Drug and food cold storage monitoring method, system and medium based on internet of things

Abstract

The present application relates to a kind of based on Internet of Things pharmaceutical food cold storage monitoring method, system and medium, method includes: using integrated temperature sensing RFID label acquisition cold store goods information and temperature data, obtain acquisition data;Acquisition data is sent into the four-layer monitoring system of perception-transmission-service-application and is carried out data processing and exception identification, obtain exception identification result, exception identification result includes goods exception and temperature exception;Based on exception identification result, execute automatic inspection, goods exception control and temperature exception control strategy, obtain processing result;Based on exhaustive algorithm and processing result optimization refrigeration equipment scheduling, realize accurate temperature control and energy-saving operation.The present application can realize the synchronous acquisition of goods information and temperature, intelligent identification and linkage control, and using exhaustive algorithm optimizes refrigeration scheduling, can be in time to discover and deal with goods exception and temperature exception, while, improve cold store temperature control precision, reduce energy consumption, guarantee pharmaceutical food storage safety.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control technology, specifically relating to a method, system, and medium for monitoring pharmaceutical and food cold storage based on the Internet of Things. Background Technology

[0002] Food and pharmaceuticals share similar management requirements, including strict control over storage temperature and humidity, high accuracy of information and data, and prominent role of product traceability. This makes the requirements for storage monitoring and process traceability more stringent for pharmaceuticals and food.

[0003] In related technical fields, there are problems such as insufficient precision in monitoring pharmaceutical and food cold storage facilities and lack of intelligent energy consumption optimization, resulting in low monitoring efficiency and increased cargo loss rate and energy consumption. Summary of the Invention

[0004] In view of the above-mentioned defects or deficiencies in the prior art, the present invention proposes a method, system and storage medium for monitoring pharmaceutical and food cold storage based on the Internet of Things.

[0005] This invention provides an IoT-based monitoring method for pharmaceutical and food cold storage facilities, comprising: collecting cargo information and temperature data within the cold storage facility using integrated temperature-sensing RFID tags to obtain collected data; sending the collected data into a four-layer monitoring system (sensing-transmission-service-application) for data processing and anomaly identification to obtain anomaly identification results, including cargo anomalies and temperature anomalies; executing automatic inspection, cargo anomaly control, and temperature anomaly control strategies based on the anomaly identification results to obtain processing results; and optimizing the scheduling of refrigeration equipment based on an exhaustive algorithm and the processing results to achieve precise temperature control and energy-saving operation.

[0006] In one alternative implementation, the integrated temperature-sensing RFID tag employs low-power circuitry, optimized radio frequency energy harvesting, and MOSFET threshold compensation to operate in a low-temperature environment of -20°C.

[0007] In one alternative implementation, the four-layer monitoring system of sensing-transmission-service-application uses one or more combinations of wired Ethernet, 4G, 5G, NB-IoT or WLAN for data transmission.

[0008] In one optional implementation, the automatic inspection involves installing RFID readers on stacker cranes, shuttle cars, AGVs, or AMRs to traverse the storage locations according to a preset route and cycle, triggering tag temperature measurement and uploading data.

[0009] In one optional implementation, the cargo anomaly control includes: on-site verification and equipment inspection when cargo information is missing; marking cargo nearing its expiration date and prioritizing its release; and handling expired cargo according to regulations and synchronizing the information to the warehouse management system (WMS).

[0010] In one optional implementation, the temperature anomaly control includes: locating the coordinates of the overheated cargo location, selecting the corresponding air cooler and compressor to form a refrigeration combination based on distance priority, starting local precision refrigeration, and automatically shutting down after the temperature returns to the set range.

[0011] In one optional implementation, the exhaustive algorithm first calculates and sorts the straight-line distance between the overheating point and each air cooler, then enumerates different refrigeration combination schemes, and determines the optimal refrigeration scheme with the goal of minimizing the total cost.

[0012] This invention also proposes an Internet of Things-based monitoring system for pharmaceutical and food cold storage, comprising:

[0013] An integrated temperature-sensing RFID tag is used to collect information about goods and temperature data in the cold storage and obtain the collected data; an RFID reader / writer device, together with the integrated temperature-sensing RFID tag, transmits the collected data to a four-layer monitoring platform; the four-layer monitoring platform (sensing-transmission-service-application) is used to process the collected data and identify anomalies, obtain anomaly identification results, and perform automatic inspection, cargo anomaly control, and temperature anomaly control based on the anomaly identification results, obtaining processing results; a refrigeration control module is used to optimize the scheduling of refrigeration equipment based on the processing results and combined with an exhaustive algorithm to achieve precise temperature control and energy-saving operation.

[0014] In one optional implementation, the four-layer monitoring platform is equipped with a data verification unit, a threshold management unit, a device linkage unit, and an alarm unit that use standardized interfaces to achieve data interoperability and collaborative work.

[0015] The present invention also proposes a storage medium storing a computer program, which, when executed by a processor, implements any of the IoT-based pharmaceutical and food cold storage monitoring methods described in the present invention.

[0016] The beneficial technical effects of this invention are as follows: This invention achieves synchronous collection, intelligent identification and linkage control of cargo information and temperature by integrating temperature-sensing RFID tags with a four-layer Internet of Things monitoring system, and optimizes refrigeration scheduling by using an exhaustive algorithm. It can improve the temperature control accuracy of cold storage, reduce energy consumption and ensure the safety of pharmaceutical and food storage while timely detecting and handling cargo and temperature anomalies. Attached Figure Description

[0017] Other features, objects, and advantages of this application will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0018] Figure 1 A flowchart illustrating an embodiment of the Internet of Things-based monitoring method for pharmaceutical and food cold storage provided by the present invention;

[0019] Figure 2 A structural diagram of a temperature-sensing RFID tag for a pharmaceutical and food cold storage monitoring method based on the Internet of Things provided in an embodiment of the present invention;

[0020] Figure 3 A monitoring system architecture diagram of an IoT-based pharmaceutical and food cold storage monitoring method provided in an embodiment of the present invention;

[0021] Figure 4 The automatic inspection flowchart of the IoT-based pharmaceutical and food cold storage monitoring method provided in an embodiment of the present invention is shown below.

[0022] Figure 5 A flowchart illustrating cargo anomaly control in an IoT-based pharmaceutical and food cold storage monitoring method provided in an embodiment of the present invention.

[0023] Figure 6 A schematic diagram of a traditional temperature monitoring layout for a pharmaceutical and food cold storage monitoring method based on the Internet of Things provided in an embodiment of the present invention;

[0024] Figure 7 A schematic diagram of the improved temperature monitoring layout of the IoT-based pharmaceutical and food cold storage monitoring method provided in an embodiment of the present invention;

[0025] Figure 8 A flowchart illustrating the temperature anomaly control process of an IoT-based pharmaceutical and food cold storage monitoring method provided in an embodiment of the present invention. Detailed Implementation

[0026] Currently, many pharmaceutical and food companies have not fully automated their warehousing, relying on traditional shelving and forklifts and manual labor for inbound and outbound operations. Typically, after production, goods are manually transported to the preparation area using forklifts. Each item has a unique identification code on its packaging, which is scanned by the ERP system for inbound storage. The shelving rules and path logic are based on experience, modifying the cloud-based warehouse database to complete the inbound process. When goods need to be shipped out, they are first manually transported to the designated outbound area. Upon arrival, they await dispatch to personnel who retrieve the required quantity based on the order and deliver it to the appropriate truck. The warehouse database is synchronized throughout this process. However, because each type of item needs to be moved from the shelving to a fixed outbound location, discrepancies arise between the quantity at each location and the total quantity in the database, necessitating manual counting. The current outbound method makes accurate order tracking difficult, and order requests cannot be precisely traced down to the individual person and the goods.

[0027] The pharmaceutical and food industries are characterized by complex product categories and high time sensitivity. Warehouse operations require sophisticated order consolidation and management, necessitating daily inventory checks, dynamic inventory counts, and a comprehensive inventory review monthly. Currently, companies conduct daily inventory checks manually by scanning identification codes, a method that is inefficient, prone to omissions and errors. Furthermore, this efficiency limitation prevents dynamic tracking and real-time monitoring of individual goods, hinders timely differentiation between batches, and makes intelligent handling of near-expiry products impossible. Additionally, the low-temperature environment of cold storage, coupled with the diverse types of pharmaceuticals and food products, varied packaging, and irregular arrangement, demands high accuracy from inventory counting equipment. This is particularly true in high-rise automated warehouses with multi-tiered racking, where environmental factors such as lighting and angles can interfere with data reading, leading to failures or errors and further complicating the identification process.

[0028] Pharmaceuticals and food have more stringent and demanding storage requirements than other categories, including constant temperature and humidity control in refrigeration and freezing. Precise monitoring and real-time control of warehouse temperature and humidity are crucial to prevent spoilage. However, the monitoring capabilities of most existing cold storage facilities are insufficient to meet the sophisticated management requirements of pharmaceuticals and food. Pharmaceutical companies typically monitor temperature and humidity by installing smart sensors in a few specific locations such as warehouse walls and shelves. These sensors upload data in real time and issue alerts when temperatures or humidity exceed set thresholds. At this point, staff activate the air conditioning compressor to regulate the warehouse temperature and humidity back to the preset range before shutting it off. While this method provides real-time monitoring, the limited number of monitoring points prevents accurate control of the temperature and humidity of every item, leading to missed monitoring points and potential damage. Furthermore, the lack of a well-optimized intelligent decision-making relationship between temperature and humidity monitoring and the start / stop of the air conditioning compressor results in imprecise temperature and humidity control and wasted air conditioning energy.

[0029] Against the backdrop of digital and intelligent development, how to conduct intelligent monitoring of pharmaceutical and food warehouses has become a core issue for pharmaceutical and food manufacturers and related logistics service providers. However, problems such as poor monitoring equipment performance and low efficiency of monitoring strategies have limited the development of intelligent warehouse monitoring technology. There is an urgent need for an intelligent monitoring technology method that is tailored to the characteristics of pharmaceutical and food cold storage and addresses the pain points and difficulties in the storage of pharmaceutical and food products.

[0030] Radio Frequency Identification (RFID) is a typical passive Internet of Things (IoT) technology and the most mature IoT technology based on wireless electromagnetic energy capture and power supply. As a core technology of the IoT sensing layer, RFID has shown broad application prospects. Because RFID can effectively improve the convenience and precision of data transmission, it can be primarily applied in the field of information monitoring, especially for data such as temperature and humidity, which are critical to product performance and difficult to monitor using traditional methods. It has already been widely used in logistics, retail, and pharmaceuticals.

[0031] This application addresses three prominent issues in pharmaceutical and food warehousing: insufficient precision and timeliness in monitoring content and methods; limited effectiveness of monitoring equipment and systems; and difficulty in meeting the specific needs of pharmaceuticals and foods during monitoring. Through innovative design integrating temperature-sensing RFID tag IoT technology, a monitoring system architecture and intelligent temperature control model are established. An intelligent decision-making algorithm is proposed to determine the applicability of RFID technology, enabling automated inspection and intelligent monitoring of goods and temperature anomalies in pharmaceutical and food warehousing. This reduces cargo damage and lowers cold storage energy consumption, thereby achieving cost reduction and efficiency improvement.

[0032] This application optimizes the design of RFID tags with integrated temperature sensing and a multi-level monitoring system, overcoming the challenges of equipment adaptation and accurate identification in low-temperature environments of cold storage, and improving the application system of the Internet of Things in warehousing scenarios. Addressing the characteristics of strict temperature and humidity control and prominent traceability requirements in pharmaceutical and food storage, it solves the pain points of insufficient precision and timeliness of traditional monitoring and high damage rates, providing a practical solution for product quality assurance and distribution safety.

[0033] The following is in conjunction with the appendix Figures 1 to 8 The present application will be further described in detail with reference to the embodiments. It is understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0034] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0035] Please refer to Figure 1 This invention provides an Internet of Things (IoT)-based method for monitoring pharmaceutical and food cold storage facilities, comprising the following steps:

[0036] Step S101: Use integrated temperature-sensing RFID tags to collect information on goods and temperature data in the cold storage to obtain the collected data.

[0037] By using integrated temperature-sensing RFID tags to collect data, cargo information and temperature data can be obtained simultaneously, enabling one item, one code for accurate traceability and improving the completeness and real-time performance of data collection.

[0038] Step S103: Send the collected data into the four-layer monitoring system of perception-transmission-service-application for data processing and anomaly identification to obtain anomaly identification results, including cargo anomalies and temperature anomalies.

[0039] By using a four-layer system of perception, transmission, service, and application for data processing and anomaly identification, it is possible to efficiently distinguish between cargo anomalies and temperature anomalies, thereby improving the accuracy of anomaly judgment and the stability of system operation.

[0040] Step S105: Based on the anomaly identification results, execute automatic inspection, cargo anomaly control, and temperature anomaly control strategies to obtain the processing results.

[0041] Based on abnormal results, the system automatically executes inspections and corresponding control strategies, enabling rapid response to abnormal situations, reducing manual intervention, and improving the intelligence level and efficiency of cold storage management.

[0042] Step S107: Optimize the scheduling of refrigeration equipment based on exhaustive algorithm and processing results to achieve precise temperature control and energy-saving operation.

[0043] By using an exhaustive algorithm combined with the processing results to optimize the scheduling of refrigeration equipment, it is possible to achieve precise temperature control in cold storage, ensuring the quality of pharmaceutical and food storage, and also to optimize energy consumption distribution, thus achieving energy-saving operation.

[0044] The integrated temperature-sensing RFID tag employs low-power circuitry, optimized radio frequency energy harvesting, and MOSFET threshold compensation to operate in environments as low as -20°C. The RFID tag structure is as follows: Figure 2 As shown.

[0045] The four-layer monitoring system (sensing-transmission-service-application) uses one or more combinations of wired Ethernet, 4G, 5G, NB-IoT, or WLAN for data transmission. The monitoring system architecture diagram is shown below. Figure 3 As shown

[0046] Automated inspection involves installing RFID readers on stacker cranes, shuttle cars, AGVs, or AMRs to traverse the storage locations according to preset routes and cycles, triggering tag temperature measurement and uploading data.

[0047] RFID reader terminals are installed on automated equipment such as stacker cranes, shuttles, automated guided vehicles (AGVs), and autonomous mobile robots (AMRs), and are linked to the system platform via a WLAN network. Warehouse staff can pre-set the inspection intervals for automated equipment on the system platform, and can also set up inventory checks for all goods or categories of goods, i.e., set an inspection plan. Once confirmed, the plan is synchronized to the reader terminals. When the inspection time arrives, the automated equipment will pass through each aisle in the shelving at a constant speed along the set route, continuously emitting communication signals. After receiving the signal from the reader terminal, each RFID tag will perform a temperature measurement. The temperature data will first be stored in the tag's memory, and then transmitted to the reader terminal along with other static information of the goods. The terminal will temporarily store all monitoring information. When the automated equipment returns to its initial position, the terminal synchronizes the inspection data to the system platform. The system platform then evaluates the inspection results. If no abnormalities are found, the monitoring information is stored in the database, and the inspection ends. If abnormalities are detected, staff will be prompted to investigate and handle the situation: for example, if missing static information of goods is detected, staff will be prompted to check for missing goods or other abnormalities; if goods are nearing or past their expiration date, the system will report this to the WMS for appropriate processing; if the temperature of a certain item exceeds a threshold, the system will issue corresponding instructions to the air conditioning system. These operations can also be performed manually by staff using a handheld terminal. The automatic inspection strategy is as follows: Figure 4 As shown.

[0048] Furthermore, cargo anomaly control includes the following steps:

[0049] Step S201: When cargo information is missing, conduct on-site verification and equipment inspection;

[0050] Step S203: Mark the goods when they are nearing their expiration date and adjust them for priority shipment;

[0051] Step S205: When goods expire, handle them according to regulations and synchronize them to the Warehouse Management System (WMS).

[0052] When the system platform alerts staff to cargo anomalies, it will prompt staff to perform corresponding operations and report back to the WMS. If cargo data is missing, indicating suspected cargo loss, staff must use a terminal to locate the abnormal cargo and check for loss. If not lost, check for communication issues and clear the anomaly alert; if cargo is indeed lost, confirm the loss, synchronize the information to the WMS, and handle it according to warehouse management procedures. If the system alerts staff that cargo is nearing its expiration date, it will automatically mark it in the WMS and adjust the outbound strategy, prioritizing near-expiration cargo. However, if cargo's expiration date has reached the minimum threshold or exceeded its expiration date, making further circulation impossible, staff will be prompted to handle the cargo promptly according to company management regulations to prevent spoiled cargo from affecting other normal cargo or the warehouse environment. This information will also be synchronized to the WMS to update the in-stock cargo information. Cargo anomaly control strategies are as follows: Figure 5 As shown.

[0053] Temperature anomaly control includes: locating the coordinates of the overheated cargo location, prioritizing the selection of the corresponding air cooler and compressor to form a refrigeration combination based on distance, starting local precision refrigeration, and automatically shutting down after the temperature returns to the set range.

[0054] Stable temperature control is crucial for ensuring proper cold storage conditions. This application only introduces the design of temperature control; control strategies for other environmental factors such as humidity can be referenced and implemented. In traditional cold storage temperature control, temperature sensors are installed at the air conditioning coolers, and air conditioning ducts are laid throughout the warehouse. The coolers are distributed in different locations, and multiple compressors are typically required to cover the overall cooling needs of the warehouse. Traditional cold storage temperature monitoring and air conditioning layout methods are as follows: Figure 6 As shown. Taking a cold storage facility as an example, the temperature generally needs to be controlled between -18 and -25 degrees Celsius. The traditional control strategy involves issuing a cooling command to the compressor when a temperature sensor detects a temperature above -18 degrees Celsius. Without intelligent control, all compressors will operate simultaneously, distributing cold air to all air coolers until the temperature at all sensor points returns to -25 degrees Celsius or below. Therefore, the traditional control strategy has two major problems: First, there are too few monitoring points, resulting in blind spots in temperature monitoring. This can easily lead to situations where the temperature in a certain area exceeds the threshold but cooling is not timely, potentially causing spoilage of temperature-sensitive pharmaceuticals and foods, resulting in waste. Second, there is no intelligent control over multiple air conditioning compressors and air coolers, and precise cooling cannot be achieved. The simultaneous start and stop of all compressors and air coolers leads to wasted cold air and energy. To address the above issues, RFID devices with integrated temperature sensing can achieve precise temperature measurement at all points, down to the cargo level. This allows for the immediate detection of abnormal temperatures, enabling precise control of the corresponding compressors to start refrigeration, thus reducing costs and increasing efficiency. A schematic diagram of the improved cold storage temperature monitoring layout is shown below. Figure 7 As shown.

[0055] When the system platform alerts to a temperature anomaly, the system first identifies the location information of the corresponding point. Then, through model algorithms, it automatically determines the location and number of air coolers to be activated, matches the corresponding number of compressors to be started, and issues activation commands to the compressors and air coolers. Simultaneously, automated equipment carrying a reader terminal moves to the anomaly location, performs multiple temperature measurements at that point, and stops the compressor once the temperature drops to -25 degrees Celsius. The automated equipment is then instructed to return to its original position, completing the temperature control operation. Furthermore, intelligent algorithms can automatically identify areas where cargo exhibits multiple temperature anomalies within a short period, indicating potential cold air leaks, exothermic reactions in the cargo, or other abnormalities. This prompts staff to investigate and address the issue, thereby timely detection and prevention of energy waste or cargo damage. Temperature anomaly control strategies are as follows: Figure 8 As shown.

[0056] To ensure that smart technologies truly reduce costs and increase efficiency for enterprises, rather than blindly applying them and increasing total costs, thus harming enterprise interests, a method for determining the boundaries of technology investment is first needed. This section constructs a cost model that, under a series of boundary conditions, directly compares the cost differences between applying and not applying RFID smart monitoring solutions, providing enterprises with a basis for deciding whether to invest in smart technologies.

[0057] Based on determining a suitable RFID intelligent monitoring solution, an exhaustive algorithm can automatically determine the appropriate air conditioning control and refrigeration scheme to be adopted when the temperature in a certain area of ​​the cold storage exceeds the threshold, thereby achieving optimal overall cost.

[0058] The exhaustive algorithm first calculates and sorts the straight-line distances between the overheating point and each air cooler, then enumerates different refrigeration combination schemes, and determines the optimal refrigeration scheme with the goal of minimizing the total cost.

[0059] To better characterize the problem scenario and establish an efficient decision-making model, based on the intelligent monitoring solution for cold storage, the following description and assumptions are made regarding the intelligent control problem of cold storage air conditioning:

[0060] (1) such as Figure 7 As shown, the cold storage is a cuboid warehouse with shelves evenly distributed inside, totaling N storage locations. A air coolers are evenly distributed on the roof of the warehouse. By establishing a three-dimensional coordinate system, the relative positions of each storage location and each air cooler can be described using coordinates.

[0061] (2) Assume that the cold storage is fully stocked, each storage location has 1 unit of goods, and there are N units of goods in the storage. The average value of each unit of goods is V.

[0062] (3) Each unit of goods is affixed with an RFID tag. The amortization price of the tag is r. The amortization price of all other investments in implementing the intelligent monitoring system, excluding the tag, such as terminals, robots, and networks, is R. The amortization price refers to the total price / amortization days, which is the daily cost of using the intelligent technology.

[0063] (4) All air coolers are connected to G compressors. The starting power of each air cooler is p, and each compressor has a minimum working power P (to simplify the model, it is assumed that the minimum working power of the compressor can be reached when 2 air coolers start together). When more than 2 air coolers start at the same time, the power of the compressor = number of air coolers * p / COP, where COP is the coefficient of performance of the compressor.

[0064] (5) Assume that the storage temperature requirement for the goods is T0~T1, and the cooling capacity required to reduce the temperature from T1 to T0 is T, and the electricity cost is e.

[0065] (6) Assume that when the goods are in an environment with a temperature above T1, the rate of spoilage per unit time is k.

[0066] (7) The daily probability of cargo loss is k when no intelligent monitoring solution is applied. m Assuming the cargo loss rate is 0 after applying the intelligent monitoring solution.

[0067] Table 1 provides the symbol and parameter definitions.

[0068] Table 1. Model Symbols and Parameter Definitions

[0069]

[0070] Taking the point n with the highest temperature within the n' storage areas as the target, calculate the straight-line distance between this point and each of the A air coolers, and sort the distances from closest to farthest as s1, s2, ..., s. A Therefore, there are A cooling schemes: turning on the 1st, 2nd, 3rd, ... up to the Ath air cooler closest to point n. The schemes of turning on 1st to Ath air coolers are denoted as the a-th cooling scheme. When the system detects an over-temperature point, it will automatically traverse and calculate the cost function of A schemes using an exhaustive algorithm, compare their values, and automatically execute the scheme with the lowest cost. The cost function model of the a-th cooling scheme is as follows:

[0071] ; (1)

[0072] in, Let T be the time required for the temperature at point n to drop from T1 to T0. According to the jet intensity superposition method, the cooling contribution of each air outlet to this point decreases with the square of the distance, and the total cooling capacity is the sum of the contributions of each air outlet. Let n' be the value of damage to n' goods per unit time. The full amortization cost of warehouse intelligent monitoring technology, This is the sum of the power of the compressor and the air cooler. Since the compressor has a minimum operating power, therefore... The function is a piecewise function, as shown below:

[0073] ; (2)

[0074] Assuming the same over-temperature situation occurs without intelligent monitoring, and due to the lack of intelligent control, when the temperature at the evaporative cooler exceeds T1, all evaporative coolers and compressors will start simultaneously. In this case, the cost function is:

[0075] ; (3)

[0076] Because of the lack of cargo-level temperature sensing, temperature anomalies are not detected in a timely manner. Furthermore, to simplify the model, we simply assume that the temperature sensor at the evaporative cooler requires the same amount of time as the cooling cycle to detect when point n exceeds the threshold. Therefore, the cargo loss is... Nk m V represents the cost of lost goods.

[0077] Based on the exhaustive algorithm, an algorithm was developed that can automatically calculate and compare different schemes. This allows the calculation results to be directly linked to the intelligent control of cold storage air conditioning, enabling intelligent control of abnormal cold storage temperatures. This achieves the goal of reducing costs and increasing efficiency while minimizing cargo loss, thus ensuring energy conservation.

[0078] An Internet of Things (IoT) based monitoring system for pharmaceutical and food cold storage facilities includes:

[0079] Integrated temperature-sensing RFID tags are used to collect information on goods and temperature data in cold storage and obtain the collected data;

[0080] The RFID reader / writer, along with the integrated temperature-sensing RFID tag, transmits the collected data to the four-layer monitoring platform.

[0081] The four-layer monitoring platform of perception-transmission-service-application is used to process the collected data and identify anomalies, obtain anomaly identification results, and perform automatic inspection, cargo anomaly control and temperature anomaly control based on the anomaly identification results to obtain processing results;

[0082] The refrigeration control module is used to optimize the scheduling of refrigeration equipment based on the processing results and combined with an exhaustive algorithm, so as to achieve precise temperature control and energy-saving operation.

[0083] The four-layer monitoring platform is equipped with a data verification unit, a threshold management unit, a device linkage unit, and an alarm unit that use standardized interfaces to achieve data interoperability and collaborative work.

[0084] like Figure 3 As shown, based on RFID tags with integrated temperature sensing and combined with the requirements of pharmaceutical and food companies for monitoring systems, a warehouse monitoring system architecture is designed. This system architecture is divided into four layers: monitoring and sensing layer, network transmission layer, data service layer, and application system layer.

[0085] The monitoring and sensing layer refers to the tag carriers and hardware terminal equipment responsible for data collection, mainly including: self-adhesive electronic tags for non-metallic packaging such as cardboard boxes, wooden boxes, and plastic boxes; cable ties for unpackaged spare parts or metal spare parts; handheld terminals or industrial tablet handheld terminals for spare parts inventory and warehouse management; and RFID printers for printing information on the tag surface and initializing chip data.

[0086] The network transmission layer refers to the communication channel for data interaction between the monitoring and sensing layer and the data service layer. It adopts a hybrid "wired + wireless" transmission mode to ensure the stability and flexibility of data transmission. Industrial Ethernet (fiber optic / network cable) transmission is used in the inbound and outbound areas to meet the real-time data transmission needs of critical equipment such as label printers. Wireless transmission technologies such as 4G / 5G and NB-IoT are used within the warehouse area to adapt to the data transmission scenarios of electronic tags.

[0087] The data service layer will manage data and execute tasks, including data query, data verification, channel management, device management, protocol management, threshold management, data acquisition and debugging, and template management.

[0088] The application system layer is the human-computer interaction interface application entry point that presents the system functions. It is an operating system platform deployed on a PC, and its main functions include online monitoring, risk analysis, early warning classification, decision support, and coordinated response.

[0089] The monitoring system connects with the warehouse management system and the air conditioning and refrigeration system to achieve dynamic management of stored goods and intelligent, efficient and precise control of warehouse temperature.

[0090] This application focuses on the technical requirements, existing problems, and solutions for intelligent monitoring of pharmaceutical and food cold storage facilities, aiming to reduce costs and increase efficiency in warehouse management through IoT technology. Firstly, based on enterprise surveys, it constructs a technical requirement framework for intelligent warehouse monitoring, covering monitoring content, methods, equipment, systems, and processes. Secondly, it accurately identifies three core industry problems: insufficient monitoring precision and timeliness, limited equipment system effectiveness, and difficulty in meeting special storage needs during monitoring. Finally, it innovatively designs RFID tags integrating temperature sensing, builds a four-layer monitoring system of "sensing-transmission-service-application," and proposes three strategies: automatic inspection, cargo anomaly control, and temperature anomaly control, forming a complete solution. The solution has been validated through engineering, effectively solving problems such as high reliance on manual labor, low identification accuracy, and blind spots in temperature sensing monitoring in traditional warehousing. It achieves dynamic tracking of goods throughout their entire lifecycle and precise environmental control, significantly reducing damage rates and energy consumption, improving warehousing operational efficiency and compliance, and providing a practical technical path for the "intelligent transformation, digitalization, and networking" of the pharmaceutical and food industry.

[0091] The present invention also proposes a storage medium storing a computer program, which, when executed by a processor, implements an Internet of Things-based method for monitoring pharmaceutical and food cold storage facilities.

[0092] In the above embodiments, the storage medium may include, but is not limited to, various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0093] Optionally, the processor executes the method steps described in the above embodiments based on the program code stored in the storage medium.

[0094] Specific examples in this embodiment can be found in the examples described in the above embodiments and optional implementations, and will not be repeated here.

[0095] Obviously, those skilled in the art should understand that the modules or steps of the present disclosure described above can be implemented using general-purpose computing devices, which can be centralized on a single computing device or distributed on a network of multiple computing devices.

[0096] Alternatively, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device. Furthermore, in some cases, the steps shown or described can be performed in a different order than those described herein, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, this disclosure is not limited to any particular combination of hardware and software.

[0097] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the inventive concept. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for monitoring pharmaceutical and food cold storage facilities based on the Internet of Things, characterized in that, include: Integrated temperature-sensing RFID tags are used to collect information on goods and temperature data in the cold storage, resulting in the collected data; The collected data is sent to a four-layer monitoring system of perception-transmission-service-application for data processing and anomaly identification to obtain anomaly identification results, which include cargo anomalies and temperature anomalies. Based on the anomaly identification results, automatic inspection, cargo anomaly control, and temperature anomaly control strategies are executed to obtain the processing results; The scheduling of refrigeration equipment is optimized based on the exhaustive algorithm and the processing results to achieve precise temperature control and energy-saving operation.

2. The IoT-based monitoring method for pharmaceutical and food cold storage according to claim 1, characterized in that, The integrated temperature-sensing RFID tag employs low-power circuitry, optimized radio frequency energy harvesting, and MOSFET threshold compensation to operate in a low-temperature environment of -20℃.

3. The IoT-based monitoring method for pharmaceutical and food cold storage according to claim 1, characterized in that, The four-layer monitoring system of perception-transmission-service-application uses one or more combinations of wired Ethernet, 4G, 5G, NB-IoT or WLAN for data transmission.

4. The IoT-based monitoring method for pharmaceutical and food cold storage according to claim 1, characterized in that, The automatic inspection involves installing RFID readers on stacker cranes, shuttle cars, AGVs, or AMRs, which then traverse the storage locations according to a preset route and cycle, triggering tag temperature measurement and uploading data.

5. The IoT-based monitoring method for pharmaceutical and food cold storage according to claim 4, characterized in that, The cargo anomaly control includes: When cargo information is missing, on-site verification and equipment checks should be conducted. Goods nearing their expiration date should be marked and prioritized for shipment. When goods expire, they should be disposed of in accordance with regulations and the information should be synchronized to the Warehouse Management System (WMS).

6. The IoT-based monitoring method for pharmaceutical and food cold storage according to claim 1, characterized in that, The temperature anomaly control includes: locating the coordinates of the overheated cargo location, prioritizing the selection of the corresponding air cooler and compressor to form a refrigeration combination based on distance, starting local precision refrigeration, and automatically shutting down after the temperature returns to the set range.

7. The IoT-based monitoring method for pharmaceutical and food cold storage according to claim 1, characterized in that, The exhaustive algorithm first calculates and sorts the straight-line distances between the overheating point and each air cooler, then enumerates different refrigeration combination schemes, and determines the optimal refrigeration scheme with the goal of minimizing the total cost.

8. A monitoring system for pharmaceutical and food cold storage based on the Internet of Things, characterized in that, include: Integrated temperature-sensing RFID tags are used to collect information on goods and temperature data in cold storage and obtain the collected data; An RFID reader / writer, together with the integrated temperature-sensing RFID tag, transmits the collected data to a four-layer monitoring platform. The four-layer monitoring platform of perception-transmission-service-application is used to process the collected data and identify anomalies, obtain anomaly identification results, and perform automatic inspection, cargo anomaly control and temperature anomaly control based on the anomaly identification results to obtain processing results. The refrigeration control module is used to optimize the scheduling of refrigeration equipment based on the processing results and in combination with an exhaustive algorithm, so as to achieve precise temperature control and energy-saving operation.

9. The IoT-based pharmaceutical and food cold storage monitoring system according to claim 1, characterized in that, The four-layer monitoring platform is equipped with a data verification unit, a threshold management unit, a device linkage unit, and an alarm unit that use standardized interfaces to achieve data interoperability and collaborative work.

10. A storage medium, characterized in that, The device contains a computer program that, when executed by a processor, implements the Internet of Things-based monitoring method for pharmaceutical and food cold storage as described in any one of claims 1 to 7.

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