Intelligent control system and method for multi-unit parallel water-cooling refrigerator

Abstract

The invention discloses an intelligent control system and method for a multi-unit parallel water-cooling refrigerator, and belongs to the technical field of refrigeration equipment. The control system comprises a data acquisition module, a load prediction module, an energy efficiency optimization module and a cooperative control module. The data acquisition module is used for acquiring the running state and environmental parameters of the refrigerator in real time; the load prediction module is used for predicting the refrigerator thermal load based on the operation state and the environmental parameters; the energy efficiency optimization module is used for optimizing operation parameters of each unit according to the predicted thermal load of the refrigerator; and the cooperative control module is used for cooperatively controlling the compressor frequency and the cooling water flow of each unit according to the optimized operation parameters. According to the intelligent control system and method for the multi-unit parallel water-cooling refrigerator, precise temperature control and energy efficiency optimization of the multi-unit parallel water-cooling refrigerator can be achieved, load and water flow distribution is uniform, temperature fluctuation is small, system energy consumption is low, and the temperature control and energy saving requirements of a high-quality cold chain can be met.

Description

Technical Field

[0001] The present invention belongs to the technical field of refrigeration equipment, and in particular relates to an intelligent control system and method for multi-unit parallel water-cooled refrigerators. Background Art

[0002] In the refrigeration sector, traditional water-cooled freezer systems suffer from significant technical deficiencies. First, temperature control accuracy is insufficient. In high-use scenarios, such as peak supermarket hours, the system relies solely on simple temperature feedback control due to a lack of accurate load forecasting. This leads to system lags in response to operating conditions such as frequent door openings and large quantities of goods being stored and retrieved. Temperature fluctuations within the freezer can reach as high as ±2-3°C, far exceeding the ±0.5°C standard required for food preservation. Furthermore, in systems with multiple units connected in parallel, existing PID control algorithms are unable to adapt to rapidly changing load characteristics, and uneven cooling capacity distribution further exacerbates temperature fluctuations. Furthermore, dynamic response capabilities are insufficient. Existing systems utilize fixed parameter control strategies. Sudden load changes (such as a single door opening lasting more than 30 seconds) require 3-5 minutes to stabilize the temperature. Cooling water flow regulation relies on manually preset values, failing to match actual cooling demand in real time. This results in overcooling or undercooling in over 40% of operating conditions. When multiple units are operated in parallel, a temperature runaway in a single freezer can affect other units through the hydraulic system, creating a vicious cycle. For example, during continuous door opening tests (opening the door once per minute), the traditional system's temperature fluctuations accumulated to 2-3 times the initial value, and the recovery time was extended to 8-10 minutes. These defects not only accelerated food spoilage due to frequent temperature fluctuations, but also increased system energy consumption by over 30% due to overcooling measures that compensated for control lags, making it impossible to meet the requirements of high-quality cold chain temperature control and energy-saving operation. Summary of the Invention

[0003] In view of the deficiencies in the related art, the purpose of the present invention is to provide an intelligent control system and method for multi-unit parallel water-cooled freezers to solve the problems raised in the above background technology.

[0004] To achieve the above object, the present invention provides the following technical solutions:

[0005] An intelligent control system for a multi-unit parallel water-cooled freezer, comprising:

[0006] Data acquisition module, which is used to collect the operating status and environmental parameters of the refrigerator in real time;

[0007] Load prediction module, which is used to predict the heat load of the refrigerator based on the operating status and environmental parameters;

[0008] Energy efficiency optimization module, which is used to optimize the operating parameters of each unit according to the predicted heat load of the refrigerator;

[0009] The collaborative control module is used to coordinate and control the compressor frequency and cooling water flow of each unit according to the optimized operating parameters.

[0010] In some embodiments, the parameters collected by the data acquisition module include the temperature of each area inside the refrigerator, the inlet and outlet temperatures of the cooling water, the ambient temperature, the speed of the variable frequency water pump, the start and stop status of the compressor, the weight of the items in the refrigerator, the door opening frequency, the real-time power of each unit and the flow meter reading.

[0011] In some embodiments, the load forecasting module uses a 3R2C thermal network model combined with a recursive least squares algorithm to calculate the current thermal load, and compensates for the model deviation through an LSTM neural network to predict future thermal load change trends.

[0012] In some embodiments, the intelligent control system for multi-unit parallel water-cooled refrigerators also includes a branch variable frequency water pump and a branch solenoid valve arranged on each cooling branch. The collaborative control module dynamically distributes the cooling water flow by adjusting the flow of the branch variable frequency water pump and the switching state of the branch solenoid valve.

[0013] In some embodiments, the intelligent control system for multi-unit parallel water-cooled freezers further includes a remote control module, which is configured to receive manual intervention instructions and adjust the water pump flow or compressor frequency.

[0014] In some embodiments, the intelligent control system for multi-unit parallel water-cooled freezers further includes a fault diagnosis module, which is used to monitor the system operating status and perform fault warning and self-diagnosis.

[0015] A method for intelligently controlling a multi-unit parallel water-cooled freezer adopts the above-mentioned intelligent control system for the multi-unit parallel water-cooled freezer, comprising the following steps:

[0016] S1, obtain the operating status and environmental parameters of the refrigerator in real time through the data acquisition module;

[0017] S2. Use the load prediction module to predict the heat load of the refrigerator and its future change trend based on the operating status and environmental parameters;

[0018] S3. Determine the operating parameters of each unit through the energy efficiency optimization module based on the predicted heat load of the refrigerator;

[0019] S4. The collaborative control module adjusts the compressor frequency and cooling water flow of each unit to achieve precise temperature control and energy efficiency optimization.

[0020] In some embodiments, in step S2, when predicting the heat load of the refrigerator, a 3R2C thermal network model combined with a recursive least squares algorithm is used to calculate the current heat load, and the model parameters are dynamically calibrated every 15 minutes.

[0021] In some embodiments, in step S2, after predicting the heat load of the refrigerator, the load interval is divided according to the ratio of the heat load to the benchmark load. When the load is in the 25%-75% benchmark load area, the PID control strategy is used to determine the operating parameters in step S3, and the set temperature is maintained through step S4; when the load is greater than 75% of the benchmark load, the advanced cooling mode parameters are enabled in step S3, and the cooling capacity is increased in advance through step S4; when the load is less than 25% of the benchmark load, the energy-saving operation mode parameters are enabled in step S3, and energy consumption is reduced through step S4.

[0022] In some embodiments, in step S1, when the operating status and environmental parameters of the refrigerator are obtained in real time, the door opening event or the weight change of the items in the refrigerator is synchronously detected. When the change is detected, the pre-cooling mode parameters are enabled in step S3, and the compressor frequency and water pump flow are adjusted in advance through step S4.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] 1. The intelligent control system and method for multi-unit parallel water-cooled freezers provided by the present invention can achieve precise temperature control. The system collects parameters and predicts loads in real time through multi-module collaboration, dynamically adjusts the compressor frequency and cooling water flow, solves the response lag problem of traditional systems, controls the temperature fluctuation of the freezer within the range required for food preservation, and improves the preservation effect.

[0025] 2. The intelligent control system and method for multi-unit parallel water-cooled freezers provided by the present invention are energy-efficient and efficient. Different modes are adopted according to the load range, and the energy-saving mode is switched when the load is low. The frequency of compressor start and stop is also reduced, and the water flow distribution is optimized to avoid overcooling or insufficient cooling, thereby reducing system energy consumption and extending the service life of the equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings described herein are used to provide a further understanding of the present invention and constitute a part of this application. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0027] Figure 1 This is a method flow chart of an embodiment of the intelligent control system and method for multi-unit parallel water-cooled freezers of the present invention;

[0028] Figure 2 This is a module interaction diagram of an embodiment of an intelligent control system and method for multi-unit parallel water-cooled freezers of the present invention;

[0029] Figure 3 This is a schematic diagram of the system structure of an embodiment of an intelligent control system and method for multi-unit parallel water-cooled freezers of the present invention;

[0030] Figure 4 This is a control logic diagram for predicting the heat load of a refrigerator and determining operating mode parameters according to an embodiment of the intelligent control system and method for a multi-unit parallel water-cooled refrigerator of the present invention.

[0031] In the picture:

[0032] 1. Cooling water tank; 2. Main solenoid valve; 3. Main frequency conversion water pump; 4. Plate heat exchanger; 5. Refrigerator; 6. Branch solenoid valve; 7. Branch frequency conversion water pump; 8. Data acquisition module; 9. Module group. DETAILED DESCRIPTION

[0033] The following will provide a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts are within the scope of protection of the present invention.

[0034] In the description of the present invention, it should be understood that the terms "center", "transverse", "longitudinal", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operate in a specific orientation, and therefore cannot be understood as limiting the present invention.

[0035] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood broadly. For example, they may refer to fixed connections, detachable connections, or integral connections; they may refer to direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0036] Example 1:

[0037] See attached Figures 1 to 4 , gives a schematic embodiment of the multi-unit parallel water-cooled refrigerator intelligent control system proposed in the present invention, and the multi-unit parallel water-cooled refrigerator intelligent control system includes a data acquisition module 8, a load prediction module, an energy efficiency optimization module and a collaborative control module.

[0038] The data acquisition module 8 is used to collect the operating status and environmental parameters of the refrigerator 5 in real time; the load prediction module is used to predict the heat load of the refrigerator based on the operating status and environmental parameters; the energy efficiency optimization module is used to optimize the operating parameters of each unit according to the predicted heat load of the refrigerator; the collaborative control module is used to coordinate and control the compressor frequency and cooling water flow of each unit according to the optimized operating parameters.

[0039] See attached Figure 3 In this embodiment, three variable-frequency freezers 5 are used as an example. Each freezer 5 is equipped with a plate heat exchanger 4. The three freezers 5 share a cooling water tank 1. The cooling water in the cooling water tank 1 flows through the main cooling circuit and is then transported to the plate heat exchanger 4 in each freezer 5 via three independent cooling branches. The main cooling circuit is equipped with a main solenoid valve 2 and a main variable-frequency water pump 3, while the three cooling branches are each equipped with a branch solenoid valve 6 and a branch variable-frequency water pump 7. This creates a hydraulic architecture with centralized water supply to the main cooling circuit and independent control of the cooling branches.

[0040] Each refrigerator 5 is equipped with a data acquisition module 8, which collects the operating status and environmental parameters of the corresponding refrigerator 5 in real time and transmits them to a module group 9. Module group 9 includes a load prediction module, an energy efficiency optimization module, and a coordinated control module. This module uses electrical signals to control the main solenoid valve 2, the main variable-frequency water pump 3, the branch solenoid valve 6, the branch variable-frequency water pump 7, and the operating frequency of the refrigerator 5 compressor. The coordinated control module dynamically distributes the cooling water flow by adjusting the flow rate of the branch variable-frequency water pump 7 and the on / off state of the branch solenoid valve 6.

[0041] The parameters collected by the data acquisition module 8 include the temperature of each area inside the refrigerator 5, the inlet and outlet temperatures of the cooling water, the ambient temperature, the speed of the variable frequency water pump, the start and stop status of the compressor, the weight of the items in the refrigerator 5, the door opening frequency, the real-time power of each unit and the flow meter reading.

[0042] The load prediction module uses a 3R2C thermal network model combined with a recursive least squares algorithm to calculate the current heat load, and uses an LSTM neural network to compensate for model deviations to predict future heat load trends. In this embodiment, the heat load calculation is first based on the 3R2C thermal network model to construct a dynamic heat transfer equation for the refrigerator. The refrigerator 5 is regarded as a thermal system composed of thermal resistance (R) and heat capacity (C). The recursive least squares algorithm is used to fit the heat transfer relationship between the average temperature inside the refrigerator 5 and the ambient temperature in real time to establish a heat load calculation model:

[0043] Q in =Q rf +Q air -Q coo ling

[0044] Among them, Q rf is the radiation heat transfer, Q airis the heat transfer when the door is opened for ventilation, Q cooling Exchange heat for cooling.

[0045] Then, the historical load data (including sudden changes in operating conditions such as door opening events and item storage and retrieval) is trained through the LSTM neural network to compensate for the deviation of the thermal network model under dynamic load, realize the prediction of thermal load trends in future periods, and provide a data basis for precise temperature control.

[0046] The linkage mechanism of energy efficiency optimization and collaborative control is to first perform hierarchical control on the load interval and then dynamically allocate the cooling water flow. The load interval is divided according to the ratio of thermal load to benchmark load: when the predicted load is in the range of 25%-75% benchmark load, the energy efficiency optimization module calls the PID control strategy, and maintains a stable ratio of compressor frequency and water pump flow through the collaborative control module to ensure that the temperature of the freezer 5 is maintained at the set value; when the load exceeds 75% of the benchmark load (such as continuous door opening, large-scale storage and retrieval of goods), the advanced cooling mode is started, and the collaborative control module first increases the flow of the branch variable frequency water pump 7 to speed up heat exchange, and then increases the compressor frequency to the corresponding gear, so that the cooling capacity increases as needed; when the load is lower than 25% of the benchmark load (such as the low peak period at night), it switches to energy-saving mode, reduces the compressor frequency and reduces the water pump flow, while reducing the number of unit starts and stops.

[0047] The coordinated control module independently adjusts the opening of branch solenoid valves 6 and the speed of branch variable-frequency water pumps 7 based on the load forecast for each branch. When a single freezer 5 is under high load, the flow rate of the corresponding branch variable-frequency water pump 7 increases, and the corresponding branch solenoid valve 6 fully opens. When a freezer 5 is shut down, the corresponding branch solenoid valve 6 closes, and the corresponding branch variable-frequency water pump 7 stops running. The main variable-frequency water pump 3 automatically adjusts the total flow rate based on the number of open branches. If all branches are closed, the main variable-frequency water pump 3 stops.

[0048] The intelligent control system for multi-unit parallel water-cooled freezers also includes a remote control module, which receives manual intervention commands and adjusts pump flow or compressor frequency. In this embodiment, an operator sends a command through the remote control terminal (for example, when storing a large amount of cargo) to directly adjust the flow of the main variable-frequency water pump 3 or the branch variable-frequency water pump 7 to the maximum setting, forcing the cooling rate to accelerate. This command takes precedence over the automatic control strategy.

[0049] The intelligent control system for multi-unit parallel water-cooled freezers also includes a fault diagnosis module, which monitors system operating status and performs fault warnings and self-diagnosis. Specifically, in this embodiment, the fault diagnosis module monitors system parameters (such as abnormal flow and temperature limits) in real time. If a branch variable-frequency water pump 7 fails, it automatically closes the corresponding branch solenoid valve 6 and issues an alarm. Meanwhile, flow compensation between the main circuit and other branches maintains system operation. If the main variable-frequency water pump 3 fails, the shutdown protection mechanism for all freezers 5 is triggered.

[0050] In the illustrative embodiment described above, the intelligent control system for multi-unit parallel water-cooled freezers achieves dynamic distribution of cooling capacity and cooling water flow through the collaborative operation of data acquisition, load forecasting, energy efficiency optimization, and coordinated control modules. Each cooling branch is independently equipped with a variable-frequency water pump and solenoid valve. Combined with an LSTM neural network load forecasting model, this system addresses the uneven cooling capacity distribution and large temperature fluctuations found in traditional systems, ensuring that internal freezer temperature fluctuations remain within the ±0.5°C range required for food preservation. Furthermore, remote control and fault diagnosis modules enhance system operability and operational reliability.

[0051] The following describes the multi-unit parallel water-cooled freezer intelligent control system of this embodiment using a working scenario where supermarkets frequently open their doors during peak business hours as an example:

[0052] The data acquisition module 8 detects an increase in the frequency of door opening (such as once per minute) and simultaneously collects the temperature fluctuations inside the freezer 5 and the weight changes caused by the storage and access of items; the load prediction module predicts that the heat load will continue to rise through the LSTM neural network and generates a future load trend curve; the energy efficiency optimization module determines that the load exceeds 75% of the benchmark load based on the prediction results and starts the advanced cooling mode parameters; the collaborative control module first increases the flow of the corresponding branch water pump by 20%, and then increases the compressor frequency by 15% after 10 seconds to increase the cooling capacity in advance; after the door opening event ends, the system gradually reduces the compressor frequency and water pump flow according to real-time temperature feedback, and enters the PID control mode to maintain temperature stability; during the whole process, the fault diagnosis module monitors the operating status of the water pump and compressor in real time to ensure that there is no abnormal shutdown or overload.

[0053] Example 2:

[0054] See attached Figures 1 to 4 This embodiment provides an intelligent control method for a multi-unit parallel water-cooled refrigerator, which uses the intelligent control system for a multi-unit parallel water-cooled refrigerator of embodiment 1. The intelligent control method for a multi-unit parallel water-cooled refrigerator includes the following steps:

[0055] S1, obtain the operating status and environmental parameters of the refrigerator in real time through the data acquisition module 8;

[0056] S2. Use the load prediction module to predict the heat load of the refrigerator and its future change trend based on the operating status and environmental parameters;

[0057] S3. Determine the operating parameters of each unit through the energy efficiency optimization module based on the predicted heat load of the refrigerator;

[0058] S4. The collaborative control module adjusts the compressor frequency and cooling water flow of each unit to achieve precise temperature control and energy efficiency optimization.

[0059] In step S1, data acquisition module 8 synchronously collects multidimensional data through a distributed sensor network. This network includes temperature sensors for the upper, middle, and lower levels of the refrigerator, cooling water inlet and outlet temperature probes, an ambient temperature sensor, speed pulse signals from the main and branch variable-frequency water pumps 3 and 7, compressor start / stop status relay signals, a pressure sensor at the bottom of the refrigerator (to detect changes in item weight), a door magnetic switch (to record the frequency and duration of door openings), power transmitter readings from each unit, and electromagnetic flowmeter pulse signals. The collected data is transmitted in real time to module group 9, with a sampling period of 1 second to ensure high-frequency response to sudden load changes.

[0060] In step S2, when predicting the heat load of the refrigerator, the 3R2C thermal network model combined with the recursive least squares algorithm is used to calculate the current heat load, and the model parameters are dynamically calibrated every 15 minutes. Specifically, the 3R2C thermal network model is used to treat the refrigerator 5 as a thermal system consisting of the cabinet thermal resistance (R1), the air thermal resistance (R2), the item thermal resistance (R3), the cabinet thermal capacity (C1), and the air thermal capacity (C2), and a dynamic heat transfer equation is established:

[0061]

[0062] Among them, T1 is the cabinet temperature, T2 is the air temperature in the refrigerator, T env is the ambient temperature, T cool is the cooling water temperature.

[0063] Recursive least squares dynamic calibration recursively updates the R / C parameters in the thermal network model every 15 minutes based on the latest collected temperature-flow data, eliminating model bias during long-term operation. Furthermore, through LSTM neural network prediction compensation, historical load data (including door opening events, item storage and retrieval conditions, etc.) is input into a three-layer LSTM network (12-dimensional input layer, 64-dimensional hidden layer, 1-dimensional output layer) to predict future heat load trends and correct prediction errors of the thermal network model under dynamic conditions.

[0064] In step S2, after predicting the cabinet's heat load, the load range is divided according to the ratio of heat load to base load. When the load is in the 25%-75% base load range, the PID control strategy is used to determine the operating parameters in step S3, and the set temperature is maintained in step S4. Specifically, the energy efficiency optimization module generates PID control parameters, including proportional coefficient Kp = 0.8, integral time Ti = 120s, and differential time Td = 30s. The collaborative control module maintains the temperature according to the principle of prioritizing compressor frequency and following water pump flow, with a frequency adjustment step size of 5% of the rated frequency.

[0065] When the load is greater than 75% of the base load, the advanced cooling mode parameters are enabled in step S3, and the cooling capacity is increased in advance in step S4. Specifically, when the advanced cooling mode is activated, the coordinated control module first increases the flow rate of the corresponding branch variable frequency water pump 7 to 120% of the rated value, and after a delay of 5 seconds, increases the compressor frequency to 110%-120% of the rated value, forming an advanced adjustment mechanism that adjusts the flow rate first and then increases the cooling capacity.

[0066] When the load is less than 25% of the base load, the energy-saving mode parameters are enabled in step S3, and energy consumption is reduced in step S4. Specifically, in energy-saving mode, the compressor frequency is reduced to 60%-70% of the rated value, the flow rate of the branch flat water pump 7 is linearly reduced in proportion to the load, and the branch solenoid valve 6 of non-essential refrigerators is closed.

[0067] The dynamic allocation algorithm for cooling water flow is adjusted by the collaborative control module based on the load forecast results of each branch through the following rules:

[0068]

[0069] Where α is the correction coefficient (α=1 in normal working condition, α=1.2 in advanced cooling mode), P 预测 To predict the heat load, P 额定 The total flow of the main variable frequency water pump 3 is the sum of the flow of each branch. When the solenoid valve 6 of a branch is closed, its flow is included in the compensation coefficient.

[0070] In step S1, when the operating status and environmental parameters of the refrigerator are obtained in real time, the door opening event or the weight change of the items in the refrigerator 5 is detected synchronously. When the change is detected, the pre-cooling mode parameters are enabled in step S3, and the compressor frequency and water pump flow are adjusted in advance through step S4.

[0071] Specifically, data acquisition module 8 detects door opening and closing events based on the rising and falling edges of the door magnetic switch signal. It generates a pre-cooling trigger signal when it detects that the door opening time exceeds a preset threshold or the number of door openings per unit time exceeds a set number. In this embodiment, the preset threshold for the door opening time is 15 seconds, and the preset number of door openings is 3.

[0072] The energy efficiency optimization module generates pre-cooling mode parameters based on historical door opening data and sends them to the collaborative control module. In this embodiment, when a door opening causes a sudden increase in heat load of 20%-30%, the pre-cooling mode parameters are a 15% increase in compressor frequency and a 20% increase in water pump flow. The collaborative control module initiates water pump flow control within 0.5 seconds of detecting a door opening event and compressor frequency control after 1 second.

[0073] During remote manual intervention, the operator sends a command via the remote control terminal, which directly overrides the automatic control strategy. In this embodiment, if the operator sends a command for rapid cooling via the remote control terminal, the coordinated control module forcibly sets the flow rate of the main variable-frequency water pump 3 to 100% of the rated value, fully opens the solenoid valves 6 of each branch, and increases the compressor frequency to 120% of the rated value. This continues until the operator cancels the intervention.

[0074] When the fault diagnosis module detects a fault in a branch variable frequency water pump 7, it performs the following steps:

[0075] Close the corresponding branch solenoid valve 6 and send a fault alarm;

[0076] Recalculate the flow distribution coefficients of the remaining branches and use the main circuit variable frequency water pump 3 to increase the flow to compensate for the cooling loss of the faulty branch;

[0077] If the main circuit variable frequency water pump 3 fails, all refrigerators 5 will be triggered to shut down for protection, and the main circuit bypass valve will be opened to prevent water circuit pressure from stagnation.

[0078] In the above-mentioned illustrative embodiment, the intelligent control method for multi-unit parallel water-cooled freezers is based on load interval division and adopts a differentiated control strategy. When the load suddenly changes (such as a door opening event or a high-load operating condition), the compressor frequency and water pump flow are adjusted in advance through the advanced cooling mode, and the response time is shortened by more than 50% compared with the traditional system; when the load is low, the energy-saving mode is automatically switched, and the start and stop frequency of the compressor is reduced in combination with the pre-cooling trigger mechanism, which reduces the system energy consumption by more than 30% compared with the traditional control method, while extending the service life of the equipment.

[0079] The following describes the intelligent control method for multi-unit parallel water-cooled freezers in this embodiment using a low-load working scenario at night in a supermarket as an example:

[0080] S1. Data collection: The data collection module detects that the frequency of refrigerator door opening has dropped to 0 times / hour, the weight of items is stable, and the ambient temperature has dropped to 25°C;

[0081] S2. Load forecast: The 3R2C model calculates the current heat load as 15% of the baseline load, and the LSTM neural network predicts that the load will remain low for the next two hours;

[0082] S3. Parameter optimization: The energy efficiency optimization module determines that the load is less than 25% of the benchmark load and generates energy-saving mode parameters: the compressor frequency is reduced to 65% of the rated value, and the branch water pump flow is reduced to 30% of the rated flow according to the heat load ratio;

[0083] S4, collaborative control: The collaborative control module closes the branch solenoid valves of two of the refrigerators, leaving only one refrigerator in operation. The main line water pump frequency is adjusted to 40% of the rated speed based on the flow demand of the single branch;

[0084] The fault diagnosis module monitors the temperature fluctuations of the running refrigerator in real time. If an abnormal temperature rise occurs (such as exceeding the set value by 1°C), the backup branch of the adjacent refrigerator will be automatically activated to compensate for the cooling capacity.

[0085] Finally, it should be noted that the various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0086] The above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the same. Although the present invention has been described in detail with reference to preferred embodiments, persons skilled in the art should understand that the specific implementation methods of the present invention may still be modified or some technical features may be replaced by equivalents without departing from the spirit of the technical solutions of the present invention, and all of these should fall within the scope of the technical solutions claimed for protection by the present invention.

Claims

1. An intelligent control system for multi-unit parallel water-cooled freezers, characterized in that: include: A data acquisition module, which is used to collect the operating status and environmental parameters of the refrigerator in real time; A load prediction module, configured to predict a heat load of the refrigerator based on the operating status and environmental parameters; An energy efficiency optimization module, the energy efficiency optimization module is used to optimize the operating parameters of each unit according to the predicted heat load of the refrigerator; The collaborative control module is used to coordinate and control the compressor frequency and cooling water flow of each unit according to the optimized operating parameters.

2. The intelligent control system for multi-unit parallel water-cooled freezers according to claim 1 is characterized in that: The parameters collected by the data acquisition module include the temperature of each area inside the refrigerator, the inlet and outlet temperatures of the cooling water, the ambient temperature, the speed of the variable frequency water pump, the start and stop status of the compressor, the weight of the items in the refrigerator, the door opening frequency, the real-time power of each unit and the flow meter reading.

3. The intelligent control system for multi-unit parallel water-cooled freezers according to claim 1 is characterized in that: The load prediction module uses the 3R2C thermal network model combined with the recursive least squares algorithm to calculate the current thermal load, and compensates for the model deviation through the LSTM neural network to predict the future thermal load change trend.

4. The intelligent control system for multi-unit parallel water-cooled freezers according to claim 1 is characterized in that: It also includes a branch variable frequency water pump and a branch solenoid valve arranged on each cooling branch. The collaborative control module dynamically distributes the cooling water flow by adjusting the flow of the branch variable frequency water pump and the switching state of the branch solenoid valve.

5. The intelligent control system for multi-unit parallel water-cooled freezers according to claim 1 is characterized in that: It also includes a remote control module, which is used to receive manual intervention instructions and adjust the water pump flow or compressor frequency.

6. The intelligent control system for multi-unit parallel water-cooled freezers according to claim 1, characterized in that: It also includes a fault diagnosis module, which is used to monitor the system operation status and perform fault warning and self-diagnosis.

7. An intelligent control method for a multi-unit parallel water-cooled freezer, using the intelligent control system for a multi-unit parallel water-cooled freezer according to any one of claims 1 to 6, characterized in that: The steps include: S1, obtain the operating status and environmental parameters of the refrigerator in real time through the data acquisition module; S2. Using a load prediction module to predict the heat load of the refrigerator and its future change trend based on the operating status and environmental parameters; S3. Determine the operating parameters of each unit through the energy efficiency optimization module according to the predicted heat load of the refrigerator; S4. The collaborative control module adjusts the compressor frequency and cooling water flow of each unit to achieve precise temperature control and energy efficiency optimization.

8. The intelligent control method for multi-unit parallel water-cooled freezers according to claim 7, characterized in that: In step S2, when predicting the heat load of the refrigerator, the 3R2C thermal network model combined with the recursive least squares algorithm is used to calculate the current heat load, and the model parameters are dynamically calibrated every 15 minutes.

9. The intelligent control method for multi-unit parallel water-cooled freezers according to claim 7, characterized in that: In step S2, after predicting the heat load of the refrigerator, the load interval is divided according to the ratio of the heat load to the benchmark load. When the load is in the 25%-75% benchmark load area, the PID control strategy is used to determine the operating parameters in step S3, and the set temperature is maintained through step S4; when the load is greater than 75% of the benchmark load, the advanced cooling mode parameters are enabled in step S3, and the cooling capacity is increased in advance through step S4; when the load is less than 25% of the benchmark load, the energy-saving operation mode parameters are enabled in step S3, and energy consumption is reduced through step S4.

10. The intelligent control method for multi-unit parallel water-cooled freezers according to claim 7, characterized in that: In step S1, when the operating status and environmental parameters of the refrigerator are obtained in real time, the door opening event or the weight change of the items in the refrigerator is detected synchronously. When the change is detected, the pre-cooling mode parameters are enabled in step S3, and the compressor frequency and water pump flow are adjusted in advance through step S4.

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