predicted load change data. This ratio represents the current energy efficiency status of the system: a lower ratio indicates that the compressor can maintain high operating efficiency under relatively low load conditions; while a higher ratio may indicate that the refrigeration system is operating inefficiently, posing a risk of energy waste. Through the calculated dynamic EER indicator, the system can assess the energy-saving effect under the current operating state in real time and quantify the energy efficiency differences under different operating modes.

[0037] Step S4: Based on the numerical range of the dynamic energy efficiency ratio indicator, the compressor frequency and fan speed are adjusted using a nonlinear predictive control algorithm to generate multi-machine collaborative control commands.

[0038] Specifically, based on the numerical range of the dynamic energy efficiency ratio indicator, the system classifies the current energy efficiency level. If the dynamic energy efficiency ratio is in the high-efficiency range, it indicates that the current system is operating well and no immediate adjustment is needed. If the dynamic energy efficiency ratio is in the medium-efficiency or low-efficiency range, the system activates a nonlinear predictive control algorithm to adjust and optimize energy efficiency. The nonlinear predictive control algorithm constructs a multivariable control state-space model based on the system's current energy efficiency status and the adjustment requirements of the compressor frequency and fan speed. This model reflects the dynamic response characteristics of the compressor and fan under different load and energy efficiency conditions. By approximating the system's nonlinear characteristics through algorithms such as neural networks, the algorithm can predict the operating behavior of each device over a future period and calculate the corresponding adjustment parameters. The system will then generate adjustment commands for the compressor frequency and fan speed based on the optimized calculations, ensuring that the equipment achieves optimal energy efficiency when working collaboratively.

[0039] Step S5: Analyze the impact of multi-unit coordinated control commands on system load through refrigerant flow tendency function, synchronously adjust defrost cycle parameters, obtain energy-saving control strategies and execute them.

[0040] Specifically, by analyzing the impact of multi-machine collaborative control commands on the system load, the energy-saving effect of the system is further optimized. The refrigerant flow tendency function is used to assess the load change trend caused by the adjustment of compressor frequency and fan speed. This tendency function predicts the impact of refrigerant flow on the system load under different operating conditions by calculating the relationship between flow rate changes and system load. If the system load changes, the flow tendency function will predict the future trend of system load changes based on the increase or decrease in flow rate. Specifically, when the flow tendency function is positive, it means that the load may increase, and the system will adjust the refrigeration parameters accordingly; while when the flow tendency function is negative, the load may decrease, and the system will take corresponding measures to reduce energy consumption. Based on the analysis results of the flow tendency function, the system synchronously adjusts the defrost cycle parameters. By combining the current humidity conditions and changes in frost thickness, the defrost triggering timing is dynamically calculated to avoid energy waste caused by excessive or insufficient defrosting. This intelligent adjustment keeps the defrost cycle synchronized with changes in refrigeration load, thereby achieving coordinated optimization of refrigeration and defrosting.

[0041] In one specific embodiment, the process of performing step S1 may specifically include the following steps:

[0042] By deploying PT100 platinum resistance temperature sensor arrays in the upper, middle and lower layers of the refrigerated display case, temperature data and temperature change rate data of each layer in the display case are collected to obtain the original temperature data matrix.

[0043] The infrared pyroelectric people flow detection sensor scans and detects the dwell information of customers within a 3-meter range in front of the display case, and obtains the time series data of the number of people and the dwell time parameters.

[0044] The original temperature data matrix and the time series data of the number of people are input into a moving average filter. A filtering algorithm with a window length of 10 sampling points is used to remove noise, and the filtered temperature data and the flow of people data are obtained.

[0045] The filtered temperature data and pedestrian flow data are fused together, and the environmental load parameters are calculated according to the ratio of temperature weight 0.7 and pedestrian flow weight 0.3 to obtain a set of preprocessed environmental parameters.

[0046] Specifically, to accurately acquire environmental information within the refrigerated display case, temperature sensor arrays were deployed in the upper, middle, and lower layers. These sensors collect real-time temperature data and temperature change rate data for each layer, forming a raw temperature data matrix inside the display case. To monitor foot traffic within a 3-meter radius in front of the display case, infrared pyroelectric sensors were used to scan customer dwell times. The collected time-series data on the number of people and customer dwell time provided valuable information for subsequent analysis. To reduce noise in this data, the raw temperature data matrix and the time-series data on the number of people were input into a moving average filter and processed using a filtering algorithm with a window length of 10 sampling points. This effectively eliminated instantaneous fluctuations and noise in the data, making the temperature and foot traffic data more stable and reliable. The filtered temperature and foot traffic data were then fused. By setting a weighting ratio of 0.7 for temperature and 0.3 for foot traffic, a comprehensive environmental load parameter was calculated by combining these two sets of data. This parameter reflects the combined impact of temperature and foot traffic changes within the display case, forming a pre-processed set of environmental parameters, providing a precise data foundation for subsequent load prediction and energy-saving control.

[0047] For example, in monitoring foot traffic, assuming customers are entering and exiting within a 3-meter radius in front of a display case, the sensor detects that the number of customers increases from 2 to 5 within 10 minutes and records their dwell time. The sensor records the number of customers every second, and when it detects that a customer has stayed in front of the display case for more than 30 seconds, it calculates the dwell time for each customer. This data forms time-series data on the number of people and dwell time parameters, providing detailed foot traffic information for subsequent environmental load calculations.

[0048] For example, in noise reduction, a moving average filter smooths the collected temperature and pedestrian flow data. Assuming the temperature data collected over 10 seconds is [4.2°C, 4.4°C, 4.1°C, 4.3°C, 4.2°C], after filtering with a moving average window of 10 sampling points, the data will be smoothed to a more stable average value, such as 4.2°C, thus eliminating errors caused by short-term fluctuations. Similarly, pedestrian flow data undergoes similar smoothing. Assuming the original data is [2 people, 3 people, 4 people, 5 people, 3 people], after moving average filtering, the smoothed data is [3 people, 4 people, 3 people], eliminating data noise caused by instantaneous fluctuations. Figure 2 The figure shows a comparison of the filtering effects on temperature and pedestrian flow data.

[0049] In one specific embodiment, the process of performing step S2 may specifically include the following steps:

[0050] The pre-processed environmental parameter set is decomposed into four components: heat dissipation of goods, heat dissipation of personnel, heat dissipation of lighting, and heat dissipation of defrosting, to obtain the cooling load component data;

[0051] A second-order difference second-order moving average time series model is constructed based on cooling load component data. Autoregressive weights and moving average weights are set to obtain the cooling load prediction model.

[0052] The prediction time domain of the cooling load forecasting model is set to sixty minutes, and the control step size is set to ten minutes. The cooling load fluctuations in the future time domain are recursively calculated to obtain the time-period load forecast values.

[0053] Differential calculations and trend analysis are performed on the time-period load forecast values ​​to calculate the load change gradient and fluctuation amplitude parameters, thereby obtaining load change forecast data.

[0054] Specifically, the pre-processed environmental parameter set is used for load decomposition calculation based on four main factors: heat dissipation from goods, heat dissipation from personnel, heat dissipation from lighting, and heating during defrosting, thus obtaining the cooling load component data for each factor. This data helps the system accurately assess the contribution of each factor to the overall cooling demand. For example, heat dissipation from goods fluctuates with the type of goods and storage temperature, heat dissipation from personnel is related to the number of customers and their dwell time, while lighting and defrosting heating are closely related to the lighting equipment and defrosting cycle in the display case, respectively. These load component data are then input into a second-order difference second-order moving average time series model for further processing. The autoregressive weights and moving average weights in the model are set to accurately describe the load variation pattern. By continuously training the model, the fluctuation trend of the cooling load is captured, resulting in an accurate cooling load prediction model.

[0055] To predict future load changes, the model's prediction time domain is set to 60 minutes, and the control step size is set to 10 minutes. This allows for recursive calculations of load fluctuations over the next 60 minutes every 10 minutes, progressively obtaining load forecasts for each future time period. Through this process, the system can monitor load change trends within the display case in real time, make predictions, and adjust strategies promptly to cope with upcoming load changes. To ensure prediction accuracy, differential calculations are performed on the load forecast values ​​for each time period to eliminate interference from potential long-term trends or periodic changes. Furthermore, trend analysis is used to calculate the gradient and fluctuation amplitude parameters of load changes. These calculation results provide detailed references for subsequent load change prediction data, ensuring the system can accurately adjust based on actual load changes, avoiding over- or under-cooling and thus effectively improving energy efficiency.

[0056] Taking a display case in a shopping mall as an example, this case stores different types of fresh produce, and the temperature needs to be maintained at around 4°C. Multiple sensors are installed inside the display case to collect environmental data, such as the heat dissipation of goods varying according to the types of items displayed, and the heat dissipation of people fluctuating with the number of customers. After load decomposition calculation, the cooling load components of heat dissipation—goods heat dissipation, people heat dissipation, lighting heat dissipation, and defrosting heating—are obtained. For example, the heat dissipation of goods is 300W, people heat dissipation is 150W, lighting heat dissipation is 100W, and defrosting heating is 50W. The system inputs this data into a second-order difference second-order moving average time series model, with an autoregression weight of 0.6 and a moving average weight of 0.4. After calculation, a cooling load prediction model is obtained, which can accurately predict load fluctuations within the next 60 minutes. Assuming the load prediction for the next 10 minutes is 450W, 420W, 460W, and 430W, the system obtains the load prediction values ​​for four time periods through recursive calculation. Differential calculations and trend analysis were performed on these forecast values ​​to obtain the gradient and fluctuation range of load changes; for example, the load change gradient was -30W, and the fluctuation range was 50W. (Reference) Figure 3 This figure illustrates a comprehensive load analysis and forecast of the refrigeration system for fresh produce display cases. These results allow the system to adjust its control strategy promptly based on load variation trends, ensuring efficient operation of the refrigeration system and maximizing energy savings.

[0057] In one specific embodiment, the process of performing step S3 may specifically include the following steps:

[0058] The compressor's real-time input power data is collected by current and voltage sensors, and the current cooling capacity is calculated by combining the temperature difference between the evaporator inlet and outlet and the refrigerant flow rate, thus obtaining the compressor power parameters and cooling capacity parameters.

[0059] The ratio of compressor power parameters to cooling capacity parameters is calculated, and the energy efficiency ratio is dynamically corrected by combining load change prediction data to obtain the real-time dynamic energy efficiency ratio value.

[0060] Based on the real-time dynamic energy efficiency ratio value, three levels are set: high efficiency, medium efficiency and low efficiency. The energy efficiency level is determined by the current operating status to obtain the energy efficiency level classification result.

[0061] The energy efficiency rating classification results are weighted and fused with the temperature deviation control target. A comprehensive evaluation function is constructed according to the preset ratio of energy efficiency weight and temperature weight to obtain a dynamic energy efficiency ratio indicator.

[0062] In one specific embodiment, the process of performing step S4 may specifically include the following steps:

[0063] The control strategy selection is determined based on the numerical range of the dynamic energy efficiency ratio indicator. When the indicator is in the high efficiency range, the current control parameters are maintained. When the indicator is in the medium efficiency or low efficiency range, the control adjustment mode is triggered to obtain the control mode selection signal.

[0064] The control mode selection signal is input into the nonlinear predictive controller to construct a multivariable control state space model that includes compressor frequency and fan speed. The nonlinear characteristics of the system are approximated by a neural network algorithm to obtain the predictive control state equation.

[0065] Based on the predictive control state equation, the parameters in the prediction time domain and the control time domain are set, a multi-objective function including temperature tracking error and energy efficiency optimization is established, and the optimal control parameter sequence is obtained by using the sequential quadratic programming algorithm for constraint optimization.

[0066] The optimal control parameter sequence is decomposed into compressor frequency adjustment commands and condenser fan speed adjustment commands. The equipment is coordinated and configured through a master-slave control architecture to obtain multi-machine collaborative control commands.

[0067] Specifically, the system selects a control strategy based on the value range of the dynamic energy efficiency ratio indicator. When the indicator is in the high-efficiency range, the system maintains the current control parameters without adjustment; when the indicator enters the medium-efficiency or low-efficiency range, a control adjustment mode is triggered and a control mode selection signal is generated. This signal is then input to the nonlinear predictive controller to construct a control mode selection signal that includes the compressor frequency. and fan speed A multivariable control state-space model is proposed. This model approximates the nonlinear characteristics of the system using a neural network algorithm, yielding the predictive control state equations: ,in It is the derivative of the system state. and These are the state matrix, input matrix, and output matrix, respectively. Represents the state vector of the system. It is the control input vector. This is the system output. Based on this equation, the system sets the control time domain. Parameters, constructing a system that includes temperature tracking error. It is a multi-objective function that balances temperature tracking error and energy efficiency optimization objectives. ,in, It is a temperature tracking error. It is the objective function. and These are the ideal compressor frequency and fan speed, respectively. These are weighting coefficients used to balance temperature control accuracy and energy efficiency optimization. Based on this, a sequential quadratic programming algorithm is used for constraint optimization to obtain the optimal control parameter sequence: The optimal control parameter sequence is decomposed into compressor frequency adjustment commands and condenser fan speed adjustment commands. A master-slave control architecture is used for coordinated configuration between devices, resulting in multi-machine collaborative control commands. These commands control the operation of the equipment, achieving precise temperature control and energy efficiency optimization. This ensures stable operation of the refrigerated display case within its high-efficiency range while preventing the system from entering inefficient zones, thus improving overall energy savings. (See reference for further details.) Figure 4 The figure shows the closed-loop optimization control diagram for the energy efficiency of refrigeration equipment.

[0068] In one specific embodiment, the process of performing step S5 may specifically include the following steps:

[0069] The refrigerant flow influence of compressor frequency regulation and fan speed regulation in multi-machine collaborative control commands is analyzed. The flow tendency coefficient is calculated by combining the evaporation temperature change trend and the condensation temperature change trend, and the refrigerant flow tendency function is obtained.

[0070] The system cooling load is dynamically predicted based on the refrigerant flow tendency function. When the flow tendency function is positive, the load increase trend is predicted, and when the flow tendency function is negative, the load decrease trend is predicted, thus obtaining the system load response prediction results.

[0071] Based on the system load response prediction results, the change in frost thickness on the evaporator surface is calculated and analyzed. Combined with the current humidity conditions and the frost rate model, the defrost trigger threshold is dynamically adjusted to obtain the optimized defrost cycle parameters.

[0072] The defrost cycle parameters are optimized and integrated with multi-machine collaborative control commands to construct a unified scheduling scheme that includes refrigeration control and defrost control. The resulting energy-saving control strategy is then distributed to each device for execution through the control execution unit.

[0073] Specifically, by analyzing the compressor frequency and fan speed adjustments in the multi-machine collaborative control commands, and combining the trends of evaporation and condensation temperature changes, a refrigerant flow tendency coefficient is calculated, thus yielding the refrigerant flow tendency function. Based on this tendency function, the system dynamically predicts the refrigeration load. When the flow tendency function is positive, it indicates that the refrigeration load will increase, and the system will predict an increase in load accordingly; conversely, when the flow tendency function is negative, the system predicts a decrease in load. Based on the load response prediction results, the change in frost thickness on the evaporator surface is calculated and analyzed. Combined with the current humidity conditions, the system uses a frosting rate model for dynamic adjustment, deriving an optimized defrost trigger threshold. In this way, the system can intelligently adjust the defrost cycle, avoiding over-defrosting or under-defrosting, ensuring the refrigeration system operates within its optimal efficiency range. The optimized defrost cycle parameters are integrated with the multi-machine collaborative control commands to construct a unified scheduling scheme that includes refrigeration control and defrost control. Energy-saving control strategies are issued to each device through the control execution unit. The execution system adjusts the operating status of each device according to these commands to achieve precise temperature control and energy-saving goals.

[0074] For example, when analyzing compressor frequency and fan speed adjustments in multi-machine collaborative control commands, the system might receive commands requiring the compressor frequency to be adjusted from 40Hz to 45Hz and the fan speed to increase from 1500RPM to 1800RPM. Based on these changes, the system will combine the trends of evaporation temperature rising from -5°C to -4°C and condensation temperature falling from 35°C to 33°C to calculate the refrigerant flow tendency coefficient. These changes indicate that the flow tendency function may be positive, meaning the system load will increase accordingly. In calculating the change in frost thickness on the evaporator surface, assuming the current humidity is 80%, the system predicts, based on the frosting rate model, that the frost thickness will increase by 0.5mm in the next hour. Combining the system's load prediction results, the system will dynamically adjust the defrost trigger threshold, deciding to initiate defrost when the frost thickness reaches 2mm. In this way, the system can ensure that frost growth does not affect the cooling effect when the load increases, and initiate defrost at the appropriate time.

[0075] In one specific embodiment, the process of calculating and analyzing the change in frost thickness on the evaporator surface based on the system load response prediction results, and dynamically adjusting the defrost trigger threshold in combination with the current humidity conditions and the frost rate model to obtain optimized defrost cycle parameters can specifically include the following steps:

[0076] By correlating and matching the load increase / decrease trend data in the system load response prediction results with the evaporator surface temperature data, the temperature fluctuation range corresponding to the load change is screened out, and the temperature change influencing factor is obtained.

[0077] The current frost thickness value on the evaporator surface is collected by the frost thickness sensor. The temperature change influence factor is multiplied with the frost thickness value to obtain the frost thickness change.

[0078] Based on the current relative humidity data obtained by the humidity sensor, the change in frost thickness is corrected by humidity. When the humidity exceeds the reference value, the change is increased, and when the humidity is below the reference value, the change is decreased, thus obtaining the corrected change in frost thickness.

[0079] The change in the corrected frost layer thickness is compared with the preset defrost trigger threshold. Based on the expected time to reach the threshold, the new defrost start time is calculated to obtain the optimized defrost cycle parameters.

[0080] Specifically, the load increase / decrease trend data in the system load response prediction results are correlated and matched with the evaporator surface temperature data. By analyzing the temperature fluctuation range corresponding to load changes, the temperature change influencing factors are extracted. For example, if an increase in load leads to enhanced cooling effect, the temperature fluctuation amplitude may be larger, while the temperature change may be more gradual when the load decreases. These fluctuation data will be combined with the temperature change factor to obtain a more accurate prediction of frost thickness change.

[0081] The system collects the current frost thickness value on the evaporator surface through a frost thickness sensor. For example, if the current frost thickness is measured to be 1.5 mm, the temperature change influence factor is multiplied by the current frost thickness value. For example, if the temperature fluctuation factor is 0.8 and the frost thickness is 1.5 mm, then the change in frost thickness is 1.2 mm.

[0082] The humidity sensor provides the current relative humidity data, for example, 85%. Based on humidity conditions and a frost rate model, the frost layer thickens more rapidly when the humidity is above a reference value. The system performs humidity correction on the change in frost thickness. For example, when the humidity is above the reference value, the system may decide to increase the change in frost thickness, for example, to 1.4 mm, to compensate for the effect of humidity. When the humidity is below the reference value, the rate of frost thickening slows down, and the system will reduce the change.

[0083] The system compares the corrected change in frost thickness with a preset defrost trigger threshold. For example, if the corrected change in frost thickness is 1.4 mm, the current frost thickness is 1.5 mm, and the preset trigger threshold is 2 mm, the system will calculate a new defrost start time based on this data. For instance, if the frost thickness will reach 2 mm within one hour, the system will optimize the defrost cycle parameters based on this calculation, preparing for defrost operation in advance. In this way, the system can precisely control the defrost cycle, ensuring cooling efficiency while avoiding a decrease in cooling effect due to excessive frost thickness.

[0084] The above describes the intelligent energy-saving control method for the refrigerated display case in the embodiments of this application. The following describes the intelligent energy-saving control system for the refrigerated display case in the embodiments of this application. Please refer to [link / reference]. Figure 5 One embodiment of the intelligent energy-saving control system for the refrigerated display case in this application includes:

[0085] The data acquisition module is used to collect temperature distribution data and people flow detection data inside the refrigerated display case through a multi-parameter sensor network, and obtain a set of preprocessed environmental parameters after processing by a moving average filtering algorithm.

[0086] The prediction module is used to input the preprocessed environmental parameter set into the ARIMA time series model, calculate the fluctuation trend of the cooling load in the preset time domain, and output load change prediction data.

[0087] The evaluation module is used to establish a dynamic energy efficiency ratio indicator based on the ratio of the compressor's real-time power to the load change prediction data, and to quantify the energy-saving effect of the current operating state.

[0088] The optimization module is used to adjust the compressor frequency and fan speed according to the numerical range of the dynamic energy efficiency ratio indicator, and generate multi-machine collaborative control commands by using a nonlinear predictive control algorithm.

[0089] The execution module is used to analyze the impact of the multi-machine collaborative control command on the system load through the refrigerant flow tendency function, synchronously adjust the defrost cycle parameters, obtain the energy-saving control strategy, and execute it.

[0090] Through the collaborative efforts of the aforementioned components, the system achieves a closed-loop optimization process from environmental data acquisition to refrigeration load prediction and energy-saving control. The acquisition module obtains temperature distribution and pedestrian flow data within the refrigerated display case through a multi-parameter sensor network and preprocesses it using a moving average filtering algorithm to remove noise and obtain an accurate set of environmental parameters. The prediction module inputs this preprocessed data into an ARIMA time series model to calculate the future refrigeration load fluctuation trend, thus outputting load change prediction data to provide a basis for subsequent energy efficiency assessment and control. The assessment module uses the ratio of real-time compressor power to load change prediction data to establish a dynamic energy efficiency ratio indicator, thereby quantifying the current energy-saving effect of the equipment and providing a system energy efficiency level classification based on the assessment results. At this point, the optimization module adjusts the compressor frequency and fan speed using a nonlinear predictive control algorithm based on the numerical range of the dynamic energy efficiency ratio indicator, generating corresponding multi-machine collaborative control commands to ensure the equipment operates under optimal energy efficiency conditions. The execution module analyzes the refrigerant flow tendency function, predicts the impact of multi-machine collaborative control commands on the system load, and optimizes adjustments in conjunction with the defrost cycle. Through this process, the system can achieve real-time monitoring and dynamic adjustment, which not only improves energy efficiency but also reduces energy consumption, while ensuring stable operation and optimal performance of the equipment. This closed-loop feedback energy-saving control scheme provides strong technical support for the efficient management and operation of intelligent refrigeration systems.

[0091] above Figure 5 The intelligent energy-saving control system of the refrigerated display cabinet in this embodiment of the invention is described in detail from the perspective of modular functional entities. The intelligent energy-saving control device of the refrigerated display cabinet in this embodiment of the invention is described in detail from the perspective of hardware processing.

[0092] Reference Figure 6 This invention also provides an intelligent energy-saving control device for a refrigerated display case. This intelligent energy-saving control device can be a server, and its internal structure can be as follows: Figure 6 As shown. The intelligent energy-saving control device of the refrigerated display case includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor, designed as a computer, provides computing and control capabilities. The memory of the intelligent energy-saving control device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database of the intelligent energy-saving control device stores the data corresponding to this embodiment. The network interface of the intelligent energy-saving control device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0093] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the intelligent energy-saving control device for the refrigerated display cabinet to which the present invention is applied.

[0094] The present invention also provides a computer-readable storage medium, which can be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium, wherein the computer-readable storage medium stores instructions that, when the instructions are executed on a computer, cause the computer to perform the steps of the intelligent energy-saving control method for the refrigerated display case.

[0095] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0096] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an intelligent energy-saving control device for a refrigerated display case (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for intelligent energy-saving control of a refrigerated display case, characterized in that, The method includes: Step S1: Collect temperature distribution data and people flow detection data inside the refrigerated display case through a multi-parameter sensor network, and obtain a set of preprocessed environmental parameters after processing by a moving average filtering algorithm; Step S2: Input the preprocessed environmental parameter set into the ARIMA time series model, calculate the fluctuation trend of the cooling load in the preset time domain, and output the load change prediction data; Step S3: Based on the ratio between the compressor's real-time power and the predicted load change data, establish a dynamic energy efficiency ratio indicator to quantify the energy-saving effect of the current operating state, including: Real-time input power data of the compressor is collected by current and voltage sensors. Combined with the evaporator inlet and outlet temperature difference and refrigerant flow rate, the current cooling capacity is calculated to obtain compressor power parameters and cooling capacity parameters. The ratio of the compressor power parameters to the cooling capacity parameters is calculated, and the energy efficiency ratio is dynamically corrected based on the load change prediction data to obtain a real-time dynamic energy efficiency ratio (EER). Based on the real-time dynamic EER, three levels—high efficiency, medium efficiency, and low efficiency—are set, and the current operating state is processed to determine the energy efficiency level, resulting in an energy efficiency level classification. The energy efficiency level classification result is weighted and fused with the temperature deviation control target, and a comprehensive evaluation function is constructed according to the preset ratio of energy efficiency weight and temperature weight to obtain a dynamic energy efficiency ratio indicator. Step S4: Based on the numerical range of the dynamic energy efficiency ratio indicator, a nonlinear predictive control algorithm is used to adjust the compressor frequency and fan speed to generate multi-machine collaborative control commands; Step S5: Analyze the impact of the multi-machine collaborative control command on the system load using the refrigerant flow tendency function, synchronously adjust the defrost cycle parameters, obtain an energy-saving control strategy, and execute it. This includes: analyzing the refrigerant flow impact of the compressor frequency adjustment and fan speed adjustment in the multi-machine collaborative control command; calculating the flow tendency coefficient by combining the evaporation temperature change trend and the condensation temperature change trend to obtain the refrigerant flow tendency function; dynamically predicting the system cooling load based on the refrigerant flow tendency function; predicting an increasing load trend when the flow tendency function is positive and a decreasing load trend when the flow tendency function is negative, obtaining the system load response prediction result; calculating and analyzing the frost layer thickness change on the evaporator surface based on the system load response prediction result; dynamically adjusting the defrost trigger threshold by combining the current humidity conditions and the frosting rate model to obtain optimized defrost cycle parameters; integrating the optimized defrost cycle parameters with the multi-machine collaborative control command to construct a unified scheduling scheme that includes refrigeration control and defrost control, obtaining an energy-saving control strategy, and issuing it to each device for execution through the control execution unit.

2. The intelligent energy-saving control method for the refrigerated display case according to claim 1, characterized in that, Step S1 includes: By deploying PT100 platinum resistance temperature sensor arrays in the upper, middle and lower layers of the refrigerated display case, temperature data and temperature change rate data of each layer in the display case are collected to obtain the original temperature data matrix. The infrared pyroelectric people flow detection sensor scans and detects the dwell information of customers within a 3-meter range in front of the display case, and obtains the time sequence data of the number of people and the dwell time parameters. The original temperature data matrix and the time series data of the number of people are input into a moving average filter, and noise is eliminated by a filtering algorithm with a window length of 10 sampling points to obtain filtered temperature data and people flow data. The filtered temperature data and pedestrian flow data are fused together, and environmental load parameters are calculated according to the ratio of temperature weight 0.7 and pedestrian flow weight 0.3 to obtain a set of preprocessed environmental parameters.

3. The intelligent energy-saving control method for refrigerated display cases according to claim 1, characterized in that, Step S2 includes: The pre-processed environmental parameter set is decomposed and calculated according to four components: heat dissipation of goods, heat dissipation of personnel, heat dissipation of lighting, and heat dissipation of defrosting, to obtain the cooling load component data; Based on the cooling load component data, a second-order difference second-order moving average time series model is constructed, and the autoregressive weight and moving average weight parameters are set to obtain the cooling load prediction model. The prediction time domain of the cooling load prediction model is set to sixty minutes, and the control step size is set to ten minutes. The cooling load fluctuations in the future time domain are recursively calculated to obtain the time-segmented load prediction values. The time-segmented load forecast values ​​are processed by differential calculation and trend analysis to calculate the load change gradient and fluctuation amplitude parameters, thereby obtaining load change forecast data.

4. The intelligent energy-saving control method for refrigerated display cases according to claim 1, characterized in that, Step S4 includes: The control strategy selection is determined based on the numerical range of the dynamic energy efficiency ratio indicator. When the indicator is in the high efficiency range, the current control parameters are maintained. When the indicator is in the medium efficiency or low efficiency range, the control adjustment mode is triggered to obtain the control mode selection signal. The control mode selection signal is input into the nonlinear predictive controller to construct a multivariable control state space model that includes compressor frequency and fan speed. A neural network algorithm is used to approximate the nonlinear characteristics of the system to obtain the predictive control state equation. Based on the predictive control state equation, the prediction time domain and control time domain parameters are set, a multi-objective function including temperature tracking error and energy efficiency optimization objective is established, and the optimal control parameter sequence is obtained by using a sequential quadratic programming algorithm for constraint optimization. The optimal control parameter sequence is decomposed into compressor frequency adjustment commands and condenser fan speed adjustment commands. The equipment is coordinated and configured through a master-slave control architecture to obtain multi-machine collaborative control commands.

5. The intelligent energy-saving control method for the refrigerated display case according to claim 1, characterized in that, The calculation and analysis of the frost thickness change on the evaporator surface based on the system load response prediction results, combined with the current humidity conditions and frost rate model, dynamically adjusts the defrost trigger threshold to obtain optimized defrost cycle parameters, including: The load increase / decrease trend data in the system load response prediction results are correlated and matched with the evaporator surface temperature data to screen out the temperature fluctuation range corresponding to the load change and obtain the temperature change influencing factor. The current frost thickness value on the evaporator surface is collected by a frost thickness sensor. The temperature change influence factor is multiplied by the frost thickness value to obtain the frost thickness change. Based on the current relative humidity data obtained by the humidity sensor, the change in frost thickness is corrected by humidity. When the humidity exceeds the reference value, the change is increased; when the humidity is below the reference value, the change is decreased, thus obtaining the corrected change in frost thickness. The modified frost layer thickness change is compared with the preset defrost trigger threshold. Based on the expected time to reach the threshold, a new defrost start time is calculated to obtain the optimized defrost cycle parameters.

6. An intelligent energy-saving control system for a refrigerated display case, characterized in that, The method for implementing the intelligent energy-saving control of the refrigerated display case as described in any one of claims 1-5, wherein the intelligent energy-saving control system of the refrigerated display case comprises: The data acquisition module is used to collect temperature distribution data and people flow detection data inside the refrigerated display case through a multi-parameter sensor network, and obtain a set of preprocessed environmental parameters after processing by a moving average filtering algorithm. The prediction module is used to input the preprocessed environmental parameter set into the ARIMA time series model, calculate the fluctuation trend of the cooling load in the preset time domain, and output load change prediction data. The evaluation module is used to establish a dynamic energy efficiency ratio indicator based on the ratio of the compressor's real-time power to the load change prediction data, and to quantify the energy-saving effect of the current operating state. The optimization module is used to adjust the compressor frequency and fan speed according to the numerical range of the dynamic energy efficiency ratio indicator, and generate multi-machine collaborative control commands by using a nonlinear predictive control algorithm. The execution module is used to analyze the impact of the multi-machine collaborative control command on the system load through the refrigerant flow tendency function, synchronously adjust the defrost cycle parameters, obtain the energy-saving control strategy, and execute it.

7. An intelligent energy-saving control device for a refrigerated display case, characterized in that, The device includes a memory and a processor, wherein the memory stores a computer program that can run on the processor, and the processor executes the computer program to implement the intelligent energy-saving control method for the refrigerated display case according to any one of claims 1 to 5.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is run by the processor, it causes the processor to execute the intelligent energy-saving control method for the refrigerated display case as described in any one of claims 1 to 5.

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