Temperature regulation method and system for cold chain storage based on multi-temperature zone real-time monitoring

Abstract

The application relates to the technical field of temperature control, in particular to a cold chain storage temperature regulation method and system based on real-time monitoring of multiple temperature zones, which comprises the following steps: acquiring real-time monitoring data of each temperature zone in a cold storage; constructing a cold quantity feature map of the cold storage, and determining whether each temperature zone is in an imminent out-of-stock state according to the cold quantity feature map; calculating an article temperature difference compatibility coefficient between two temperature zones to determine the compatibility of the two temperature zones, and constructing an undirected graph; using a heuristic search algorithm to select a temperature zone with a high comprehensive score as a near neighbor temperature zone of the temperature zone in the imminent out-of-stock state and capable of article diversion; based on the searched near neighbor temperature zone, using an optimization algorithm to calculate a target refrigeration power of a refrigeration device, controlling the refrigeration device to perform pre-refrigeration, and after the pre-refrigeration is completed, diverting the articles to the near neighbor temperature zone. The application aims to avoid the problem of lag response after the out-of-stock occurs and improve the efficiency of article diversion.

Description

Technical Field

[0001] This application relates to the field of temperature control technology, specifically to a method and system for temperature control in cold chain storage based on real-time monitoring of multiple temperature zones. Background Technology

[0002] Cold chain warehousing is a crucial link in maintaining a suitable low-temperature environment for stored goods throughout the entire process from production and transportation to consumption. It ensures precise temperature control at every stage of the cold chain system to minimize losses. Temperature control in multi-temperature zones of warehousing requires the combined efforts of key equipment such as evaporative condensers, drum pump units, screw compressor units, fans, and piston compressor units. This ensures the efficient transfer of cold energy between different high and low temperature zones, such as cold storage and variable temperature rooms, while maintaining the economic efficiency of multi-temperature zone control.

[0003] Existing multi-temperature zone temperature control methods involve real-time monitoring and data collection of storage temperatures across multiple zones. This is followed by precise management of fan speeds and nighttime pre-cooling energy storage based on time intervals, with independent thresholds set for each zone. Temperature control is then based on comparisons between real-time temperatures and these independent thresholds. However, when a cold storage facility is nearing capacity, goods need to be diverted. The instability of various factors, such as the duration and frequency of cold storage door openings, the internal transport paths of goods, and the real-time distribution and timing of inventory, makes it difficult to effectively control the temperature across multiple zones using only independent thresholds. This can easily lead to significant temperature differences and energy waste. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a method and system for controlling the temperature of cold chain storage based on real-time monitoring of multiple temperature zones. The specific technical solution adopted is as follows: In a first aspect, one embodiment of this application provides a method for controlling the temperature of cold chain storage based on real-time monitoring of multiple temperature zones, the method comprising the following steps: Step 1: Obtain real-time monitoring data for each temperature zone in the cold storage. The real-time monitoring data includes the storage status of items inside the cold storage, external environmental data, and the operating status of the refrigeration equipment. Step 2: Construct a cold storage capacity characteristic map based on real-time monitoring data to reflect the supply and demand balance of cold storage capacity in real time; and determine whether each temperature zone is about to be overloaded based on the cold storage capacity characteristic map. Step 3: Treat each temperature zone in the cold storage as a node in an undirected graph. Based on the difference in the allowable storage temperature range of items between two temperature zones, the difference in the target temperature of items between two temperature zones, and the equivalent heat transfer coefficient of the contact surface when items are placed in adjacent storage locations, calculate the temperature difference compatibility coefficient between the two temperature zones. This coefficient is used to assess the possibility of separating items for storage between the two temperature zones. Based on the temperature difference compatibility coefficient, determine the compatibility between the two temperature zones, connect the nodes of compatible temperature zones, and construct an undirected graph. Step 4: Using a heuristic search algorithm, with grid proportion as the path cost constraint, and combining distance weight, cooling method weight, and cold energy load weight, calculate the comprehensive score of each node, and select the temperature zone with the highest comprehensive score as the nearest temperature zone that is about to be overloaded and can be used for item diversion. Step 5: Based on the searched neighboring temperature zones, use the optimization algorithm to calculate the target cooling power of the refrigeration equipment, control the refrigeration equipment to perform pre-cooling, and after the pre-cooling is completed, divert the items to the neighboring temperature zones.

[0005] Preferably, the step of constructing a cold storage cold load characteristic map based on real-time monitoring data includes: The space of the cold storage is divided into several grids. For each grid, the net cold energy intensity required to maintain the target temperature per unit time is calculated. The net cold energy intensity is determined by positively fusing the near-expiration time of the stored items in the grid, the fitting model of the daily heat load change over time, the thermal characteristic parameters of the stored items in the grid, and the instantaneous cold load generated by opening the cold storage door. The net cooling energy intensity corresponding to each grid per unit time is mapped to a cooling capacity feature map, where the value of each grid in the cooling capacity feature map represents the net cooling energy intensity required to maintain the target temperature per unit time within the unit grid of the storage area.

[0006] Preferably, determining whether each temperature zone is about to be overloaded based on the cold energy characteristic map includes: Statistical methods were used to determine the safe threshold for net cooling energy intensity of all grids within any temperature zone; Calculate the percentage of grid cells in the temperature range where the net cooling energy intensity exceeds the safety threshold. When the proportion of the grid exceeds a preset decision threshold, the temperature zone is determined to be in a state of imminent overload.

[0007] Preferably, the temperature difference compatibility coefficient of the items is positively correlated with the difference in the allowable storage temperature range of the items between the two temperature zones, and negatively correlated with the difference in the target temperature of the items between the two temperature zones and the equivalent heat transfer coefficient of the contact surface when the items between the two temperature zones are placed in adjacent storage locations.

[0008] Preferably, the two temperature zones are compatible when the temperature difference compatibility coefficient of the items is greater than or equal to a preset compatibility threshold; otherwise, the two temperature zones are incompatible.

[0009] Preferably, the step of calculating the target cooling power of the refrigeration equipment using an optimization algorithm based on the searched nearest-neighbor temperature zones includes: For any grid in the cold energy feature map of the searched neighboring temperature zone, based on the laws of thermodynamics, the optimization algorithm is used to solve the target cooling power at any grid when the items in the temperature zone about to be overcrowded are diverted, so as to control the cooling equipment in the temperature zone where the grid is located to pre-cool, so that the grid reaches the target cooling power. The objective function in the optimization algorithm for solving the target cooling power is used to minimize energy consumption and final temperature difference. The set of constraints in the optimization algorithm for solving the target cooling power includes power range constraints, power change rate constraints, and temperature difference threshold constraints. In the process of solving the optimization algorithm, the sequential quadratic programming algorithm is used on the edge computing device to solve the problem and output the target cooling power at each time step within the prediction step.

[0010] Preferably, the controlled refrigeration equipment performs pre-cooling, including: The target cooling power curve is generated by linear fitting of the target cooling power at each time step within the prediction step of the solution output. The target cooling power curve is sent to the refrigeration equipment in the adjacent temperature zone to control the refrigeration equipment in the temperature zone where the grid is located to perform pre-cooling.

[0011] Preferably, any grid is a storage location to be put into storage in the cold energy feature map of the searched nearest temperature zone.

[0012] Preferably, the method further includes: during the item diversion process, if the current nearest temperature zone has completed diversion or no longer meets the diversion conditions, then the method switches to the next nearest temperature zone for item diversion according to the comprehensive scoring order, until the item diversion in the temperature zone that is about to be overloaded is completed.

[0013] Secondly, another embodiment of this application also provides a cold chain storage temperature control system based on real-time monitoring of multiple temperature zones, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the above-described cold chain storage temperature control method based on real-time monitoring of multiple temperature zones.

[0014] This application has at least the following beneficial effects: 1. This application solves for the target cooling power by using a three-dimensional gridded temperature zone storage location, combined with real-time temperature field and gradient field. Compared with the traditional black box control of single-point temperature feedback, it regulates the balance between cooling supply and demand by considering the spatial distribution. 2. Compared with traditional passive follow-up control, this application first assesses the storage status of the temperature zone and predicts and controls the temperature zone that is about to be overwhelmed, forming a control method of proactive judgment and early intervention, avoiding the problem of delayed response after the overwhelmed situation occurs. 3. This application uses a heuristic search algorithm to recommend neighboring temperature zones that can be used for goods diversion, which satisfies the requirement of ensuring the warehouse temperature is up to standard while taking into account the risk of crosstalk due to temperature differences between neighboring goods, thus making the goods diversion more effective. Attached Figure Description

[0015] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 A flowchart of a cold chain storage temperature control method based on real-time monitoring of multiple temperature zones, provided as an embodiment of this application. Detailed Implementation

[0017] Example 1 One embodiment of this application provides a cold chain storage temperature control method based on real-time monitoring of multiple temperature zones, for details please refer to... Figure 1 The method includes the following steps: Step 1: Obtain real-time monitoring data for each temperature zone in the cold storage. The real-time monitoring data includes the storage status of items inside the cold storage, external environmental data, and the operating status of the refrigeration equipment.

[0018] This application uses UWB positioning tags and non-contact temperature measurement equipment to monitor the storage status of goods in each temperature zone of the cold storage in real time, including the type of goods, storage time, shelf life, specific heat capacity, density, and real-time temperature of each storage location; at the same time, it uses industrial temperature and humidity sensors to collect the temperature and humidity outside the cold storage; and it uses smart meters deployed on each refrigeration unit to monitor the energy consumption of the refrigeration equipment in real time.

[0019] Furthermore, the duration, opening angle, and opening frequency per hour of each opening are recorded by vault door sensors (such as magnetic door switches and infrared beams) deployed on the vault doors.

[0020] It should be noted that, for the real-time monitoring data of each temperature zone in the cold storage, to avoid the impact of a large amount of redundant data on subsequent data analysis, the sampling frequency of the monitoring data is set separately according to the peak and off-peak periods of cold storage operations. The sampling frequency is high during peak periods (in this embodiment, it is set to sample data once every 1 minute), and low during off-peak periods (in this embodiment, it is set to sample data once every 5 minutes). In other embodiments, the sampling frequency of monitoring data during different operating periods can be set according to the size of the storage compartments within the cold storage.

[0021] In this embodiment, the peak and off-peak periods of cold storage operations can be divided into time periods based on the frequency of entry and exit from the cold storage.

[0022] It should be noted that the frequency of inbound and outbound operations = number of inbound and outbound operations / unit time. Large cold chain warehouses can achieve multiple inbound and outbound operations per hour on peak days, while perishable foods (such as fresh and frozen products) may be handled 1-2 times daily; during off-peak periods, operations may occur once every 2-3 days to balance costs and inventory turnover. The unit time depends on the type of stored goods and is generally set to 1-24 hours. Considering that the stored goods in this embodiment are food, the unit time is set to 1 hour. Furthermore, the frequency of inbound and outbound operations is determined by a pre-set frequency threshold; in this embodiment, the frequency threshold is set to 5 times / 1 hour.

[0023] Step 2: Construct a cold storage capacity characteristic map based on real-time monitoring data to reflect the supply and demand balance of cold storage capacity in real time; and determine whether each temperature zone is about to be overloaded based on the cold storage capacity characteristic map. This application models the environmental heat load of the cold storage facility using meteorological data from the area where the cold storage is located. This model is then used to periodically generate a cold load characteristic map of the cold storage facility, reflecting the real-time balance between cold load supply and demand within the cold storage. To ensure the data is accurate and effective and to avoid wasting algorithm time, this embodiment updates the cold load characteristic map every minute.

[0024] First, this application divides the space of the cold storage into several grids, each grid corresponding to a unique storage area. It then uses various monitoring data collected within one minute to solve the problem and generate a cold energy characteristic map that is updated every minute.

[0025] Furthermore, considering that the length of time an item is nearing its expiration date and the alternation of peak and off-peak periods each day will affect the priority of cold energy in the area where the inventory is located, for example, the shorter the time before the expiration date, the more priority the item needs to be transported, that is, the higher the priority of cold energy transport.

[0026] Based on the above analysis, this application constructs a cooling capacity characteristic map using the following method: For each grid in the cold storage, the net cold energy intensity required to maintain the target temperature per unit time for each grid is calculated. The net cold energy intensity is determined by positively fusing the near-expiration time of the stored items in the grid, the fitting model of the daily heat load change over time, the thermal characteristic parameters of the stored items in the grid, and the instantaneous cold load generated by opening the cold storage door. The net cooling energy intensity corresponding to each grid per unit time is mapped to a cooling capacity feature map, where the value of each grid in the cooling capacity feature map represents the net cooling energy intensity required to maintain the target temperature per unit time within the unit grid of the storage area.

[0027] It is understood that positive correlation fusion refers to fusion methods such as addition and multiplication between data. The specific positive correlation fusion method shall be determined by the implementer based on the actual situation, and this application does not impose any special restrictions.

[0028] It should be noted that the grid size or number of grids can be set according to the size of the cold storage compartments. The empirical range is 1 cubic meter to 2 cubic meters. In this embodiment, the grid size is set to 1 cubic meter.

[0029] Specifically, in this embodiment, the net cooling energy intensity (in units of t) required to maintain the target temperature at the grid (x,y,z) within the library at time t is used. ) For example: In the formula, This represents the sensitivity weight at grid (x,y,z) within the warehouse at time t, used to weight and strengthen the allocation of cooling capacity for products nearing expiration or temperature-sensitive goods. This represents the time decay factor at grid (x,y,z) within the library at time t, which quantifies the impact of the environmental heat load at the current time and is used to dynamically reduce the aforementioned cooling power. This refers to the cooling power required at grid (x, y, z) within the warehouse. It represents the instantaneous cooling demand at time t caused by the item's thermal characteristics and the temperature difference relaxation process (which can be understood as the relaxation process of gradually restoring thermal equilibrium through mechanisms such as heat conduction and diffusion after a temperature difference exists). The unit is 1 / 2 kilometre oz. ; The true impact coefficient representing the instantaneous cooling load per unit time, in units of... ; Let Q be the value of the equivalent characteristic model of door opening cold loss at time t.

[0030] against , In the formula, , These represent the maximum and minimum near-expiration dates of stored goods in the cold storage, respectively. This represents the expiration time of an item in the grid (x, y, z) at time t. This is a constant parameter used to prevent the denominator from being 0; its value is taken as an empirical value of 0.01. and , , The units are consistent; in actual warehousing, the near-expiration time of goods is usually quantified in hours. Therefore, here... , , as well as All units are hours.

[0031] In this embodiment, the formula aims to take the reciprocal of the expiration time of the inventory items in the grid (x,y,z) at time t after max-min normalization, and use it as the sensitivity weight of the grid (x,y,z) in the inventory at time t.

[0032] In other embodiments, items can be categorized based on their expiration date, with each category assigned a positive integer label starting from 1. The longer the expiration date, the larger the numerical label value. Sensitivity weights are determined based on the ratio of the largest numerical label to the numerical label value for each category. The specific number of categories should be sufficient to ensure that the sensitivity weight of the same category of items is the same across different embodiments; no other special restrictions apply.

[0033] against , In the formula, This indicates the maximum heat load for the day. , , These represent the baseline models of daytime and nighttime heat loads predicted by regression from historical data, respectively. A fitted model representing the change of daily heat load over time. The heat load at time t is , It is a sine function; , These represent the sunrise and sunset times obtained from the local weather API, respectively. By overlaying the baseline models of daytime and nighttime heat loads predicted by regression from historical data, this setup ensures that... The value range is greater than 0 and less than 1, which ensures that the time decay factor can adapt to the differences in day and night time periods within a day in different seasons.

[0034] against , In the formula, Let represent the set of all storage locations within the grid (x, y, z). This represents the density of the item at the k-th storage location within the grid (x, y, z) (Note: This is not the packaging density, but the true density of the contents, pre-obtained from the "Handbook of Physical Properties of Food"), in units of... ; This represents the specific heat capacity of the item in the k-th storage location within the grid (x, y, z), in units of... ; , These represent the temperature of the stored items in grid (x,y,z) at time t and the target temperature of the temperature zone where grid (x,y,z) is located (in this embodiment, the target temperature of the temperature zone is the midpoint of the preset temperature range of the temperature zone, which is determined by the system according to the temperature range that the items diverted to the temperature zone can be stored), respectively, in °C; This represents the theoretical time required for the stocked items within the grid (x, y, z) to reach the target temperature, in seconds. This represents the volume of the k-th storage location within the grid (x, y, z), in units of... , This represents the sum of volumes of all locations within the grid (x, y, z), in units of... .

[0035] against , In the formula, This indicates the proportion of the instantaneous cooling load that can affect the local area of ​​the cold storage door. Because when the cold storage door is opened, the movement of warm, humid outside air into the door follows the turbulent jet attenuation law in fluid dynamics, it cannot act 100% on the local area of ​​the door. Typically, only 85%-92% of the load can be converted into heat energy through turbulent mixing in the local area of ​​the door, while the remainder diffuses to a wider area. Take the empirical value of 0.85; This indicates the local area affected by the instantaneous cooling load at the warehouse door. It is a circular area with the center of the warehouse door as the center and a preset radius r (taken as an empirical value of 1.2m). h represents the height of the warehouse door in meters.

[0036] against , In the formula, The temperature difference correction coefficient represents the door opening angle (the smaller the door opening angle, the smaller the weighting coefficient; here, it is set to 0.2 when the angle is less than 30 degrees; 0.5 when the angle is between 30 and 90 degrees; and 1 when the angle is at its maximum). This represents the duration of the door being open at time t, in seconds. The unit of time is 1 second (here); q is the instantaneous cooling load generated per unit time (1 second) when the cold storage door is opened, calculated using the heat transfer coefficient, and its unit is W. , Represents the heat transfer coefficient, with units of . ; This indicates the difference between indoor and outdoor temperatures per unit time, expressed in °C. This indicates the area affected by the instantaneous cooling load, i.e., the area of ​​the cold storage's enclosure structure, expressed in units of... .

[0037] Furthermore, in this scheme, when the inventory rate of a certain temperature zone exceeds the capacity threshold and the proportion of inventory distribution with high demand increases, it indicates that the temperature zone is about to enter a state of overcapacity, and it is necessary to divert goods using adjacent temperature zones. The diversion refers to automatically sending goods originally intended for the temperature zone that is about to be overcapacity into the cold storage to the selected neighboring temperature zones through sealed channels or shuttle ports between cold chain temperature zones within the internal environment.

[0038] Accordingly, this application determines whether each temperature zone is about to be overwhelmed based on the cold energy characteristic map, so that the items in the overwhelmed temperature zone can be sent to the adjacent temperature zone through the cold chain temperature zone.

[0039] Preferably, this application uses statistical methods to determine the safety threshold of net cooling energy intensity for all grids within any temperature zone; calculates the percentage of grids within the temperature zone whose net cooling energy intensity is higher than the safety threshold; and determines that the temperature zone is in a state of imminent overload when the percentage of grids is greater than a preset decision threshold.

[0040] Specifically, taking any temperature zone at time t as an example, the net cooling energy intensity of all grids in the temperature zone is calculated, and a safe threshold for the net cooling energy intensity of all grids in the temperature zone is determined using statistical methods. In this embodiment, the upper limit of three times the standard deviation of the net cooling energy intensity of all grids in the temperature zone is selected as the safe threshold. In other embodiments, the upper quartile of the net cooling energy intensity of all grids in the temperature zone can also be selected as the safe threshold.

[0041] Further statistics show that the percentage of grids with net cooling energy intensity exceeding the safety threshold in this temperature zone is higher. The higher the percentage, the greater the potential for high cooling demand to spread beyond localized areas and the greater the likelihood that the temperature zone is on the verge of overflowing.

[0042] In this embodiment, the decision threshold is set to a range of [0.35, 0.6]. During implementation, the decision threshold can be set according to the specifications of the cold storage facility: the more storage units and the larger the storage volume, the higher the tolerance for grid ratio; conversely, the lower the tolerance for grid ratio, the more likely diversion should be initiated when the ratio is low. This embodiment takes a small-sized cold storage facility as an example, with the decision threshold set to 0.35. When the grid ratio at time t is greater than the decision threshold, it indicates that the facility is about to be overloaded.

[0043] Step 3: Treat each temperature zone in the cold storage as a node in an undirected graph. Based on the difference in the allowable storage temperature range of items between two temperature zones, the difference in the target temperature of items between two temperature zones, and the equivalent heat transfer coefficient of the contact surface when items are placed in adjacent storage locations, calculate the temperature difference compatibility coefficient between the two temperature zones. This coefficient is used to assess the possibility of separating items for storage in the two temperature zones. Based on the temperature difference compatibility coefficient, determine the compatibility between the two temperature zones, connect the nodes of compatible temperature zones, and construct an undirected graph.

[0044] Furthermore, within the entire storage area, each temperature zone in the cold storage is treated as a node in an undirected graph. A multi-objective graph search algorithm is then used to identify the nearest neighboring temperature zones from which to divert heat from temperature zones nearing capacity shortages. The specific process is as follows: First, this application calculates the temperature difference compatibility coefficient between two temperature zones based on the difference in the allowable storage temperature range of the items between the two temperature zones, the difference in the target temperature of the items between the two temperature zones, and the equivalent heat transfer coefficient of the contact surface when the items are placed in adjacent storage locations. This is used to assess the possibility of separating the items for storage in the two temperature zones.

[0045] The temperature difference compatibility coefficient of the items is positively correlated with the difference in the allowable storage temperature range of the items between the two temperature zones, and negatively correlated with the difference in the target temperature of the items between the two temperature zones and the equivalent heat transfer coefficient of the contact surface when the items between the two temperature zones are placed in adjacent storage locations.

[0046] It is understandable that a positive correlation means that the dependent variable increases as the independent variable increases, and decreases as the independent variable decreases; a negative correlation means that the dependent variable decreases as the independent variable increases, and increases as the independent variable decreases. This is determined by the actual application, and this application does not impose any special restrictions.

[0047] Specifically, in this embodiment, the temperature difference compatibility coefficient between the i-th temperature zone and the j-th temperature zone, which are about to be overwhelmed, is expressed as: : In the formula, This represents a function that takes the minimum value. , Let i and j represent the set of items that need to be diverted in the i-th temperature zone and the set of items in the j-th temperature zone warehouse, respectively. , These represent the maximum and minimum allowable storage temperature ranges for Category A items that need to be diverted in the i-th temperature zone, respectively. , These represent the maximum and minimum values ​​of the permissible storage temperature range for Category B items in the j-th temperature zone, respectively. This represents the absolute value of the difference between the target temperatures of the items in the i-th temperature zone and the j-th temperature zone; This represents the equivalent heat transfer coefficient of the contact surface when Class A and Class B items are placed in adjacent storage locations (obtained in advance from a table based on the packaging materials of the two types of items). This represents a constant parameter, which is a positive number less than 1. In this embodiment, it is set to an empirical value of 0.1.

[0048] The numerator represents the compatibility between the storage temperature of Category A items and the preset storage temperature range of Category B items. The larger the numerator, the more compatible the temperature conditions are when the items are diverted to adjacent storage locations of Category B items. The denominator reflects the thermal conductivity of the contact surface and the magnitude of the temperature difference between the two temperature zones. The greater the temperature difference between the two temperature zones and the better the thermal conductivity of the contact surface, the greater the impact of the temperature difference between the two temperature zones on the storage of the two types of items when Category A items are diverted to adjacent storage locations of Category B items. This makes it easier for "temperature crosstalk between adjacent items" to occur, leading to quality deterioration, and the poorer the compatibility of the temperature conditions between the two temperature zones.

[0049] Furthermore, this application is based on a preset compatibility threshold. The temperature difference compatibility coefficient of an item is used to determine the temperature difference compatibility weight between two temperature zones. When the temperature difference compatibility coefficient of an item is greater than or equal to... If the two temperature zones are compatible, then they are compatible; otherwise, they are incompatible, indicating that the conditions for separate storage are not met.

[0050] Specifically, this embodiment uses the temperature difference compatibility weight between the i-th temperature zone and the j-th temperature zone. For example, The compatibility threshold in this embodiment This was obtained by binary classification of the temperature difference compatibility coefficients of all items.

[0051] Accordingly, this application connects the nodes corresponding to two temperature zones with a temperature difference compatibility weight of 1 for each node in the undirected graph, and does not connect the nodes corresponding to two temperature zones with a temperature difference compatibility weight of 0, thus obtaining the undirected graph at the corresponding time.

[0052] Step 4: Using a heuristic search algorithm, with grid percentage as the path cost constraint, and combining distance weight, cooling method weight, and cold energy load weight, calculate the comprehensive score of each node, and select the temperature zones with the highest comprehensive scores as the nearest temperature zones that are about to be overwhelmed and can be used for item diversion.

[0053] A comprehensive evaluation is conducted based on the proportion of grids within a temperature zone whose net cooling energy intensity exceeds the safety threshold. The higher the proportion of grids exceeding the decision threshold, the lower the likelihood of participating in item diversion. When searching for a path, the cost of passing through the corresponding node in that temperature zone should be higher. When searching for a path, the incentive algorithm tends to select nodes with lower costs to avoid diverting items between temperature zones that are close to each other and are about to be overwhelmed.

[0054] Accordingly, this application uses a heuristic search algorithm, with grid ratio as path cost constraint, and combines distance weight, cooling method weight and cold energy load weight to calculate the comprehensive score of each node, and selects the temperature zone with the highest comprehensive score as the neighboring temperature zone that is about to be overwhelmed and can be used for item diversion.

[0055] In this embodiment, taking the i-th temperature zone and the j-th temperature zone as examples, the comprehensive score of the i-th temperature zone to the j-th temperature zone in the heuristic search algorithm is calculated. Heuristic search algorithms include, but are not limited to, the A* algorithm and the A* algorithm. This embodiment uses the A* algorithm as an example: In the formula, , Let $\mathbf{i}$ and $\mathbf{j}$ represent the path cost and estimated cost from the i-th temperature zone to the corresponding node in the j-th temperature zone, respectively. This represents the distance weight from the i-th temperature zone to the j-th temperature zone; This represents the weight of the cooling method from the i-th temperature zone to the j-th temperature zone; This represents the cooling load weight from the i-th temperature zone to the j-th temperature zone; This represents the percentage of grid cells in the j-th temperature zone whose net cooling energy intensity exceeds the safety threshold. Using this percentage as a cost constraint, a lower percentage indicates a lower net cooling energy demand in the j-th temperature zone, a higher probability of it being used as a backup energy source for neighboring temperature zones, and a lower cost for that node during the search.

[0056] against : In the formula, This represents the straight-line distance between the geographical center points of the i-th and j-th temperature zones, in meters (m). This represents a preset distance parameter used to eliminate the influence of dimensions; in this embodiment, it is set to an empirical value of 50m. This represents an exponential function with base e. The negative mapping of the exponential function controls the range of distance weights within (0,1). The closer the distance, the smaller the refrigerant / electricity transmission loss, the faster the response, and the larger the distance weight.

[0057] against : The reason for this setting is that if the same structure of equipment is used for refrigeration in two temperature zones, the control strategy can be reused directly without parameter retuning.

[0058] against : In the formula, This represents the maximum cooling power that the refrigeration equipment in the j-th temperature zone can theoretically output at time t. The method for obtaining this power is: , Indicates the rated cooling capacity of the refrigeration equipment. The environmental impact factors are obtained by referring to a table using the condenser outlet water temperature and ambient humidity. The current health coefficient of the equipment can be predicted using a time-series prediction model based on the historical trends of the refrigeration equipment's operating current, exhaust temperature, and coefficient of performance (COP). It represents the real-time total heat load power of the j-th temperature zone at time t, which includes the sum of ambient heat leakage power and instantaneous cooling load power when the door is opened.

[0059] For the i-th temperature zone that is about to be overwhelmed, traverse all nodes connected to its corresponding node in the undirected graph. For each temperature zone corresponding to a connected node (taking the j-th temperature zone as an example in this embodiment), calculate the comprehensive score of the heuristic search algorithm from the i-th temperature zone to the j-th temperature zone. The comprehensive scores are sorted in descending order, and the temperature zones corresponding to the top U nodes in the ranking are recommended as the nearest neighbor temperature zones that the i-th temperature zone can divert to. Here, U is set to an empirical value of 3. The specific A* path search process is a well-known procedure and will not be described in detail here.

[0060] Step 5: Based on the searched neighboring temperature zones, use the optimization algorithm to calculate the target cooling power of the refrigeration equipment, control the refrigeration equipment to perform pre-cooling, and after the pre-cooling is completed, divert the items to the neighboring temperature zones.

[0061] First, this application evaluates the cooling efficiency of each grid based on the distance between the center coordinates of the corresponding compartment and the fan in the cooling capacity characteristic map of the adjacent temperature zone.

[0062] For any neighboring temperature zone, this embodiment takes the j-th temperature zone as the first temperature zone in the neighboring temperature zone arrangement result of the i-th temperature zone which is about to be overloaded as an example. This application focuses on the grid in the cooling capacity characteristic map of the j-th temperature zone. Based on the laws of thermodynamics, an optimization algorithm is used to solve the mesh for item splitting in the i-th temperature zone. The target cooling power at the grid is used to control the refrigeration equipment in the temperature zone where the grid is located to pre-cool, so that the grid reaches the target cooling power, and after pre-cooling, the items are diverted to that temperature zone j. The grid... This refers to the storage location to be received within the j-th temperature zone.

[0063] It should be noted that for the i-th temperature zone that is about to overflow, the other U-1 neighboring temperature zones are processed in the same way, that is, the items are diverted first from the 1st neighboring temperature zone, then the 2nd, and finally the U-th. If the intermediate items are diverted, the items diverted from the i-th temperature zone will not continue.

[0064] The objective function in the optimization algorithm for solving the target cooling power is used to minimize energy consumption and final temperature difference. The set of constraints in the optimization algorithm for solving the target cooling power includes power range constraints, power change rate constraints, and temperature difference threshold constraints. During the solution process, a sequential quadratic programming algorithm is used to solve the problem on an edge computing device, and the target cooling power at each time step within the prediction step is output.

[0065] Specifically, in this embodiment, the mesh is solved when the item is split in the i-th temperature zone. The method for achieving the target cooling capacity is as follows: First, based on the first law of thermodynamics (thermal energy storage rate = effective cooling rate - ambient heat leakage power), the derivative is calculated: Where, on the left side of the equation: the unit is , This represents the grid at time t in the j-th temperature zone. Temperature difference (the difference between the actual temperature and the target temperature of the j-th temperature zone at time t). This represents the grid at time t in the j-th temperature zone. Temperature field at a given temperature difference; right side of the equation: This represents the target cooling power at time t in the j-th temperature zone, in watts (W). For grid Cooling efficiency This represents the equivalent thermal coefficient for the j-th temperature zone, in units of . ; This represents the thermal time constant for the j-th temperature zone, expressed in seconds. The process is as follows: All refrigeration equipment is shut down (before use in the cold chain), allowing the storage unit to heat up naturally without active intervention. Temperature data is recorded, and the thermal time constant is solved using a first-order thermal inertia model. This is a well-known technology, and the specific process will not be described in detail.

[0066] against , In the formula, This represents the environmental heat influence coefficient for the j-th temperature zone. , This represents the overall heat transfer coefficient specified during the design of the j-th temperature zone cold storage, in units of... , This represents the surface area of ​​the j-th temperature zone cold storage, in units of... .

[0067] against In this embodiment, the grid in the cooling capacity characteristic diagram of the j-th temperature zone is used as the basis. The distance between the center coordinates of the corresponding storage unit and the wind turbine is used to evaluate the grid. The cooling efficiency. Specifically, this embodiment calculates the mesh. The ratio of the distance between the center coordinate of each compartment and the fan to the effective working distance of the fan is used. Subtracting this ratio from 1 gives the cooling efficiency of the fan in each compartment. The grid is formed by the quadratic summation of the cooling efficiencies of all fans in all compartments. Cooling efficiency For compartments where the distance from the fan exceeds the effective working range, the cooling efficiency is set to 0.

[0068] Then, set the constraint set when solving for the target cooling power: In the formula, the first constraint represents the power range that the target cooling power must not exceed. The first constraint represents the maximum cooling power; the second constraint indicates that the rate of change of the target cooling power within a unit step size must not be too fast and cannot exceed the upper limit of the rate of change. In this embodiment, the value is set to an empirical value of 5% / min; the third constraint indicates that the final temperature difference must not exceed the temperature difference threshold, which is also set to an empirical value in this embodiment. J is the grid set for the j-th temperature zone, and t11 is the preset upper limit of the time step, meaning that 12 time steps are used for prediction and control (from t0 to t11, for example, a time step of 30 seconds, which is a 6-minute prediction window). Empirical parameters can be reasonably adjusted by the implementer according to the cold chain situation, without special restrictions.

[0069] Furthermore, the objective function for solving the target cooling power is set as follows: In the formula, the first term is the integral result of the power, i.e., energy consumption; the second term is the final temperature difference. The purpose of using the square is to prevent the temperature difference from being negative and affecting the logic (in other embodiments, the absolute value can also be used).

[0070] Finally, in the process of solving the optimization algorithm, this application uses a sequential quadratic programming algorithm on an edge computing device to obtain the target cooling power sequence that minimizes the objective function, that is, the target cooling power at each time step within the prediction step, which is used for subsequent temperature control.

[0071] Specifically, this embodiment uses a Sequential Quadratic Programming (SQP) algorithm (such as IPOPT) to solve the problem on an edge GPU device in the temperature control system. The prediction step size is set to 12 steps, and the control time domain is set to 4 steps, meaning that only the first 4 steps are implemented for rolling optimization. The algorithm outputs the target cooling power at the corresponding time points of the 12 steps. , .

[0072] Additionally, it should be noted that, in order to ensure smoothness during regulation, this embodiment also performs linear fitting on the target cooling power at each time step within the prediction step size of the solution output to generate a target cooling power curve (which can be divided into multiple fitting segments, as long as the slope of the fitted line also satisfies the second constraint), and sends the target cooling power curve to the cooling equipment in the j-th temperature zone to control the cooling equipment in the temperature zone where the grid is located to perform pre-cooling; after pre-cooling is completed, the items are diverted.

[0073] Example 2 Based on the above-described method for controlling the temperature of cold chain storage based on real-time monitoring of multiple temperature zones, another embodiment of this application provides a system for controlling the temperature of cold chain storage based on real-time monitoring of multiple temperature zones, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the above-described method for controlling the temperature of cold chain storage based on real-time monitoring of multiple temperature zones.

[0074] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the invention herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not invented in this application.

[0075] It should be understood that this application is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope.

Claims

1. A method for controlling the temperature of cold chain storage based on real-time monitoring of multiple temperature zones, characterized in that, The method includes the following steps: Step 1: Obtain real-time monitoring data for each temperature zone in the cold storage. The real-time monitoring data includes the storage status of items inside the cold storage, external environmental data, and the operating status of the refrigeration equipment. Step 2: Construct a cold storage capacity characteristic map based on real-time monitoring data to reflect the supply and demand balance of cold storage capacity in real time; and determine whether each temperature zone is about to be overloaded based on the cold storage capacity characteristic map. Step 3: Treat each temperature zone in the cold storage as a node in an undirected graph. Based on the difference in the allowable storage temperature range of items between two temperature zones, the difference in the target temperature of items between two temperature zones, and the equivalent heat transfer coefficient of the contact surface when items are placed in adjacent storage locations, calculate the temperature difference compatibility coefficient between the two temperature zones. This coefficient is used to assess the possibility of separating items for storage between the two temperature zones. Based on the temperature difference compatibility coefficient, determine the compatibility between the two temperature zones, connect the nodes of compatible temperature zones, and construct an undirected graph. Step 4: Using a heuristic search algorithm, with grid proportion as the path cost constraint, and combining distance weight, cooling method weight, and cold energy load weight, calculate the comprehensive score of each node, and select the temperature zone with the highest comprehensive score as the nearest temperature zone that is about to be overloaded and can be used for item diversion. Step 5: Based on the searched neighboring temperature zones, use the optimization algorithm to calculate the target cooling power of the refrigeration equipment, control the refrigeration equipment to perform pre-cooling, and after the pre-cooling is completed, divert the items to the neighboring temperature zones.

2. The cold chain storage temperature control method based on real-time monitoring of multiple temperature zones as described in claim 1, characterized in that, The construction of the cold storage cold capacity characteristic map based on real-time monitoring data includes: The space of the cold storage is divided into several grids. For each grid, the net cold energy intensity required to maintain the target temperature per unit time is calculated. The net cold energy intensity is determined by positively fusing the near-expiration time of the stored items in the grid, the fitting model of the daily heat load change over time, the thermal characteristic parameters of the stored items in the grid, and the instantaneous cold load generated by opening the cold storage door. The net cooling energy intensity corresponding to each grid per unit time is mapped to a cooling capacity feature map, where the value of each grid in the cooling capacity feature map represents the net cooling energy intensity required to maintain the target temperature per unit time within the unit grid of the storage area.

3. The cold chain storage temperature control method based on real-time monitoring of multiple temperature zones as described in claim 2, characterized in that, The step of determining whether each temperature zone is about to be overloaded based on the cold energy characteristic map includes: Statistical methods were used to determine the safe threshold for net cooling energy intensity of all grids within any temperature zone; Calculate the percentage of grid cells in the temperature range where the net cooling energy intensity exceeds the safety threshold. When the proportion of the grid exceeds a preset decision threshold, the temperature zone is determined to be in a state of imminent overload.

4. The cold chain storage temperature control method based on real-time monitoring of multiple temperature zones as described in claim 1, characterized in that, The temperature difference compatibility coefficient of the items is positively correlated with the difference in the allowable storage temperature range of the items between the two temperature zones, and negatively correlated with the difference in the target temperature of the items between the two temperature zones and the equivalent heat transfer coefficient of the contact surface when the items between the two temperature zones are placed in adjacent storage locations.

5. The cold chain storage temperature control method based on real-time monitoring of multiple temperature zones as described in claim 4, characterized in that, When the temperature difference compatibility coefficient of the items is greater than or equal to the preset compatibility threshold, the two temperature zones are compatible; otherwise, the two temperature zones are incompatible.

6. The cold chain storage temperature control method based on real-time monitoring of multiple temperature zones as described in claim 1, characterized in that, The calculation of the target cooling power of the refrigeration equipment based on the searched nearest temperature zone using an optimization algorithm includes: For any grid in the cold energy feature map of the searched neighboring temperature zone, based on the laws of thermodynamics, the optimization algorithm is used to solve the target cooling power at any grid when the items in the temperature zone about to be overcrowded are diverted, so as to control the cooling equipment in the temperature zone where the grid is located to pre-cool, so that the grid reaches the target cooling power. The objective function in the optimization algorithm for solving the target cooling power is used to minimize energy consumption and final temperature difference. The set of constraints in the optimization algorithm for solving the target cooling power includes power range constraints, power change rate constraints, and temperature difference threshold constraints. In the process of solving the optimization algorithm, the sequential quadratic programming algorithm is used on the edge computing device to solve the problem and output the target cooling power at each time step within the prediction step.

7. The cold chain storage temperature control method based on real-time monitoring of multiple temperature zones as described in claim 6, characterized in that, The controlled refrigeration equipment performs pre-cooling, including: The target cooling power curve is generated by linear fitting of the target cooling power at each time step within the prediction step of the solution output. The target cooling power curve is sent to the refrigeration equipment in the adjacent temperature zone to control the refrigeration equipment in the temperature zone where the grid is located to perform pre-cooling.

8. The cold chain storage temperature control method based on real-time monitoring of multiple temperature zones as described in claim 7, characterized in that, Each grid cell represents a storage location to be received into the warehouse in the cold energy characteristic map of the nearest temperature zone found during the search.

9. The cold chain storage temperature control method based on real-time monitoring of multiple temperature zones as described in claim 1, characterized in that, The method further includes: during the item diversion process, if the current nearest temperature zone has completed diversion or no longer meets the diversion conditions, the method switches to the next nearest temperature zone for item diversion according to the comprehensive score order, until the item diversion in the temperature zone that is about to be overloaded is completed.

10. A cold chain storage temperature control system based on real-time monitoring of multiple temperature zones, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the cold chain storage temperature control method based on real-time monitoring of multiple temperature zones as described in any one of claims 1-9.

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