Intelligent operation regulation and control method and system for cold storage supercooling refrigerator

Abstract

This invention discloses an intelligent operation and control method and system for cold storage and subcooling cold storage. The method includes: acquiring basic parameters and real-time monitoring data of the cold storage; calculating the real-time load and short-term load change trends of the cold storage through a dynamic load prediction model; formulating an operation strategy based on load factor and time-of-use electricity price information, and intelligently selecting normal cooling, combined cooling with cold storage, or combined cooling with cold release and subcooling modes; achieving smooth switching between the three modes by adjusting the operating status of the compressor unit, electronic expansion valve, electric valve, and water pump; and continuously optimizing the load prediction model and control strategy using real-time feedback of the system's operating status. This invention achieves coordinated control of the cold storage and subcooling processes, improving the system's energy efficiency and operational economy under variable load conditions, and is applicable to the energy-saving and optimized operation of various types of cold storage and subcooling cold storage facilities.

Description

Technical Field

[0001] This invention relates to the fields of refrigeration system control and building energy conservation technology, specifically to an intelligent operation control method and system for cold storage and subcooling cold storage. Background Technology

[0002] As a core infrastructure of cold chain logistics, cold storage facilities consistently account for 20% to 40% of the operating expenses of related enterprises. To ensure reliability under extreme conditions (such as high summer temperatures, high temperatures of goods entering the warehouse, and frequent door opening operations), traditional cold storage refrigeration systems are typically selected and designed based on the maximum possible load. This results in the unit's cooling capacity far exceeding actual demand for most of the normal operating time. This overly redundant design not only increases initial investment but also causes the system to operate at low loads for extended periods (load rate often below 50%). Under these conditions, the coefficient of performance (COP) of the refrigeration unit deteriorates significantly, and key equipment such as compressors and condenser fans deviate from their high-efficiency operating range for extended periods, resulting in continuous low energy consumption and poor overall system economics.

[0003] To improve energy efficiency under partial load and reduce costs by utilizing time-of-use electricity pricing policies, integrating cold storage technology into refrigeration systems has become an important solution. While traditional cold storage systems can achieve basic peak shaving and valley filling, their operation modes are often relatively independent and fixed. The cold storage and release processes of the cold storage device are usually simply coupled with the main refrigeration unit's operating schedule, or merely used as a load supplement, lacking deep coordination and dynamic matching with the main refrigeration system under different operating conditions. More importantly, the operating strategies of existing systems mostly rely on preset fixed schedules or simple temperature / pressure threshold control, which is a passive response control. This control method fails to introduce accurate prediction of future load changes in the cold storage, and therefore cannot proactively and dynamically optimize the timing and rate of cold storage and release, as well as the output distribution between the main unit and the cold storage device. This limits the overall energy efficiency improvement of the system, fails to fully tap the potential for equipment synergy, and fails to maximize economic benefits.

[0004] Previously, to further improve energy efficiency and operational flexibility, an advanced cold storage refrigeration system integrating cold storage and subcooling technologies was proposed. This system, by introducing key components such as cold storage units and subcoolers, constructs diverse operating modes, including normal cooling mode, cold storage + cooling mode, and cold release subcooling + cooling mode. Compared to traditional cold storage systems, it provides the possibility of deep synergy and energy efficiency leaps between the main unit, cold storage, and subcooling at the hardware level. However, it is precisely this multi-mode, multi-degree-of-freedom characteristic that dramatically increases the complexity of its operating strategy. If traditional fixed strategies or simple threshold control are still used, it is not only difficult to manage the smooth switching and optimal matching between numerous operating modes, but it may even lead to energy efficiency degradation due to improper mode selection or switching, failing to fully realize the theoretical energy-saving potential of this advanced system. Furthermore, as cold chain logistics develops towards higher frequency, smaller batches, and higher timeliness, the load fluctuations of cold storage are more drastic and unpredictable, further highlighting the inadequacy of traditional fixed strategies. For the aforementioned cold storage and subcooling cold storage with multi-mode operating capabilities, the drastically fluctuating load environment places higher demands on its dynamic control capabilities.

[0005] To address the aforementioned technical challenges, existing cold storage refrigeration technology typically relies on deploying temperature and pressure sensors inside the cold storage facility or at key equipment nodes. Refrigeration equipment is started, stopped, or adjusted by comparing monitored values ​​with set thresholds to maintain stable storage temperatures. While related patented technologies have made some improvements in certain areas, they still suffer from common problems such as complex modeling, slow response times, limited application scenarios, and insufficient hardware-software coordination. A related patent search and analysis are as follows.

[0006] Existing Chinese patent document (CN121230356A) proposes a dynamic energy consumption optimization system for cold storage based on digital twins. This system uses a high-precision digital twin model for energy consumption prediction and multi-objective optimization. While this method achieves refined simulation and medium-to-long-term energy efficiency management, the modeling relies on a large amount of historical data and complex parameter calibration, resulting in high implementation barriers and long cycles. Furthermore, it is not integrated with physical energy-saving technologies such as cold storage and subcooling, making it difficult to achieve real-time and economical load response. Chinese patent document (CN120926687A) discloses a multi-temperature zone coordinated refrigeration system and energy-saving control method for variable-temperature cold storage. This system achieves multi-temperature zone coordination and equipment safety assurance through dynamic load prediction and central oil circuit management. This solution is suitable for multi-temperature zone scenarios and improves system reliability. However, it does not incorporate electricity price signals and real-time load prediction, lacks energy-saving strategies for peak-valley electricity pricing, has limited economic optimization capabilities, and is difficult to apply to single-temperature zone or conventional cold storage. Chinese patent document (CN121052520A) discloses a dynamic monitoring system and method for refrigeration energy efficiency in cold storage. Based on cargo thermal state modeling and multi-objective optimization functions, combined with the TOPSIS method to screen the optimal energy-saving scheme, it realizes the transformation from "controlling the environment" to "controlling the cargo." While this scheme is relatively advanced in terms of energy efficiency theoretical optimization, it relies on RFID / QR code tags to obtain cargo information, making implementation complex, with a long optimization cycle and insufficient real-time performance. Furthermore, it does not integrate hardware energy-saving technologies such as cold storage and subcooling, making it difficult to cope with frequently fluctuating load demands.

[0007] Therefore, in response to the common problems of design redundancy, low efficiency at low loads in existing cold storage systems, and the inability of traditional operating strategies to unleash their energy-saving potential when facing advanced systems with multiple modes such as cold storage and subcooling due to shallow coupling, rigidity, passivity, and lack of prediction and optimization capabilities, there is an urgent need for a new system and method that can achieve intelligent coordination and dynamic control of refrigeration units, cold storage devices, and auxiliary systems based on accurate real-time and short-term load forecasting. This would enable the system to truly achieve the comprehensive optimization of energy efficiency, stability, and economy under complex load fluctuations and electricity pricing policies. Summary of the Invention

[0008] To address the shortcomings of the existing technologies, the present invention aims to propose an intelligent operation and control method and system for cold storage and subcooling cold storage. This system can accurately predict the short-term load of the cold storage and, based on the prediction results, electricity price signals, and system status, intelligently switch to the optimal operating mode, thereby achieving safe, stable, efficient, and economical operation of the cold storage and subcooling cold storage.

[0009] To achieve the above-mentioned technical objectives, the present invention adopts the following technical solution: A method for intelligent operation and control of cold storage and subcooling cold storage facilities includes the following steps: S1. Obtain the basic parameter dataset and setting dataset for the cold storage; S2. Obtain the real-time monitoring dataset for the cold storage; S3. Based on the data obtained in S1 and S2, the real-time load and load change trend of the cold storage are calculated using the dynamic load prediction model. S4. Based on the real-time load and load change trend calculated by S3, formulate cold storage operation strategies and determine cold storage operation modes; S5. Based on the cold storage operation mode determined by S4, issue operation instructions to adjust the refrigeration system operation mode; S6. Summarize the operating status of each component of the refrigeration system, perform feedback optimization, return to S3, adjust the cold storage load calculation, and continuously correct the dynamic load prediction model and strategy parameters to make them increasingly suitable for the operating characteristics of the specific cold storage.

[0010] The cold storage basic parameter dataset in step S1 includes basic cold storage size data and cold storage enclosure structure attributes, including the length, width, and height of the cold storage, the width and height of the cold storage door, and the material type and thickness of the cold storage enclosure structure; the cold storage setting dataset includes cold storage cargo attribute information and cold storage set temperature and humidity, including the type and weight of stored goods, entry and exit temperatures, indoor set temperature, and relative humidity.

[0011] The real-time monitoring dataset for the cold storage in step S2 includes indoor measured temperature, humidity, and air pressure; outdoor measured temperature, humidity, and air pressure; outdoor solar radiation; indoor lighting parameters; refrigeration system defrosting parameters; door opening and closing status parameters; goods storage parameters; and personnel entry and exit frequency.

[0012] Step S3 specifically includes the following steps: S3.1 Data preprocessing: For the data in the cold storage basic parameter dataset and setting dataset input by the data input module and the cold storage real-time monitoring dataset obtained by the data acquisition module, perform preliminary processing, check for missing values ​​and outliers, impute missing values, remove outliers, and standardize and normalize the data. S3.2 Calculation of heat transfer load of cold storage enclosure structure: Based on the properties of cold storage enclosure structure, the RC thermal network model is adopted to convert the cold storage enclosure structure into thermal resistance and heat capacity, convert solar radiation into equivalent temperature, and combine indoor and outdoor temperatures to establish nodal temperature differential equations. Solve the differential equations to obtain the wall temperature and calculate the output wall heat transfer load. S3.3 Calculation of air infiltration and personnel entry / exit load: Based on the indoor and outdoor air pressure and door opening / closing parameters, the air infiltration load is divided into the state of door opening and no personnel entering / exiting and the state of personnel entering / exiting. The Gosney-Olama air infiltration model is called to calculate the air infiltration volume. Considering that cold storage is generally equipped with air curtain machines, the air infiltration volume is corrected by combining the air curtain machine operating parameters, and the air infiltration load and latent heat load are calculated and output. S3.4 Internal heat source load calculation, including cargo breathing heat, lighting heat, personnel operation heat, and defrosting heat. Calculate the internal heat source load separately based on cargo storage conditions, indoor lighting conditions, indoor personnel conditions, and system defrosting conditions, and summarize and output the internal heat source load. S3.5 Total Load Summary and Real-time Output: Summarize the loads calculated in steps S3.2, S3.3 and S3.4, and output the real-time cooling load value. S3.6 Short-term load forecasting: Based on historical operating data of cold storage and real-time load calculation, supervised machine learning is performed using neural networks to output a load forecast curve for future periods in the short term, thus obtaining the load change trend.

[0013] Step S4 specifically includes the following steps: S4.1 Read input data, including real-time predicted load. Q pred , set load Q set Electricity price period t; S4.2 Calculate the load factor, i.e., predict the load in real time. Q pred With set load Q set The ratio r; S4.3. Based on the load factor and electricity price period, make logical judgments to determine the cold storage operation mode as normal cooling supply, cold storage and cooling supply combined operation, or cold release and cooling supply combined operation. Try to store more cold during the low electricity price period and release more cold during the peak period to make full use of the peak and off-peak electricity prices.

[0014] In step S5, adjusting the refrigeration system operating mode includes adjusting the compressor unit operating status, adjusting the evaporator unit operating status, adjusting the electronic expansion valve opening, adjusting the electric valve switching path of the cold storage subcooling plate, and adjusting the water pump operating status.

[0015] The system for implementing the intelligent operation and control method of the cold storage and subcooling cold storage includes: The data input module is used to acquire basic parameter data and cold storage setting data of the cold storage and input them into the predictive analysis module. The data acquisition module collects information from multiple sensors according to data acquisition instructions and inputs it into the predictive analysis module; The predictive analysis module calculates the real-time load and load change trend of the cold storage based on the dynamic load prediction model. The strategy generation module generates cold storage operation strategies based on load calculation and analysis, determines the cold storage operation mode, and sends operation instructions to the refrigeration execution module. The refrigeration execution module, based on the input operating commands, regulates the operating status of the compressor, pump, motor, and the on / off state of the electric valves to achieve intelligent control of the cold storage operation mode; The feedback optimization module is used to summarize the operating status of each component of the refrigeration system and feed it back to the predictive analysis module to continuously optimize and adjust the system load prediction analysis.

[0016] Compared with the prior art, the present invention has the following advantages: 1. By using a dynamic load forecasting model, the system monitors real-time load and predicts future load changes, intelligently adjusting the cold storage operation mode. This solves the problem of insufficient adaptability of traditional fixed strategies, enabling the system to operate in the high-efficiency range as much as possible and improving the overall energy efficiency ratio (COP) of the system. 2. Through intelligent decision-making, the control system stores more cold during off-peak hours and releases more cold during peak hours, making full use of peak-valley electricity prices to significantly save on operating costs and reduce operating expenses.

[0017] 3. Through feedback optimization, the system can continuously revise the load forecasting model and strategy parameters using historical operating data, making them increasingly suitable for the operating characteristics of specific cold storage facilities, thus achieving continuous optimization. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating the steps of an intelligent operation and control method for a cold storage and subcooling cold storage facility according to an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the structure of an intelligent operation and control system for a cold storage and subcooling cold storage in an embodiment of the present invention.

[0020] Figure 3 This is a structural diagram of the device corresponding to the intelligent operation control system in an embodiment of the present invention.

[0021] Figure 4 This is a flowchart illustrating the steps of real-time load forecasting in an embodiment of the present invention.

[0022] Figure 5 This is a flowchart illustrating the steps involved in generating a cold storage operation mode strategy in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this invention clearer and more concise, the invention will be further described in detail below with reference to the accompanying drawings and an embodiment. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of the invention.

[0024] This embodiment provides a method for intelligent operation and control of cold storage and subcooling cold storage, such as... Figure 1 As shown, it includes the following steps: S1. Obtain the cold storage basic parameter dataset and the setting dataset. The cold storage basic parameter dataset includes basic cold storage dimension data and cold storage enclosure structure attributes, including the length, width, and height of the cold storage, the width and height of the cold storage door, and the material type and thickness of the cold storage enclosure structure. The cold storage setting dataset includes cold storage cargo attribute information and cold storage set temperature and humidity, including the type and weight of the stored cargo, the entry and exit temperatures, the indoor set temperature, and the relative humidity. In one embodiment, the stored cargo is set to apples, 38 tons, with an entry temperature of 25°C and an exit temperature of 0°C, a cold storage temperature of 0°C, and a relative humidity of 85%.

[0025] S2. Obtain real-time monitoring datasets for the cold storage, including indoor measured temperature, humidity, and air pressure; outdoor measured temperature, humidity, and air pressure; outdoor solar radiation; indoor lighting parameters; system defrosting parameters; door opening and closing status parameters; goods storage parameters; and personnel entry and exit frequency.

[0026] S3. Based on the data obtained from S1 and S2, use the dynamic load prediction model to calculate the real-time load and load change trend of the cold storage, such as... Figure 4 As shown, it includes the following steps: S3.1 Data preprocessing: For the data in the cold storage basic parameter dataset and setting dataset input by the data input module and the cold storage real-time monitoring dataset obtained by the data acquisition module, perform preliminary processing, check for missing values ​​and outliers, impute missing values, remove outliers, and standardize and normalize the data. S3.2 Calculation of heat transfer load of cold storage enclosure structure, as detailed below: S3.2.1. Based on the properties of the cold storage enclosure structure, an RC thermal network model is established. In one embodiment, a 3R2C heat transfer model is adopted, and the cold storage enclosure structure is equivalent to 3 thermal resistances and 2 heat capacities in the horizontal and vertical directions, respectively. The total thermal resistance and total heat capacity are determined by the material properties of the enclosure structure. Temperature nodes are established at the outdoor, indoor, and heat capacity locations. Based on the physical parameters of the enclosure structure, a theoretical heat transfer model is established, and its transfer function matrix in the Laplace domain is calculated to obtain the frequency response characteristics of the theoretical heat transfer model. Based on the transfer function of the 3R2C heat transfer model, its frequency response characteristics in the same frequency domain range are derived. A genetic algorithm is used to optimize the thermal resistance and heat capacity parameters. The optimization objective is to make the frequency response characteristics of the 3R2C heat transfer model consistent with the frequency response characteristics of the theoretical heat transfer model. S3.2.2, Convert solar radiation into equivalent temperature, as follows: in, T d The equivalent temperature for solar radiation. P The solar radiation absorption coefficient of the outer surface of the cold storage enclosure wall. Jp The average radiation intensity of the outer surface of the cold storage. α w The heat transfer coefficient of the outer surface of the cold storage; S3.2.3. Based on the measured indoor and outdoor temperatures, establish the nodal temperature differential equation. The outdoor temperature is the sum of the measured temperature and the equivalent temperature from solar radiation. Solve the differential equation to obtain the wall temperature. The specific differential equation is as follows: in, R rf,1 , R rf,3 , R rf,5 These are the three equivalent thermal resistances from top to bottom along the vertical direction from top to bottom. R wi,1 , R wi,3 , R wi,5 The three equivalent thermal resistances are from the outside to the inside along the horizontal direction of the wall. C rf,2 , C rf,4 These are the two equivalent heat capacities from top to bottom along the vertical direction from top to bottom. C wi,2 ,、 C wi,4 These are two equivalent heat capacities along the horizontal direction of the wall, from the outside to the inside. T rf,2 (t), T rf,4 (t) represents the temperature change over time at two nodes with varying heat capacities along the vertical direction from the top. T wi,2 (t),、 T wi,4 (t) represents the temperature change over time at two nodes with varying heat capacity along the horizontal direction of the wall. T out,rf (t), T out,wi (t) represents the temperature change over time at outdoor nodes located on the vertical roof and horizontal wall. T in, (t) represents the temperature change of the indoor node over time, where t is time; S3.2.4, Calculate and output the wall heat transfer load, as follows: in, Qenvelpo For the thermal load of the building envelope, A i For the first i Wall area, A rf This represents the area of ​​the top.

[0027] S3.3 Calculation of air seepage and personnel entry / exit load: Based on indoor and outdoor air pressure and door opening / closing parameters, the air seepage load is divided into two states: door open with no personnel entering / exiting and personnel entering / exiting. Details are as follows: S3.3.1. Call the Gosney-Olama air infiltration model to calculate the air infiltration volume. Considering that cold storage facilities are generally equipped with air curtain machines, the calculation is corrected based on the operating parameters of the air curtain machines, as follows: in, V 1 represents the air leakage rate when the warehouse door is open and no one is entering or exiting. k This is the correction factor for the air curtain machine. A For the area of ​​the warehouse door, g For local gravitational acceleration, H The height of the warehouse door, ρ in , ρ out The density of the air inside and outside the cold storage. V 2 represents the air leakage rate when personnel are opening the door to enter or exit. T h The dimensionless coefficient for door opening time per hour is obtained by making the total door opening time per hour, caused by people entering and exiting, dimensionless. Δp oi Due to the pressure difference between the inside and outside of the cold storage, when Δp oi When >0, i =0, when Δp oi When <0, i =1, C Dave , D Dave These are the average flow coefficient and the average flow correction value, respectively, obtained by fitting the operating parameters of the air curtain machine. S3.3.2 Calculate and output the infiltration load and latent heat load, as follows: in, Q infil For air infiltration and latent heat load, Qinfil,1 , Q infil,2 The loads are respectively the load when the warehouse door is open and no one is entering or exiting, and the load when people are entering or exiting. c out The specific heat capacity of the outside air. λ c The thermal conductivity of the curtain. r c The heat released during the condensation of water.

[0028] S3.4 Calculation of internal heat source load, as detailed below: S3.4.1 Calculate the respiratory heat of the cargo, as follows: in, Q breath For cargo respiration heat, m For the quality of goods, q s For the respiratory heat flow of goods; S3.4.2 Calculate the heat generated by lighting, personnel operation, and defrosting. Determine whether the heat generated is present based on the indoor lighting conditions, the number of people in the room, and the defrosting status of the system. Generally, a fixed value is used. S3.4.3 Summarize and output the internal heat source load, as follows: in, Q im For internal heat source load, Q light For lighting heat load, Q r For personnel operating heat load, Q defrost This is the defrosting heat load.

[0029] S3.5 Total Load Summary and Real-time Output: Summarize the loads calculated in each step and output the real-time cooling load value, as detailed below: in, Q total For real-time total load, Q envelpo For the thermal load of the building envelope, Q infil For air infiltration and latent heat load, Q im For internal heat source load.

[0030] S3.6 Short-term load forecasting: Based on historical operating data of cold storage and real-time load calculation, supervised machine learning using neural networks is adopted to output load forecast curves for future periods in the short term.

[0031] S4. Based on the real-time load and load change trend calculated in S3, formulate a cold storage operation strategy and determine the cold storage operation mode, such as Figure 5 shown below, including the following steps: S4.1. Read the input data, including the real-time predicted load Q pred , the set load Q set , and the electricity price period t; in one embodiment, the set load Q set is 16 kW; S4.2. Calculate the load rate, that is, the ratio r of the actual predicted load Q pred to the set load Q set ; S4.3. According to the load rate and the electricity price period, make a logical judgment to determine the cold storage operation mode, specifically as follows: 1) When r > 0.85, it is in a high-load state, and the system enters the combined operation mode of cold release subcooling and cooling supply; 2) When 0.4 < r < 0.85, it is in a medium-load state. If it is the valley electricity price period at this time, the system enters the combined operation mode of cold storage and cooling supply; 3) When 0.4 < r < 0.85, it is in a medium-load state. If it is the peak electricity price period at this time, the system enters the combined operation mode of cold release subcooling and cooling supply; 4) When 0.4 < r < 0.85, it is in a medium-load state. If it is the flat electricity price period at this time, the system enters the normal cooling supply operation mode; 5) When r < 0.4, it is in a low-load state. If it is the valley electricity price period at this time, the system enters the combined operation mode of cold storage and cooling supply; 6) When r < 0.4, it is in a low-load state. If it is the peak electricity price period at this time, the system enters the normal cooling supply operation mode; 7) When r < 0.4, it is in a low-load state. If it is the flat electricity price period at this time, the system enters the combined operation mode of cold storage and cooling supply; S5. Based on the cold storage operation mode determined in S4, issue an operation instruction to adjust the operation mode of the refrigeration system, including adjusting the operation state of the compressor unit, adjusting the operation state of the evaporator unit, adjusting the opening of the electronic expansion valve, adjusting the switching path of the electric valve of the cold storage subcooling plate, and adjusting the operation state of the water pump, specifically as follows: The refrigeration system meets cooling demands by adjusting the start-up and shutdown frequency of parallel compressor units, the start-up and shutdown of evaporator units, and the opening of electronic expansion valves based on real-time load requirements. Under this common adjustment mechanism, the system supports three operating modes, the main differences being the working status of the cold storage and subcooling section and the linkage method of valves and water pumps: 1) Normal cooling mode: The cold storage and subcooling plate is not working. The electric valve is adjusted to connect the cold release and subcooling passage. All water pumps are turned off. At this time, the cold release and subcooling passage is only used as a section of pipeline and does not play a role. 2) Combined operation mode of cold storage and cooling supply: The cold storage and subcooling section is in cold storage mode. The electric valve is adjusted to connect the cold storage passage and the cold release and subcooling passage. The water pump in the cold storage passage is turned on and the water pump in the cold release and subcooling passage is turned off. At this time, the cold release and subcooling passage is only a section of pipeline and does not play a role. The refrigerant entering the cold storage passage enters the cold storage unit for cold storage after being throttled by the electronic expansion valve. The refrigerant entering the cold release and subcooling passage enters the evaporator for cooling. 3) Combined operation mode of cold release and subcooling and cooling supply: The cold storage subcooling section is in the cold release and subcooling mode. The regulating electric valve connects the cold release and subcooling passage. The cold release and subcooling passage water pump is turned on, and the refrigerant enters the subcooler for subcooling, and then enters the evaporator for cooling.

[0032] S6. Summarize the operating status of each component of the system, perform feedback optimization, return to S3, and adjust the cold storage load calculation.

[0033] A smart operation and control system for cold storage and subcooling, such as Figure 2 As shown, it includes a data input module, a data acquisition module, a predictive analysis module, a strategy generation module, a cooling execution module, and a feedback optimization module; The data input module is used to acquire basic parameter data and setting data of the cold storage and input them into the predictive analysis module. In one embodiment, such as... Figure 2 As shown, the data input module has operating software for manually inputting basic parameters and setting data for the cold storage. The data acquisition module collects information from multiple sensors according to the data acquisition instructions and inputs it into the predictive analysis module; The predictive analysis module is used to receive data from the data input module and the data acquisition module, perform data preprocessing, call the dynamic load prediction model, and complete the calculation of the real-time load and load change trend of the cold storage. The strategy generation module generates a cold storage operation strategy based on the calculation and analysis of the load and the electricity price period, determines the cold storage operation mode, and sends operation instructions to the refrigeration execution module. The refrigeration execution module regulates the operating status of the compressor, pump, and motor, as well as the on / off state of the electric valves, according to the input operating instructions, to achieve intelligent control of the cold storage operation mode; The feedback optimization module is used to summarize the operating status of each component of the system and feed it back to the predictive analysis module to continuously optimize and adjust the system load predictive analysis. The multi-sensor information includes measured indoor and outdoor temperature, humidity, air pressure, outdoor solar radiation, indoor lighting conditions, system defrosting status, warehouse door opening and closing status, goods storage conditions, and personnel entry and exit frequency.

[0034] In one embodiment, such as Figure 3 As shown, a data acquisition device is used to collect information from the multiple sensors, including an outdoor temperature sensor, an outdoor humidity sensor, and an outdoor air pressure sensor installed on the outside of the cold storage where there is no sunlight or obvious wind; an indoor temperature sensor, an indoor humidity sensor, and an indoor air pressure sensor installed in the center of the cold storage; a solar radiation sensor installed on the roof of the cold storage where there is sunlight; a lighting status sensor installed in the lighting circuit; a defrost status sensor installed in the refrigeration system; a door opening / closing sensor and a personnel entry / exit photoelectric counter installed at the door; and a cargo temperature sensor installed in the cargo storage area.

[0035] In one embodiment, such as Figure 3 As shown, a data transmission device is used to transmit multi-sensor information collected by the data acquisition device. The data transmission device includes a wired signal transmitter, a wireless signal transmitter, and a signal converter. Data collected by indoor and outdoor air temperature sensors, indoor and outdoor air humidity sensors, indoor and outdoor air pressure sensors, lighting status sensors, and defrosting status sensors are transmitted using the wired signal transmitter. Data collected by solar radiation sensors, warehouse door opening and closing sensors, cargo temperature sensors, and personnel entry and exit photoelectric counters are transmitted using the wireless signal transmitter. The electrical signals of various sensors are transmitted to the local area network system via the signal converter.

[0036] In one embodiment, such as Figure 3 As shown, a data processor is used to implement the predictive analysis module and the strategy generation module. After receiving the data, the data processor preprocesses the data and uses the processed data to calculate the real-time load of the cold storage and predict the load change trend. Then, it generates the cold storage operation strategy in combination with the electricity price period, determines the cold storage operation mode, and sends the operation instructions to the control actuator.

[0037] In one embodiment, such as Figure 3 As shown, a control actuator is used to realize the functions of the refrigeration execution module and the feedback optimization module. After receiving the operation command, the control actuator controls the start-up and shutdown and operating frequency of the parallel compressor unit, controls the start-up and shutdown of the evaporator unit, controls the opening of the electronic expansion valve, controls the connection / bypass of the electric valve to the cold storage subcooling plate, controls the start-up and shutdown of the water pump body, and summarizes the operating status of each component of the system and feeds it back to the data transmitter for further optimization of load calculation.

[0038] It should be understood that the present invention is not limited to the specific examples described above. Any modifications, substitutions, combinations, simplifications, etc., made by those skilled in the art within the scope of the technology disclosed in the present invention, under the spirit and principle of the present invention, are equivalent substitutions and should be included within the protection scope of the present invention.

Claims

1. A method for intelligent operation and control of a cold storage and subcooling cold storage facility, characterized in that: Includes the following steps: S1. Obtain the basic parameter dataset and setting dataset for the cold storage; S2. Obtain the real-time monitoring dataset for the cold storage; S3. Based on the data obtained in S1 and S2, the real-time load and load change trend of the cold storage are calculated using the dynamic load prediction model. S4. Based on the real-time load and load change trend calculated by S3, formulate cold storage operation strategies and determine cold storage operation modes; S5. Based on the cold storage operation mode determined by S4, issue operation instructions to adjust the refrigeration system operation mode; S6. Summarize the operating status of each component of the refrigeration system, perform feedback optimization, return to S3, adjust the cold storage load calculation, and continuously correct the dynamic load prediction model and strategy parameters to make them increasingly suitable for the operating characteristics of the specific cold storage.

2. The intelligent operation regulation and control method of a cold accumulation and supercooling refrigerator according to claim 1, characterized in that: The cold storage basic parameter dataset in step S1 includes basic cold storage size data and cold storage enclosure structure attributes, including the length, width, and height of the cold storage, the width and height of the cold storage door, and the material type and thickness of the cold storage enclosure structure; the cold storage setting dataset includes cold storage cargo attribute information and cold storage set temperature and humidity, including the type and weight of stored goods, entry and exit temperatures, indoor set temperature, and relative humidity.

3. The intelligent operation and control method for a cold storage and subcooling cold storage facility according to claim 1, characterized in that: The real-time monitoring dataset for the cold storage in step S2 includes indoor measured temperature, humidity, and air pressure; outdoor measured temperature, humidity, and air pressure; outdoor solar radiation; indoor lighting parameters; refrigeration system defrosting parameters; door opening and closing status parameters; goods storage parameters; and personnel entry and exit frequency.

4. The intelligent operation and control method for a cold storage and subcooling cold storage facility according to claim 1, characterized in that: Step S3 specifically includes the following steps: S3.1 Data preprocessing: For the data in the cold storage basic parameter dataset and setting dataset input by the data input module and the cold storage real-time monitoring dataset obtained by the data acquisition module, perform preliminary processing, check for missing values ​​and outliers, impute missing values, remove outliers, and standardize and normalize the data. S3.2 Calculation of heat transfer load of cold storage enclosure structure: Based on the properties of cold storage enclosure structure, the RC thermal network model is adopted to convert the cold storage enclosure structure into thermal resistance and heat capacity, convert solar radiation into equivalent temperature, and combine indoor and outdoor temperatures to establish nodal temperature differential equations. Solve the differential equations to obtain the wall temperature and calculate the output wall heat transfer load. S3.3 Calculation of air infiltration and personnel entry / exit load: Based on the indoor and outdoor air pressure and door opening / closing parameters, the air infiltration load is divided into the state of door opening and no personnel entering / exiting and the state of personnel entering / exiting. The Gosney-Olama air infiltration model is called to calculate the air infiltration volume. Considering that cold storage is generally equipped with air curtain machines, the air infiltration volume is corrected by combining the air curtain machine operating parameters, and the air infiltration load and latent heat load are calculated and output. S3.4 Internal heat source load calculation, including cargo breathing heat, lighting heat, personnel operation heat, and defrosting heat. Calculate the internal heat source load separately based on cargo storage conditions, indoor lighting conditions, indoor personnel conditions, and system defrosting conditions, and summarize and output the internal heat source load. S3.5 Total Load Summary and Real-time Output: Summarize the loads calculated in steps S3.2, S3.3 and S3.4, and output the real-time cooling load value. S3.6 Short-term load forecasting: Based on historical operating data of cold storage and real-time load calculation, supervised machine learning is performed using neural networks to output a load forecast curve for future periods in the short term, thus obtaining the load change trend.

5. The intelligent operation and control method for a cold storage and subcooling cold storage facility according to claim 1, characterized in that: Step S4 Specifically, the following steps are included: S4.1 Read input data, including real-time predicted load. Q pred , set load Q set Electricity price period t; S4.2 Calculate the load factor, i.e., predict the load in real time. Q pred With set load Q set The ratio r; S4.

3. Based on the load factor and electricity price period, make logical judgments to determine the cold storage operation mode as normal cooling supply, cold storage and cooling supply combined operation, or cold release and cooling supply combined operation. Try to store more cold during the low electricity price period and release more cold during the peak period to make full use of the peak and off-peak electricity prices.

6. The intelligent operation and control method for a cold storage and subcooling cold storage facility according to claim 1, characterized in that: In step S5, adjusting the refrigeration system operating mode includes adjusting the compressor unit operating status, adjusting the evaporator unit operating status, adjusting the electronic expansion valve opening, adjusting the electric valve switching path of the cold storage subcooling plate, and adjusting the water pump operating status.

7. A system for implementing the intelligent operation and control method for a cold storage and subcooling cold storage as described in claim 1, characterized in that... include: The data input module is used to acquire basic parameter data and cold storage setting data of the cold storage and input them into the predictive analysis module. The data acquisition module collects information from multiple sensors according to data acquisition instructions and inputs it into the predictive analysis module; The predictive analysis module calculates the real-time load and load change trend of the cold storage based on the dynamic load prediction model. The strategy generation module generates cold storage operation strategies based on load calculation and analysis, determines the cold storage operation mode, and sends operation instructions to the refrigeration execution module. The refrigeration execution module, based on the input operating commands, regulates the operating status of the compressor, pump, motor, and the on / off state of the electric valves to achieve intelligent control of the cold storage operation mode; The feedback optimization module is used to summarize the operating status of each component of the refrigeration system and feed it back to the predictive analysis module to continuously optimize and adjust the system load prediction analysis.

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