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

Abstract

The invention 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: acquiring temperature distribution and human traffic data in the refrigeration display cabinet through a multi-parameter sensor network, inputting the data into an ARIMA model after moving average filtering processing, and predicting a refrigeration load fluctuation trend; establishing a dynamic energy efficiency ratio indicator based on the ratio of the compressor power to the load prediction value, and quantifying the energy-saving effect; the compressor frequency and the fan rotating speed are adjusted by combining a nonlinear predictive control algorithm, and a cooperative control instruction is generated; and the influence of the instruction on the system load is analyzed through the refrigerant flow tendency function, the defrosting period is synchronously optimized, and an energy-saving control strategy is formed and executed. According to the invention, predictability and collaboration of energy efficiency management of the refrigeration display cabinet are improved.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent control of refrigeration equipment, and in particular to an intelligent energy-saving control method and system for a refrigerated display cabinet. Background Art

[0002] Existing intelligent energy-saving control methods for refrigerated display cabinets primarily employ temperature-feedback-based PID control strategies, which monitor temperature fluctuations within the display cabinet to adjust the operating status of the compressor and fan. These traditional control methods typically utilize temperature and humidity sensors to collect environmental parameters. When the temperature deviates from the setpoint, the control system adjusts the refrigeration unit's operating frequency accordingly. Some advanced systems also integrate timed defrost functions and simple energy consumption monitoring modules, enabling the defrost cycle to be initiated at preset intervals and recording basic energy consumption data.

[0003] However, traditional single-variable temperature control cannot effectively handle the combined impact of multiple factors on cooling demand, such as changes in passenger flow and fluctuations in commodity load, resulting in a lack of foresight in the control strategy. Secondly, the existing PID control method is a passive response control method that can only be adjusted after a temperature deviation occurs. It cannot predict future load change trends, and control lags and energy waste often occur. Thirdly, the fixed defrost cycle setting ignores the dynamic changes in the actual frosting rate with environmental conditions, resulting in problems such as excessive defrosting or untimely defrosting.

[0004] Since it is impossible to establish a predictive relationship between environmental parameters and refrigeration load, the system cannot adjust the 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 refrigeration 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 the defrost cycle. Summary of the Invention

[0005] The present application provides an intelligent energy-saving control method and system for a refrigerated display cabinet, which are used to improve the predictability and coordination of energy efficiency management of the refrigerated display cabinet.

[0006] In a first aspect, the present application provides an intelligent energy-saving control method for a refrigerated display cabinet, the intelligent energy-saving control method for a refrigerated display cabinet comprising:

[0007] Step S1: collecting temperature distribution data and human flow detection data in the refrigerated display cabinet through a multi-parameter sensor network, and obtaining a pre-processed environmental parameter set after processing by a sliding average filter algorithm;

[0008] Step S2: inputting the pre-processed environmental parameter set into the ARIMA time series model, calculating the fluctuation trend of the cooling load within a preset time domain, and outputting load change prediction data;

[0009] Step S3: establishing a dynamic energy efficiency ratio indicator based on the ratio of the real-time power of the compressor to the load change prediction data to quantify the energy saving effect of the current operating state;

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

[0011] Step S5: Analyze the impact of the multi-machine coordinated control instruction 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] In a second aspect, the present application provides an intelligent energy-saving control system for a refrigerated display cabinet, the intelligent energy-saving control system for the refrigerated display cabinet comprising:

[0013] The acquisition module is used to collect temperature distribution data and human flow detection data in the refrigerated display cabinet through a multi-parameter sensor network, and obtain a pre-processed environmental parameter set after processing by a sliding average filter algorithm;

[0014] A prediction module is used to input the pre-processed environmental parameter set into an ARIMA time series model, calculate the fluctuation trend of the cooling load within a preset time domain, and output load change prediction data;

[0015] An evaluation module is used to establish a dynamic energy efficiency ratio indicator based on the ratio of the real-time power of the compressor to the load change prediction data, so as to quantify the energy saving effect of the current operating state;

[0016] an optimization module for adjusting the compressor frequency and fan speed using a nonlinear predictive control algorithm according to the numerical range of the dynamic energy efficiency ratio indicator and generating a multi-machine coordinated control instruction;

[0017] The execution module is used to analyze the impact of the multi-machine collaborative control instruction 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] In a third aspect, an intelligent energy-saving control device for a refrigerated display cabinet is provided, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the intelligent energy-saving control device for the refrigerated display cabinet executes the above-mentioned intelligent energy-saving control method for the refrigerated display cabinet.

[0019] In a fourth aspect, a computer-readable storage medium is provided, wherein instructions are stored in the computer-readable storage medium. When the computer-readable storage medium is executed on a computer, the computer executes the above-mentioned intelligent energy-saving control method for a refrigerated display cabinet.

[0020] In the technical solution provided by the present application, the temperature distribution data and human flow detection data in the refrigerated display cabinet are collected through a multi-parameter sensor network, and the pre-processing environmental parameter set is obtained by processing with a sliding average filter algorithm, which solves the problem of incomplete environmental information caused by the single temperature parameter control in the prior art, and enables the system to comprehensively consider the impact of temperature changes and human activities on the refrigeration load. The pre-processing environmental parameter set is input into the ARIMA time series model to calculate the refrigeration load fluctuation trend and output the load change prediction data, which overcomes the lag of the traditional passive response control and enables the system to have forward-looking control capabilities. A dynamic energy efficiency ratio indicator is established based on the ratio relationship between the real-time power of the compressor and the load change prediction data, which realizes the quantitative evaluation of the energy-saving effect of the current operating state and provides a scientific basis for subsequent control decisions. According to 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 instructions, breaking through the limitations of traditional single-device independent control and realizing unified coordination and optimized configuration among multiple devices. The impact of multi-machine collaborative control instructions on system load is analyzed through the refrigerant flow tendency function, and the defrost cycle parameters are adjusted synchronously, which changes the rigid mode of fixed defrost cycle and realizes the intelligent linkage of defrost control and refrigeration control.

[0021] In particular, the application of the ARIMA time series model in refrigeration load forecasting for refrigerated display cabinets fully considers the temporal characteristics and periodic patterns of load changes in commercial environments. Its second-order difference and moving average characteristics can effectively capture the trend and random components of load fluctuations, providing a reliable data basis 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 refrigerated display cabinets. By constructing a multivariable control state space model and a multi-objective optimization function, it achieves a balance between temperature control accuracy and energy saving effects. The refrigerant flow rate tendency function, as a bridge connecting device control instructions and system response, fully reflects the mechanism by which flow changes in the refrigeration cycle system affect the overall performance, enabling the adjustment of the defrost cycle to keep pace with the changes in the refrigeration load, avoiding mutual interference between control strategies, and significantly improving the overall intelligent control level and energy saving effects of refrigerated display cabinets. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

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

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

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

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

[0027] Figure 5 This is a schematic diagram of an embodiment of an intelligent energy-saving control system for a refrigerated display cabinet in an embodiment of the present application;

[0028] Figure 6 It is a schematic block diagram of the structure of an intelligent energy-saving control device for a refrigerated display cabinet in an embodiment of the present invention. DETAILED DESCRIPTION

[0029] The embodiments of the present application provide an intelligent energy-saving control method and system for a refrigerated display cabinet. The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, 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 that are clearly listed, but may include other steps or units that are not clearly listed or that are inherent to these processes, methods, products or devices.

[0030] For ease of understanding, the specific process of the embodiment of the present application is described below. Figure 1 In one embodiment of the present application, an intelligent energy-saving control method for a refrigerated display cabinet includes:

[0031] Step S1: The temperature distribution data and the human flow detection data in the refrigerated display cabinet are collected through a multi-parameter sensor network, and the pre-processed environmental parameter set is obtained after being processed by a sliding average filter algorithm.

[0032] Specifically, to accurately capture the environmental parameters of refrigerated display cases, multiple sensors are deployed at various locations within the display cases to collect real-time temperature distribution and traffic data. These sensors are located at different levels of the display cases to ensure comprehensive capture of temperature variations within the internal environment. Simultaneously, a traffic flow detection module monitors customer activity in front of the display cases, including the number of customers and their duration of stay. Because raw data is often affected by external environmental factors and equipment operating conditions, a sliding average filter algorithm is used to preprocess the data. This algorithm uses a fixed window length to smooth out short-term fluctuations and noise in the data, making it more stable and reliable. This process removes errors caused by transient changes and ensures more stable collected data. After filtering, the resulting set of environmental parameters accurately reflects temperature and traffic variations within the display cases, providing a reliable basis for subsequent load forecasting and energy-saving control.

[0033] Step S2: Input the pre-processed 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.

[0034] Specifically, to predict future refrigeration load changes for refrigerated display cases, a set of preprocessed environmental parameters is input into the ARIMA time series model. By analyzing trends, seasonality, and random fluctuations in historical data, the ARIMA model captures the temporal characteristics of the refrigeration load, effectively predicting load fluctuations. The ARIMA model performs differential processing on the input data to eliminate non-stationarity and ensure that the model captures the true load variation patterns. Using autoregressive and moving average calculations, the model combines historical time series data to predict future refrigeration load fluctuation trends. This prediction enables the system to understand load changes for refrigerated display cases in advance and output load change forecast data. This data provides an important basis for subsequent energy-saving control, enabling the system to make adjustments 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 the real-time power of the compressor to the load change prediction data, a dynamic energy efficiency ratio indicator is established to quantify the energy-saving effect of the current operating state.

[0036] Specifically, in order to quantify the energy-saving effect of the refrigerated display cabinet's current operating state, a dynamic energy efficiency ratio indicator is established based on the ratio between the compressor's real-time power data and the load change prediction data. This indicator reflects the relationship between the power consumed by the compressor during actual operation and the predicted load. During this process, the system monitors the compressor's power consumption in real time and calculates the ratio between the two in combination with the load change prediction data. This ratio represents the current energy efficiency status of the system: when the ratio is low, it means that the compressor can still maintain a high operating efficiency under relatively low load conditions; when the ratio is high, it may indicate that the refrigeration system is operating inefficiently and there is a risk of energy waste. Through the calculated dynamic energy efficiency ratio indicator, the system can evaluate 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: According to the numerical range of the dynamic energy efficiency ratio indicator, the compressor frequency and the fan speed are adjusted using a nonlinear predictive control algorithm to generate a multi-machine coordinated control instruction.

[0038] Specifically, the system categorizes the current energy efficiency level based on the numerical range of the dynamic energy efficiency ratio indicator. If the dynamic energy efficiency ratio is in the high efficiency range, the system is operating well and no immediate adjustment is required. If the dynamic energy efficiency ratio is in the medium or low efficiency range, the system activates a nonlinear predictive control algorithm to 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 required adjustments for 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 period of time and calculate the corresponding adjustment parameters. The system then generates adjustment commands for the compressor frequency and fan speed based on the control commands obtained through the optimization calculation, ensuring that the devices achieve optimal energy efficiency when working together.

[0039] Step S5: Analyze the impact of the multi-machine collaborative control instructions on the system load through the refrigerant flow tendency function, adjust the defrost cycle parameters synchronously, obtain the energy-saving control strategy and execute it.

[0040] Specifically, by analyzing the impact of multi-machine coordinated control instructions on system load, the system's energy-saving performance is further optimized. A refrigerant flow rate tendency function is used to assess the load variation trends caused by adjustments to compressor frequency and fan speed. By calculating the relationship between flow rate changes and system load, this tendency function predicts the impact of refrigerant flow rate on system load under different operating conditions. If the system load changes, the flow rate tendency function predicts future load trends based on the increase or decrease in flow rate. Specifically, a positive value of the flow rate tendency function indicates a potential load increase, and the system will adjust cooling parameters accordingly. A negative value indicates a potential load decrease, and the system will take appropriate measures to reduce energy consumption. Based on the analysis of the flow rate tendency function, the system simultaneously adjusts defrost cycle parameters. By combining current humidity conditions and frost layer thickness changes, the system dynamically calculates the defrost triggering timing to avoid energy waste caused by excessive or insufficient defrosting. This intelligent adjustment synchronizes the defrost cycle with changes in cooling load, achieving coordinated optimization of cooling and defrosting.

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

[0042] The PT100 platinum resistance temperature sensor arrays arranged on the upper, middle and lower layers of the refrigerated display cabinet collect the temperature data and temperature change rate data of each layer in the display cabinet to obtain the original temperature data matrix;

[0043] The infrared pyroelectric human flow detection sensor scans and detects the customer stay information within 3 meters in front of the display cabinet to obtain the time series data of the number of people and the parameters of the stay time;

[0044] The original temperature data matrix and the time series data of the number of people are input into the sliding average filter, and the noise is eliminated by using the filtering algorithm with a window length of 10 sampling points to obtain the filtered temperature data and human flow data;

[0045] The filtered temperature data and pedestrian flow data were fused, and the environmental load parameters were calculated according to the ratio of temperature weight 0.7 and pedestrian flow weight 0.3 to obtain the preprocessing environmental parameter set.

[0046] Specifically, to accurately capture environmental information within a refrigerated display case, an array of temperature sensors was placed on the upper, middle, and lower levels of the display case. These sensors collected real-time temperature data and temperature change rate data for each level, generating a raw temperature data matrix within the display case. To monitor customer traffic within a 3-meter radius in front of the display case, infrared pyroelectric sensors were used to scan customer dwelling patterns. The collected time-series data on customer counts and customer dwell time provided valuable information for subsequent analysis. To reduce the impact of noise in these data, the raw temperature matrix and customer count time-series data were filtered using a sliding average filter with a window length of 10 sampling points. This effectively eliminated transient fluctuations and noise, making the temperature and customer flow data more stable and reliable. The filtered temperature and customer flow data were fused, and a comprehensive environmental load parameter was calculated by combining the two data sets, assigning a weight of 0.7 for temperature and 0.3 for flow. This parameter reflects the combined impact of temperature and flow fluctuations within the display case, forming a preprocessed set of environmental parameters that provides an accurate data foundation for subsequent load forecasting and energy-saving control.

[0047] For example, in monitoring pedestrian flow, suppose customers enter and exit within 3 meters of a display case. The sensor detects an increase in the number of customers from 2 to 5 over 10 minutes and records their dwell time. The sensor records the number of customers every 1 second. If a customer remains in front of the display case for more than 30 seconds, the sensor calculates the dwell time for each customer. This data forms a time series of customer count data and dwell time parameters, providing detailed pedestrian flow information for subsequent environmental load calculations.

[0048] For example, in terms of noise elimination, the sliding average filter will smooth the collected temperature data and pedestrian flow data. Assuming that the temperature data collected within 10 seconds is [4.2℃, 4.4℃, 4.1℃, 4.3℃, 4.2℃], after filtering with a sliding window of 10 sampling points, the data will be smoothed to a more stable average value, such as 4.2℃, thereby eliminating the errors caused by short-term fluctuations. At the same time, the pedestrian flow data will also undergo similar smoothing processing. Assuming that the original data is [2 people, 3 people, 4 people, 5 people, 3 people], after sliding average filtering, the smoothed data obtained is [3 people, 4 people, 3 people], eliminating the data noise caused by instantaneous fluctuations. Figure 2 ,This figure shows the comparison of the filtering effects of temperature and pedestrian flow data.

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

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

[0051] 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;

[0052] The prediction time domain of the cooling load prediction model is set to 60 minutes, the control step is set to 10 minutes, and the cooling load fluctuation in the future time domain is recursively calculated to obtain the load prediction value by time period;

[0053] Perform differential calculation and trend analysis on the load forecast values ​​of different time periods, calculate the load change gradient and fluctuation amplitude parameters, and obtain the load change forecast data.

[0054] Specifically, the preprocessed set of environmental parameters is used to perform load decomposition calculations based on four main factors: heat dissipation from goods, heat dissipation from personnel, heat dissipation from lighting, and heat dissipation from defrosting. This results in refrigeration load component data for each item. This data helps the system accurately assess the contribution of each factor to the overall refrigeration 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 length of stay, and lighting and defrosting heat are closely related to the lighting equipment and defrosting cycle within the display cabinet, 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 weight parameters in the model are set to accurately describe the load variation pattern. By continuously training the model, the fluctuation trend of the refrigeration load is captured, and an accurate refrigeration load prediction model is obtained.

[0055] To predict future load changes, the model's prediction horizon is set to sixty minutes, with a control step size of ten minutes. This allows a recursive calculation of load fluctuations over the next sixty minutes every ten minutes, gradually obtaining load forecasts for each future time period. This process enables the system to understand load trends within the display cabinet in real time, make predictions, and promptly adjust strategies to address upcoming load changes. To ensure forecast accuracy, a differential calculation is performed on the load forecast values ​​for each time period to eliminate possible interference from long-term trends or cyclical changes. Trend analysis is also used to further calculate the gradient and fluctuation amplitude parameters of the load changes. These calculation results provide a detailed reference for subsequent load change forecast data, ensuring that the system can accurately control and regulate according to actual load changes, avoiding over- or under-cooling, and effectively improving energy efficiency.

[0056] For example, a display case in a shopping mall stores a variety of fresh produce, which must be maintained at a temperature of around 4°C. Multiple sensors are installed within the display case to collect environmental data. For example, the heat dissipation from merchandise varies depending on the type of items on display, while the heat dissipation from people fluctuates with the number of customers. Load decomposition calculations yield cooling load component data for heat dissipation from merchandise, people, lighting, and defrost heating. For example, merchandise heat dissipation is 300W, people heat dissipation is 150W, lighting heat dissipation is 100W, and defrost heating is 50W. The system inputs this data into a second-differenced, second-order moving average time series model with an autoregressive weight of 0.6 and a moving average weight of 0.4. This calculation yields a cooling load forecasting model that accurately predicts load fluctuations over the next 60 minutes. Assuming the load forecast for the next 10 minutes is 450W, 420W, 460W, and 430W, the system uses recursive calculations to derive load forecasts for the four time periods. By performing differential calculations and trend analysis on these predicted values, we can obtain the gradient and fluctuation range of the load change. For example, the load change gradient is -30W and the fluctuation range is 50W. Figure 3 This figure shows a comprehensive load analysis and forecast for the fresh produce display cabinet refrigeration system. These results allow the system to adjust the control strategy in a timely manner based on load trends, ensuring efficient operation of the refrigeration system and maximizing energy savings.

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

[0058] The real-time input power data of the compressor is collected through the current and voltage sensors, and the current cooling capacity value is calculated by combining the evaporator inlet and outlet temperature difference and the refrigerant flow rate to obtain the compressor power parameters and cooling capacity parameters;

[0059] The ratio of the compressor power parameter to the cooling capacity parameter is calculated, and the energy efficiency ratio is dynamically corrected and calculated in combination with the 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 level ranges are set: high efficiency range, medium efficiency range, and low efficiency range. The energy efficiency level is determined for the current operating state to obtain the energy efficiency level classification result.

[0061] The energy efficiency grade classification results and the temperature deviation control target are weightedly fused and calculated, 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.

[0062] Specifically, the system collects real-time compressor power data using current and voltage sensors and calculates the current cooling capacity based on the evaporator inlet and outlet temperature difference and refrigerant flow rate. For example, if the compressor input power is 1500W, the evaporator inlet and outlet temperature difference is 5°C, and the refrigerant flow rate is 0.8L / min, the calculated cooling capacity is 450W. These data are used as compressor power and cooling capacity parameters for the subsequent energy efficiency ratio calculation. The system calculates the ratio of compressor power to cooling capacity, assuming a value of 3.33 (1500W / 450W). It then dynamically adjusts the EER based on load change forecasts to produce a real-time dynamic EER value. For example, a corrected dynamic EER of 2.9 indicates a decrease in system efficiency compared to the ideal state. Based on this dynamic EER value, the system sets three efficiency ranges: high efficiency (>2.5), medium efficiency (1.5-2.5), and low efficiency (<1.5). The system then determines the current system operating status as a medium efficiency level. Finally, the energy efficiency grade classification result is weightedly fused with the temperature deviation control target. The temperature deviation weight is 0.7, and the energy efficiency weight is 0.3. After weighted calculation, the value of the comprehensive evaluation function is 2.4, thus generating a dynamic energy efficiency ratio indicator.

[0063] In a specific embodiment, the process of executing step S4 may specifically include the following steps:

[0064] 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 a control mode selection signal.

[0065] The control mode selection signal is input into the nonlinear predictive controller to build a multivariable control state space model including the compressor frequency and fan speed. The neural network algorithm is used to approximate the nonlinear characteristics of the system to obtain the predictive control state equation.

[0066] According to 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 objectives is established, and a sequential quadratic programming algorithm is used for constrained optimization to obtain the optimal control parameter sequence.

[0067] The optimal control parameter sequence is decomposed into compressor frequency adjustment instructions and condenser fan speed adjustment instructions. The master-slave control architecture is used to coordinate the configuration between devices and obtain multi-machine collaborative control instructions.

[0068] Specifically, the system makes a control strategy selection decision based on the numerical 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 making any adjustments; when the indicator enters the medium efficiency or low efficiency range, the control adjustment mode is triggered and a control mode selection signal is generated. This signal is then input into the nonlinear predictive controller to construct a control strategy containing the compressor frequency f c (t) and fan speed f v (t) is a multivariable control state space model. This model approximates the nonlinear characteristics of the system through a neural network algorithm and obtains the predictive control state equation: in, is the derivative of the system state, A, B and C are the state matrix, input matrix and output matrix respectively, x(t) represents the state vector of the system, u(t) is the control input vector, and y(t) is the system output. Based on this equation, the system sets the control domain T c Parameters, construct a multi-objective function including temperature tracking error ∈(t), which is the temperature tracking error and energy efficiency optimization target: Where, ∈(t) is the temperature tracking error, J is the objective function, and are the ideal compressor frequency and fan speed respectively, and α, β, and γ are weight coefficients used to balance temperature control accuracy and energy efficiency optimization. On this basis, the sequential quadratic programming algorithm is used to solve the constrained optimization problem and obtain the optimal control parameter sequence: The optimal control parameter sequence is decomposed into compressor frequency adjustment instructions and condenser fan speed adjustment instructions. The master-slave control architecture is used to coordinate the configuration of the devices, thus obtaining multi-machine collaborative control instructions. These instructions achieve precise temperature control and energy efficiency optimization by controlling the operation of the devices, ensuring that the refrigerated display cabinet operates stably within the high-efficiency range while preventing the system from entering the low-efficiency range, thereby improving the overall energy saving effect. Figure 4 ,This figure shows the closed-loop optimization control diagram of refrigeration equipment energy efficiency.

[0069] In a specific embodiment, the process of executing step S5 may specifically include the following steps:

[0070] The refrigerant flow rate is analyzed by analyzing the influence of the compressor frequency adjustment value and the fan speed adjustment value in the multi-machine coordinated control instruction. The flow rate tendency coefficient is calculated by combining the evaporation temperature change trend and the condensation temperature change trend to obtain the refrigerant flow rate tendency function.

[0071] Based on the refrigerant flow tendency function, the dynamic response prediction of the system cooling load is carried out. When the flow tendency function is positive, the load increase trend is predicted. When the flow tendency function is negative, the load decrease trend is predicted. The system load response prediction result is obtained.

[0072] The change in frost thickness on the evaporator surface is calculated and analyzed based on the system load response prediction results. The defrost trigger threshold is dynamically adjusted based on the current humidity conditions and the frost rate model to obtain the optimized defrost cycle parameters.

[0073] The optimized defrost cycle parameters are integrated with the multi-machine collaborative control instructions to build a unified scheduling plan that includes refrigeration control and defrost control. The energy-saving control strategy is obtained and sent to each device for execution through the control execution unit.

[0074] Specifically, by analyzing the compressor frequency and fan speed adjustments in the multi-machine coordinated control instructions and combining them with the evaporating and condensing temperature trends, the refrigerant flow rate tendency coefficient is calculated, resulting in a refrigerant flow rate tendency function. Based on this tendency function, the system dynamically predicts the cooling load response. When the flow rate tendency function is positive, the cooling load will increase, and the system predicts a corresponding load increase; when the flow rate tendency function is negative, the system predicts a load decrease. Based on the load response prediction results, the system calculates and analyzes the change in frost thickness on the evaporator surface. Based on current humidity conditions, the system dynamically adjusts the defrost trigger threshold using a frost rate model to determine the optimized defrost trigger threshold. This intelligently adjusts the defrost cycle to avoid over-defrosting or under-defrosting, ensuring that the refrigeration system operates within the optimal efficiency range. The optimized defrost cycle parameters are integrated with the multi-machine coordinated control instructions to construct a unified scheduling scheme that encompasses both cooling and defrosting control. Energy-saving control strategies are distributed to each device through the control execution unit. The execution system adjusts the operating status of each device based on these instructions to achieve precise temperature control and energy saving goals.

[0075] For example, when analyzing the compressor frequency and fan speed adjustments in multi-machine coordinated control instructions, the system may receive instructions to adjust the compressor frequency from 40Hz to 45Hz and increase the fan speed from 1500RPM to 1800RPM. Based on these changes, the system calculates the refrigerant flow rate tendency coefficient, taking into account the trends of the evaporating temperature rising from -5°C to -4°C and the condensing temperature falling from 35°C to 33°C. These changes indicate that the flow rate tendency function may be positive, indicating an increase in system load. When calculating the change in frost thickness on the evaporator surface, assuming a current humidity of 80%, the system predicts, based on the frost formation rate model, that the frost thickness will increase by 0.5mm over the next hour. Based on the system's load forecast, the system dynamically adjusts the defrost trigger threshold, initiating defrost when the frost thickness reaches 2mm. This ensures that frost growth does not affect cooling performance as the load increases, and that defrost is initiated at the appropriate time.

[0076] In a 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, dynamically adjusting the defrost trigger threshold in combination with the current humidity conditions and the frost rate model, and obtaining optimized defrost cycle parameters may specifically include the following steps:

[0077] The load increase and decrease trend data in the system load response prediction results are correlated and matched with the evaporator surface temperature data to filter out the temperature fluctuation range corresponding to the load change and obtain the temperature change influencing factor;

[0078] The frost thickness sensor collects the current frost thickness value on the evaporator surface, and multiplies the temperature change influencing factor by the frost thickness value to obtain the frost thickness change;

[0079] Based on the current relative humidity data obtained by the humidity sensor, the humidity correction processing is performed on the frost thickness change. When the humidity exceeds the reference value, the change is increased, and when the humidity is lower than the reference value, the change is reduced to obtain the corrected frost thickness change;

[0080] The corrected frost layer thickness change is compared with the preset defrost trigger threshold, and the new defrost start time point is calculated according to the expected time to reach the threshold to obtain the optimized defrost cycle parameters.

[0081] Specifically, the load increase / decrease trend data from the system load response prediction results is correlated with the evaporator surface temperature data. By analyzing the temperature fluctuation range corresponding to the load change, the influencing factors of temperature change are extracted. For example, if the cooling effect is enhanced due to an increase in load, the temperature fluctuation amplitude may increase, while the temperature change may be more gradual when the load decreases. This fluctuation data is combined with the temperature change factor to produce a more accurate prediction of the frost thickness change.

[0082] The system uses the frost thickness sensor to collect the current frost thickness value on the evaporator surface. For example, if the current frost thickness is 1.5 mm, the system multiplies the temperature change factor by the current frost thickness value. For example, if the temperature fluctuation factor is 0.8 and the frost thickness is 1.5 mm, the change in frost thickness is 1.2 mm.

[0083] The humidity sensor provides current relative humidity data, for example, 85%. Based on humidity conditions and the frost formation rate model, when the humidity is above a baseline value, the frost layer grows faster. The system applies humidity correction to the frost thickness variation. For example, when the humidity is above the baseline value, the system may increase the frost thickness variation, for example, to 1.4 mm, to compensate for the humidity effect. When the humidity is below the baseline value, the frost layer grows slower, and the system reduces the variation.

[0084] The corrected frost thickness change is compared with the preset defrost trigger threshold. For example, if the corrected frost thickness change is 1.4mm, the current frost thickness is 1.5mm, and the preset trigger threshold is 2mm, the system will calculate a new defrost start time based on this data. For example, if the frost thickness will reach 2mm within 1 hour, the system will optimize the defrost cycle parameters based on this calculation and prepare for defrost operation in advance. In this way, the system can accurately control the defrost cycle, ensuring cooling efficiency while avoiding the loss of cooling effect caused by excessive frost.

[0085] The above describes the intelligent energy-saving control method of the refrigerated display cabinet in the embodiment of the present application. The following describes the intelligent energy-saving control system of the refrigerated display cabinet in the embodiment of the present application. Figure 5 In one embodiment of the present application, an intelligent energy-saving control system for a refrigerated display cabinet includes:

[0086] The acquisition module is used to collect temperature distribution data and human flow detection data in the refrigerated display cabinet through a multi-parameter sensor network, and obtain a pre-processed environmental parameter set after processing by a sliding average filter algorithm;

[0087] A prediction module is used to input the pre-processed environmental parameter set into an ARIMA time series model, calculate the fluctuation trend of the cooling load within a preset time domain, and output load change prediction data;

[0088] An evaluation module is used to establish a dynamic energy efficiency ratio indicator based on the ratio of the real-time power of the compressor to the load change prediction data, so as to quantify the energy saving effect of the current operating state;

[0089] an optimization module for adjusting the compressor frequency and fan speed using a nonlinear predictive control algorithm according to the numerical range of the dynamic energy efficiency ratio indicator and generating a multi-machine coordinated control instruction;

[0090] The execution module is used to analyze the impact of the multi-machine collaborative control instruction 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.

[0091] Through the collaborative efforts of these components, the system achieves a closed-loop optimization process, from environmental data collection to refrigeration load prediction and energy-saving control. The acquisition module acquires temperature distribution and traffic flow data within the refrigerated display cabinets through a multi-parameter sensor network and preprocesses it using a sliding average filter 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 future refrigeration load fluctuation trends. This output then provides load change forecasts, providing a basis for subsequent energy efficiency evaluation and control. The evaluation module uses the ratio of real-time compressor power to predicted load change data to establish a dynamic energy efficiency ratio indicator (DERI), quantifying the energy efficiency of the current equipment and providing a system energy efficiency classification based on the evaluation results. The optimization module then uses a nonlinear predictive control algorithm to adjust the compressor frequency and fan speed based on the dynamic EER indicator's numerical range, generating corresponding multi-machine coordinated control commands to ensure optimal equipment operation. The execution module analyzes the refrigerant flow rate trend function, predicts the impact of the DERI control commands on the system load, and optimizes and adjusts them based on the defrost cycle. Through this process, the system enables real-time monitoring and dynamic adjustment, improving energy efficiency and reducing energy consumption while ensuring stable equipment operation and optimal performance. This closed-loop feedback energy-saving control solution provides strong technical support for the efficient management and operation of intelligent refrigeration systems.

[0092] above Figure 5 The intelligent energy-saving control system of the refrigerated display cabinet in the embodiment of the present invention is described in detail from the perspective of modular functional entities. The intelligent energy-saving control device of the refrigerated display cabinet in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0093] Reference Figure 6 In an embodiment of the present invention, there is also provided an intelligent energy-saving control device for a refrigerated display cabinet. The intelligent energy-saving control device for the refrigerated display cabinet may be a server, and its internal structure may be as follows: Figure 6 As shown. The intelligent energy-saving control device of the refrigerated display cabinet includes a processor, a memory, a display screen, an input device, a network interface and a database connected via a system bus. Among them, the computer-designed processor is used to provide computing and control capabilities. The memory of the intelligent energy-saving control device of the refrigerated display cabinet includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the intelligent energy-saving control device of the refrigerated display cabinet is used to store the corresponding data in this embodiment. The network interface of the intelligent energy-saving control device of the refrigerated display cabinet is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, the above method is implemented.

[0094] Those skilled in the art will understand that Figure 6 The structure shown in the figure is merely a block diagram of a portion of the structure related to the solution of the present invention, and does not constitute a limitation on the intelligent energy-saving control device for the refrigerated display cabinet to which the solution of the present invention is applied.

[0095] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. The computer-readable storage medium stores instructions. When the instructions are executed on a computer, the computer executes the steps of the intelligent energy-saving control method for the refrigerated display cabinet.

[0096] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0097] If the integrated unit is implemented in the form of 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, 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. The computer software product is stored in a storage medium and includes several instructions for enabling an intelligent energy-saving control device for a refrigerated display cabinet (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0098] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. An intelligent energy-saving control method for a refrigerated display cabinet, characterized in that: The method comprises: Step S1: collecting temperature distribution data and human flow detection data in the refrigerated display cabinet through a multi-parameter sensor network, and obtaining a pre-processed environmental parameter set after processing by a sliding average filter algorithm; Step S2: inputting the pre-processed environmental parameter set into the ARIMA time series model, calculating the fluctuation trend of the cooling load within a preset time domain, and outputting load change prediction data; Step S3: establishing a dynamic energy efficiency ratio indicator based on the ratio of the real-time power of the compressor to the load change prediction data to quantify the energy saving effect of the current operating state; Step S4: According to the numerical range of the dynamic energy efficiency ratio indicator, a nonlinear predictive control algorithm is used to adjust the compressor frequency and the fan speed to generate a multi-machine coordinated control instruction; Step S5: Analyze the impact of the multi-machine coordinated control instruction 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.

2. The intelligent energy-saving control method for a refrigerated display cabinet according to claim 1, characterized in that: The step S1 comprises: The PT100 platinum resistance temperature sensor arrays arranged on the upper, middle and lower layers of the refrigerated display cabinet collect the temperature data and temperature change rate data of each layer in the display cabinet to obtain the original temperature data matrix; The infrared pyroelectric human flow detection sensor scans and detects the customer stay information within 3 meters in front of the display cabinet to obtain the time series data of the number of people and the parameters of the stay time; The original temperature data matrix and the time series data of the number of people are input into a sliding average filter, and a filtering algorithm with a window length of 10 sampling points is used to perform noise elimination processing to obtain filtered temperature data and human flow data; The filtered temperature data and the human flow data are subjected to data fusion processing, and the environmental load parameters are calculated according to a ratio of a temperature weight of 0.7 and a human flow weight of 0.3 to obtain a pre-processed environmental parameter set.

3. The intelligent energy-saving control method for a refrigerated display cabinet according to claim 1, characterized in that: The step S2 includes: The pre-processing environmental parameter set is subjected to load decomposition calculation according to four components: heat dissipation of goods, heat dissipation of personnel, heat dissipation of lighting, and defrosting heating, to obtain refrigeration load component data; Constructing a second-order difference second-order moving average time series model based on the refrigeration load component data, setting autoregressive weight and moving average weight parameters, and obtaining a refrigeration load prediction model; The prediction time domain of the cooling load prediction model is set to 60 minutes, the control step length is set to 10 minutes, and the cooling load fluctuation in the future time domain is recursively calculated to obtain the load prediction value by time period; The load forecast values ​​for each time period are subjected to differential calculation and trend analysis, and load change gradient and fluctuation amplitude parameters are calculated to obtain load change forecast data.

4. The intelligent energy-saving control method for a refrigerated display cabinet according to claim 1, characterized in that: The step S3 comprises: The real-time input power data of the compressor is collected through the current and voltage sensors, and the current cooling capacity value is calculated by combining the evaporator inlet and outlet temperature difference and the refrigerant flow rate to obtain the compressor power parameters and cooling capacity parameters; Performing a ratio calculation on the compressor power parameter and the cooling capacity parameter, and dynamically correcting the energy efficiency ratio in combination with the load change prediction data to obtain a real-time dynamic energy efficiency ratio value; Based on the real-time dynamic energy efficiency ratio value, three level ranges are set: high efficiency range, medium efficiency range and low efficiency range, and the energy efficiency level is determined for the current operating state to obtain an energy efficiency level classification result; The energy efficiency grade classification result and the temperature deviation control target are weightedly fused and calculated, and a comprehensive evaluation function is constructed according to a preset ratio of the energy efficiency weight and the temperature weight to obtain a dynamic energy efficiency ratio indicator.

5. The intelligent energy-saving control method for a refrigerated display cabinet according to claim 1, characterized in that: The step S4 comprises: Performing a control strategy selection judgment based on the numerical range of the dynamic energy efficiency ratio indicator, maintaining the current control parameters when the indicator is in a high efficiency range, and triggering a control adjustment mode when the indicator is in a medium efficiency or low efficiency range, thereby obtaining a control mode selection signal; Inputting the control mode selection signal into a nonlinear predictive controller, constructing a multivariable control state space model including compressor frequency and fan speed, and using a neural network algorithm to approximate the nonlinear characteristics of the system to obtain a predictive control state equation; According to 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 objectives is established, and a sequential quadratic programming algorithm is used to perform constrained optimization to obtain the optimal control parameter sequence; The optimal control parameter sequence is decomposed into compressor frequency adjustment instructions and condenser fan speed adjustment instructions, and coordinated configuration processing is performed between devices through a master-slave control architecture to obtain multi-machine collaborative control instructions.

6. The intelligent energy-saving control method for a refrigerated display cabinet according to claim 1, characterized in that: The step S5 comprises: The refrigerant flow rate is analyzed by analyzing the influence of the compressor frequency adjustment amount and the fan speed adjustment amount in the multi-machine coordinated control instruction, and the flow rate tendency coefficient is calculated in combination with the evaporation temperature change trend and the condensation temperature change trend to obtain the refrigerant flow rate tendency function; Based on the refrigerant flow tendency function, a dynamic response prediction of the system refrigeration load is performed, when the flow tendency function is positive, a load increase trend is predicted, and when the flow tendency function is negative, a load decrease trend is predicted, to obtain a system load response prediction result; The change in frost thickness on the evaporator surface is calculated and analyzed based on the system load response prediction results, and the defrost trigger threshold is dynamically adjusted in combination with the current humidity conditions and the frost rate model to obtain the optimized defrost cycle parameters; The optimized defrost cycle parameters are integrated with the multi-machine collaborative control instructions to construct a unified scheduling plan including refrigeration control and defrost control, and an energy-saving control strategy is obtained and sent to each device for execution through a control execution unit.

7. The intelligent energy-saving control method for a refrigerated display cabinet according to claim 6, characterized in that: The calculation and analysis of the change in frost thickness on the evaporator surface based on the system load response prediction result, and the dynamic adjustment of the defrost trigger threshold in combination with the current humidity conditions and the frost rate model to obtain the optimized defrost cycle parameters include: Correlating and matching the load increase and decrease trend data in the system load response prediction result with the evaporator surface temperature data, screening out the temperature fluctuation range corresponding to the load change, and obtaining the temperature change influencing factor; The frost thickness sensor collects the current frost thickness value on the evaporator surface, and multiplies the temperature change influencing factor by the frost thickness value to obtain the frost thickness change; Based on the current relative humidity data obtained by the humidity sensor, the humidity correction processing is performed on the frost layer thickness change, when the humidity exceeds the reference value, the change is increased, and when the humidity is lower than the reference value, the change is reduced, so as to obtain a corrected frost layer thickness change; The modified frost layer thickness variation is compared with a preset defrost trigger threshold, and a new defrost start time point is calculated according to the estimated time of reaching the threshold to obtain optimized defrost cycle parameters.

8. An intelligent energy-saving control system for a refrigerated display cabinet, characterized in that: The intelligent energy-saving control method for a refrigerated display cabinet according to any one of claims 1 to 7 is implemented, wherein the intelligent energy-saving control system for the refrigerated display cabinet comprises: The acquisition module is used to collect temperature distribution data and human flow detection data in the refrigerated display cabinet through a multi-parameter sensor network, and obtain a pre-processed environmental parameter set after processing by a sliding average filter algorithm; A prediction module is used to input the pre-processed environmental parameter set into an ARIMA time series model, calculate the fluctuation trend of the cooling load within a preset time domain, and output load change prediction data; An evaluation module is used to establish a dynamic energy efficiency ratio indicator based on the ratio of the real-time power of the compressor to the load change prediction data, so as to quantify the energy saving effect of the current operating state; an optimization module for adjusting the compressor frequency and fan speed using a nonlinear predictive control algorithm according to the numerical range of the dynamic energy efficiency ratio indicator and generating a multi-machine coordinated control instruction; The execution module is used to analyze the impact of the multi-machine collaborative control instruction 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.

9. An intelligent energy-saving control device for a refrigerated display cabinet, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, the intelligent energy-saving control method for the refrigerated display cabinet according to any one of claims 1 to 7 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the processor is enabled to execute the intelligent energy-saving control method for a refrigerated display cabinet according to any one of claims 1 to 7.

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