A dynamic temperature control system for cold chain cargo

Abstract

The application relates to the technical field of cold chain transportation, in particular to a dynamic temperature control system for cold chain goods, which comprises a data acquisition module used for acquiring coordinate data of each good in a cold chain space; an acquisition module used for polling and acquiring real-time temperature data of each good in the cold chain space at fixed time intervals; a partition module used for obtaining a plurality of target cold chain space temperature partitions according to the real-time temperature data of each good, the coordinate data of each acquired good and a target temperature zone of each good; a priority determination module used for obtaining an adjustment priority of each target cold chain space temperature partition according to the target temperature zone and a temperature allowable fluctuation threshold of each target cold chain space temperature partition; and a regulation and control module used for performing cold quantity regulation and control according to the adjustment priority of each target cold chain space temperature partition. The application effectively balances the relationship among temperature control precision, energy consumption and hardware cost.

Description

Technical Field

[0001] This application relates to the field of cold chain transportation technology, and in particular to a dynamic temperature control system for cold chain goods. Background Technology

[0002] In the cold chain logistics industry, mixed-cargo transportation has become a mainstream demand. The same cold chain space (such as refrigerated trucks and containers) often needs to simultaneously carry goods with different temperature zone requirements and temperature fluctuation tolerance capabilities, such as transporting "2-8℃ medicines", "0-4℃ fresh produce", and "-18℃ frozen food" at the same time. Different goods have significantly different allowable temperature fluctuation thresholds. The allowable fluctuation threshold for some highly sensitive goods (such as blood products and vaccines) is only ±0.5℃, while the allowable fluctuation threshold for ordinary goods (such as vegetables and frozen meat) can be relaxed to ±2℃ or even greater. This differentiated demand places extremely high demands on the accuracy and flexibility of cold chain temperature control.

[0003] Existing cold chain temperature control solutions struggle to address this core pain point: either they employ a "unified threshold control" strategy, setting a global temperature control standard based on the most stringent fluctuation threshold among all goods, leading to excessive energy consumption to meet the needs of highly sensitive goods, or sacrificing the quality of high-requirement goods to save energy; or they deploy multiple independent cold sources to achieve zoned temperature control, resulting in significantly increased hardware costs, increased equipment maintenance complexity, and decreased temperature control accuracy due to interference between multiple cold sources. The key issue is that existing systems lack a technical solution capable of accurately identifying the target temperature zone and allowable fluctuation threshold for each type of goods, dynamically zoning them based on the spatial distribution of goods, and then allocating control priorities based on the characteristics of each zone. This makes it impossible to achieve differentiated and precise temperature control for mixed-category goods under a single cold source architecture, and it is difficult to balance the relationship between temperature control accuracy, energy consumption, and hardware costs, becoming a significant technical bottleneck restricting the efficient development of the cold chain logistics industry. Summary of the Invention

[0004] To address the aforementioned technical problems, this application provides a dynamic temperature control system for cold chain goods, which at least partially solves the problems existing in the prior art.

[0005] In a first aspect of this application, a dynamic temperature control system for cold chain goods is provided, the system comprising: The data acquisition module is used to acquire the coordinate data of each item inside the cold chain space. Each item inside the cold chain space is equipped with a corresponding item tag. The item tag includes a temperature sensor and a wireless communication unit. The item tag is pre-stored with the target temperature zone and the allowable temperature fluctuation threshold for the corresponding item. The item tag determines the coordinate data through the wireless communication unit. The data acquisition module is used to poll and collect real-time temperature data of each item inside the cold chain space at fixed time intervals. The partitioning module is used to obtain several target cold chain space temperature partitions based on the real-time temperature data of each cargo, the coordinate data of each acquired cargo, and the target temperature zone of each cargo. The priority determination module is used to determine the adjustment priority of each target cold chain space temperature zone based on the target temperature zone and the allowable temperature fluctuation threshold of each target cold chain space temperature zone. The control module is used to control the cooling capacity according to the adjustment priority of each target cold chain space temperature zone.

[0006] This application has at least the following beneficial effects: This application pre-stores the target temperature zone and allowable temperature fluctuation threshold for each cargo using cargo tags. Combined with the precise acquisition of cargo coordinate data by the data acquisition module and the periodic collection of real-time temperature data by the data acquisition module, it provides a refined data foundation for zoned control. The zoning module dynamically partitions cargo based on its real-time temperature, spatial coordinates, and target temperature zone. This logically aggregates cargo with the same or similar temperature control requirements into independent target cold chain space temperature zones, avoiding temperature control interference between cargo with different temperature zone requirements. The priority determination module allocates adjustment priorities based on the target temperature zone and allowable temperature fluctuation threshold of each zone. This allows zones containing highly sensitive cargo to receive higher control weights, ensuring that their temperature fluctuations are strictly controlled within the allowable range, while avoiding over-control of cargo with high tolerance. The control module allocates cold energy based on this priority, enabling differentiated temperature control for different zones under a single cold source architecture. This eliminates the need to deploy multiple independent cold sources, reducing hardware costs and maintenance complexity. Furthermore, by accurately matching the temperature control requirements of each zone, it reduces unnecessary energy consumption, ultimately effectively balancing the relationship between temperature control accuracy, energy consumption, and hardware costs. This overcomes the technical bottleneck of existing solutions in scenarios involving mixed loading of multiple types of cargo. Attached Figure Description

[0007] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying 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.

[0008] Figure 1 This is a structural block diagram of the dynamic temperature control system for cold chain goods provided in an embodiment of this application. Detailed Implementation

[0009] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0010] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or server that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0011] It should be noted that the following description covers various aspects of embodiments within the scope of the appended claims. It will be apparent that the aspects described herein can be embodied in a wide variety of forms, and any particular structure and / or function described herein is merely illustrative. Based on this application, those skilled in the art will understand that one aspect described herein can be implemented independently of any other aspect, and two or more of these aspects can be combined in various ways. For example, any number of aspects set forth herein can be used to implement the device and / or practice the method. Additionally, this device and / or method can be implemented using structures and / or functionalities other than one or more of the aspects set forth herein.

[0012] Please refer to Figure 1 As shown, an embodiment of this application provides a dynamic temperature control system for cold chain goods, the system including: a data acquisition module 110, a data collection module 120, a partitioning module 130, a priority determination module 140, and a control module 150.

[0013] The data acquisition module 110 is used to acquire the coordinate data of each item inside the cold chain space. Each item inside the cold chain space is equipped with a corresponding item tag. The item tag includes a temperature sensor and a wireless communication unit. The item tag pre-stores the target temperature zone and the allowable temperature fluctuation threshold for the corresponding item. The item tag determines the coordinate data through the wireless communication unit.

[0014] Specifically, the cargo tag is a passive RFID tag or Bluetooth positioning tag powered by a built-in battery. Its small size allows it to be affixed to the surface of the cargo packaging or embedded inside, without affecting cargo stacking or transportation. The temperature sensor in the cargo tag uses a high-precision digital temperature sensor to collect the cargo's own temperature in real time. The wireless communication unit supports UWB (Ultra-Wideband) positioning or Bluetooth 5.0 positioning protocols, achieving coordinate positioning through interaction with positioning base stations deployed within the cold chain space.

[0015] The information pre-stored on the cargo tags is entered using a dedicated writing device before the cargo is loaded. The target temperature zone refers to the standard temperature range required for cargo storage and transportation, and the allowable temperature fluctuation threshold refers to the maximum acceptable temperature deviation of the cargo within the target temperature zone (e.g., ±0.5℃ for blood products, ±2℃ for ordinary vegetables). The coordinate data acquisition process is as follows: the positioning base station broadcasts positioning signals to the cold chain space at a preset frequency. After receiving signals from at least three different positioning base stations, the wireless communication unit of the cargo tag calculates its own three-dimensional coordinates using the Time Difference of Arrival (TDOA) algorithm and uploads the coordinate data to the data acquisition module 110 in real time via the wireless communication unit, ensuring that the module can accurately grasp the physical location of each batch of goods within the cold chain space.

[0016] The data acquisition module 120 is used to poll and collect real-time temperature data of each item inside the cold chain space at fixed time intervals.

[0017] Specifically, the fixed time interval can be flexibly configured according to the sensitivity of the goods. For example, the time interval for highly sensitive goods (such as vaccines and blood products) can be set to 1 minute, and the time interval for ordinary goods can be set to 5 minutes. The default time interval is 3 minutes. The acquisition module 120 sends temperature acquisition commands to each goods tag sequentially via a wireless communication link (matching the wireless communication unit protocol of the goods tag). After receiving the command, the goods tag uses a temperature sensor to acquire the current temperature of the goods and feeds it back to the acquisition module 120 via the wireless communication unit. To ensure data reliability, the acquisition module 120 is equipped with a data retransmission mechanism: if no feedback is received on the first acquisition, the acquisition command will be resent within 10 seconds, up to a maximum of 3 times; if no feedback is still received, the goods tag is marked as abnormal and an alarm is triggered. For example, if there are 100 batches of goods in the cold chain space, and the acquisition module 120 is polling and collecting data at a fixed time interval of 3 minutes, it will communicate with the goods tags numbered 1 to 100 in sequence. After completing one round of acquisition, it will wait 3 minutes to enter the next round, ensuring that the temperature status of all goods is monitored in real time.

[0018] The partitioning module 130 is used to obtain several target cold chain space temperature partitions based on the real-time temperature data of each cargo, the coordinate data of each acquired cargo, and the target temperature zone of each cargo.

[0019] Specifically, the partitioning module 130 further includes: The initial partitioning unit 131 is used to obtain several initial cold chain space temperature partitions based on the real-time temperature data of each cargo, the coordinate data of each acquired cargo, and the target temperature zone of each cargo.

[0020] Here, multiple environmental sensors are fixedly installed on the inner walls and top of the cold chain space at a preset density. These sensors collect ambient temperature and detect airflow velocity. Each sensor has corresponding fixed installation coordinates. The preset density is determined based on the volume of the cold chain space. For example, 15 environmental sensors are installed in a 10-cubic-meter cold chain compartment, evenly distributed on the front, rear, left, right, and top walls to ensure no blind spots. The fixed installation coordinates of the environmental sensors are measured and entered into the system during installation using a positioning device, serving as the reference for subsequent data binding. The initial partition unit includes: The acquisition subunit 1311 is used to poll and acquire real-time space temperature data from each space environment sensor at fixed time intervals.

[0021] The fixed time interval here is consistent with the time interval of the acquisition module 120 to ensure the time synchronization of temperature data. The acquisition subunit 1311 shares a wireless communication link with the acquisition module 120 to synchronously acquire real-time temperature data of all goods; at the same time, it acquires ambient temperature data and airflow velocity data of each spatial environment sensor through a wired communication interface (such as RS485) (the airflow velocity data is used for reference by the subsequent control module, and the ambient temperature data is mainly used in the initial zoning stage).

[0022] The binding subunit 1312 is used to bind the real-time temperature data of each cargo to the corresponding coordinate data, and to bind the real-time temperature data of each space to the corresponding fixed installation coordinates, so as to obtain cargo temperature data pairs and space temperature data pairs.

[0023] Here, the format for cargo temperature data pairs is: cargo label identifier, real-time temperature value, and three-dimensional coordinates (x, y, z). The cargo label identifier is a unique identifier (e.g., A001, B002) to ensure a one-to-one correspondence between data and cargo. For example, if cargo A has a label identifier of A001, a real-time temperature of 5.2℃, and coordinates (2.1m, 1.5m, 0.8m), then the bound data pair would be (A001, 5.2℃, (2.1, 1.5, 0.8)). The format for spatial temperature data pairs is: sensor identifier, ambient temperature value, and fixed installation coordinates (x, y, z). The sensor identifier is a unique code (e.g., S001, S002). For example, if sensor 1 has an identifier of S001, an ambient temperature of 4.9℃, and fixed coordinates (0.5m, 0.5m, 2.0m), then the bound data pair would be (S001, 4.9℃, (0.5, 0.5, 2.0)). The binding process is achieved through identifier matching, ensuring that each set of temperature data corresponds to a specific physical location.

[0024] Matching subunit 1313 is used to match cargo temperature data pairs and space temperature data pairs to the grid cells corresponding to the preset three-dimensional grid model; wherein, the preset three-dimensional grid model divides the cold chain space into several uniform three-dimensional grid cells according to a preset precision, and each grid cell corresponds to a unique spatial coordinate range.

[0025] Here, the preset 3D mesh model is a digital model constructed based on the actual dimensions of the cold chain space. For example, a cold chain truck with a length of 10m, a width of 2.5m, and a height of 2m is divided into mesh units with a preset precision of 0.5m × 0.5m × 0.5m, resulting in a total of 10 / 0.5 × 2.5 / 0.5 × 2 / 0.5 = 400 mesh units. The coordinate range of each mesh unit is clearly defined (e.g., the x-range of a certain mesh unit is 1.0-1.5m, the y-range is 0.5-1.0m, and the z-range is 1.0-1.5m). The matching process involves extracting the coordinate values ​​from the cargo temperature data pairs and the space temperature data pairs, determining which mesh unit's coordinate range the coordinate belongs to, and then associating the corresponding temperature data with that mesh unit. As an example: if the coordinates of cargo A (2.1m, 1.5m, 0.8m) belong to the grid cell of x2.0-2.5m, y1.5-2.0m, z0.5-1.0m, then the temperature data of 5.2℃ will be matched to that grid cell.

[0026] The calculation subunit 1314 is used to calculate the estimated temperature value of each blank grid cell based on the adjacent grid cells of each blank grid cell in the preset three-dimensional grid model; wherein, the blank grid cell is a grid cell that does not directly match the cargo temperature data pair and the space temperature data pair.

[0027] Here, due to limitations in cargo stacking gaps and spatial environment sensor deployment, some grid cells may not be directly associated with temperature data, i.e., blank grid cells. Computation sub-cell 1314 uses the Kriging interpolation algorithm to calculate the estimated temperature value of blank grid cells. This algorithm achieves accurate prediction by analyzing the temperature distribution patterns of adjacent valid grid cells (grid cells with matched temperature data). Adjacent grid cells refer to grid cells that share faces, edges, or vertices with blank grid cells, with a minimum of 3 and a maximum of 8 adjacent valid grid cells referenced. As an example, if 5 of the 8 adjacent grid cells of a blank grid cell are valid, with temperatures of 4.8℃, 5.0℃, 5.1℃, 4.9℃, and 5.2℃ respectively, the estimated temperature of this blank grid cell is calculated to be 5.0℃ using the Kriging interpolation algorithm.

[0028] Furthermore, in this embodiment, the specific implementation steps of the Kriging interpolation algorithm are as follows: Step 1: Define the interpolation object and neighborhood. Identify the blank grid cell to be calculated and extract its 3D coordinates. Define the neighborhood as all adjacent grid cells that share faces, edges, or vertices with the blank grid cell. Filter out the valid adjacent grid cells that have matched temperature data (3-8 cells), and exclude invalid adjacent grid cells that have no temperature data.

[0029] Step 2: Calculate the spatial distance to obtain the three-dimensional coordinates of each effective adjacent grid cell. Using the Euclidean distance formula, calculate the straight-line distance between the blank grid cell and each effective adjacent grid cell, and record all distance values.

[0030] Step 3: Initial weight allocation is based on the principle that the closer the distance, the greater the influence. The initial weight of each effective adjacent grid cell is calculated using the inverse distance weighting method: First, calculate the inverse of the distance of each effective adjacent grid cell, then calculate the sum of the inverse distances of all effective adjacent grid cells, and finally divide the inverse distance of a single effective adjacent grid cell by the sum to obtain the initial weight of each effective adjacent grid cell (the sum of all initial weights is 1).

[0031] Step 4: Spatial Variance Modeling and Weight Correction. A spherical model is selected as the spatial variability function for temperature (adapting to the continuous temperature distribution characteristics of the cold chain space). The nugget value, sill value, and range parameter of the variability function are set (the nugget value represents random error, the sill value represents total variability, and the range represents the maximum distance of spatial temperature correlation). Based on the distance between the blank grid cell and each effective adjacent grid cell, the corresponding variability function value is calculated by substituting it into the variability function model. The variability function matrix is ​​constructed, and the Lagrange multiplier is solved by solving the system of equations. The initial weights are corrected using this multiplier to obtain the final weights (after correction, the sum of all weights is still 1, and the weight ratio of effective adjacent grid cells that are closer and have stronger spatial correlation is increased).

[0032] Step 5: The estimated temperature is calculated using a weighted summation method. The temperature value of each effective adjacent grid cell is multiplied by the corresponding final weight to obtain the weighted temperature value of each effective adjacent grid cell. The summation of all weighted temperature values ​​gives the estimated temperature of the blank grid cell.

[0033] Step 6: Extract the minimum and maximum values ​​of the temperature values ​​of all valid adjacent grid cells, and determine whether the estimated temperature is within the range. If it is within the range, the estimated result is considered reasonable, and the estimated temperature is output. If it is outside the range, the variogram parameters are readjusted or more distant valid grid cells are added (no more than 8). Steps 2 to 5 are repeated until the estimated temperature is within a reasonable range.

[0034] The mapping sub-unit 1315 is used to map all grid units in the preset three-dimensional grid model to the corresponding colors according to the preset temperature gradient color scale, so as to obtain the real-time temperature field thermogram corresponding to the cold chain space.

[0035] Here, the preset temperature gradient color scale is a pre-defined rule for corresponding temperatures to colors. For example: -20℃ to -15℃ corresponds to dark blue, -15℃ to -10℃ corresponds to light blue, 0℃ to 5℃ corresponds to light green, and 5℃ to 10℃ corresponds to dark green. The higher the temperature, the darker the color, and the lower the temperature, the lighter the color (this can be adjusted according to the actual temperature control range). The temperature value (directly matched value or estimated temperature value) of each grid cell is compared with the color scale and assigned the corresponding color. Then, all the colored grid cells are combined to form a visualized real-time temperature field heat map. Through this heat map, the temperature distribution within the cold chain space can be observed intuitively, such as which areas have higher temperatures and which areas have lower temperatures, providing a visual basis for subsequent clustering and partitioning.

[0036] Clustering subunit 1316 is used to cluster the real-time temperature field thermogram according to the target temperature zone of each cargo to obtain several initial cold chain space temperature partitions.

[0037] The clustering subunit 1316 includes: The similar temperature zone determination component 13161 is used to obtain at least one similar target temperature zone group according to a preset similar temperature zone determination algorithm; wherein, the proportion of the overlap between any two target temperature zones in the similar target temperature zone group to the total range of any one of the two target temperature zones exceeds a preset proportion threshold.

[0038] As an example, the default preset ratio threshold is 70%, which can be adjusted according to the actual scenario. The total range of the target temperature zone refers to the difference between the upper and lower limits of the temperature zone. For example, the total range of the target temperature zone 2-8℃ is 6℃, and the total range of the target temperature zone 3-7℃ is 4℃. The overlap range between the two is 3-7℃ (4℃). Calculate the percentage of overlap: 4℃ / 6℃≈66.7% (not reaching 70%), 4℃ / 4℃=100% (reaching 70%). Since the percentage of either temperature zone meets the standard, they are determined to be the same type of temperature zone and are classified into the same target temperature zone group. For example, there are three types of goods in the cold chain space, with target temperature zones of 2-8℃, 3-7℃, and 0-4℃, and the preset ratio threshold is 70%. The overlap between 2-8℃ and 3-7℃ is 4℃, accounting for 100% of the total range of 3-7℃ (meets the standard), and is classified into group 1; the overlap between 0-4℃ and any temperature zone in group 1 is 2-4℃ (2℃), accounting for 50% of the total range of 0-4℃ (does not meet the standard), so it is classified into group 2 separately, resulting in two groups of the same type of target temperature zone.

[0039] The association component 13162 is used to associate the coordinate data of all goods in the same target temperature zone group in a real-time temperature field thermogram.

[0040] Here, the component extracts the 3D coordinate data of all goods within the same group by mapping the goods' labels to similar target temperature zones, and establishes a coordinate set. For example, group 1 contains goods A, B, and C, with coordinates (2.1, 1.5, 0.8), (2.3, 1.6, 0.9), and (2.2, 1.4, 1.0) respectively. After association, they form the coordinate set {(2.1, 1.5, 0.8), (2.3, 1.6, 0.9), (2.2, 1.4, 1.0)}, which is then used to determine the partition boundaries.

[0041] Boundary determination component 13163 is used to calculate the boundary range of coordinate data corresponding to all goods within the same target temperature zone group; wherein, the boundary range is the smallest geometric area that can cover the spatial coordinates of all labels within the same target temperature zone group.

[0042] In this embodiment, the minimum geometric region adopts a cuboid structure. The boundary is determined by calculating the extreme values ​​of the coordinate set in the three dimensions of x, y, and z: the minimum (x_min) and maximum (x_max) of all x-coordinates in the x-direction, y_min and y_max in the y-direction, and z_min and z_max in the z-direction. The cuboid boundary is formed with (x_min, y_min, z_min) as the lowest point at the bottom left and (x_max, y_max, z_max) as the highest point at the top right. For example, in the coordinate set of group 1, x_min = 2.1m, x_max = 2.3m, y_min = 1.4m, y_max = 1.6m, z_min = 0.8m, and z_max = 1.0m. Therefore, the boundary range is x 2.1-2.3m, y 1.4-1.6m, z 0.8-1.0m. This cuboid can completely cover the coordinates of all goods within the group.

[0043] The partitioning component 13164 is used to determine each minimum geometric region as an initial cold chain space temperature partition to obtain several initial cold chain space temperature partitions.

[0044] Here, the smallest geometric region corresponding to each target temperature zone group of the same type is an initial partition, and the partition number is assigned according to the group order (e.g., group 1 corresponds to initial partition 1, group 2 corresponds to initial partition 2). The boundary information of the initial partition (extreme values ​​in the x, y, and z directions) and the corresponding target temperature zone group information (e.g., target temperature zone range, allowable fluctuation threshold) will be stored in the system database to provide a basis for subsequent standard deviation calculation and subdivision.

[0045] The standard deviation acquisition unit 132 is used to acquire the temperature zone standard deviation of each initial cold chain space temperature zone; wherein, the temperature zone standard deviation is the standard deviation between the real-time temperature data of all goods contained in the corresponding initial cold chain space temperature zone.

[0046] Here, the standard deviation of the temperature zone is used to characterize the uniformity of temperature distribution within the initial zone. The larger the standard deviation, the more significant the temperature difference between different goods within the zone, and the worse the temperature control accuracy. The calculation process is as follows: First, extract the real-time temperature data of all goods within the initial zone to form a temperature dataset (e.g., the temperature data for initial zone 1 are 5.2℃, 5.3℃, and 5.1℃); then calculate the average value of this dataset ((5.2+5.3+5.1) / 3=5.2℃); finally, calculate the sum of squares of the differences between each data point and the average value ((5.2-5.2)). 2 +(5.3-5.2) 2 +(5.1-5.2) 2=0.02); Finally, divide the sum of squared differences by the number of data (3), and then take the square root to get the standard deviation (√(0.02 / 3)≈0.08℃). The preset standard deviation threshold is determined according to the allowable fluctuation threshold of the goods, and is set to 50% of the allowable fluctuation threshold by default. For example, if the allowable fluctuation threshold corresponding to a certain initial partition is ±1℃, then the preset standard deviation threshold is 0.5℃.

[0047] The subdivision unit 133 is used to subdivide the initial cold chain space temperature partition into several target cold chain space temperature partitions if the temperature zone standard deviation of any initial cold chain space temperature partition is greater than a preset standard deviation threshold.

[0048] Specifically, the subdivision unit 133 includes: The coordinate acquisition subunit 1331 is used to acquire the coordinates of the highest temperature and the lowest temperature in any initial cold chain space temperature zone if the standard deviation of the temperature zone of any initial cold chain space temperature zone is greater than a preset standard deviation threshold.

[0049] The dividing line determination subunit 1332 is used to connect the coordinates corresponding to the highest temperature and the lowest temperature in the initial cold chain space temperature partition to obtain a virtual dividing line.

[0050] Here, the virtual dividing line is a straight line in three-dimensional space connecting the highest temperature coordinates and the lowest temperature coordinates. Its core function is to divide the space into zones along the direction of the greatest temperature difference, ensuring optimal temperature uniformity in the sub-zones. For example, the line connecting the highest temperature coordinates (3.1, 1.2, 0.7) and the lowest temperature coordinates (3.5, 1.3, 0.8) is the virtual dividing line. The direction vector of this line is (3.5-3.1, 1.3-1.2, 0.8-0.7) = (0.4, 0.1, 0.1), representing the main direction of the temperature gradient within this initial zone.

[0051] The segmentation subunit 1333 is used to divide the initial cold chain space temperature partition into two initial cold chain space temperature partitions using virtual dividing lines as boundaries. It ensures that each initial cold chain space temperature partition contains at least one item and jumps to the coordinate acquisition subunit until the temperature zone standard deviation of each initial cold chain space temperature partition is equal to or less than the preset standard deviation threshold. Then the subdivision ends and each initial cold chain space temperature partition is determined as the target cold chain space temperature partition.

[0052] The specific segmentation process is as follows: First, calculate the coordinates of the midpoint of the virtual segmentation line (e.g., ((3.1+3.5) / 2,(1.2+1.3) / 2,(0.7+0.8) / 2) = (3.3,1.25,0.75)). Then, using the midpoint as the foot of the perpendicular, establish a virtual segmentation plane perpendicular to the virtual segmentation line (the normal vector of the segmentation plane is consistent with the direction vector of the virtual segmentation line). Next, traverse the coordinates of all goods in the original initial partition, calculate the distance from each coordinate to the segmentation plane, and assign goods with positive distances to the first sub-partition and goods with negative distances to the second sub-partition, ensuring that each sub-partition contains at least one goods (if a sub-partition has no goods, adjust the position of the segmentation plane, offset it by 10% of the line length along the virtual segmentation line towards the extreme temperature value, and then re-segment). Taking the initial partition 2 above as an example, the first sub-partition after segmentation contains goods D (5.8℃) and goods F (5.6℃), with a temperature dataset of [5.8, 5.6] and a standard deviation of ≈0.14℃ (≤0.5℃); the second sub-partition contains goods E (4.5℃) and goods G (4.7℃), with a temperature dataset of [4.5, 4.7] and a standard deviation of ≈0.14℃ (≤0.5℃). Both meet the uniformity requirement and do not need to be further subdivided. These two sub-partitions are the target cold chain space temperature partitions. If the standard deviation of a certain sub-partition is still greater than the threshold after subdivision (e.g., the temperature difference within the initial partition is extremely large, and the standard deviation of a certain sub-partition is 0.6℃ after one subdivision), then the coordinate acquisition, segmentation line determination, and segmentation steps are repeated until the standard deviation of all sub-partitions meets the threshold.

[0053] In one embodiment of this application, the system combines airflow velocity data collected by space environment sensors to ensure that the extension direction of the virtual dividing line is perpendicular to the main airflow direction within the cold chain space (e.g., if the airflow mainly flows along the positive x-axis, the dividing line should extend along the yz plane as much as possible), thus preventing airflow interference after subdivision from exacerbating temperature unevenness within the sub-zones. This allows for fine-tuning of the straight line connecting the highest and lowest temperature coordinates, and, without changing the core direction, slight adjustments to the extension angle of the dividing line, preventing the subdivided sub-zones from being penetrated by the temperature gradient of the airflow.

[0054] Furthermore, the refrigerated airflow within the cold chain space is the primary carrier of temperature transfer (for example, cold airflow blown out of the vent and flowing in a fixed direction). The airflow itself carries a certain temperature gradient (e.g., the temperature is lower at the front end and slightly higher at the back end, or there is a temperature difference between the edge and the center of the airflow). If the division direction of the sub-zone is consistent with the airflow direction, the airflow will directly pass through the entire sub-zone, bringing its own temperature gradient into the zone, resulting in a larger temperature difference between different locations within the zone (front end vs. back end, center vs. edge). Space environment sensors collect real-time airflow velocity data (including wind speed and direction) at various locations. The system analyzes the wind direction data from all sensors to determine the airflow direction with the highest percentage and strongest wind speed (i.e., the "main airflow direction"). When the virtual dividing line extends "perpendicular to the main airflow direction," it divides the cold chain space into multiple sub-zones "across the airflow," rather than sub-zones "following the airflow." The specific avoidance process is as follows: Assuming the main airflow flows along the positive x-axis (cold air blows from x=0 to x=10, and the airflow itself may have a temperature gradient of "5℃ at x=0, 7℃ at x=10"): If the dividing line follows the airflow direction (extending along the x-axis): the sub-zones will be long strips like "x1-x2, x3-x4," and each sub-zone will be complete. If the dividing line covers a section of the airflow (e.g., x1=2-x2=4), the temperature gradient of the airflow (5℃→7℃) will directly penetrate the sub-section, resulting in a large temperature difference at x=2 (5℃) and x=4 (7℃), rendering the subdivision meaningless. If the dividing line is perpendicular to the airflow direction (extending along the yz plane), the sub-sections will be short blocks like "x=2-x=3, x=3-x=4". Each sub-section occupies only a small segment in the airflow direction, and the temperature gradient within this small segment is extremely small (e.g., within the x=2-x=3 interval, the airflow temperature is 5.1℃-5.2℃). Furthermore, the airflow will evenly sweep across the entire cross-section (yz plane) of each sub-section, preventing the situation where "some areas are covered by cold airflow while others are not," thus avoiding uneven temperature within the sub-sections.

[0055] Through the above steps, the airflow can be fully covered when passing through each sub-section (the sub-section cross-section is perpendicular to the airflow direction, and the airflow can cover the entire cross-section); and the sub-section has a small span in the airflow direction, so the temperature gradient brought by the airflow can be ignored. Each position in the sub-section can be evenly covered by airflow at the same temperature level, thereby avoiding temperature unevenness caused by airflow interference after subdivision and ensuring a more uniform temperature distribution in each sub-section.

[0056] The priority determination module 140 is used to determine the adjustment priority of each target cold chain space temperature zone based on the target temperature zone and the allowable temperature fluctuation threshold of each target cold chain space temperature zone.

[0057] The priority determination module 140 includes: The boundary determination unit 141 is used to determine the preset three-layer boundary of each target cold chain space temperature zone. The preset three-layer boundary includes a safety boundary, a warning boundary, and a critical boundary. The temperature range of the critical boundary is greater than the temperature range of the warning boundary. The temperature range of the warning boundary is greater than the temperature range of the safety boundary. The temperature range of the preset three-layer boundary is determined based on the midpoint of the corresponding target temperature zone and the allowable temperature fluctuation threshold. The level determination unit 142 is used to determine the warning level to which the current average temperature of each target cold chain space temperature zone belongs based on the preset three-layer boundary of each target cold chain space temperature zone. The trend value determination unit 143 is used to determine the boundary crossing trend value of each target cold chain space temperature zone according to the warning level of each target cold chain space temperature zone; wherein, the boundary crossing trend value is determined according to the warning level of the target cold chain space temperature zone and the current average temperature. The priority determination unit 144 is used to determine the adjustment priority of each target cold chain space temperature zone based on the out-of-bounds trend value and the level sensitivity score of each target cold chain space temperature zone; wherein, the level sensitivity score is proportional to both the sensitivity coefficient and the preset base score of the corresponding warning level; the sensitivity coefficient is inversely proportional to the temperature allowable fluctuation threshold.

[0058] Specifically, the three preset boundaries include a safety boundary, a warning boundary, and a critical boundary; the temperature range of the safety boundary is the product of the midpoint of the temperature zone of the corresponding target cold chain space temperature partition plus or minus the allowable temperature fluctuation threshold and a first proportion; the temperature range of the warning boundary is the product of the midpoint of the temperature zone of the corresponding target cold chain space temperature partition plus or minus the allowable temperature fluctuation threshold and a second proportion; the temperature range of the critical boundary is the midpoint of the temperature zone of the corresponding target cold chain space temperature partition plus or minus the allowable temperature fluctuation threshold; wherein, the first proportion is less than the second proportion; The midpoint of the target temperature zone is the arithmetic mean of the upper and lower limits of the target temperature zone. For example, the midpoint of the target temperature zone 2-8℃ is (2+8) / 2=5℃, and the allowable temperature fluctuation threshold is ±3℃; the midpoint of the target temperature zone -18~-12℃ is -15℃, and the allowable fluctuation threshold is ±3℃. The calculation rules for the three-layer boundary are as follows: Safety boundary: Midpoint of the target temperature zone ± (allowable fluctuation threshold × 30%). The safety boundary for the 2-8℃ zone is 5℃ ± 0.9℃, i.e., 4.1℃~5.9℃; the safety boundary for the -18~-12℃ zone is -15℃ ± 0.9℃, i.e., -15.9℃~-14.1℃. Warning boundary: Midpoint of the target temperature zone ± (allowable fluctuation threshold × 60%). The warning boundary for the 2-8℃ zone is 5℃ ± 1.8℃, i.e., 3.2℃~6.8℃; the warning boundary for the -18~-12℃ zone is -15℃ ± 1.8℃, i.e., -16.8℃~-13.2℃. Critical boundary: Midpoint of the target temperature zone ± allowable fluctuation threshold (i.e., the upper and lower limits of the target temperature zone). The critical boundary of the 2-8℃ zone is 2℃~8℃; the critical boundary of the -18~-12℃ zone is -18℃~-12℃. The range relationship of the three boundaries satisfies: critical boundary ⊃ warning boundary ⊃ safety boundary.

[0059] Therefore, the alert levels are divided as follows: if the real-time average temperature of the target cold chain space temperature zone is within the temperature range of the safety boundary, the alert level is the safe level; if the real-time average temperature of the target cold chain space temperature zone is outside the temperature range of the safety boundary but within the temperature range of the warning boundary, the alert level is the warning level; if the real-time average temperature of the target cold chain space temperature zone is outside the temperature range of the warning boundary but within the temperature range of the critical boundary, the alert level is the critical level. Based on the warning level of each target cold chain space temperature zone, determine the out-of-bounds trend value of each target cold chain space temperature zone.

[0060] The safety level is defined as follows: Out-of-bounds trend value = temperature change rate ÷ (distance from the warning boundary to the current average temperature). The temperature change rate is the ratio of the average temperature difference between the two most recent sampling periods for that zone to the sampling interval (e.g., if the average temperature of the previous period was 5.0℃, the current temperature is 5.2℃, and the sampling interval is 3 minutes, then the change rate is (5.2-5.0) / 3≈0.067℃ / minute). The distance from the warning boundary to the current temperature is the difference between the current average temperature and the most recent warning boundary (e.g., if the current temperature is 5.2℃, the upper limit of the safety boundary is 5.9℃, and the upper limit of the warning boundary is 6.8℃, then the distance is 5.9℃-5.2℃=0.7℃). The larger the ratio, the more significant the trend of moving towards the warning boundary. Warning level: Out-of-bounds trend value = temperature change rate ÷ (distance from the critical boundary to the current average temperature). The distance from the critical boundary to the current temperature is the difference between the current average temperature and the nearest critical boundary (e.g., if the current temperature is 6.2℃ and the upper limit of the critical boundary is 8℃, then the distance is 8℃-6.2℃=1.8℃). The larger the ratio, the more significant the trend of moving towards the critical boundary. Critical Level: Out-of-bounds trend value = temperature change rate ÷ (distance from the critical boundary to the current average temperature). If the calculated result is > 5, it is fixed at 5 (setting an upper limit to avoid numerical overflow); if the temperature change rate is negative (temperature decreases, risk decreases), the trend value is 0. Emergency Level: The out-of-bounds trend value is fixed at 10 (preset maximum value), directly triggering the highest priority response. For example: If the temperature change rate of a certain safety-level zone is 0.067℃ / minute and the warning boundary distance is 0.7℃, then the out-of-bounds trend value = 0.067 ÷ 0.7 ≈ 0.096; if the temperature change rate of a certain critical-level zone is 0.1℃ / minute and the critical boundary distance is 0.8℃, then the out-of-bounds trend value = 0.1 ÷ 0.8 = 0.125.

[0061] Finally, the sensitivity coefficient is set according to the following rules: the smaller the allowable fluctuation threshold, the larger the coefficient. For example, an allowable fluctuation threshold of ±0.5℃ (blood products) corresponds to a sensitivity coefficient of 3.0, ±1℃ (vaccines) corresponds to 2.0, ±2℃ (common vegetables) corresponds to 1.0, and ±3℃ (frozen meat) corresponds to 0.8, ensuring that the zone containing highly sensitive goods receives a higher weight. The preset base score is set according to the alert level: as an example: safe level 1 point, warning level 3 points, critical level 5 points, emergency level 10 points, with higher levels having higher base scores.

[0062] The formula for calculating the tiered sensitivity score is: Tiered Sensitivity Score = Sensitivity Coefficient × Preset Base Score; The formula for calculating the adjustment priority is: Adjustment Priority = Tiered Sensitivity Score + (Out-of-bounds Trend Value × 2) (The trend value is magnified by 2 times to strengthen the dynamic risk weight).

[0063] As an example: Zone 1 (blood products, allowable fluctuation ±0.5℃, sensitivity coefficient 3.0): alert level critical level (base score 5 points), out-of-bounds trend value 0.125; level sensitivity score = 3.0 × 5 = 15 points, adjustment priority = 15 + (0.125 × 2) = 15.25 points; Zone 2 (common vegetables, allowable fluctuation ±2℃, sensitivity coefficient 1.0): Alert level (basic score 3 points), out-of-bounds trend value 0.096; Level sensitivity score = 1.0 × 3 = 3 points, adjustment priority = 3 + (0.096 × 2) = 3.192 points; Zone 3 (frozen meat, allowable fluctuation ±3℃, sensitivity coefficient 0.8): Alert level safety level (base score 1 point), out-of-bounds trend value 0.05; level sensitivity score = 0.8 × 1 = 0.8 points, adjustment priority = 0.8 + (0.05 × 2) = 0.9 points; the final priority ranking is: Zone 1 (15.25 points) > Zone 2 (3.192 points) > Zone 3 (0.9 points), ensuring that high-sensitivity and high-risk zones receive priority in obtaining regulatory resources.

[0064] The control module 150 is used to control the cooling capacity according to the adjustment priority of the temperature zone of each target cold chain space.

[0065] Specifically, the control module 150 includes The compensation calculation unit 151 is used to calculate the amount of cooling compensation required for the target cold chain space temperature zone with the highest adjustment priority.

[0066] Here, the cooling compensation amount is the amount of cooling required to lower the target zone's temperature from the current average temperature to the midpoint of the target temperature zone. The calculation formula is: Cooling compensation amount = K × zone volume × (current average temperature - midpoint of target temperature zone) × cargo density coefficient; where: K is the coefficient of performance of the cold chain space (calibrated by the rated power of the refrigeration system, such as 1.2 kW・h / m³・℃, which means that 1.2 kW・h of cooling energy is required to lower the temperature of each cubic meter of space by 1℃). The partition volume is the volume of the cuboid corresponding to the boundary range of the target partition (e.g., x2.1-2.3m, y1.4-1.6m, z0.8-1.0m, volume = 0.2×0.2×0.2=0.008m³). The cargo density coefficient is set according to the cargo stacking density within the zone. When the stacking density is >80%, it is 1.2; when it is 50%-80%, it is 1.0; and when it is <50%, it is 0.8 (the denser the stacking, the slower the cold transfer, and the more compensation is needed). If the current average temperature is lower than the midpoint of the target temperature zone (e.g., the target midpoint is 5℃, and the current temperature is 4.0℃), then the cooling compensation amount is 0, and no additional cooling is required.

[0067] The air outlet determination unit 152 is used to determine several target air outlets based on the preset airflow field model of the cold chain space.

[0068] Here, the "airflow field model of the pre-set cold chain space" is a digital model established based on the airflow propagation law of the cold chain space structure, air outlet layout (such as 8 air outlets evenly distributed at the top), fan speed and air guide angle. The model stores the airflow coverage, wind speed attenuation curve and cold energy transfer efficiency of each air outlet under different operating parameters.

[0069] The process of determining the target air outlet is as follows: Enter the boundary range and cooling compensation amount of the highest priority partition; The model iterates through all air outlets, simulating the process of each air outlet delivering cooling capacity to the target zone, and calculates the "cooling capacity loss rate" for each air outlet (loss rate = (cooling capacity output from air outlet - cooling capacity reaching the target zone) / cooling capacity output from air outlet). Sort the air outlets by loss rate from low to high, and select the N outlets with the lowest loss rates (N is dynamically adjusted according to the cooling capacity compensation: for example, 2 outlets are selected when the compensation is ≤0.02kW·h, 3 outlets are selected when the compensation is 0.02-0.05kW·h, and 4 outlets are selected when the compensation is >0.05kW·h). These N outlets form the target air outlet combination. For example, if the loss rates of the 8 air outlets are 5%, 8%, 10%, 12%, 15%, 18%, 20%, and 25%, and the cooling capacity compensation is 0.024kW·h, select the 3 air outlets with the lowest loss rates (loss rates of 5%, 8%, and 10%) as the target air outlets.

[0070] The adjustment unit 153 is used to adjust the angle of the air guide plate of several target air outlets and increase the fan speed of several air outlets.

[0071] Here, the specific adjustment process is as follows: Air guide vane angle adjustment: Based on the airflow field model, the target angle of the air guide vane for each target air outlet is calculated to ensure that the airflow axis points to the geometric center of the target zone (e.g., if the center coordinates of the target zone are (2.2, 1.5, 0.9) and the coordinates of a target air outlet are (2.2, 1.5, 2.0), then the air guide vane angle is tilted downwards by 45° so that the airflow is perpendicular to the center); the adjustment accuracy of the air guide vane angle is 1°, which is achieved by stepper motor drive; Fan speed adjustment: Based on the cooling capacity compensation and the number of target air outlets, calculate the required increase in fan speed for each target air outlet. The fan speed range of the cold chain system is 500-2000 r / min, with a base speed of 1000 r / min (corresponding to the base cooling capacity output). The speed is directly proportional to the cooling capacity output (e.g., a 10% increase in speed results in a 10% increase in cooling capacity output). For example, with a cooling capacity compensation of 0.024 kW·h, and the cooling capacity is evenly distributed among three target air outlets (each requiring an output of 0.008 kW·h), if the cooling capacity output of each air outlet at the base speed is 0.004 kW·h, then the speed needs to be increased to 2000 r / min (doubling the speed doubles the cooling capacity output) to meet the compensation requirements. Speed ​​adjustment is achieved by controlling the fan driver using a PWM (Pulse Width Modulation) signal, with an adjustment step of 50 r / min to avoid sudden speed changes that could cause airflow turbulence.

[0072] Meanwhile, differentiated control is implemented for non-target air outlets (i.e., other air outlets not selected for the target air outlet combination): if their current speed is higher than the base speed (1000r / min), the speed is reduced by 30% (e.g., if the current speed is 1500r / min, it is reduced to 1050r / min); if the speed is at the base speed, it remains unchanged to ensure that the basic temperature control requirements of non-priority zones are not affected, while avoiding waste of cooling capacity.

[0073] In one exemplary embodiment of this application, the system further includes: The acquisition module is used to acquire the temperature data of the target cold chain space temperature zone with the highest adjustment priority again after delivering cold energy to the target cold chain space temperature zone with the highest adjustment priority for a period of time.

[0074] Specifically, the duration of cold energy delivery is dynamically set based on the cold energy compensation amount: 5 minutes for compensation amounts ≤0.02 kW·h, 8 minutes for 0.02-0.05 kW·h, and 10 minutes for amounts greater than 0.05 kW·h, ensuring sufficient cold energy is transferred to the target zone before temperature detection. The data acquisition process is consistent with the implementation logic of the acquisition module 120 described earlier, polling and collecting real-time temperature data of all goods within the target zone, calculating a new current average temperature as a correction basis. For example, a cold energy compensation amount of 0.024 kW·h corresponds to an 8-minute acquisition interval; after 8 minutes, the temperature of goods within the target zone is re-acquired, resulting in a new average temperature of 6.0℃.

[0075] The correction value calculation module is used to calculate the temperature correction value; wherein the temperature correction value is the difference between the midpoint of the target temperature zone of the target cold chain space temperature partition with the highest adjustment priority and the current measured average temperature.

[0076] Specifically, the formula for calculating the temperature correction value is: Temperature Correction Value = Midpoint of Target Temperature Zone - Current Measured Average Temperature. If the correction value is positive, it means the current temperature is still higher than the target midpoint, and the cooling supply needs to be increased. If the correction value is negative, it means the current temperature is lower than the target midpoint, and the cooling supply needs to be reduced. If the absolute value of the correction value is ≤0.1℃ (temperature deviation is within the allowable range), no correction is needed. As an example, if the midpoint of the target temperature zone is 5℃ and the current measured average temperature is 6.0℃, then the temperature correction value = 5 - 6.0 = -1.0℃ (absolute value 1.0℃ > 0.1℃, correction is needed); if the current measured average temperature is 5.05℃, then the correction value = 5 - 5.05 = -0.05℃ (absolute value ≤0.1℃, no correction is needed).

[0077] The fine-tuning module is used to fine-tune the fan speed and air guide angle of the corresponding air outlet in reverse according to the temperature correction value.

[0078] Specifically, the core logic of reverse fine-tuning is to dynamically adjust the operating parameters of the target air outlet based on the magnitude and sign of the correction value until the temperature deviation meets the standard: Fan speed fine-tuning: As an example: when the absolute value of the correction value is >0.5℃, adjust the speed proportionally to the correction value (e.g., a correction value of -1.0℃ means an additional 1.0℃ of cooling is needed, so increase the target outlet speed by 20%; a correction value of 0.3℃ means an excessive 0.3℃ of cooling is needed, so decrease the speed by 10%); when the absolute value of the correction value is between 0.1-0.5℃, fine-tune it by a fixed proportion (increase or decrease the speed by 5%). Fine-tuning of air guide angle: As an example: if the absolute value of the correction value is >0.3℃, and the current measured temperature is unevenly distributed within the zone boundary (e.g., the temperature at the front of the zone is 5.5℃ and the temperature at the rear is 6.5℃), then adjust the air guide angle ±3° to make the airflow more accurately cover the area with higher temperature; if the temperature distribution is uniform, then only adjust the rotation speed without changing the air guide angle.

[0079] As an example: If the target zone correction value is -1.0℃ (requiring an additional 1.0℃ temperature reduction) and the temperature distribution is uniform, then the rotation speed of the three target air outlets will be increased by 20% from 2000r / min to 2400r / min (not exceeding the upper limit of rotation speed); if the correction value is 0.4℃ (excessive cooling of 0.4℃), then the rotation speed will be reduced by 10% to 1800r / min. After fine-tuning, the system continues to monitor the temperature at the time intervals of the supplementary acquisition module until the absolute value of the correction value is ≤0.1℃, completing the closed-loop control.

[0080] The complete workflow of this dynamic temperature control system for cold chain goods is as follows: First, the data acquisition module obtains the coordinate data and preset temperature control parameters of all goods, while the data collection module periodically collects real-time temperature data. Then, the zoning module dynamically divides the goods into zones based on temperature, coordinates, and target temperature zones. Through initial zoning, standard deviation verification, and subdivision optimization, target zones with uniform temperature are obtained. The priority determination module calculates the adjustment priority of each target zone based on three-layer warning boundaries, out-of-bounds trend values, and sensitivity coefficients. The control module determines the target air outlet and adjusts operating parameters according to the cooling demand of the highest priority zone to achieve directional cooling. Finally, through supplementary data collection, correction value calculation, and reverse fine-tuning, a closed-loop control is formed to ensure temperature control accuracy. The entire process does not require multiple independent cold sources to achieve differentiated temperature control for mixed-category goods, effectively balancing temperature control accuracy, energy consumption, and hardware costs.

[0081] Embodiments of this application also provide a computer program product including program code that, when the program product is run on an electronic device, causes the electronic device to perform the steps of the methods described above according to various exemplary embodiments of this application.

[0082] Furthermore, although the steps of the method in this application are described in a specific order in the accompanying drawings, this does not require or imply that the steps must be performed in that specific order, or that all the steps shown must be performed to achieve the desired result. Additional or alternative steps may be omitted, multiple steps may be combined into one step, and / or a step may be broken down into multiple steps.

[0083] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, mobile terminal, or network device, etc.) to execute the methods according to the embodiments of this application.

[0084] In an exemplary embodiment of this application, an electronic device capable of implementing the above-described method is also provided.

[0085] Those skilled in the art will understand that various aspects of this application can be implemented as a system, method, or program product. Therefore, various aspects of this application can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software implementations, collectively referred to herein as a "circuit," "module," or "system."

[0086] An electronic device according to this embodiment of the present application. The electronic device is merely an example and should not be construed as limiting the functionality and scope of use of the embodiments of this application.

[0087] Electronic devices are manifested in the form of general-purpose computing devices. Components of an electronic device may include, but are not limited to: at least one processor, at least one memory, and buses connecting different system components (including memory and processor).

[0088] The memory stores program code that can be executed by a processor, causing the processor to perform the steps described in the "Exemplary Methods" section above, according to various exemplary embodiments of this application.

[0089] The storage may include readable media in the form of volatile storage, such as random access memory (RAM) and / or cache memory, and may further include read-only memory (ROM).

[0090] The storage may also include programs / utilities having a set (at least one) of program modules, including but not limited to: an operating system, one or more applications, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0091] A bus can represent one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus that uses any of the various bus architectures.

[0092] The electronic device can also communicate with one or more external devices (such as keyboards, pointing devices, Bluetooth devices, etc.), one or more devices that enable a user to interact with the electronic device, and / or any device that enables the electronic device to communicate with one or more other computing devices (such as routers, modems, etc.). This communication can be performed via input / output (I / O) interfaces. Furthermore, the electronic device can communicate with one or more networks (such as local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter. As shown in the figure, the network adapter communicates with other modules of the electronic device via a bus. It should be understood that, although not shown in the figure, other hardware and / or software modules can be used in conjunction with the electronic device, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0093] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, terminal device, or network device, etc.) to execute the methods according to the embodiments of this application.

[0094] In exemplary embodiments of this application, a computer-readable storage medium is also provided, on which a program product capable of implementing the methods described above is stored. In some possible implementations, various aspects of this application may also be implemented as a program product including program code, which, when the program product is run on a terminal device, causes the terminal device to perform the steps of the various exemplary embodiments of this application described in the "Exemplary Methods" section above.

[0095] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0096] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable signal medium may also be any readable medium other than a readable storage medium, capable of sending, propagating, or transmitting programs for use by or in conjunction with an instruction execution system, apparatus, or device.

[0097] The program code contained on the readable medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0098] Program code for performing the operations of this application can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0099] Furthermore, the above figures are merely illustrative of the processes included in the method according to exemplary embodiments of this application, and are not intended to be limiting. It is readily understood that the processes shown in the above figures do not indicate or limit the temporal order of these processes. Additionally, it is readily understood that these processes may be executed synchronously or asynchronously, for example, in multiple modules.

[0100] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0101] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A dynamic temperature control system for cold chain goods, characterized in that, The system includes: The data acquisition module is used to acquire the coordinate data of each item inside the cold chain space. Each item inside the cold chain space is equipped with a corresponding item tag. The item tag includes a temperature sensor and a wireless communication unit. The item tag is pre-stored with the target temperature zone and the allowable temperature fluctuation threshold for the corresponding item. The item tag determines the coordinate data through the wireless communication unit. The data acquisition module is used to poll and collect real-time temperature data of each item inside the cold chain space at fixed time intervals. The partitioning module is used to obtain several target cold chain space temperature partitions based on the real-time temperature data of each cargo, the coordinate data of each acquired cargo, and the target temperature zone of each cargo. The priority determination module is used to determine the adjustment priority of each target cold chain space temperature zone based on the target temperature zone and the allowable temperature fluctuation threshold of each target cold chain space temperature zone. The control module is used to control the cooling capacity according to the adjustment priority of each target cold chain space temperature zone; The partitioning module also includes: The initial partitioning unit is used to obtain several initial cold chain space temperature partitions based on the real-time temperature data of each cargo, the coordinate data of each acquired cargo, and the target temperature zone of each cargo. The standard deviation acquisition unit is used to acquire the temperature zone standard deviation of each initial cold chain space temperature zone; wherein, the temperature zone standard deviation is the standard deviation between the real-time temperature data of all goods contained in the corresponding initial cold chain space temperature zone. The subdivision unit is used to subdivide the initial cold chain space temperature zone into several target cold chain space temperature zones if the temperature zone standard deviation of any initial cold chain space temperature zone is greater than a preset standard deviation threshold. Multiple environmental sensors are fixedly installed on the inner walls and top of the cold chain space at a preset density. These sensors are used to collect ambient temperature and detect airflow velocity. Each environmental sensor has corresponding fixed installation coordinates. The initial partitioning unit includes: The acquisition subunit is used to poll and acquire real-time temperature data of each space environment sensor at fixed time intervals. The binding subunit is used to bind the real-time temperature data of each cargo to the corresponding coordinate data, and to bind the real-time temperature data of each space to the corresponding fixed installation coordinates, so as to obtain cargo temperature data pairs and space temperature data pairs. The matching sub-unit is used to match cargo temperature data pairs and space temperature data pairs to the corresponding grid cells of the preset three-dimensional grid model. The preset three-dimensional grid model divides the cold chain space into several uniform three-dimensional grid cells with a preset precision, and each grid cell corresponds to a unique spatial coordinate range. The calculation sub-unit is used to calculate the estimated temperature value of each blank grid cell based on the adjacent grid cells of each blank grid cell in the preset three-dimensional grid model; wherein, the blank grid cell is a grid cell that does not directly match the cargo temperature data pair and the space temperature data pair. The mapping sub-unit is used to map all grid units in the preset three-dimensional grid model to the corresponding colors according to the preset temperature gradient color scale, so as to obtain the real-time temperature field thermogram corresponding to the cold chain space. Clustering subunits are used to cluster the real-time temperature field thermograms according to the target temperature zone of each cargo to obtain several initial cold chain space temperature partitions. The clustering subunit includes: The component for determining similar temperature zones is used to obtain at least one group of similar target temperature zones according to a preset similar temperature zone determination algorithm; wherein the overlap between any two target temperature zones in the similar target temperature zone group is more than a preset ratio threshold as a proportion of the total range of any one of the two target temperature zones. The association component is used to associate the coordinate data of all goods within the same target temperature zone group in a real-time temperature field thermogram. A boundary determination component is used to calculate the boundary range of coordinate data corresponding to all goods within the same target temperature zone group; wherein, the boundary range is the smallest geometric area that can cover the spatial coordinates of all labels within the same target temperature zone group; A partitioning component is used to determine each minimum geometric region as an initial cold chain space temperature partition, so as to obtain several initial cold chain space temperature partitions.

2. The dynamic temperature control system for cold chain goods according to claim 1, characterized in that, The subdivision unit includes: The coordinate acquisition subunit is used to acquire the coordinates of the highest temperature and the lowest temperature in any initial cold chain space temperature zone if the standard deviation of the temperature zone is greater than a preset standard deviation threshold. The dividing line defines the sub-unit, which is used to connect the coordinates corresponding to the highest temperature and the lowest temperature in the initial cold chain space temperature partition to obtain a virtual dividing line. The segmentation sub-unit is used to divide the initial cold chain space temperature zone into two initial cold chain space temperature zones using virtual dividing lines as boundaries. It ensures that each initial cold chain space temperature zone contains at least one item and jumps to the coordinate acquisition sub-unit until the standard deviation of the temperature zone of each initial cold chain space temperature zone is equal to or less than the preset standard deviation threshold. Then the sub-segmentation ends and each initial cold chain space temperature zone is determined as the target cold chain space temperature zone.

3. The dynamic temperature control system for cold chain goods according to claim 1, characterized in that, The priority determination module includes: The boundary determination unit is used to determine the preset three-layer boundary of each target cold chain space temperature zone. The preset three-layer boundary includes a safety boundary, a warning boundary, and a critical boundary. The temperature range of the critical boundary is greater than the temperature range of the warning boundary. The temperature range of the warning boundary is greater than the temperature range of the safety boundary. The temperature range of the preset three-layer boundary is determined based on the midpoint of the corresponding target temperature zone and the allowable temperature fluctuation threshold. The level determination unit is used to determine the warning level to which the current average temperature of each target cold chain space temperature zone belongs based on the preset three-layer boundary of each target cold chain space temperature zone. The trend value determination unit is used to determine the out-of-bounds trend value of each target cold chain space temperature zone based on the warning level of each target cold chain space temperature zone; wherein, the out-of-bounds trend value is determined based on the warning level of the target cold chain space temperature zone and the current average temperature. The priority determination unit is used to determine the adjustment priority of each target cold chain space temperature zone based on the out-of-bounds trend value and the level sensitivity score of each target cold chain space temperature zone; wherein, the level sensitivity score is proportional to both the sensitivity coefficient and the preset base score of the corresponding warning level; the sensitivity coefficient is inversely proportional to the temperature allowable fluctuation threshold.

4. The dynamic temperature control system for cold chain goods according to claim 1, characterized in that, The control module includes: The compensation calculation unit is used to calculate the amount of cooling compensation required for the target cold chain space temperature zone with the highest adjustment priority. The air outlet determination unit is used to determine several target air outlets based on the preset airflow field model of the cold chain space; The adjustment unit is used to adjust the angle of the air guide plate of several target air outlets and increase the fan speed of several air outlets.

5. The dynamic temperature control system for cold chain goods according to claim 4, characterized in that, The system also includes: The acquisition module is used to acquire the temperature data of the target cold chain space temperature zone with the highest adjustment priority again after delivering cold energy to the target cold chain space temperature zone with the highest adjustment priority for a period of time. The correction value calculation module is used to calculate the temperature correction value; wherein, the temperature correction value is the difference between the midpoint of the target temperature zone of the target cold chain space temperature partition with the highest adjustment priority and the current measured average temperature. The fine-tuning module is used to fine-tune the fan speed and air guide angle of the corresponding air outlet in reverse according to the temperature correction value.

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