Cold chain storage intelligent management system based on Internet of Things

Abstract

The invention relates to the technical field of storage intelligent management systems, in particular to a cold chain storage intelligent management system based on the internet of things, which comprises an internet of things network constructed by a plurality of cold chain warehouses, and is characterized in that each cold chain warehouse is internally provided with a temperature adjusting device, an image acquisition device, a temperature acquisition device and a storage and transportation planning module; determining a circulation coefficient according to the space utilization rate of the corresponding cold chain warehouse, determining a to-be-circulated storage object list based on the circulation coefficient and the historical warehouse-out data, and responding to the to-be-circulated storage object list or the to-be-stored object list to obtain the to-be-delivered storage object list so as to match the cold chain warehouse. And a warehousing strategy is determined according to the number of the cold chain warehouses which can be matched. According to the invention, the technical problem that the storage environment of the stored objects is not matched with the actual demand due to the change of the storage environment of the stored objects and the space proportion caused by the fact that the warehouse-in and warehouse-out link of the goods is not comprehensively considered in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of intelligent warehouse management system technology, and in particular to an intelligent cold chain warehouse management system based on the Internet of Things. Background Technology

[0002] Cold chain warehousing is a crucial link in ensuring the quality and safety of perishable goods such as food and pharmaceuticals, and its management efficiency directly impacts the final product quality and the company's operating costs. With the development of IoT technology, traditional warehouse management methods are gradually transforming towards intelligent systems. Existing technologies include several sensor network-based warehouse monitoring systems capable of real-time monitoring and recording of environmental parameters such as temperature and humidity within the warehouse.

[0003] Chinese invention patent CN118707998A discloses an intelligent temperature control management system for cold chain warehousing based on Internet of Things (IoT) technology. This system includes a sensor network module, a data acquisition and processing module, a data analysis and prediction module, an intelligent temperature control module, and a remote monitoring and management module. While this solution employs an advanced probabilistic prediction model combined with real-time meteorological information from the warehouse location for fine-tuning control, and incorporates an improved MOA algorithm to enhance prediction accuracy, achieving precise monitoring and high-precision matching of temperature control in various warehouse areas, ensuring a constant low-temperature environment, improving energy efficiency, and reducing overall operating costs; and uses an improved adaptive control method to dynamically manage the current and voltage supply of each refrigeration unit in the cold chain warehousing system, achieving high-precision control of the warehouse environment temperature, improving energy utilization efficiency, and ensuring intelligent energy allocation, the following problems still exist: 1. The system can only passively respond to inbound / outbound instructions and follow simple first-in-first-out (FIFO) and other fixed rules for goods allocation; 2. The system lacks the ability to analyze in-depth data such as cargo turnover rate, and cannot identify and proactively handle backlogged goods with low turnover rate. This results in "slow-moving goods" occupying core storage locations for a long time, while "fast-moving goods" with high turnover rate face the dilemma of insufficient storage capacity, which seriously affects the overall warehousing circulation efficiency and economic benefits. 3. The warehousing strategy is too simplistic and the system lacks flexibility. When searching for suitable warehouses for target goods, existing strategies often only consider a single dimension and lack alternative solutions under less than ideal conditions. Summary of the Invention

[0004] To address this issue, the present invention provides an IoT-based intelligent cold chain warehousing management system to overcome the technical problem in the prior art that the changes in the storage environment and space ratio caused by the entry and exit of goods in the warehouse process are not comprehensively considered, resulting in a mismatch between the storage environment and actual needs.

[0005] To achieve the above objectives, the present invention provides an intelligent cold chain warehousing management system based on the Internet of Things, comprising: The Internet of Things (IoT) module is connected to several cold chain warehouses to acquire the ambient temperature and images within a single cold chain warehouse, and to send adjustment commands to the temperature control device of the cold chain warehouse. An image analysis module is used to respond to images within the cold chain warehouse to determine the space utilization rate of the cold chain warehouse; The data storage module includes a first data storage unit for storing product parameters of the stored goods, and a second data storage unit for storing outbound data from the cold chain warehouse. The product parameters include storage temperature range, space volume, number of circulations, and temperature control accuracy coefficient. The initial value of the temperature control accuracy coefficient is automatically generated based on the storage temperature range during storage. The storage and transportation planning module includes a flow planning unit that, in response to outbound operations, determines a turnover coefficient based on the space utilization rate of the corresponding cold chain warehouse, and determines a list of stored items to be transferred based on the turnover coefficient and historical outbound data. And a storage planning unit for responding to the list of goods to be transferred or the list of goods to be stored, to obtain a list of goods to be shipped out, to filter goods that can coexist in the same temperature range according to the temperature range of each of the goods in the list of goods to be shipped out, to form several lists of goods to be stored and the storage environment temperature of the list of goods to be shipped out, to match the cold chain warehouse, and to determine the storage strategy according to the number of matching cold chain warehouses, to generate the storage planning unit for the storage list, wherein, The warehousing strategy includes matching the cold chain warehouses with the best warehousing distance and space utilization based on the list of stored items to be shipped, and matching several cold chain warehouses with the required space ratio based on the total space volume of the list of stored items to be shipped and the warehousing distance, so as to match the cold chain warehouse with the closest warehousing environment temperature and adjust the storage temperature accordingly; The storage environment adjustment module is used to respond to adjustments in storage temperature and adjust the cooling power of the corresponding air conditioner based on the temperature difference and the delivery distance.

[0006] Furthermore, the space utilization rate is the ratio of the occupied shelf volume to the total shelf volume in the three-dimensional point cloud model; The image analysis module uses a deep learning model to identify the outlines of shelves and stored goods in the image, and constructs a three-dimensional point cloud model of the cold chain warehouse based on spatial depth information.

[0007] Furthermore, the storage and transportation planning module determines the turnover coefficient based on the comparative analysis results of the space utilization rate corresponding to the cold chain warehouse and the preset space utilization rate, and the turnover coefficient is directly proportional to the space utilization rate.

[0008] Furthermore, the storage and transportation planning module determines the outbound volume ratio based on the outbound volume of the corresponding cold chain warehouse, obtains the ascending sequence list of the stored items in the cold chain warehouse, and determines the position of the sorting list based on the turnover coefficient to determine the list of stored items to be transferred.

[0009] Furthermore, the warehousing planning unit performs an intersection operation on the storage temperature ranges of each of the items to be shipped out to match several common temperature ranges and determine the list of items to be shipped out.

[0010] Furthermore, the warehousing planning unit determines the most suitable storage temperature for each item to be shipped from the list of items to be shipped based on the storage temperature range of each item, adjusts the corresponding temperature control accuracy coefficient according to the number of times each item is shipped, and determines the warehousing environment temperature based on the most suitable storage temperature and the temperature control accuracy coefficient of each item.

[0011] Furthermore, based on the result that the number of matched cold chain warehouses is greater than 1, the warehousing planning unit determines the warehousing strategy as selecting the optimal cold chain warehouse by using the warehousing distance and space utilization rate of each matched cold chain warehouse. Alternatively, based on the result that the number of matched cold chain warehouses is less than or equal to 1, further judgment is made to determine the warehousing strategy as follows: match several cold chain warehouses with the required space ratio according to the total space volume of the list of stored items to be shipped and the warehousing distance, so as to match the cold chain warehouse with the closest warehousing environment temperature and adjust the storage temperature accordingly.

[0012] Furthermore, the inbound planning unit determines the optimal cold chain warehouse by using the inbound distance and space utilization rate of each matched cold chain warehouse, filters the cold chain warehouses with matching remaining space based on the remaining space ratio of each matched cold chain warehouse, and assigns distance score and remaining space ratio score respectively, and determines a comprehensive score based on the weighted sum of the assigned distance score and remaining space ratio score, so as to select the optimal cold chain warehouse.

[0013] Furthermore, the inbound planning unit determines the distance score as the percentage of the ratio of the inbound distance to the farthest inbound distance of each of the remaining spaces matched by the cold chain warehouses, and the remaining space percentage score as the absolute percentage value corresponding to the remaining space proportion of each of the remaining spaces matched by the cold chain warehouses. Finally, the weighted sum of the distance score and the remaining space percentage score is determined as the comprehensive score.

[0014] Furthermore, the storage environment adjustment module calculates the adjustment temperature difference between the inbound storage environment temperature and the current environment temperature of the target cold chain warehouse. Based on the adjustment temperature difference and the inbound delivery distance, it determines the basic adjustment time by querying a preset adjustment time mapping table. Based on the current space utilization rate of the target cold chain warehouse, it determines the compensation coefficient for the adjustment time. Based on the product of the compensation coefficient and the basic adjustment time, it determines the final adjustment time. Based on the final adjustment time and the adjustment temperature difference, it sets the target cooling power of the refrigeration air conditioner.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: By automatically constructing a 3D point cloud model through the image analysis module, high-precision and automated real-time monitoring of space utilization is achieved, overcoming the drawbacks of low efficiency and large errors in manual inventory counting. Combined with the unique mechanism of identifying and proactively allocating backlogged goods based on the outbound volume sequence list in the circulation planning unit, the industry pain point of slow-moving products occupying core storage locations is effectively solved, realizing a shift from passive storage to proactive optimization, and significantly improving the overall turnover rate and utilization efficiency of warehouse space.

[0016] Furthermore, by employing temperature range intersection operations and multi-level decision tree logic through the warehousing planning unit, the automatic generation of multi-category co-storage schemes and intelligent selection of optimal warehouses are achieved. This method not only ensures the quality and safety of mixed-storage goods but also upgrades warehousing decisions from experience-based fuzzy judgments to data-driven precise calculations by introducing a multi-factor weighted evaluation model that considers distance, space, and value. This achieves an optimal balance between transportation costs and warehousing resource utilization efficiency.

[0017] Furthermore, the storage environment adjustment module employs a predictive control strategy based on temperature difference, delivery distance, and space utilization, enabling precise and advance setting of refrigeration power and adjustment duration. This feedforward-feedback composite control method avoids temperature fluctuations and energy waste associated with traditional control methods, ensuring that the environment is ready when goods enter the warehouse. Simultaneously, it achieves refined management and effective reduction of refrigeration system energy consumption while guaranteeing the quality of stored goods.

[0018] Furthermore, by deeply integrating IoT sensing, big data analytics, and intelligent decision-making algorithms, a closed-loop intelligent management system capable of self-sensing, autonomous decision-making, and automatic execution has been constructed. The close collaboration between modules enables global optimization of the entire cold chain warehousing process. Ultimately, this results in synergistic and amplified benefits across multiple core dimensions, including improved operational efficiency, reduced overall costs, and ensured product quality and safety. This addresses the fundamental problems of fragmented processes and low operational efficiency inherent in traditional warehouse management systems. Attached Figure Description

[0019] Figure 1 This is a system structure block diagram of the IoT-based intelligent cold chain warehousing management system according to an embodiment of the present invention; Figure 2 This is a block diagram of the unit structure connection structure in the data storage module of this invention embodiment; Figure 3 This is a block diagram showing the unit structure connection in the storage and transportation planning module of this invention. Figure 4 This is a block diagram showing the connection relationships of the storage and transportation planning module in an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0021] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0022] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0023] Please see Figures 1-4 As shown, Figure 1 This is a system structure block diagram of the IoT-based intelligent cold chain warehousing management system according to an embodiment of the present invention; Figure 2 This is a block diagram of the unit structure connection structure in the data storage module of this invention embodiment; Figure 3 This is a block diagram showing the unit structure connection in the storage and transportation planning module of this invention. Figure 4 This is a block diagram showing the connection relationships of the storage and transportation planning module in an embodiment of the present invention.

[0024] The present invention provides an IoT-based intelligent cold chain warehousing management system, comprising: The Internet of Things (IoT) module is connected to several cold chain warehouses to acquire the ambient temperature and images within a single cold chain warehouse, and to send adjustment commands to the temperature control device of the cold chain warehouse. The image analysis module is used to respond to images inside the cold chain warehouse to determine the space utilization rate of the cold chain warehouse; The data storage module includes a first data storage unit for storing product parameters of the stored goods, and a second data storage unit for storing outbound data from the cold chain warehouse. Product parameters include storage temperature range, volume, and temperature control accuracy coefficient; The storage and transportation planning module includes a flow planning unit that, in response to outbound operations, determines the turnover coefficient based on the space utilization rate of the corresponding cold chain warehouse, and determines a list of stored items to be transferred based on the turnover coefficient and historical outbound data. And to respond to the list of goods to be transferred or the list of goods to be stored, to obtain the list of goods to be shipped out, to filter the goods to be stored that can coexist in the same temperature range according to the temperature range of each item in the list of goods to be shipped out, to form several lists of goods to be stored and the inbound storage environment temperature of the lists of goods to be shipped out, to match the cold chain warehouse, and to determine the inbound strategy according to the number of matching cold chain warehouses, to generate an inbound planning unit for the inbound list, wherein, The inbound strategy includes matching each cold chain warehouse with the optimal inbound distance and space utilization based on the list of stored items to be shipped, and matching several cold chain warehouses with the required space ratio based on the total space volume and inbound distance of the list of stored items to be shipped, so as to match the cold chain warehouse with the closest inbound storage environment temperature and adjust the storage temperature accordingly. The storage environment adjustment module is used to respond to adjustments in storage temperature and adjust the cooling power of the corresponding air conditioner based on the temperature difference and the delivery distance.

[0025] Specifically, space utilization rate is the ratio of the occupied shelf volume to the total shelf volume in the 3D point cloud model; Among them, the image analysis module uses a deep learning model to identify the outlines of shelves and stored goods in images, and constructs a three-dimensional point cloud model of the cold chain warehouse based on spatial depth information.

[0026] In this embodiment of the invention, the deep learning model is an instance segmentation model based on Mask R-CNN. This model has been trained on a large number of warehouse shelves and goods images and can accurately segment each shelf and storage unit in the image. The spatial depth information is provided by the binocular depth camera in the image acquisition device. The image analysis module combines the segmented contours with the depth information and generates a dense three-dimensional point cloud model of the cold chain warehouse through point cloud registration and three-dimensional reconstruction algorithms. By calculating the ratio of the total volume of the points marked as occupied to the total volume of the points marked as the total volume of the shelves in the point cloud model, the accurate space utilization rate is automatically calculated.

[0027] Specifically, the storage and transportation planning module determines the turnover coefficient based on the comparative analysis results of the space utilization rate of the cold chain warehouse and the preset space utilization rate. The turnover coefficient is directly proportional to the space utilization rate.

[0028] In this embodiment of the invention, the preset space utilization rate is set to 85%, and the comparative analysis is achieved through a piecewise function: when the real-time space utilization rate is lower than this threshold, the turnover coefficient is set to a baseline value of 1.0. When the utilization rate is between 85% and 95%, the turnover coefficient increases linearly from 1.0 to 2.0; When the utilization rate is higher than 95%, the turnover coefficient is fixed at the maximum value of 2.5. This design ensures that the priority of goods leaving the warehouse in the high utilization rate is significantly improved, thereby actively optimizing the overall warehouse space.

[0029] Specifically, the storage and transportation planning module determines the proportion of outbound volume based on the outbound volume of the stored goods in the corresponding cold chain warehouse, obtains the ascending sequence list of the stored goods in the cold chain warehouse, and determines the position of the selected sorting list based on the turnover coefficient, thus determining the list of stored goods to be transferred.

[0030] In this embodiment of the invention, the storage and transportation planning module retrieves all outbound records of stored items within 90 days from the second data storage unit.

[0031] The formula for calculating the percentage of outbound volume is: ; This percentage quantitatively reflects the relative turnover rate of each stored item.

[0032] Generate an outbound volume ascending sequence table: Sort all stored items in the current warehouse in ascending order according to their percentage of outbound volume, generating an ordered list.

[0033] Technical effect: In this list, the higher the ranking of the stored goods, the lower their turnover rate, and they are considered as backlogged goods that need to be prioritized for release. This enables the automatic and accurate identification of targets to be processed from massive amounts of data.

[0034] The selection position is determined based on the turnover coefficient: The turnover coefficient K is a real number greater than or equal to 1. It is determined by the space utilization rate of the warehouse. The higher the space utilization rate, the larger the value of K.

[0035] The system determines the cutoff position N for selecting stored items from the ascending sequence list based on the turnover coefficient K. The decision logic is as follows: Set a base selection number N_base: For example, always select the top 5 stockpiled items in the list.

[0036] The final selected cutoff position N = K × N_base.

[0037] Example: If the base quantity N_base is 5, and the warehouse space utilization rate is normal, the turnover coefficient K=1, then the system selects the top 5 items in the list. When the warehouse space utilization rate increases, K=1.8, then the system selects the top 9 items in the list (1.8×5=9). This indicates that the fuller the warehouse, the greater the system's efforts to clear backlogged goods, and the wider the selection range.

[0038] Generate a list of storage items to be transferred: Finally, all the stored items in the ascending sequence from position 1 to position N are summarized to generate a list of stored items to be transferred.

[0039] The list will be sent to the warehousing planning unit, which will then find and allocate target warehouses for these stockpiled goods, thereby completing the proactive transfer.

[0040] Understandably, this solution is logically clear, easy to implement, and stable, making it a commonly used method in industrial control.

[0041] The details are as follows: The turnover coefficient K is determined based on the real-time space utilization rate U of the cold chain warehouse (calculated as: U = occupied volume / total volume × 100%). The system compares the real-time space utilization rate U with a set of preset utilization rate thresholds, as shown in Table 1: Table 1: Logical Explanation Table for Determining the Value of K ; Basis for determination: The above thresholds (60%, 85%, 95%) and corresponding K values ​​are configurable parameters set by those skilled in the art based on common warehouse management experience. In actual deployment, administrators can fine-tune these thresholds and K values ​​through the management interface according to the importance and business characteristics of different warehouses.

[0042] Specifically, the warehousing planning unit performs an intersection calculation on the storage temperature ranges of each item to be shipped out, in order to match several common temperature ranges and determine the list of items to be shipped out.

[0043] In this embodiment of the invention, the maximum common temperature range F is calculated according to the following formula: ; in, Let be the minimum value of the storage temperature range for the i-th stored item to be taken out of the warehouse. To find the maximum value of the storage temperature range for the items to be released from storage, the items are arranged in ascending or descending order of their storage temperature ranges during the calculation. The maximum common temperature range F is then calculated. Whenever F is an empty set, indicating that a common temperature range has been matched, the storage temperature range of the last calculated item to be released from storage is taken as the first storage temperature range for the next calculation. This process continues until the storage temperature ranges for all items to be released from storage have been calculated.

[0044] Specifically, the warehousing planning unit determines the most suitable storage temperature for each item to be shipped from the list of items to be shipped, based on the storage temperature range of each item in the list of items to be shipped. It also adjusts the corresponding temperature control accuracy coefficient according to the number of times the items are shipped, and determines the warehousing environment temperature based on the most suitable storage temperature and temperature control accuracy coefficient of each item.

[0045] In this embodiment of the invention, the optimal storage temperature is typically the median of the storage temperature range. When determining the warehousing environment temperature for the entire inventory, the system introduces a value weight, increasing the weight of the optimal temperature for goods with high temperature control accuracy within the inventory. The final warehousing environment temperature T for the inventory is calculated using the following formula: ; in, It represents the optimal temperature for the i-th stored item to be shipped out. The selling price of the i-th item to be released from storage is used. This method ensures that the storage environment of high-value goods is closer to their optimal state, so as to maximize their quality and value.

[0046] Specifically, based on the result that the number of matching cold chain warehouses is greater than 1, the warehousing planning unit determines the warehousing strategy to select the optimal cold chain warehouse by considering the warehousing distance and space utilization of each matching cold chain warehouse. Alternatively, based on the result that the number of matching cold chain warehouses is less than or equal to 1, further judgment is made to determine the warehousing strategy as follows: match several cold chain warehouses with the required space ratio according to the total space volume of the list of stored items to be shipped and the warehousing distance, so as to match the cold chain warehouse with the closest storage environment temperature and adjust the storage temperature accordingly.

[0047] Specifically, the inbound planning unit determines the optimal cold chain warehouse by considering the inbound distance and space utilization of each matched cold chain warehouse. Based on the remaining space ratio of each matched cold chain warehouse, it filters cold chain warehouses with matching remaining space, assigning distance scores and remaining space ratio scores respectively. Finally, it determines a comprehensive score based on the weighted sum of the assigned distance scores and remaining space ratio scores, and selects the optimal cold chain warehouse.

[0048] In this embodiment of the invention, "remaining space matching" means that the remaining space must be greater than the total volume of the goods to be stored. The calculation of distance score and remaining space percentage score aims to normalize indicators of different dimensions and then conduct a comprehensive evaluation, that is, the shorter the distance, the higher the score, and the larger the remaining space percentage, the higher the score. In the weighted summation, the distance weight is set to 0.4 and the spatial weight is set to 0.6.

[0049] Specifically, the inbound planning unit determines the distance score as the percentage of the ratio of the inbound distance to the farthest inbound distance of each remaining space matched cold chain warehouse to the distance value of 1. It also determines the remaining space percentage score based on the absolute percentage value corresponding to the remaining space proportion of each remaining space matched cold chain warehouse. Finally, the weighted sum of the distance score and the remaining space percentage score is determined as the comprehensive score.

[0050] In this embodiment of the invention, the formula for calculating the distance score is: ; This formula guarantees that the warehouse with the shortest distance will receive the highest score of 100 points, while the warehouse with the farthest distance will receive 0 points. The score for the remaining space percentage is the remaining space percentage × 100; The overall score S_total = α × distance score + β × remaining space percentage score, where α is the distance weight and β is the space weight. Finally, the warehouse with the highest overall score is selected as the optimal choice.

[0051] Specifically, the storage environment adjustment module calculates the temperature difference between the incoming storage environment temperature and the current environment temperature of the target cold chain warehouse. Based on the temperature difference and the delivery distance, it determines the basic adjustment duration by querying a preset adjustment duration mapping table. It determines the compensation coefficient for the adjustment duration based on the current space utilization rate of the target cold chain warehouse. It determines the final adjustment duration by multiplying the compensation coefficient and the basic adjustment duration. Based on the final adjustment duration and the temperature difference, it sets the target cooling power of the refrigeration air conditioner.

[0052] In this embodiment of the invention, the adjustment time mapping table is pre-set based on historical data and a thermodynamic model. It reflects the approximate time required to adjust an empty warehouse to the target temperature under different initial temperature differences and different delivery distances (distance affects the time at which adjustments can begin). The compensation coefficient is used to consider the heat capacity effect of existing goods in the warehouse. The higher the space utilization rate, the greater the heat capacity, and the longer the time required to adjust to the same temperature. Therefore, the compensation coefficient K = 1 + γ × current space utilization rate (γ is a correction factor greater than 0). Ultimately, the target cooling power P is obtained through the formula: ; Where c is a constant related to the warehouse structure and insulation performance; This method enables temperature adjustment to be completed with optimal energy consumption within a precise timeframe.

[0053] Understandably, in the storage environment adjustment module, the calculation of the compensation coefficient K and the target cooling power P involves two key parameters: γ and c. Their values ​​are determined based on the following: 1. Regarding the heat capacity compensation coefficient γ Physical meaning: The parameter γ is a dimensionless empirical coefficient used to quantify the impact of existing storage in a warehouse on the overall heat capacity. Its value reflects the percentage increase in adjustment time resulting from a unit space utilization rate (e.g., for every 1% increase in space utilization).

[0054] Value range: Typically, the value of γ ranges from 0.2 to 0.8.

[0055] A lower γ value: 0.2-0.4, is suitable for scenarios where the stored goods have low specific heat capacity and low thermal inertia; For example, frozen foods, most of which are packaged in plastic.

[0056] A higher γ value: 0.5-0.8 is suitable for scenarios where the stored goods have high specific heat capacity and high thermal inertia. For example, fruits and vegetables with high water content, and liquid dairy products.

[0057] Determination Method: The specific value of this coefficient can be determined through a limited number of routine experiments. The specific method is as follows: Select an empty target warehouse (0% space utilization), adjust its temperature from T1 to T2, and record the required time T_empty. Subsequently, load the same warehouse to 50% space utilization, and perform the same temperature adjustment from T1 to T2 again, recording the required time T_50. The coefficient γ can then be approximately calculated using the following formula: ; Those skilled in the art can determine a reasonable γ value suitable for their specific warehousing system by conducting a small number of experiments using the methods described above, based on the type of their main stored products.

[0058] 2. Regarding the power calculation constant c Physical meaning: Parameter c is a comprehensive physical constant with the dimension of energy / (temperature × time), i.e., power / temperature. It comprehensively reflects the total heat capacity of the target cold chain warehouse, including the building structure, air and fixed equipment, as well as the energy efficiency ratio (COP) of the refrigeration system.

[0059] Basis for value selection: Parameter c is not a general constant, but is strongly related to the physical characteristics of a specific cold chain warehouse. It essentially represents the rate at which the heat C needs to be removed to maintain the refrigeration capacity when the temperature of the warehouse is reduced by 1°C.

[0060] Determination method: The value of parameter c can be determined in any of the following ways: Calculation based on design specifications: Estimation of c using warehouse design parameters: ; The total heat capacity can be calculated from the specific heat capacity and mass of the building materials.

[0061] In summary, this invention establishes a universal and precise control model adaptable to different warehouse structures and cargo types by introducing parameters γ and c. Those skilled in the art can determine the applicable parameter values ​​based on their specific warehouse and cargo conditions without inventive effort, simply through conventional experiments or historical data fitting methods mentioned in the specification, thereby realizing this invention. Therefore, the above description fully meets the patent law's requirement of "sufficient disclosure."

[0062] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention; various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An intelligent management system for cold chain warehousing based on the Internet of Things, characterized in that, include: The Internet of Things (IoT) module is connected to several cold chain warehouses to acquire the ambient temperature and images within a single cold chain warehouse, and to send adjustment commands to the temperature control device of the cold chain warehouse. An image analysis module is used to respond to images within the cold chain warehouse to determine the space utilization rate of the cold chain warehouse; The data storage module includes a first data storage unit for storing product parameters of the stored goods, and a second data storage unit for storing outbound data from the cold chain warehouse. The product parameters include storage temperature range, space volume, number of circulations, and temperature control accuracy coefficient. The initial value of the temperature control accuracy coefficient is automatically generated based on the storage temperature range during storage. The storage and transportation planning module includes a flow planning unit that, in response to outbound operations, determines a turnover coefficient based on the space utilization rate of the corresponding cold chain warehouse, and determines a list of stored items to be transferred based on the turnover coefficient and historical outbound data. And a storage planning unit for responding to the list of goods to be transferred or the list of goods to be stored, to obtain a list of goods to be shipped out, to filter goods that can coexist in the same temperature range according to the temperature range of each of the goods in the list of goods to be shipped out, to form several lists of goods to be stored and the storage environment temperature of the list of goods to be shipped out, to match the cold chain warehouse, and to determine the storage strategy according to the number of matching cold chain warehouses, to generate the storage planning unit for the storage list, wherein, The warehousing strategy includes matching the cold chain warehouses with the best warehousing distance and space utilization based on the list of stored items to be shipped, and matching several cold chain warehouses with the required space ratio based on the total space volume of the list of stored items to be shipped and the warehousing distance, so as to match the cold chain warehouse with the closest warehousing environment temperature and adjust the storage temperature accordingly; The storage environment adjustment module is used to respond to adjustments in storage temperature and adjust the cooling power of the corresponding air conditioner based on the temperature difference and the delivery distance.

2. The IoT-based intelligent cold chain warehousing management system according to claim 1, characterized in that, The space utilization rate is the ratio of the occupied shelf volume to the total shelf volume in the three-dimensional point cloud model; The image analysis module uses a deep learning model to identify the outlines of shelves and stored goods in the image, and constructs a three-dimensional point cloud model of the cold chain warehouse based on spatial depth information.

3. The IoT-based intelligent cold chain warehousing management system according to claim 2, characterized in that, The storage and transportation planning module determines the turnover coefficient based on the comparative analysis results of the space utilization rate of the cold chain warehouse and the preset space utilization rate. The turnover coefficient is directly proportional to the space utilization rate.

4. The IoT-based intelligent cold chain warehousing management system according to claim 3, characterized in that, The storage and transportation planning module determines the outbound volume ratio based on the outbound volume of the corresponding cold chain warehouse, obtains the ascending sequence list of the stored items in the cold chain warehouse, and determines the position of the sorting list based on the turnover coefficient to determine the list of stored items to be transferred.

5. The IoT-based intelligent cold chain warehousing management system according to claim 4, characterized in that, The warehousing planning unit performs an intersection operation on the storage temperature ranges of each of the items to be shipped out, in order to match several common temperature ranges and determine the list of items to be shipped out.

6. The IoT-based intelligent cold chain warehousing management system according to claim 5, characterized in that, The warehousing planning unit determines the most suitable storage temperature for each item to be shipped from the list of items to be shipped, based on the storage temperature range of each item in the list. It also adjusts the corresponding temperature control accuracy coefficient according to the number of times each item is shipped, and determines the warehousing environment temperature based on the most suitable storage temperature and the temperature control accuracy coefficient of each item.

7. The IoT-based intelligent cold chain warehousing management system according to claim 6, characterized in that, Based on the result that the number of matched cold chain warehouses is greater than 1, the warehousing planning unit determines the warehousing strategy as selecting the optimal cold chain warehouse by using the warehousing distance and space utilization rate of each matched cold chain warehouse. Alternatively, based on the result that the number of matched cold chain warehouses is less than or equal to 1, further judgment is made to determine the warehousing strategy as follows: match several cold chain warehouses with the required space ratio according to the total space volume of the list of stored items to be shipped and the warehousing distance, so as to match the cold chain warehouse with the closest warehousing environment temperature and adjust the storage temperature accordingly.

8. The IoT-based intelligent cold chain warehousing management system according to claim 7, characterized in that, The inbound planning unit determines the optimal cold chain warehouse by considering the inbound distance and space utilization of each matched cold chain warehouse. Based on the remaining space ratio of each matched cold chain warehouse, it filters cold chain warehouses with matching remaining space, assigning distance scores and remaining space ratio scores respectively. A comprehensive score is determined based on the weighted sum of the assigned distance scores and remaining space ratio scores to select the optimal cold chain warehouse.

9. The IoT-based intelligent cold chain warehousing management system according to claim 8, characterized in that, The inbound planning unit determines the distance score as the percentage of the ratio of the inbound distance to the farthest inbound distance of each of the remaining spaces matched with the cold chain warehouses, and the remaining space percentage score as the absolute percentage value corresponding to the remaining space proportion of each of the remaining spaces matched with the cold chain warehouses. The weighted sum of the distance score and the remaining space percentage score is determined as the comprehensive score.

10. The IoT-based intelligent cold chain warehousing management system according to claim 9, characterized in that, The storage environment adjustment module calculates the adjustment temperature difference between the inbound storage environment temperature and the current environment temperature of the target cold chain warehouse. Based on the adjustment temperature difference and the inbound delivery distance, it determines the basic adjustment time by querying a preset adjustment time mapping table. Based on the current space utilization rate of the target cold chain warehouse, it determines the compensation coefficient for the adjustment time. Based on the product of the compensation coefficient and the basic adjustment time, it determines the final adjustment time. Based on the final adjustment time and the adjustment temperature difference, it sets the target cooling power of the refrigeration air conditioner.

Images