Medical refrigerator temperature and humidity wireless monitoring system and method

Abstract

The present application relates to the technical field of medical equipment monitoring, and particularly relates to a wireless temperature and humidity monitoring system and method for medical refrigerators. The system of the present application comprises a distributed wireless sensing module, an edge data processing module, a cloud monitoring platform, an intelligent early warning module and a blockchain storage module. The distributed wireless sensing terminal adopts a cellular distribution mode, integrates a high-precision temperature and humidity sensor, a laser scattering compensation component and an RFID-WiFi dual-mode communication unit, realizes global accurate collection and reliable transmission; the edge data processing module generates a three-dimensional temperature and humidity distribution model through space-time calibration and temperature field reconstruction, and predicts potential equipment failures; the intelligent early warning module performs hierarchical early warning and links the standby refrigeration unit; the blockchain storage module stores all data on the chain to ensure that the data cannot be tampered with. The present application is suitable for intelligent temperature and humidity monitoring and compliance control in medical material storage scenes such as vaccines, blood products and biological reagents.

Description

Technical Field

[0001] This invention relates to the field of medical equipment monitoring technology, and in particular to a wireless temperature and humidity monitoring system and method for medical refrigerators. Background Technology

[0002] Medical refrigerators are core storage devices for special medical supplies such as vaccines, medicines, blood, and biological samples. Their temperature and humidity stability directly affects medical quality, patient safety, and public health safety. Domestic and international regulations concerning medical and health care, drug regulation, and hospital infection control, such as Good Supply Practice (GSP), requirements for cold chain management in medical institutions, and biosafety management standards, all clearly stipulate that medical refrigeration / freezing equipment must continuously monitor, record, and alarm on temperature and humidity 24 hours a day, and be traceable.

[0003] Traditional monitoring methods rely on manual periodic checks and handwritten records, which are prone to problems such as missed records, incorrect records, interrupted records, and untimely detection. In the event of a power outage, a door not being closed properly, or equipment failure, there is no way to issue an early warning in time, which can easily lead to the waste of a large number of materials and is difficult to meet the stringent requirements of clinical practice and supervision.

[0004] To overcome the shortcomings of manual monitoring, various automated monitoring solutions have been developed in the industry. However, existing technologies still have the following deficiencies: 1. Incomplete monitoring coverage and blind spots in the temperature field. Existing temperature and humidity monitoring solutions for medical refrigerators mostly use fixed point probes for temperature measurement, and the limited sensors can only collect local data. For example, Chinese utility model patent CN211695594U discloses a mobile refrigerator temperature monitoring device, which does not integrate humidity monitoring function and cannot meet the regulatory and practical needs for dual-parameter temperature and humidity monitoring in medical cold chain. Chinese utility model patent CN219532130U is a general design for civilian refrigerators and does not consider the special requirements of medical refrigerators storing vaccines, biological agents, and blood products. It lacks accurate temperature and humidity control, anti-condensation / frost design, and data traceability function. In actual use, there is a significant temperature gradient inside the medical refrigerator due to the distribution of shelves and cooling vents. The temperature difference between the door seal area, corners, and the middle of the refrigerator can reach 3-5℃. Single-point data cannot reflect the true storage environment inside the refrigerator. Although some existing technologies have proposed to place multiple temperature sensors in different positions in the cabinet, they only achieve multi-point temperature measurement and do not involve dual temperature and humidity monitoring, let alone integrate multi-point data into a three-dimensional temperature field visualization model through algorithms.

[0005] 2. Weak anti-interference capability and poor transmission stability. Hospital settings are characterized by dense medical equipment and complex electromagnetic environments. Existing wireless IoT monitoring technologies rely on single communication modules, which are easily shielded by metal enclosures. Signal blind spots easily occur in hospital basements and shielded areas, resulting in poor transmission stability and a data packet loss rate of 5% to 15%. Additional signal amplifiers are required in basements and shielded equipment rooms, increasing hospital operating costs. Although Chinese invention patent CN121462630A discloses a cold chain medicine temperature warning system using a 433MHz self-organizing network and NB-IoT / 4G remote transmission dual-mode communication, its dual-mode solution does not address the shielding problem of metal enclosures in medical refrigerators, lacks an automatic and seamless switching mechanism, and does not involve redundant backups for edge processing and local storage.

[0006] 3. Measurement distortion and lack of dynamic calibration. The surface of sensors inside medical refrigerators is prone to condensation and sample obstruction, leading to data drift. Capacitive humidity sensors are highly susceptible to temperature coupling; for every 1°C change in temperature, the humidity error is approximately ±0.5% RH; long-term use can result in drift of ±5% RH, requiring calibration every 6-12 months. Chinese invention patent CN120538608A discloses an intelligent temperature and humidity monitoring system for medical refrigerators, which employs a laser scattering compensation algorithm to dynamically identify measurement distortion caused by condensation and biological sample obstruction. However, this solution does not organically integrate laser scattering compensation with multi-point distributed sensing and three-dimensional temperature field reconstruction technologies, leaving a risk that local measurement deviations may affect overall judgment.

[0007] 4. Early warning relies on the cloud and lacks preventative maintenance capabilities. Existing solutions mostly rely on servers comparing thresholds before issuing alarm commands, failing to identify hidden anomalies such as short-term fluctuations and slow drifts, leading to missed alarms. If communication between the server and the host is interrupted, the local system cannot trigger alarms autonomously, posing a risk of "alarm failure." Although some solutions disclose a three-tiered early warning mechanism, its triggering still relies on comparison and judgment by platform-level modules. Furthermore, existing technologies generally lack the ability to predict potential faults such as refrigeration system degradation and door seal failure, allowing only passive responses and increasing the risk of material deterioration.

[0008] 5. Insufficient compliance and susceptibility to data tampering. Current mainstream temperature and humidity monitoring technologies mainly include manual inspection and paper recording, and wired sensors paired with local recorders for automatic data storage. Manual inspection involves reading data daily at set times using mechanical or electronic thermometers built into the refrigerator and making handwritten records. Manual recording is prone to omissions, errors, additions, and tampering. Existing methods for storing monitoring data using wired sensors are mostly local storage or ordinary cloud storage, which cannot meet the industry compliance requirements of immutability and traceability throughout the entire lifecycle of cold chain data. Although there are solutions that apply blockchain technology to cold chain temperature monitoring, they only achieve data recording for single temperature monitoring, do not cover both temperature and humidity parameters, and are not deeply integrated with the specific monitoring scenario of medical refrigerators.

[0009] In summary, existing medical refrigerator temperature and humidity monitoring technologies still have significant shortcomings in terms of comprehensive coverage, anti-interference ability, measurement accuracy, early warning reliability, and compliance. Therefore, there is an urgent need in this field for an integrated monitoring solution that can achieve accurate data collection across the entire area, stable transmission, intelligent analysis, hierarchical early warning, and compliant evidence storage. Summary of the Invention

[0010] This invention aims to overcome the shortcomings of existing technologies and provide an integrated wireless monitoring solution for temperature and humidity of medical refrigerators, enabling precise data collection, stable transmission, intelligent analysis, hierarchical early warning, and compliant data storage across the entire domain.

[0011] To achieve the above objectives, the present invention provides a wireless temperature and humidity monitoring system and method for medical refrigerators.

[0012] A wireless temperature and humidity monitoring system for a medical refrigerator includes a main body of the medical refrigerator and a monitoring and control system installed inside and outside the main body of the medical refrigerator. The monitoring and control system includes: A distributed wireless sensing module is used to collect temperature and humidity data from multiple points inside a medical refrigerator. An edge data processing module is used to receive, calibrate, and analyze the data collected by the distributed wireless sensing module; The cloud-based monitoring platform is used for centralized management and display of the data uploaded by the edge data processing module; The intelligent early warning module is used to provide tiered early warnings based on the data. The blockchain evidence storage module is used to store the data in an immutable manner; The distributed wireless sensing module includes distributed wireless sensing terminals located in multiple positions inside the medical refrigerator, and the number of the distributed wireless sensing terminals is no less than 6, and they are distributed in a honeycomb pattern in the top central area, the middle area of ​​the shelf, the four corner areas of the bottom and the door seal area. Each of the distributed wireless sensing terminals includes: a high-precision temperature and humidity sensor; a laser scattering compensation component for detecting condensation, frost, or obstruction interference on the sensor surface and compensating for and correcting the sampled data; an RFID-WiFi dual-mode communication unit for automatically switching communication modes in case of communication abnormalities; and a low-power power supply module. The distributed wireless sensing terminal is also configured to sample according to an adaptive sampling strategy. When the temperature and humidity fluctuation is less than or equal to 0.3℃, the sampling interval is a first duration. When the temperature and humidity fluctuation is greater than 0.3℃, the sampling interval is a second duration, and the second duration is less than the first duration. The edge data processing module includes: The spatiotemporal calibration unit is used to eliminate sampling time differences and hardware deviations between different sensing terminals; Temperature field reconstruction unit, used to generate a three-dimensional temperature and humidity distribution model inside the medical refrigerator based on multi-point data; The fault prediction unit is used to identify potential faults such as refrigeration system degradation, door seal failure, or condenser frost based on temperature and humidity change characteristics. The intelligent early warning module is used to divide multiple early warning levels according to the degree of temperature and humidity deviation from the preset threshold, and output local and / or remote early warning signals of the corresponding level when the preset conditions are met, and output linkage control signals to start the backup cooling unit when the highest level early warning is triggered. The blockchain evidence storage module is used to perform hash processing on the original collected data, correction data, analysis data, early warning records, and equipment maintenance records, and store them on the blockchain.

[0013] Furthermore, the high-precision temperature and humidity sensor has a temperature measurement accuracy of ±0.1℃, a humidity measurement accuracy of ±2%RH, and an operating temperature range covering -80℃ to 60℃.

[0014] Furthermore, the first duration is 5 minutes, and the second duration is 1 minute.

[0015] Furthermore, the RFID-WiFi dual-mode communication unit is configured to automatically switch to another communication mode for data transmission when the signal of the current communication mode is lower than a preset threshold or is interrupted.

[0016] Furthermore, the intelligent early warning module constructs a three-level hierarchical early warning mechanism, specifically including: Level 1 warning is triggered when the temperature and humidity deviate from the set threshold for less than 5 minutes, or when the temperature difference collected by different distributed wireless sensor terminals is greater than or equal to 3°C and the temperature does not exceed the limit. When triggered, the local audible and visual alarm will issue the first warning signal. Level 2 warning is triggered when the temperature and humidity deviate from the set threshold for a duration between 5 and 15 minutes. When triggered, the local audible and visual alarm will issue a second warning signal and send the warning information to the designated person in charge via remote communication. The Level 3 warning is triggered when the temperature and humidity deviate from the set threshold for more than 15 minutes. When triggered, the local audible and visual alarm will issue a third warning signal and generate a linkage control signal to start the backup cooling unit.

[0017] Furthermore, the first warning signal is a flashing green indicator light, the second warning signal is a constantly lit yellow indicator light accompanied by intermittent beeping, and the third warning signal is a constantly lit red indicator light accompanied by continuous loud beeping.

[0018] Furthermore, the cloud-based monitoring platform also includes a multi-material dedicated benchmark library, which presets and stores temperature and humidity thresholds and monitoring rules corresponding to various medical materials such as vaccines, blood products, biological reagents, and diagnostic reagents; by selecting the type of material to be stored, the cloud-based monitoring platform automatically matches the corresponding temperature and humidity thresholds, monitoring rules, and / or sampling strategies.

[0019] Furthermore, the consortium blockchain nodes of the blockchain evidence storage module include hospitals, disease control centers, and drug regulatory authorities. The blockchain evidence storage module also supports regulatory authorization access, and regulatory authorities can verify the authenticity and integrity of monitoring data through dedicated nodes.

[0020] Furthermore, the edge data processing module is also configured to: when a potential fault is identified, generate a prompt message containing the fault type and maintenance suggestions, and upload the prompt message to the cloud monitoring platform.

[0021] A method for wirelessly monitoring the temperature and humidity of a medical refrigerator based on the system of claim 1, comprising: S1. Sensor Deployment and Initialization: Deploy distributed wireless sensor terminals according to a cellular deployment method, and complete device registration and parameter configuration; S2. Data Acquisition and Compensation: Temperature and humidity data are acquired based on an adaptive sampling strategy, and data with interference is corrected by a laser scattering compensation component; S3. Edge processing and modeling: Spatiotemporal calibration of the collected data and construction of a three-dimensional temperature and humidity distribution model; S4. Data Upload and Control: Upload data to the cloud-based monitoring platform and synchronize it to the blockchain evidence storage module; S5. Early Warning and Emergency Response: Based on threshold triggering, graded early warnings are issued, and linkage control is executed when a high-level early warning is triggered; when the highest level early warning is triggered, it also includes starting the backup cooling unit and issuing a door locking reminder; S6. Data Traceability and Compliance: Generate monitoring reports based on stored data and support regulatory verification.

[0022] The beneficial effects of this invention are as follows: First, this invention effectively eliminates the temperature blind spots of traditional single-point monitoring by using a cellular deployment scheme for the sensing terminals combined with the three-dimensional temperature field reconstruction of the edge data processing module; the laser scattering compensation component can detect and correct measurement distortions caused by condensation, frost, or sample obstruction in real time, ensuring high accuracy of temperature and humidity measurements; the RFID-WiFi dual-mode communication unit automatically and seamlessly switches when the signal is abnormal, overcoming the limitations of metal enclosure shielding and electromagnetic interference, ensuring continuous and reliable data transmission; the adaptive sampling strategy effectively reduces power consumption while ensuring monitoring accuracy, meeting the requirements for long-term stable operation.

[0023] Secondly, this invention constructs a multi-level hierarchical early warning mechanism, which triggers local audible and visual alarms, remote information pushes, and linkage control based on the degree and duration of temperature and humidity deviations. When the highest level of warning is triggered, the backup refrigeration unit is automatically activated and the door opening is restricted, solving the problem of traditional solutions relying on the cloud for early warning and having the risk of "alarm failure". The edge data processing module has a built-in fault prediction unit, which actively identifies and predicts potential faults such as refrigeration system attenuation, door sealing failure, and condenser frosting by continuously analyzing the fluctuation patterns and change rates of temperature and humidity, realizing the transformation from "passive response" to "active prevention" and significantly reducing the risk of material deterioration.

[0024] Third, this invention employs a blockchain evidence storage module with a consortium blockchain architecture to store all original collected data, correction data, analysis data, early warning records, and equipment maintenance records on the blockchain. This supports authorized access and remote compliance verification by nodes such as hospitals, disease control centers, and drug regulatory departments, meeting the stringent industry requirements for the immutability and traceability of cold chain monitoring data throughout its entire lifecycle. The cloud-based monitoring platform has a built-in multi-material-specific benchmark library with preset temperature and humidity thresholds and monitoring rules for different medical materials. Users only need to select the type of material to be stored to automatically complete the parameter configuration, greatly simplifying the system deployment and maintenance process. Attached Figure Description

[0025] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will now be described in detail with reference to the accompanying drawings, wherein... Figure 1 This is a structural block diagram of a wireless temperature and humidity monitoring system for a medical refrigerator according to an embodiment of the present invention; Among them, 1-distributed wireless sensing terminal, 2-edge data processing module, 3-cloud monitoring platform, 4-intelligent early warning module, 5-blockchain evidence storage module, 6-medical refrigerator body, 7-shelf, 8-backup refrigeration unit, 9-laser scattering compensation component, 10-RFID-WiFi dual-mode communication unit, 11-lithium battery power supply module, 12-control panel; Figure 2 This is a schematic block diagram of the honeycomb layout of the distributed wireless sensing terminal in an 800L medical refrigerator according to an embodiment of the present invention. Figure 3 This is a schematic diagram of the honeycomb layout of the distributed wireless sensing terminal in an 800L medical refrigerator according to an embodiment of the present invention. Figure 4 This is a flowchart illustrating a method for wirelessly monitoring the temperature and humidity of a medical refrigerator according to an embodiment of the present invention. Detailed Implementation

[0026] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Unless otherwise specified, the following embodiments and features can be combined with each other.

[0027] The accompanying drawings are for illustrative purposes only and are schematic diagrams, not actual pictures, and should not be construed as limiting the invention. To better illustrate the embodiments of the invention, some parts in the drawings may be omitted, enlarged, or reduced, and do not represent the actual product dimensions. It is understandable to those skilled in the art that some well-known structures and their descriptions may be omitted in the drawings.

[0028] Example 1 This embodiment provides a wireless temperature and humidity monitoring system for a medical refrigerator, and its system structure block diagram is as follows. Figure 1 As shown, the system mainly comprises five core modules: a distributed wireless sensing module 1, an edge data processing module 2, a cloud-based monitoring platform 3, an intelligent early warning module 4, and a blockchain-based evidence storage module 5. Each module operates independently yet is deeply interconnected. Through the collaborative work of hardware and software, it achieves comprehensive and accurate data collection, stable transmission, intelligent analysis, tiered early warning, and compliant evidence storage of temperature and humidity within the medical refrigerator.

[0029] 1. Distributed wireless sensing module This module is the core of the sensing layer of this system and adopts a cellular deployment scheme. To achieve comprehensive coverage of the three-dimensional temperature and humidity field inside the medical refrigerator and eliminate the blind spots of traditional single-point monitoring, no fewer than six distributed wireless sensing terminals 1 are deployed inside the main body 6 of a single medical refrigerator. These sensing terminals 1 are scientifically arranged in key locations based on factors such as the refrigerator volume and the distribution of shelves 7, such as the central area at the top, the middle area of ​​each shelf 7, the four corner areas at the bottom, and the door seal area.

[0030] Specifically, each sensing terminal integrates: (1) A high-precision temperature and humidity sensor is used to collect the temperature and humidity values ​​of the location. The temperature measurement accuracy of the sensor is ±0.1℃, the humidity measurement accuracy is ±2%RH, and the working temperature range covers -80℃ to 60℃, thus adapting to different types of medical refrigerators such as refrigeration, freezing, and ultra-low temperature refrigerators.

[0031] (2) Laser scattering compensation component 9 is used to detect the physical state of the surface of the high-precision temperature and humidity sensor in real time, such as whether there are interference factors such as condensation, frost, or obstruction by biological samples. When the above interference is detected, the component corrects the collected temperature and humidity sampling data through a preset compensation algorithm, thereby effectively avoiding measurement distortion.

[0032] (3) RFID-WiFi dual-mode communication unit 10, used to wirelessly transmit the collected and corrected temperature and humidity data to the edge data processing module 2. This unit supports automatic switching between RFID communication mode and WiFi communication mode. When the signal of the currently used communication mode is interrupted or falls below a preset threshold, the unit can seamlessly switch to another communication mode for data transmission, thereby overcoming the shielding of wireless signals by the metal body of the medical refrigerator and the interference of the complex electromagnetic environment of the hospital, ensuring the continuity and reliability of data transmission.

[0033] (4) Low-power power supply module, i.e., in this embodiment Figure 1 The lithium battery power supply module 11 shown includes a built-in lithium battery and power management circuitry. This module provides operating power to the sensing terminal 1 and supports USB charging and online remote calibration, with a single charge providing a battery life of no less than 6 months. Furthermore, each sensing terminal 1 also incorporates an adaptive sampling algorithm. This algorithm is configured to: when the detected temperature and humidity fluctuation is less than or equal to 0.3℃, control the sampling interval of the sensing terminal 1 to a first duration, such as 5 minutes; when the temperature and humidity fluctuation is greater than 0.3℃, automatically shorten the sampling interval to a second duration, such as 1 minute. By dynamically adjusting the sampling frequency, the device's power consumption is minimized while ensuring monitoring accuracy.

[0034] 2. Edge data processing module like Figure 1As shown, this module is the local data processing hub of this system, and its core is the ESP32 microcontroller. Preferably, this edge data processing module 2 is integrated into the original control panel 12 of the medical refrigerator, and is used to receive, calibrate, analyze and preprocess data from various distributed wireless sensing terminals in real time.

[0035] This module has the following built-in features: (1) Temperature field reconstruction algorithm is used to integrate the temperature and humidity data collected and corrected by all distributed sensing terminals 1. By eliminating the sampling time difference and hardware deviation between different terminals through spatiotemporal calibration, the data consistency is improved, and a three-dimensional temperature field visualization model of the inside of the medical refrigerator is generated. The model can intuitively present the temperature and humidity distribution, differences and real-time change trends of different areas inside the refrigerator.

[0036] (2) Equipment fault prediction algorithm, used to analyze the fluctuation patterns and change rates of temperature and humidity data, thereby identifying and predicting potential faults such as performance degradation of the refrigeration system, door seal failure, condenser frosting, and blockage of refrigeration pipes. When a potential fault is identified, this module will generate maintenance prompts containing the fault type and suggestions.

[0037] 3. Cloud-based monitoring platform This platform is a centralized control center built on a cloud server, supporting unified access and remote management of distributed wireless sensor terminals across multiple hospital areas and multiple medical refrigerators. The platform has the following functions: a. Temperature field visualization: Real-time reception and display of 3D temperature field models and real-time temperature and humidity curves of each medical refrigerator uploaded by edge data processing module 2.

[0038] b. Multi-dimensional data query, supporting users to query and replay historical data by device number, time range, temperature and humidity range, etc.

[0039] c. Automatic report generation: Automatically generate routine monitoring reports such as daily, weekly, and monthly reports.

[0040] d. A dedicated benchmark database for multiple medical supplies, pre-set and stored temperature and humidity thresholds and monitoring rules for various medical supplies such as vaccines, blood products, biological reagents, and diagnostic reagents. During system initialization, users only need to select the type of medical supply to be stored, and the platform will automatically match and configure the relevant parameters.

[0041] e. Device management, used to view the operating status, battery level, communication signal strength and other information of each sensor terminal 1 and refrigerator device in real time, and supports remote calibration, parameter modification and firmware upgrade.

[0042] 4. Intelligent Early Warning Module This module establishes a three-tiered early warning mechanism, classifying temperature and humidity deviations from set thresholds into three levels: Level 1 (Slight Deviation), Level 2 (Significant Deviation), and Level 3 (Severe Risk). Each level is matched with differentiated early warning methods and response strategies to achieve accurate early warning and rapid response. Specifically, it includes: Level 1 Warning (Slight Deviation). Triggered when temperature and humidity deviate from the set threshold but to a minor degree, such as a duration of less than 5 minutes, or a temperature difference between the main and auxiliary sensors greater than or equal to 3°C but the temperature does not exceed the limit. Upon triggering, the local audible and visual alarm will issue the first warning signal, such as a flashing green indicator light.

[0043] Level 2 Warning (Significant Deviation). Triggered when the temperature and humidity deviate significantly from the set threshold or the duration of deviation increases, for example, when the duration is between 5 and 15 minutes. Upon triggering, the local audible and visual alarm emits a second warning signal, such as a solid yellow indicator light accompanied by intermittent beeping; simultaneously, a remote warning message is sent to the designated responsible person via APP push, SMS, email, etc.

[0044] Level 3 Warning (Severe Risk). Triggered when temperature and humidity deviate significantly from the set threshold or remain abnormal for an extended period, such as exceeding 15 minutes. Upon triggering, the local audible and visual alarm emits a third warning signal, such as a constantly lit red indicator light accompanied by a continuous loud beep. Simultaneously with sending remote warning information, this module also generates and sends a linkage control signal to the medical refrigerator's control system to activate the backup refrigeration unit 8 and can issue a door lock reminder to prevent unauthorized personnel from opening the refrigerator and exacerbating temperature and humidity fluctuations.

[0045] 5. Blockchain Evidence Storage Module This module is built using a consortium blockchain architecture, and its nodes can include hospitals, disease control centers, drug regulatory departments, etc. This module receives all data from the cloud-based monitoring platform 3 and stores it on the blockchain in real time. The all-data includes: raw data collected by distributed sensing terminals 1, data corrected by the edge data processing module 2, analysis data from the cloud platform, early warning and response records from the intelligent early warning module 4, and equipment maintenance records, etc. Hash verification technology ensures that the data is tamper-proof during transmission and storage. This module also supports authorized access by regulators, allowing regulatory departments to directly verify the authenticity and integrity of monitoring data through dedicated nodes, achieving remote compliance verification.

[0046] Example 2 This embodiment uses an 800L medical refrigerator used for storing vaccines in a tertiary hospital as an example to illustrate the system implementation and workflow of the present invention in detail. The refrigerator has four shelves. Figure 4 As shown, the wireless temperature and humidity monitoring method for medical refrigerators of the present invention mainly includes the following steps: S1. Sensor Deployment and Initialization Based on the volume, number of shelves, and internal structure of the medical refrigerator, distributed wireless sensor terminals 1 are installed in a honeycomb layout, ensuring that the terminals are unobstructed and maintain a 5cm distance from the refrigerator wall to avoid contact measurement deviations. All sensor terminals 1 are connected to the hospital's local network, and terminal registration, device binding, and information entry are completed through the cloud monitoring platform 3. The entered information includes the refrigerator number, installation location, and type of stored materials. Staff select the corresponding type of stored materials on the cloud platform, and the platform automatically matches the preset temperature and humidity thresholds, early warning rules, and sampling strategies. After completing the system parameter configuration, all sensor terminals 1 enter a low-power standby state, waiting to start data collection.

[0047] S11: As Figure 2 and Figure 3 As shown, eight distributed wireless sensor terminals 1 are deployed inside the 800L medical refrigerator. The specific layout is as follows: one in the center of the top of the refrigerator, one in the middle of each of the four shelves, and one in each of the four corners of the bottom of the refrigerator. All sensor terminals 1 are kept 5cm away from the inner wall of the refrigerator and are avoided from areas with strong interference such as the cooling air vents.

[0048] S12: Connect all sensor terminals 1 to the hospital's local WiFi network, complete terminal registration and binding with the refrigerator equipment through the cloud monitoring platform 3, and enter information such as refrigerator number and installation location.

[0049] S13: Staff select "vaccine" as the type of stored material on the cloud monitoring platform 3. The platform automatically matches and presets parameters from the multi-material exclusive benchmark database: temperature threshold 2~8℃, humidity threshold 35~75%RH; and configures three-level early warning thresholds.

[0050] S2, Data Acquisition and Compensation The sensor terminal 1 initiates temperature and humidity data acquisition according to the adaptive sampling strategy. During the acquisition process, the laser scattering compensation component 9 detects in real time whether there is interference such as condensation, sample obstruction, or frost on the sensor. If interference is detected, the acquired data is immediately corrected through the laser scattering compensation algorithm to eliminate measurement distortion and generate valid raw data. The valid raw data is transmitted to the edge data processing module 2 through the RFID-WiFi dual-mode communication unit 10. During the transmission, the system detects the communication status in real time. If the single-mode communication is interrupted or packet loss occurs, it automatically and seamlessly switches to another communication mode to ensure continuous and lossless data transmission.

[0051] S21: After the system completes initialization, all sensor terminals 1 enter the low-power acquisition state and collect temperature and humidity data inside the refrigerator in real time according to the preset adaptive sampling strategy, that is, sampling once every 5 minutes when the temperature fluctuation is ≤0.3℃ and sampling once every 1 minute when the temperature fluctuation is >0.3℃.

[0052] S22: During the data collection process, the laser scattering compensation component 9 of the sensing terminal 1 on a certain shelf 7 detects the occlusion interference caused by the close proximity of the vaccine sample box, and then performs real-time correction on the temperature and humidity data collected by the terminal through the preset laser scattering compensation algorithm to generate valid raw data.

[0053] S23: The corrected data is transmitted to the edge data processing module 2 via the RFID-WiFi dual-mode communication unit 10. Assuming a brief fluctuation occurs in the hospital's WiFi network during transmission, the communication unit automatically and seamlessly switches to RFID communication mode to ensure no data loss.

[0054] S3, Edge Processing and Modeling Edge data processing module 2 receives valid raw data from each sensor terminal 1 in real time. It first performs spatiotemporal calibration of multi-terminal data to eliminate sampling time difference and hardware deviation. Then, it integrates the calibrated data through temperature field reconstruction algorithm to generate a three-dimensional temperature field visualization model inside the medical refrigerator, which intuitively presents the temperature and humidity distribution and real-time changes of each area inside the refrigerator. At the same time, it analyzes the changing patterns of temperature and humidity data through fault prediction algorithm to identify potential faults such as refrigeration system degradation and door sealing failure. If a potential fault is found, it immediately generates a maintenance prompt and uploads it to the cloud monitoring platform 3.

[0055] S31: The edge data processing module 2 integrated into the refrigerator control panel 12 receives data from 8 sensor terminals 1 in real time. It first completes the spatiotemporal calibration of the multi-terminal data to eliminate sampling time difference and hardware deviation.

[0056] S32: Edge data processing module 2 calls the temperature field reconstruction algorithm to integrate the calibrated multi-point data and generate a three-dimensional temperature field visualization model of the refrigerator's interior. At this time, the platform can intuitively display the temperature at the top center as 4.2℃ and the humidity as 52%RH, while the lower shelves, being closer to the cooling vents, have a temperature of 4.5℃ and a humidity of 55%RH.

[0057] S33: The equipment fault prediction algorithm continuously analyzes the temperature and humidity change patterns and has not found any potential fault hazards such as refrigeration system degradation or sealing failure.

[0058] S4. Data Upload and Management Edge data processing module 2 uploads the corrected valid data, 3D temperature field model, fault hazard warnings, and other information to cloud monitoring platform 3. The platform updates the temperature field visualization interface and equipment operating status in real time for remote viewing by staff. At the same time, the platform pushes all uploaded data to blockchain evidence storage module 5. The evidence storage module hashes and encrypts the data before storing it on the blockchain in real time, generating a unique blockchain evidence number to ensure that the data is tamper-proof and traceable. The cloud platform classifies, stores, and manages the data, providing data support for subsequent queries, analysis, and report generation.

[0059] S41: Edge data processing module 2 synchronously uploads the corrected valid data, three-dimensional temperature field model and other information to the cloud monitoring platform 3, and the platform updates the visualization interface in real time.

[0060] S42: The cloud-based monitoring platform 3 simultaneously pushes all uploaded data to the blockchain evidence storage module 5, which hashes and encrypts the data before uploading it to the blockchain in real time, generating a unique evidence storage number.

[0061] S5. Early Warning and Emergency Response The cloud-based monitoring platform 3 compares the monitoring data with preset thresholds in real time. If the data deviates from the threshold, it immediately triggers the corresponding level of intelligent early warning. The intelligent early warning module 4 activates local and remote multi-channel early warning methods according to the three-level early warning mechanism. After receiving the early warning information, the staff promptly goes to the site to investigate the cause of the fault and carry out emergency handling. If it is a level three early warning, the system has activated the backup cooling unit 8. The staff must prioritize handling the core fault until the temperature and humidity return to the set range. After the fault handling is completed, the staff records the handling process, handling results, and other information on the cloud platform. This record will be stored on the blockchain simultaneously, completing the full-process data traceability of the early warning handling.

[0062] S51: During operation, the cooling efficiency decreased due to the air vents on the back of the refrigerator being blocked by debris, and the sensor terminal 1 in the top corner detected a continuous rise in temperature.

[0063] S52: When the temperature reaches 8.6℃ and the humidity reaches 78%RH for more than 5 minutes, a level 2 warning is triggered. The system immediately activates the warning mechanism: the yellow indicator light on the local refrigerator control panel 12 remains constantly lit and emits an intermittent buzzer; at the same time, the intelligent warning module 4 pushes warning information containing the location of the abnormal refrigerator, real-time temperature and humidity, and warning level to the mobile APP of the department's maintenance personnel.

[0064] S53: After receiving the alert, staff promptly investigated and cleared the blockage in the ventilation vents, thus resolving the malfunction. Subsequently, staff recorded the handling process and results on the cloud monitoring platform 3, and this record was simultaneously uploaded to the blockchain evidence storage module 5.

[0065] S6. Data Traceability and Compliance The cloud-based monitoring platform 3 automatically generates compliance monitoring reports based on stored monitoring data, handling records, equipment maintenance records, etc. The reports include basic refrigerator information, temperature and humidity change curves, early warning records, handling records, equipment operating status, etc., meeting the reporting requirements of hospital internal management and industry supervision. Regulatory authorities can access the blockchain evidence storage module 5 through authorization to verify the authenticity, completeness, and continuity of monitoring data, realizing remote compliance verification. Hospital staff can query historical data through the cloud platform according to work needs, based on refrigerator number, time, material type, and other conditions, realizing full lifecycle traceability of monitoring data.

[0066] S61: Staff can view the temperature and humidity change curves and three-dimensional temperature field model of the refrigerator at any time through the cloud monitoring platform 3, and generate weekly and monthly reports as needed.

[0067] S62: The drug regulatory authority accesses the blockchain evidence storage module 5 through an authorized node to verify the original monitoring data and early warning records of the refrigerator during a certain period. The verification results show that the data is authentic, complete, and tamper-proof, meeting the remote compliance verification requirements of industry standards such as GSP.

[0068] Based on the above embodiments, those skilled in the art will understand that various modified embodiments are also possible, all of which fall within the protection scope of this invention. For example: Although the above embodiments use an integrated temperature and humidity sensor, in some variations, a combination of independent temperature and humidity sensors can also be used. Furthermore, other types of sensors can be included, such as sensors for detecting door open / closed states or sensors for detecting compressor vibration states; the data collected by these sensors can also be incorporated into this system for comprehensive analysis.

[0069] This invention is not limited to RFID-WiFi dual-mode communication. In other variations, the dual-mode communication can also be a combination of Bluetooth and WiFi, or a combination of Zigbee and 4G / 5G, etc., as long as it can provide redundant wireless data transmission paths.

[0070] The specific duration and temperature difference threshold in the three-level early warning mechanism described in this invention are merely exemplary. In actual implementation, they can be dynamically adjusted based on factors such as the sensitivity of stored materials, season, and region, or can be customized by the user.

[0071] Although the present invention integrates the edge processing module into the refrigerator control panel, in some variations, the module can also be a standalone external device that communicates with the refrigerator and the sensor terminal via a network.

[0072] In addition to built-in lithium batteries and USB charging, in some variations, the power supply module may also include an energy harvesting unit, such as a thermoelectric generator that uses the temperature difference between the inside and outside of a refrigerator to generate electricity, in order to further extend the maintenance-free operating time of the terminal.

[0073] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A wireless temperature and humidity monitoring system for a medical refrigerator, comprising: A medical refrigerator body and a monitoring and control system installed inside and outside the medical refrigerator body, the monitoring and control system including: A distributed wireless sensing module is used to collect temperature and humidity data from multiple points inside a medical refrigerator. An edge data processing module is used to receive, calibrate, and analyze the data collected by the distributed wireless sensing module; The cloud-based monitoring platform is used for centralized management and display of the data uploaded by the edge data processing module; The intelligent early warning module is used to provide tiered early warnings based on the data. The blockchain evidence storage module is used to store the data in an immutable manner; Its features are: The distributed wireless sensing module includes distributed wireless sensing terminals located in multiple positions inside the medical refrigerator, and the number of the distributed wireless sensing terminals is no less than 6, and they are distributed in a honeycomb pattern in the top central area, the middle area of ​​the shelf, the four corner areas of the bottom and the door seal area. Each of the distributed wireless sensing terminals includes: a high-precision temperature and humidity sensor; a laser scattering compensation component for detecting condensation, frost, or obstruction interference on the sensor surface and compensating for and correcting the sampled data; an RFID-WiFi dual-mode communication unit for automatically switching communication modes in case of communication abnormalities; and a low-power power supply module. The distributed wireless sensing terminal is also configured to sample according to an adaptive sampling strategy. When the temperature and humidity fluctuation is less than or equal to 0.3℃, the sampling interval is a first duration. When the temperature and humidity fluctuation is greater than 0.3℃, the sampling interval is a second duration, and the second duration is less than the first duration. The edge data processing module includes: The spatiotemporal calibration unit is used to eliminate sampling time differences and hardware deviations between different sensing terminals; Temperature field reconstruction unit, used to generate a three-dimensional temperature and humidity distribution model inside the medical refrigerator based on multi-point data; The fault prediction unit is used to identify potential faults such as refrigeration system degradation, door seal failure, or condenser frost based on temperature and humidity change characteristics. The intelligent early warning module is used to divide multiple early warning levels according to the degree of temperature and humidity deviation from the preset threshold, and output local and / or remote early warning signals of the corresponding level when the preset conditions are met, and output linkage control signals to start the backup cooling unit when the highest level early warning is triggered. The blockchain evidence storage module is used to perform hash processing on the original collected data, correction data, analysis data, early warning records, and equipment maintenance records, and store them on the blockchain.

2. The medical refrigerator temperature and humidity wireless monitoring system according to claim 1, characterized in that, The high-precision temperature and humidity sensor has a temperature measurement accuracy of ±0.1℃ and a humidity measurement accuracy of ±2%RH, and its operating temperature range covers -80℃ to 60℃.

3. The wireless temperature and humidity monitoring system for a medical refrigerator according to claim 1, characterized in that, The first duration is 5 minutes, and the second duration is 1 minute.

4. The wireless temperature and humidity monitoring system for a medical refrigerator according to claim 1, characterized in that, The RFID-WiFi dual-mode communication unit is configured to automatically switch to another communication mode for data transmission when the signal of the current communication mode is lower than a preset threshold or is interrupted.

5. The wireless temperature and humidity monitoring system for a medical refrigerator according to claim 1, characterized in that, The intelligent early warning module has a three-level hierarchical early warning mechanism, specifically including: Level 1 warning is triggered when the temperature and humidity deviate from the set threshold for less than 5 minutes, or when the temperature difference collected by different distributed wireless sensor terminals is greater than or equal to 3°C and the temperature does not exceed the limit. When triggered, the local audible and visual alarm will issue the first warning signal. Level 2 warning is triggered when the temperature and humidity deviate from the set threshold for a duration between 5 and 15 minutes. When triggered, the local audible and visual alarm will issue a second warning signal and send the warning information to the designated person in charge via remote communication. The Level 3 warning is triggered when the temperature and humidity deviate from the set threshold for more than 15 minutes. When triggered, the local audible and visual alarm will issue a third warning signal and generate a linkage control signal to start the backup cooling unit.

6. The wireless temperature and humidity monitoring system for a medical refrigerator according to claim 5, characterized in that, The first warning signal is a flashing green indicator light, the second warning signal is a constantly lit yellow indicator light accompanied by intermittent beeping, and the third warning signal is a constantly lit red indicator light accompanied by continuous loud beeping.

7. The wireless temperature and humidity monitoring system for a medical refrigerator according to claim 1, characterized in that, The cloud-based monitoring platform also includes a multi-material-specific benchmark database, which pre-sets and stores temperature and humidity thresholds and monitoring rules for various medical supplies such as vaccines, blood products, biological reagents, and diagnostic reagents. By selecting the type of material to be stored, the cloud-based monitoring platform automatically matches the corresponding temperature and humidity thresholds, monitoring rules, and / or sampling strategies.

8. The wireless temperature and humidity monitoring system for a medical refrigerator according to claim 1, characterized in that, The blockchain evidence storage module's consortium blockchain nodes include hospitals, disease control centers, and drug regulatory authorities. The blockchain evidence storage module also supports regulatory authorization access, allowing regulatory authorities to verify the authenticity and integrity of monitoring data through dedicated nodes.

9. The wireless temperature and humidity monitoring system for a medical refrigerator according to claim 1, characterized in that, The edge data processing module is also configured to: when a potential fault is identified, generate a prompt message containing the fault type and maintenance suggestions, and upload the prompt message to the cloud monitoring platform.

10. A method for wirelessly monitoring the temperature and humidity of a medical refrigerator based on the system described in any one of claims 1 to 9, characterized in that, include: S1. Sensor Deployment and Initialization: Deploy distributed wireless sensor terminals according to a cellular deployment method, and complete device registration and parameter configuration; S2. Data Acquisition and Compensation: Temperature and humidity data are acquired based on an adaptive sampling strategy, and data with interference is corrected by a laser scattering compensation component; S3. Edge processing and modeling: Spatiotemporal calibration of the collected data and construction of a three-dimensional temperature and humidity distribution model; S4. Data Upload and Control: Upload data to the cloud-based monitoring platform and synchronize it to the blockchain evidence storage module; S5. Early Warning and Emergency Response: Based on threshold triggering, graded early warnings are issued, and linkage control is executed when a high-level early warning is issued; When the highest level of warning is triggered, it also includes activating the backup cooling unit and issuing a door lock reminder; S6. Data Traceability and Compliance: Generate monitoring reports based on stored data and support regulatory verification.

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