Module butt joint self-sealing energy-saving supermarket refrigeration display cabinet device and system

Abstract

The invention belongs to the technical field of refrigeration equipment, and particularly relates to a module butt joint self-sealing energy-saving supermarket refrigeration display cabinet device and system.The instant energy efficiency ratio of each display cabinet device and the comprehensive energy efficiency ratio of the system are reasonably calculated, a dual-objective optimization function is constructed, meanwhile, the mean square root of the deviation between the comprehensive energy efficiency ratio of the system and the temperature in the cabinet is included, and the energy-saving supermarket refrigeration display cabinet device is obtained. The frequency of the compressor of each display cabinet unit and the power of the centralized refrigerating unit are dynamically adjusted through gradient descent iteration, global optimization scheduling of the frequency of the compressor of each display cabinet unit and the power of the centralized refrigerating unit can be achieved, the contradiction that energy conservation and accurate temperature control are difficult to consider at the same time is solved, and in the gradient descent iteration process, energy conservation is achieved. And if the target function does not descend twice continuously, the gradient descent learning rate is automatically attenuated to be 0.8 times of the original value, and a lower limit is set, so that not only can parameter oscillation divergence be avoided, but also optimization can be prevented from falling into local stagnation, and rolling optimization of a five-minute period can be reliably converged in a full working condition range.

Description

Technical Field

[0001] This invention belongs to the field of refrigeration equipment technology, specifically relating to an energy-saving supermarket refrigerated display cabinet device and system with modular docking and self-sealing. Background Technology

[0002] Supermarket refrigerated display cases, as a core category of commercial refrigeration equipment, are widely used in various supermarkets and convenience stores. Their operational stability, ease of maintenance, sealing reliability, and energy efficiency directly affect the operational efficiency and costs of supermarkets. Currently, most supermarket display case refrigeration units on the market are installed in a split manner. During maintenance, professional welders are required to cut and re-weld the refrigerant pipes, resulting in long maintenance cycles and a high risk of refrigerant leakage. Moreover, existing display cases mostly use fixed-frequency or simple variable-frequency control with single temperature feedback, without adaptive adjustment based on dynamic changes in refrigeration load. They are mostly controlled independently for each case, lacking system-level global energy efficiency optimization. There is no coordinated scheduling mechanism between display case units, which easily leads to local overload and low overall energy efficiency, making it impossible to achieve a synergistic improvement in energy saving and temperature control accuracy.

[0003] To address the aforementioned issues, this application presents a modular, self-sealing, energy-efficient supermarket refrigerated display case device and system. Summary of the Invention

[0004] To address the shortcomings of the prior art mentioned in the background section, this application proposes an energy-saving supermarket refrigerated display case device and system with modular docking and self-sealing. Through modular structural design, self-sealing docking technology, multi-dimensional monitoring, and system-level energy efficiency optimization, dual innovations in the device and system are achieved to solve the problems in the background section.

[0005] To achieve the above objectives, this application provides an energy-saving supermarket refrigerated display cabinet device with modular docking and self-sealing, including a cabinet body, a drawer-type modular refrigeration unit, a self-sealing docking component, and an intelligent control system. The side wall of the cabinet body is provided with a drawer-type installation cavity, and the inner side of the drawer-type installation cavity is provided with a self-sealing docking seat. The self-sealing docking seat includes a refrigerant interface, an electrical interface, and an airflow interface. The drawer-type modular refrigeration unit includes a variable frequency compressor, a microchannel evaporator, a condenser, an electronic expansion valve, and a docking flange, with a sealing structure on the outer periphery of the docking flange; The self-sealing docking assembly includes a docking drive mechanism, a sealing performance monitoring module, and an automatic locking mechanism; The intelligent control system includes a main controller, a cabinet temperature sensor, a door status monitoring module, an ambient temperature and humidity sensor, a sealing pressure sensor, and an alarm module. After the drawer-type modular refrigeration unit is inserted into the drawer-type installation cavity, the self-sealing docking component automatically completes the sealing docking. The sealing performance monitoring module collects the sealing surface clamping force in real time. The intelligent control system adaptively adjusts the compressor frequency and fan speed according to the cabinet temperature, door status, sealing parameters and environmental parameters.

[0006] Based on the above-mentioned preferred embodiment, a guide rail and a positioning protrusion are provided between the drawer-type installation cavity and the drawer-type modular refrigeration unit to ensure precise alignment during insertion and uniform contact of the sealing surface.

[0007] In a preferred embodiment based on the above scheme, the sealing performance monitoring module includes a thin-film pressure sensor and a temperature sensor, which are used to collect the sealing surface clamping force distribution value Fs and the sealing surface temperature in real time. The main controller compares the sealing surface clamping force distribution value Fs with the preset sealing clamping force threshold Fmin, and combines the temperature difference between the sealing surface and the cabinet to comprehensively determine whether the seal has failed.

[0008] Based on the above scheme, the preferred embodiment of the door status monitoring module includes a reed switch and a permanent magnet, which are used to detect the opening and closing status of the cabinet door and the duration of opening. If the door is open for too long, the air curtain is strengthened to reduce the loss of cold energy.

[0009] In a preferred embodiment based on the above scheme, the front side of the drawer-type installation cavity is provided with a dustproof sealing door, which automatically closes when the drawer-type modular refrigeration unit is pulled out to reduce the intrusion of hot air.

[0010] The modular self-sealing energy-saving supermarket refrigerated display case system includes multiple display case units, as well as a centralized refrigeration unit, zone control valve group, central monitoring platform, communication module, load prediction module and energy efficiency optimization module; The centralized refrigeration unit is used to provide a centralized cold source to each display cabinet device, and its operating power is adjusted by the frequency converter according to the power command issued by the energy efficiency optimization module. The zone control valve group is installed on the refrigerant output pipeline of the centralized refrigeration unit and is used to adjust the refrigerant flow distribution of each display cabinet device according to the instructions issued by the energy efficiency optimization module. The central monitoring platform receives real-time data on the sealing status, temperature, and power of each display cabinet device, performs fault diagnosis and graded early warning, and generates operation and maintenance suggestions. The communication module is used to realize data interaction between the central monitoring platform and the main controller of each display cabinet device, as well as to receive instructions from the load forecasting module and the energy efficiency optimization module. The load prediction module is used to predict the cooling load demand of each display cabinet device within a preset time period based on the historical operating data and environmental parameters of each display cabinet device, and output the prediction results to the energy efficiency optimization module as a feedforward input. The energy efficiency optimization module is used to calculate the energy efficiency ratio of each display cabinet device and the overall system energy efficiency ratio, and to construct an objective function and perform iterative optimization using a gradient descent algorithm. The specific optimization steps include: Step S1: Calculate the instantaneous energy efficiency ratio of each display case unit. The formula for calculating the instantaneous energy efficiency ratio is: Where Ei is the instantaneous energy efficiency ratio of the i-th display case, and Qi is the cooling capacity of the i-th display case. Let the power of the i-th display case compressor be . Let be the power of the fan in the i-th display case; Step S2: Calculate the system's instantaneous comprehensive energy efficiency ratio (Esys) based on the instantaneous energy efficiency ratio (EHR) calculation results. The formula for calculating the system's instantaneous comprehensive energy efficiency ratio (Esys) is as follows: Where Qcen is the total cooling capacity of the centralized refrigeration unit, Pcen is the power of the centralized refrigeration unit, and N is the number of display cases; Step S3: Construct a bi-objective optimization function J to balance maximizing system energy efficiency and temperature control accuracy. The expression for the bi-objective optimization function is: Where Emax is the rated energy efficiency ratio under the system design conditions, α and β are weighting coefficients, ei is the temperature deviation inside the i-th display case, and ΔT is the preset allowable temperature deviation. Step S4: Iteratively optimize the bi-objective optimization function J using the gradient descent algorithm, and adjust the compressor frequency of each display case device. The iterative formula for the power Pcen of the centralized chiller unit is: Where η and μ are the gradient descent learning rates, Let be the frequency of the compressor of the i-th display case during the k-th iteration. Let be the power of the centralized chiller unit in the k-th iteration. and The two objective optimization functions J are respectively related to the compressor frequency. The partial derivatives of the power Pcen of the centralized chiller unit are approximated using the finite difference method. Step S5: Optimize the compressor frequency The main controller of each display case is sent to adjust the opening of the frequency converter and zone control valve group of the centralized refrigeration unit.

[0011] Based on the above scheme, the intelligent control systems of each display cabinet unit communicate with each other through a CAN bus or wireless network to form a distributed collaborative control network and execute a collaborative load balancing strategy. The collaborative load balancing strategy includes each display cabinet device acquiring the current cooling power and compressor frequency of adjacent units in real time. When the instantaneous total power of the unit and adjacent units exceeds a preset threshold, the unit delays or reduces the compressor frequency according to a preset priority order to avoid multiple units being in peak load state at the same time. At the same time, each display cabinet device reports its operating status to the central monitoring platform in real time for global auxiliary scheduling.

[0012] Based on the above scheme, the preferred embodiment of the load forecasting module adopts a Long Short-Term Memory (LSTM) network model. The input features include the relative temperature of each unit, ambient temperature, cumulative door opening time and timestamp encoding of the past M time points, and the output is the predicted value of cooling load demand for the next K time points.

[0013] In a preferred embodiment based on the above scheme, the gradient descent optimization in step S4 is performed once every 5 minutes. The gradient descent learning rates η and μ are dynamically adjusted according to the system response. If the objective function J does not decrease in two consecutive iterations, the gradient descent learning rate is decayed to 0.8 times the original value, and the lower limit of decay is set to 0.1 times the initial gradient descent learning rate.

[0014] Compared with the prior art, the beneficial effects of the present invention are: 1. Through the combination of the drawer-type installation cavity and the self-sealing docking component, the replacement of the drawer-type modular refrigeration unit does not require welding or cutting refrigerant pipes, reducing the maintenance time from several hours to several minutes. The sealing performance monitoring module collects the compression force distribution value and sealing surface temperature in real time, and can issue an early warning before leakage occurs, reducing refrigerant leakage from the source. It is both environmentally friendly and economical. Moreover, by providing a system-level centralized cold source through the centralized refrigeration unit, it can reduce overall energy consumption and achieve unified cooling for a large area. The variable frequency compressor built into the drawer-type modular refrigeration unit can undertake the terminal fine refrigeration of a single display cabinet, precise temperature adjustment, and rapid response to load fluctuations, and can perform terminal execution. Through the energy efficiency optimization module, the centralized refrigeration unit and the variable frequency compressor built into the drawer-type modular refrigeration unit work together to achieve a dual improvement in energy efficiency and temperature control accuracy.

[0015] Second, by reasonably calculating the instantaneous energy efficiency ratio and the overall system energy efficiency ratio of each display cabinet unit, a dual-objective optimization function is constructed. This function incorporates both the overall system energy efficiency ratio (Esys) and the root mean square of the temperature deviation within the cabinet. Through gradient descent iteration, the compressor frequency of each display cabinet unit and the power of the centralized refrigeration unit are dynamically adjusted. This achieves global optimization scheduling of the compressor frequency and centralized refrigeration unit power, resolving the contradiction between energy saving and precise temperature control. It effectively improves the overall energy efficiency ratio and reduces the standard deviation of temperature fluctuations. Furthermore, during the gradient descent iteration, if the objective function fails to decrease for two consecutive iterations, the gradient descent learning rate automatically decays to 0.8 times its original value and a lower limit is set. This avoids parameter oscillation and divergence and prevents optimization from getting stuck in local stagnation, ensuring reliable convergence of the 5-minute rolling optimization across the entire operating range.

[0016] Third, each display cabinet unit exchanges the power status of adjacent units via CAN bus or wireless network. When a local total power over-limit is detected, the lower priority unit automatically starts with a delay or reduces its frequency, which can prevent multiple compressors from entering peak conditions at the same time. This mechanism does not rely on the high-speed command issuance of the central controller, and the response delay is less than 100 milliseconds, which can significantly reduce the risk of instantaneous voltage drop in the power distribution system. Moreover, by introducing a long short-term memory network to model multi-dimensional features such as historical temperature, door opening time, and time coding, the cooling demand at time K in the future can be predicted, enabling the optimization algorithm to adjust the cooling strategy in advance. This can effectively cope with the sudden change in heat load caused by peak customer flow or restocking door opening, and can reduce the overshoot of the cabinet temperature by more than 40%. Attached Figure Description

[0017] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings: Figure 1 This is a system block diagram of the self-sealing, energy-saving supermarket refrigerated display case system with module docking of the present invention. Figure 2 This is a flowchart illustrating how the energy efficiency optimization module in this invention calculates the energy efficiency ratio of each display cabinet device and the overall system energy efficiency ratio, constructs an objective function, and uses a gradient descent algorithm for iterative optimization. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0019] Example: To address the technical problems raised in the background art, this application provides a preferred embodiment: such as Figure 1-2 As shown, the modular self-sealing energy-saving supermarket refrigeration display cabinet device includes a cabinet body, a drawer-type modular refrigeration unit, a self-sealing docking component, and an intelligent control system. The side wall of the cabinet body is provided with a drawer-type installation cavity, and the inner side of the drawer-type installation cavity is provided with a self-sealing docking seat. The self-sealing docking seat includes a refrigerant interface, an electrical interface, and an airflow interface. The drawer-type modular refrigeration unit includes a variable frequency compressor, a microchannel evaporator, a condenser, an electronic expansion valve, and a docking flange. The docking flange has a sealing structure on its outer periphery. The self-sealing docking assembly includes a docking drive mechanism, a sealing performance monitoring module, and an automatic locking mechanism; The intelligent control system includes a main controller, a cabinet temperature sensor, a door status monitoring module, an ambient temperature and humidity sensor, a sealing pressure sensor, and an alarm module. After the drawer-type modular refrigeration unit is inserted into the drawer-type installation cavity, the self-sealing docking component automatically completes the sealing docking. The sealing performance monitoring module collects the sealing surface clamping force in real time, and the intelligent control system adaptively adjusts the compressor frequency and fan speed according to the cabinet temperature, door status, sealing parameters and environmental parameters.

[0020] The advantages of the above solution are as follows: by combining the drawer-type installation cavity with the self-sealing docking component, the replacement of the drawer-type modular refrigeration unit does not require welding or cutting of refrigerant pipes, and the maintenance time is shortened from several hours to several minutes. The sealing performance monitoring module collects the compression force distribution value and sealing surface temperature in real time, and can issue an early warning before leakage occurs, which can reduce refrigerant leakage from the source, and is both environmentally friendly and economical.

[0021] In an optional embodiment, a guide rail and a positioning protrusion are provided between the drawer-type mounting cavity and the drawer-type modular refrigeration unit for precise alignment during insertion and to ensure uniform contact of the sealing surfaces.

[0022] In an optional embodiment, the sealing performance monitoring module includes a thin-film pressure sensor and a temperature sensor, which are used to collect the sealing surface clamping force distribution value Fs and the sealing surface temperature in real time. The main controller compares the sealing surface clamping force distribution value Fs with the preset sealing clamping force threshold Fmin, and combines the temperature difference between the sealing surface and the cabinet to comprehensively determine whether the seal has failed.

[0023] In an optional embodiment, the door status monitoring module includes a reed switch and a permanent magnet for detecting the opening and closing status of the cabinet door and the duration of opening. If the door is open for too long, the air curtain is strengthened to reduce the loss of cold energy.

[0024] In an optional embodiment, a dustproof sealing door is provided on the front side of the drawer-type mounting cavity. The dustproof sealing door automatically closes when the drawer-type modular refrigeration unit is pulled out to reduce the intrusion of hot air.

[0025] It should be noted that the drawer-type modular refrigeration unit integrates a variable frequency compressor, a microchannel evaporator, and a condenser into one unit. The docking flange is located at its rear, and an O-ring and guide pin are installed on the end face of the docking flange. During installation, the drawer-type modular refrigeration unit is pushed in with the help of guide rails. The positioning protrusions ensure that the flange faces are parallel and pressed tightly. After installation, the electric push rod of the self-sealing docking assembly drives the locking mechanism to lock, completing the synchronous docking of refrigerant, electrical, and airflow. The sealing performance monitoring module consists of four sets of thin-film pressure sensors. The main controller collects the pressure distribution value Fs every second. If the temperature difference between the sealing surface and the ambient temperature is less than 2°C, the seal is deemed to have failed, triggering the alarm module to prompt the user to re-insert or replace the sealing ring.

[0026] The modular self-sealing energy-saving supermarket refrigerated display case system includes multiple display case units, as well as a centralized refrigeration unit, zone control valve group, central monitoring platform, communication module, load prediction module and energy efficiency optimization module; The centralized refrigeration unit is used to provide a centralized cooling source to each display cabinet device. Its operating power is adjusted by the frequency converter according to the power command issued by the energy efficiency optimization module. The zone control valve group is installed on the refrigerant output pipeline of the centralized refrigeration unit and is used to adjust the refrigerant flow distribution of each display cabinet device according to the instructions issued by the energy efficiency optimization module. The central monitoring platform is used to receive real-time data on the sealing status, temperature, and power of each display cabinet device, perform fault diagnosis and graded early warning, and generate operation and maintenance suggestions. The communication module is used to realize data interaction between the central monitoring platform and the main controller of each display cabinet device, as well as to receive instructions from the load forecasting module and the energy efficiency optimization module; The load forecasting module is used to predict the cooling load demand of each display cabinet unit within a preset time period based on the historical operating data and environmental parameters of each display cabinet unit, and outputs the forecast results to the energy efficiency optimization module as a feedforward input. The energy efficiency optimization module is used to calculate the energy efficiency ratio of each display cabinet device and the overall system energy efficiency ratio, and to construct an objective function, using the gradient descent algorithm for iterative optimization; The specific optimization steps include: Step S1: Calculate the instantaneous energy efficiency ratio of each display case unit. The formula for calculating the instantaneous energy efficiency ratio is: Where Ei is the instantaneous energy efficiency ratio of the i-th display case, and Qi is the cooling capacity of the i-th display case. Let the power of the i-th display case compressor be . Let be the power of the fan in the i-th display case; Step S2: Calculate the system's instantaneous comprehensive energy efficiency ratio (Esys) based on the instantaneous energy efficiency ratio (EHR) calculation results. The formula for calculating the system's instantaneous comprehensive energy efficiency ratio (Esys) is as follows: Where Qcen is the total cooling capacity of the centralized refrigeration unit, Pcen is the power of the centralized refrigeration unit, and N is the number of display cases; It should be noted that the system employs a centralized refrigeration unit to provide the basic cooling source, while each display case's built-in inverter compressor handles precise cooling and rapid response at the terminal level in a coordinated working mode. The centralized unit provides most of the system's basic cooling capacity, reducing overall energy consumption. The inverter compressors in each display case dynamically compensate for load fluctuations within the individual case, ensuring accurate temperature control. The total system input power consists of the power of the centralized unit and the power of the compressors and fans within each case. The total cooling capacity is the sum of the cooling capacity of the centralized unit and the cooling capacity of each display case. The system's instantaneous comprehensive energy efficiency ratio (Esys) strictly adheres to the definition of the cooling system's coefficient of performance (COP) and is calculated based on the instantaneous energy efficiency ratio (Ei) of the individual case obtained in step S1. According to step S1 The definition is equivalent to the sum of the compressor power and fan power of each display case. This expression unifies the physical dimensions to the total input electrical power of the system, and intuitively reflects the contribution of each cabinet's energy efficiency ratio (Ei) to the overall energy efficiency of the system. It has a clear physical meaning, rigorous logic, and is fully in line with the working principle of the refrigeration system.

[0027] Step S3: Construct a bi-objective optimization function J to balance maximizing system energy efficiency and temperature control accuracy. The expression for the bi-objective optimization function is: Where Emax is the rated energy efficiency ratio under the system design conditions, α and β are weighting coefficients, ei is the temperature deviation inside the i-th display case, and ΔT is the preset allowable temperature deviation. Step S4: Iteratively optimize the bi-objective optimization function J using the gradient descent algorithm, and adjust the compressor frequency of each display case device. The iterative formula for the power Pcen of the centralized chiller unit is: Where η and μ are the gradient descent learning rates, Let be the frequency of the compressor of the i-th display case during the k-th iteration. Let be the power of the centralized chiller unit in the k-th iteration. and The two objective optimization functions J are respectively related to the compressor frequency. The partial derivatives of the power Pcen of the centralized chiller unit are approximated using the finite difference method. Step S5: Optimize the compressor frequency The main controller of each display case is sent to adjust the opening of the frequency converter and zone control valve group of the centralized refrigeration unit.

[0028] The advantages of the above scheme are as follows: By reasonably calculating the instantaneous energy efficiency ratio and the overall system energy efficiency ratio of each display cabinet device, a dual-objective optimization function is constructed. At the same time, the overall system energy efficiency ratio Esys and the root mean square of the temperature deviation inside the cabinet are incorporated. By dynamically adjusting the compressor frequency of each display cabinet unit and the power of the centralized refrigeration unit through gradient descent iteration, the global optimization scheduling of the compressor frequency of each display cabinet unit and the power of the centralized refrigeration unit can be achieved. This solves the contradiction between energy saving and precise temperature control, effectively improves the overall energy efficiency ratio, and effectively reduces the standard deviation of temperature fluctuation. Furthermore, during the gradient descent iteration, if the objective function fails to decrease for two consecutive times, the gradient descent learning rate automatically decays to 0.8 times the original value and a lower limit is set. This can avoid parameter oscillation and divergence, and prevent the optimization from getting stuck in local stagnation. This ensures that the 5-minute cycle rolling optimization can reliably converge across the entire operating range.

[0029] Furthermore: In an optional embodiment, the intelligent control systems of each display cabinet device communicate with each other via a CAN bus or wireless network to form a distributed collaborative control network and execute a collaborative load balancing strategy. The collaborative load balancing strategy includes each display cabinet device acquiring the current cooling power and compressor frequency of adjacent units in real time. When the instantaneous total power of the unit and adjacent units exceeds a preset threshold, the unit delays or reduces the compressor frequency according to a preset priority order to avoid multiple units being in peak load state at the same time. At the same time, each display cabinet device reports its operating status to the central monitoring platform in real time for global auxiliary scheduling.

[0030] In an optional embodiment, the load forecasting module employs a Long Short-Term Memory (LSTM) network model. The input features include the relative temperature of each unit, ambient temperature, cumulative door opening time, and timestamp encoding over the past M time periods. The output is the predicted cooling load demand for the next K time periods.

[0031] It should be noted that each display cabinet unit exchanges power status with adjacent units via CAN bus or wireless network. When a local total power over-limit is detected, the lower priority unit automatically starts with a delay or reduces its frequency, which can prevent multiple compressors from entering peak conditions at the same time. This mechanism does not rely on the high-speed command issuance of the central controller, and the response delay is less than 100 milliseconds, which can significantly reduce the risk of instantaneous voltage drop in the power distribution system. Moreover, by introducing a long short-term memory network to model multi-dimensional features such as historical temperature, door opening time, and time encoding, the cooling demand at time K in the future can be predicted, enabling the optimization algorithm to adjust the cooling strategy in advance. This can effectively cope with the sudden change in heat load caused by peak customer flow or restocking door opening, and can reduce the overshoot of the cabinet temperature by more than 40%.

[0032] In an optional embodiment, the gradient descent optimization in step S4 is performed every 5 minutes. The gradient descent learning rates η and μ are dynamically adjusted according to the system response. If the objective function J does not decrease in two consecutive iterations, the gradient descent learning rate is decayed to 0.8 times the original value, and the lower limit of decay is set to 0.1 times the initial gradient descent learning rate.

[0033] It should be noted that during the gradient descent iteration, if the objective function fails to decrease for two consecutive times, the gradient descent learning rate is automatically reduced to 0.8 times the original value and a lower limit is set. This can prevent parameter oscillation and divergence, and also prevent the optimization from getting stuck in a local stagnation, so that the 5-minute cycle rolling optimization can reliably converge in the entire operating range.

[0034] When in use, the energy efficiency optimization module triggers global optimization calculations at fixed intervals. The specific execution process is as follows: First, data acquisition and preprocessing are performed as follows: The central monitoring platform polls the main controller of each display cabinet device through the communication module to collect real-time operating data of each display cabinet device, including the internal temperature, set temperature, current operating frequency of the compressor, compressor input power, fan input power, estimated cooling capacity, and current power of the centralized refrigeration unit. Based on the collected data, the deviation between the current temperature and the set value of each display cabinet device is calculated. At the same time, the sealing pressure status of each display cabinet device is read from the sealing performance monitoring module. If the sealing pressure of any display cabinet device is lower than the preset sealing pressure threshold and continues for more than a specified time, the display cabinet device is marked as abnormal. In this round of optimization, the compressor frequency of the display cabinet device is locked at the current value and only participates in load prediction, not in frequency updates. Then, the load forecast feedforward correction is specifically performed as follows: the deep learning model of the load forecast module is called, and historical feature data from several past moments are input, including the temperature difference between each display cabinet unit and the ambient temperature, the ambient temperature collected by the ambient temperature sensor, the cumulative duration of cabinet door opening recorded by the door status monitoring module, and the time code representing the periodicity of business hours. The load forecast module outputs the predicted cooling load values ​​of each display cabinet unit for several future moments, calculates the trend of the predicted cooling load and the current cooling load, and if the trend shows that the cooling demand will increase significantly in the short term, then a larger frequency adjustment space is preset for the display cabinet unit in subsequent optimizations to respond to load changes in advance. Next, the objective function value is constructed as follows: Based on the collected real-time data, the instantaneous energy efficiency ratio of each display cabinet device under the current operating conditions is calculated, and then the instantaneous comprehensive energy efficiency ratio of the entire module-connected self-sealing energy-saving supermarket refrigerated display cabinet system is calculated. Based on the difference between the instantaneous comprehensive energy efficiency ratio and the rated energy efficiency ratio under the system design conditions, and the mean square statistics of the temperature deviation inside each display cabinet device, a dual-objective optimization function is constructed according to the preset weight coefficients to obtain the objective function value corresponding to the current operating state, which serves as the starting point for this round of optimization. Secondly, the gradient descent iterative update is specifically performed as follows: using the compressor frequency of each display case device and the power of the centralized refrigeration unit as decision variables, the finite difference method is used to approximate the partial derivative of the objective function with respect to each decision variable, i.e., the gradient direction. New candidate values ​​of decision variables are generated along the gradient descent direction and constrained within the allowable physical operating range of the variable frequency compressors of each display case device and the variable frequency drives of the centralized refrigeration unit. For display case devices marked as having sealing abnormalities, the compressor frequency update operation is skipped, and the objective function value is recalculated based on the candidate value. If the new value is less than the old value, the update is accepted. If it does not decrease, the original parameters are maintained and a stall count is recorded. When the stall count reaches the set number, the gradient descent learning rate is reduced according to the preset decay ratio, and a lower limit for the gradient descent learning rate is set to prevent the optimization from completely stalling. The above calculation, projection, evaluation, and update steps are repeated until the maximum number of iterations is reached or the change in the objective function value is less than the convergence threshold. Then, the coordinated load balancing intervention is specifically as follows: Before the optimized compressor frequency command is sent to the main controller of each display cabinet, the intelligent control system of each display cabinet broadcasts the target compressor frequency that the display cabinet will execute to each other through the CAN bus or wireless network. The intelligent control system of each display cabinet listens to the broadcast value of the adjacent display cabinet and calculates the instantaneous power estimate corresponding to the combined compressor frequency of the display cabinet and the adjacent display cabinet. If the local instantaneous total power exceeds the preset threshold, intervention is carried out according to the preset priority order of each display cabinet. The display cabinet with lower priority actively limits the rise rate of its own compressor frequency and adopts a delayed start or frequency reduction strategy until the local instantaneous total power falls back to the safe range or the temperature deviation inside the display cabinet exceeds the allowable range and the frequency needs to be forcibly increased. This mechanism is completed autonomously by the intelligent control system of each display cabinet, with a rapid response and no need for the central monitoring platform to intervene with each command. Finally, the instruction issuance and status recording are as follows: The central monitoring platform issues the final compressor frequency instruction after collaborative load balancing intervention to the main controller of each display cabinet device for execution. At the same time, it issues the updated power adjustment instruction to the inverter of the centralized refrigeration unit and adjusts the opening of the zone control valve group. The final value of the objective function after this round of optimization, the instantaneous energy efficiency ratio of each display cabinet device, the gradient descent learning rate adjustment, and the number of collaborative interventions are all recorded in the database for subsequent operation and maintenance analysis and algorithm performance traceability.

[0035] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A modular, self-sealing, energy-saving supermarket refrigerated display case device, comprising a cabinet body, a drawer-type modular refrigeration unit, a self-sealing connection assembly, and an intelligent control system, characterized in that... The cabinet side wall is provided with a drawer-type mounting cavity, and the inner side of the drawer-type mounting cavity is provided with a self-sealing docking seat, which includes a refrigerant interface, an electrical interface, and an airflow interface; The drawer-type modular refrigeration unit includes a variable frequency compressor, a microchannel evaporator, a condenser, an electronic expansion valve, and a docking flange, with a sealing structure on the outer periphery of the docking flange; The self-sealing docking assembly includes a docking drive mechanism, a sealing performance monitoring module, and an automatic locking mechanism; The intelligent control system includes a main controller, a cabinet temperature sensor, a door status monitoring module, an ambient temperature and humidity sensor, a sealing pressure sensor, and an alarm module. After the drawer-type modular refrigeration unit is inserted into the drawer-type installation cavity, the self-sealing docking component automatically completes the sealing docking. The sealing performance monitoring module collects the sealing surface clamping force in real time. The intelligent control system adaptively adjusts the compressor frequency and fan speed according to the cabinet temperature, door status, sealing parameters and environmental parameters.

2. The modular self-sealing energy-saving supermarket refrigerated display case device according to claim 1, characterized in that: The drawer-type mounting cavity and the drawer-type modular refrigeration unit are provided with guide rails and positioning protrusions for precise alignment during insertion and to ensure uniform contact of the sealing surfaces.

3. The modular self-sealing energy-saving supermarket refrigerated display case device according to claim 2, characterized in that: The sealing performance monitoring module includes a thin-film pressure sensor and a temperature sensor, which are used to collect the sealing surface clamping force distribution value Fs and the sealing surface temperature in real time. The main controller compares the sealing surface clamping force distribution value Fs with the preset sealing clamping force threshold Fmin, and combines the temperature difference between the sealing surface and the cabinet to comprehensively determine whether the seal has failed.

4. The modular self-sealing energy-saving supermarket refrigerated display case device according to claim 3, characterized in that: The door status monitoring module includes a reed switch and a permanent magnet, which are used to detect the opening and closing status of the cabinet door and the duration of opening. If the door is open for too long, the air curtain is strengthened to reduce the loss of cold air.

5. The modular self-sealing energy-saving supermarket refrigerated display case device according to claim 4, characterized in that: The front of the drawer-type mounting cavity is equipped with a dustproof sealing door. When the drawer-type modular refrigeration unit is pulled out, the dustproof sealing door automatically closes to reduce the intrusion of hot air.

6. A modular, self-sealing, energy-saving supermarket refrigerated display case system, comprising multiple display case devices as described in any one of claims 1-5, characterized in that, It also includes centralized refrigeration units, zone control valve groups, a central monitoring platform, a communication module, a load forecasting module, and an energy efficiency optimization module; The centralized refrigeration unit is used to provide a centralized cold source to each display cabinet device, and its operating power is adjusted by the frequency converter according to the power command issued by the energy efficiency optimization module. The zone control valve group is installed on the refrigerant output pipeline of the centralized refrigeration unit and is used to adjust the refrigerant flow distribution of each display cabinet device according to the instructions issued by the energy efficiency optimization module. The central monitoring platform is used to receive real-time data on the sealing status, temperature, and power of each display cabinet device, perform fault diagnosis and graded early warning, and generate operation and maintenance suggestions. The communication module is used to realize data interaction between the central monitoring platform and the main controller of each display cabinet device, as well as to receive instructions from the load forecasting module and the energy efficiency optimization module. The load prediction module is used to predict the cooling load demand of each display cabinet device within a preset time period based on the historical operating data and environmental parameters of each display cabinet device, and output the prediction results to the energy efficiency optimization module as a feedforward input. The energy efficiency optimization module is used to calculate the energy efficiency ratio of each display cabinet device and the overall system energy efficiency ratio, and to construct an objective function and perform iterative optimization using a gradient descent algorithm. The specific optimization steps include: Step S1: Calculate the instantaneous energy efficiency ratio of each display case unit. The formula for calculating the instantaneous energy efficiency ratio is: Where Ei is the instantaneous energy efficiency ratio of the i-th display case, and Qi is the cooling capacity of the i-th display case. Let the power of the i-th display case compressor be . Let be the power of the fan in the i-th display case; Step S2: Calculate the system's instantaneous comprehensive energy efficiency ratio (Esys) based on the instantaneous energy efficiency ratio (EHR) calculation results. The formula for calculating the system's instantaneous comprehensive energy efficiency ratio (Esys) is as follows: Where Qcen is the total cooling capacity of the centralized refrigeration unit, Pcen is the power of the centralized refrigeration unit, and N is the number of display cases; Step S3: Construct a bi-objective optimization function J to balance maximizing system energy efficiency and temperature control accuracy. The expression for the bi-objective optimization function is: Where Emax is the rated energy efficiency ratio under the system design conditions, α and β are weighting coefficients, ei is the temperature deviation inside the i-th display case, and ΔT is the preset allowable temperature deviation. Step S4: Iteratively optimize the bi-objective optimization function J using the gradient descent algorithm, and adjust the compressor frequency of each display case device. The iterative formula for the power Pcen of the centralized chiller unit is: Where η and μ are the gradient descent learning rates, Let be the frequency of the compressor of the i-th display case during the k-th iteration. Let be the power of the centralized chiller unit in the k-th iteration. and The two objective optimization functions J are respectively related to the compressor frequency. The partial derivatives of the power Pcen of the centralized chiller unit are approximated using the finite difference method. Step S5: Optimize the compressor frequency The main controller of each display case is sent to adjust the opening of the frequency converter and zone control valve group of the centralized refrigeration unit.

7. The modular self-sealing energy-saving supermarket refrigerated display case system according to claim 6, characterized in that: The intelligent control systems of each display case unit communicate with each other via CAN bus or wireless network to form a distributed collaborative control network and execute a collaborative load balancing strategy. The collaborative load balancing strategy includes each display cabinet device acquiring the current cooling power and compressor frequency of adjacent units in real time. When the instantaneous total power of the unit and adjacent units exceeds a preset threshold, the unit delays or reduces the compressor frequency according to a preset priority order to avoid multiple units being in peak load state at the same time. At the same time, each display cabinet device reports its operating status to the central monitoring platform in real time for global auxiliary scheduling.

8. The modular self-sealing energy-saving supermarket refrigerated display case system according to claim 6, characterized in that: The load forecasting module uses a Long Short-Term Memory (LSTM) network model. The input features include the relative temperature of each unit, ambient temperature, cumulative door opening time, and timestamp encoding of the past M time points. The output is the predicted cooling load demand value for the next K time points.

9. The modular self-sealing energy-saving supermarket refrigerated display case system according to claim 6, characterized in that: The gradient descent optimization in step S4 is performed every 5 minutes. The gradient descent learning rates η and μ are dynamically adjusted according to the system response. If the objective function J does not decrease in two consecutive iterations, the gradient descent learning rate is decayed to 0.8 times the original rate, and the lower limit of decay is set to 0.1 times the initial gradient descent learning rate.

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