Temperature and humidity redundancy monitoring and energy-saving control system for medical cold storage

Abstract

This invention relates to the field of cold storage management technology, specifically to a temperature and humidity redundancy monitoring and energy-saving control system for pharmaceutical cold storage. The invention proposes the following solution: a data acquisition module collects real-time temperature and humidity data, equipment operating parameters, and operational behavior data from various areas within the cold storage, generating a multi-dimensional monitoring data set; a dynamic risk assessment module calculates the risk value of temperature and humidity imbalance in each area based on the multi-dimensional monitoring data set, constructs a real-time dynamic risk heat map, and generates regional risk warning signals; a collaborative control module intelligently schedules the main refrigeration unit and standby refrigeration unit using a pre-loading strategy based on the regional risk warning signals and equipment status assessment results; and a blockchain storage module ensures the immutability and traceability of all monitoring data and scheduling instructions. This invention, through an intelligent collaborative scheduling and risk warning mechanism, effectively improves the intelligence level and energy efficiency of cold storage management while ensuring the storage safety of pharmaceuticals.

Description

Technical Field

[0001] This invention relates to the field of cold storage management technology, specifically to a temperature and humidity redundancy monitoring and energy-saving control system for pharmaceutical cold storage. Background Technology

[0002] Traditional cold storage systems rely heavily on basic refrigeration equipment and manual inspections for environmental monitoring, which suffers from problems such as discontinuous temperature and humidity data collection, delayed response, and susceptibility to data tampering. Especially in medical settings, power outages, equipment malfunctions, or human error, if not detected and addressed promptly, can easily lead to economic losses and medical risks. Furthermore, the large-scale application of high-value biological products such as vaccines places higher demands on the traceability of the entire cold chain. Every step of the pharmaceutical process, from warehousing and storage to delivery, must have complete, accurate, and tamper-proof environmental data records to achieve end-to-end quality traceability, ensuring "source verifiable, destination traceable, and accountability traceable."

[0003] Currently, although some pharmaceutical cold storage facilities have introduced automatic temperature and humidity monitoring systems, such as temperature and humidity sensors and data loggers, problems still exist, including low system integration, imperfect alarm mechanisms, data silos, and a lack of a unified management platform. Furthermore, existing systems often fail to meet stringent regulatory requirements regarding data compliance, regular equipment calibration, and validation report generation. In addition, technical bottlenecks remain in multi-point deployment, remote monitoring, anomaly warning, and emergency response, including slow response times and insufficient automation. Summary of the Invention

[0004] The technical problem this invention aims to solve is to address the shortcomings of existing technologies by providing a redundant temperature and humidity monitoring and energy-saving control system for pharmaceutical cold storage. The data acquisition module collects real-time temperature and humidity data, equipment operating parameters, and operational behavior data from various areas within the cold storage, generating a multi-dimensional monitoring data set. The dynamic risk assessment module calculates the risk value of temperature and humidity imbalance in each area based on the multi-dimensional monitoring data set, constructs a real-time dynamic risk heat map, and generates regional risk warning signals. The collaborative control module, based on the regional risk warning signals and equipment status assessment results, employs a pre-loading strategy to intelligently schedule the main and standby refrigeration units. A blockchain-based evidence storage module ensures the immutability and traceability of all monitoring data and scheduling instructions. This invention, through its intelligent collaborative scheduling and risk warning mechanism, effectively improves the intelligence level and energy efficiency of cold storage management while ensuring the storage safety of pharmaceutical products.

[0005] To achieve the above objectives, the present invention provides the following technical solution:

[0006] A redundant temperature and humidity monitoring and energy-saving control system for pharmaceutical cold storage, the system comprising:

[0007] The data acquisition module is used to collect temperature and humidity data, equipment operating parameters and operational behavior data in various areas of the cold storage in real time, and generate a multi-dimensional monitoring data set;

[0008] The dynamic risk assessment module is used to calculate the risk value of temperature and humidity imbalance in each area based on the multi-dimensional monitoring data set, construct a real-time dynamic risk heat map, and generate regional risk warning signals based on the real-time dynamic risk heat map.

[0009] The collaborative control module is used to coordinate the main refrigeration unit and the standby refrigeration unit according to the regional risk warning signal through a pre-loading strategy, wherein the pre-loading strategy includes controlling the standby refrigeration unit to enter a preset low-power standby mode when the regional risk warning signal is received.

[0010] The blockchain evidence storage module is used to record multi-dimensional monitoring data sets, real-time dynamic risk heat maps, regional risk early warning signals, and corresponding instructions for collaborative scheduling.

[0011] The dynamic risk assessment module includes a risk calculation unit, a heatmap generation unit, and an early warning triggering unit, comprising:

[0012] The risk calculation unit is used to calculate the risk value of temperature and humidity imbalance in each area based on the current temperature and humidity data, equipment operating parameters and operation behavior data.

[0013] The heat map generation unit is used to map the temperature and humidity imbalance risk values ​​of each area to the spatial coordinates of the cold storage, generate a real-time dynamic risk heat map, and display it with color coding according to the preset risk level.

[0014] The early warning triggering unit is used to predict the temperature and humidity imbalance trend within a future time window based on a real-time dynamic risk heat map and historical temperature and humidity imbalance risk values, and generate a regional risk early warning signal.

[0015] The risk calculation unit is configured with risk value calculation logic, which includes:

[0016] Acquire real-time temperature and humidity data, equipment operating parameters, and operational behavior data for each area of ​​the cold storage;

[0017] Perform time-series analysis on the real-time temperature and humidity data to calculate the rate of change of temperature and humidity;

[0018] Based on the equipment operating parameters, the vibration frequency characteristics and energy consumption fluctuation characteristics of the equipment are extracted, and the equipment health is calculated through preset operating stability indicators.

[0019] Based on the operational behavior data, the frequency of goods entering and exiting and the duration of door opening within a fixed time window are statistically analyzed to generate an area activity index.

[0020] The temperature and humidity change rate, equipment health, and regional activity index are weighted and summed using preset risk value weighting coefficients to generate a regional temperature and humidity imbalance risk value.

[0021] The warning triggering unit is configured with warning triggering logic, which includes:

[0022] Based on the real-time dynamic risk heat map and historical temperature and humidity imbalance risk values, the LSTM neural network algorithm is used to predict the temperature and humidity imbalance risk values ​​of each region within the future time window.

[0023] When the risk value of temperature and humidity imbalance in any area exceeds the preset risk threshold, a regional risk warning signal is generated.

[0024] The collaborative control module includes a status monitoring unit and a collaborative scheduling unit, comprising:

[0025] The status monitoring unit is used to monitor the operating status parameters of the main refrigeration unit in real time, determine the operating status of the main refrigeration unit through a fault diagnosis algorithm, and generate equipment status assessment results.

[0026] The collaborative scheduling unit is used to generate a preload start command based on the regional risk warning signal and equipment status assessment results, control the standby refrigeration unit to execute the preload strategy, and realize intelligent scheduling of the main refrigeration unit and the standby refrigeration unit when the main refrigeration unit fails.

[0027] The collaborative scheduling unit is configured with preloading control logic, which includes:

[0028] Obtain regional risk warning signals and generate preloaded startup instructions;

[0029] According to the preload start command, the standby compressor is controlled to operate at a preset minimum operating frequency to maintain stable lubricating oil circulation and refrigerant pressure.

[0030] Keep the evaporator fan off so that the standby refrigeration unit remains in a preset low-power standby mode when not in refrigeration operation.

[0031] The preloading control logic is also configured with intelligent scheduling sub-logic, which includes:

[0032] The key operating parameters of the main refrigeration unit are monitored in real time by the status monitoring unit. These key operating parameters include compressor current, refrigerant pressure, outlet air temperature, and vibration frequency.

[0033] When any critical operating parameter of the main refrigeration unit exceeds the preset normal operating range, the main refrigeration unit will be marked as faulty and an emergency switchover command will be generated.

[0034] According to the emergency switching command, the standby refrigeration unit in the preload state is controlled to switch to full-power refrigeration operation mode within a preset time, while the main refrigeration unit is shut down.

[0035] After the main refrigeration unit is troubleshooted, the main refrigeration unit is controlled to enter a preset low-power standby mode.

[0036] The blockchain evidence storage module includes a data upload unit and a traceability query unit, comprising:

[0037] The data on-chain unit is used to store the multi-dimensional monitoring data set, real-time dynamic risk heat map, regional risk warning signal and corresponding collaborative scheduling instructions to the blockchain network after hash encryption.

[0038] The traceability query unit is used to provide blockchain-based data traceability query services.

[0039] The data upload unit is configured with blockchain storage logic, which includes:

[0040] The collected temperature and humidity data, equipment operating parameters, and operational behavior data are packaged in batches at preset time intervals to generate basic data blocks;

[0041] Record real-time dynamic risk heat maps and regional risk early warning signals, and generate risk assessment records;

[0042] For each scheduling instruction, the triggering conditions, execution content, and execution time are recorded to form a decision chain;

[0043] The basic data blocks, risk assessment records, and decision-making chains are hashed and encrypted using the SHA-256 algorithm to generate digital fingerprints.

[0044] The digital fingerprint and timestamp are combined and uploaded to the blockchain network, and the blockchain transaction hash value is obtained as a proof of evidence.

[0045] Establish a mapping index between the local database and the blockchain network for querying and verification.

[0046] The system also includes an energy-saving control module, which is configured with energy-saving control logic, including:

[0047] Low-risk areas are identified based on real-time dynamic risk heat maps, and refrigeration equipment in low-risk areas is operated in an intermittent mode. These low-risk areas are divided according to preset risk levels.

[0048] Acquire equipment operating parameters and historical energy efficiency data, calculate the real-time energy efficiency ratio (COP) of each chiller unit, sort the COP values ​​in descending order, and prioritize the chiller unit with the highest COP value as the main chiller unit.

[0049] Historical operational data is extracted from the blockchain evidence storage module, and the risk threshold and risk value weight coefficient are optimized through reinforcement learning algorithms.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] 1. This invention uses multi-dimensional data acquisition and time-series analysis to calculate the risk of temperature and humidity imbalance in various areas of a cold storage facility in real time, and presents the risk distribution intuitively in the form of a dynamic risk heat map. Combined with an LSTM neural network prediction model, potential risks can be identified in advance and warning signals can be issued, thereby realizing the transformation from post-event response to pre-event prevention, and significantly improving the intelligence level and safety assurance capability of cold storage operation.

[0052] 2. By combining dynamic risk heat maps with pre-loading coordinated control, this invention achieves dual optimization of energy saving and safety. In low-risk areas, intermittent operation and priority scheduling of high-efficiency units can be adopted to reduce overall energy consumption; in high-risk areas or when equipment malfunctions, the main and standby units can be quickly switched to ensure that the cold storage environment always meets the stringent requirements for pharmaceutical storage. This invention not only extends the service life of equipment but also significantly improves the energy efficiency ratio and reliability of operation. Attached Figure Description

[0053] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0054] Figure 1 This is a schematic diagram of the temperature and humidity redundancy monitoring and energy-saving control system for a pharmaceutical cold storage according to an embodiment of the present invention;

[0055] Figure 2 This is a flowchart illustrating the configuration strategy of the temperature and humidity redundancy monitoring and energy-saving control system for a pharmaceutical cold storage according to an embodiment of the present invention.

[0056] Figure 3 This is a schematic diagram illustrating the working mode of the temperature and humidity redundant monitoring and energy-saving control system for a pharmaceutical cold storage according to an embodiment of the present invention. Detailed Implementation

[0057] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0058] Example 1:

[0059] Please see Figure 1The present invention provides an embodiment of a temperature and humidity redundancy monitoring and energy-saving control system for pharmaceutical cold storage, the system comprising a data acquisition module, a dynamic risk assessment module, a collaborative control module and a blockchain evidence storage module;

[0060] The data acquisition module is used to collect temperature and humidity data, equipment operating parameters and operation behavior data of various areas in the cold storage in real time, and generate a multi-dimensional monitoring data set.

[0061] The dynamic risk assessment module is used to calculate the risk value of temperature and humidity imbalance in each area based on the multi-dimensional monitoring data set, construct a real-time dynamic risk heat map, and generate regional risk warning signals based on the real-time dynamic risk heat map.

[0062] The collaborative control module is used to coordinate the main refrigeration unit and the standby refrigeration unit according to the regional risk warning signal through a pre-loading strategy, wherein the pre-loading strategy includes the standby refrigeration unit entering a preset low-power standby mode when it receives the regional risk warning signal.

[0063] The blockchain evidence storage module is used to record temperature and humidity data, equipment operating parameters, operational behavior data, real-time dynamic risk heat maps, regional risk warning signals, and corresponding instructions for collaborative scheduling.

[0064] While some cold storage systems have incorporated automatic temperature and humidity monitoring and alarm mechanisms, existing technologies still fall short in complex pharmaceutical cold chain scenarios. First, traditional systems largely rely on single-point sensors and fixed threshold alarms, lacking dynamic fusion analysis of multi-dimensional data. This makes it difficult to effectively capture the coupled impact of environmental disturbances, equipment health status, and operational behavior on temperature and humidity stability, leading to delayed warnings and biased risk assessments. Second, existing refrigeration units often employ a primary / standby separation operation mode, meaning the standby unit remains completely idle or in a cold-start state for extended periods. If the primary unit fails, the switching is often slow, easily causing excessive temperature fluctuations and posing a risk of drug spoilage. Furthermore, frequent start-ups and shutdowns of the standby unit increase energy consumption and mechanical wear, reducing the overall system's economy and reliability. In addition, traditional data recording and monitoring mechanisms rely heavily on centralized databases, leading to difficulties in tracing data tampering and insufficient compliance verification, failing to meet the stringent requirements of the pharmaceutical cold chain: traceable origin, trackable destination, and accountable responsibility.

[0065] This application proposes a temperature and humidity redundancy monitoring and energy-saving control system for pharmaceutical cold storage, achieving a systematic innovation in architecture and control logic. Through a dynamic risk assessment module, it weights and integrates temperature and humidity change rates, equipment health, and regional activity indices to construct a real-time dynamic risk heat map. This data, combined with historical data and predictive models, generates regional risk early warning signals, transforming from single-point alarms to multi-dimensional dynamic early warning. Furthermore, this application introduces a primary and backup unit collaborative pre-loading mechanism in the collaborative control module: upon receiving a risk warning, the backup unit enters a low-power standby mode, maintaining refrigerant circulation and lubricating oil flow, thus enabling rapid and seamless switching in case of primary unit malfunction, ensuring stable storage temperature. Compared to existing technologies, this mechanism not only shortens emergency response time but also reduces idle energy consumption and cold-start risk of the backup unit, balancing energy efficiency and high redundancy.

[0066] Furthermore, this application utilizes a blockchain notarization module to hash and encrypt monitoring data, risk assessment records, and scheduling instructions, ensuring the immutability and traceability of operational data. The dual-layer architecture combining a local database and a blockchain index guarantees both data security and compliance while maintaining query efficiency, meeting compliance and auditing requirements. In the energy-saving control module, this invention also introduces a reinforcement learning algorithm to adaptively optimize risk thresholds and weight coefficients based on historical data stored on the blockchain. It employs an intermittent operation strategy for low-risk areas based on a risk heatmap, while prioritizing the scheduling of refrigeration units with high energy efficiency ratios (large COP values), achieving a dynamic balance between optimal energy efficiency and operational stability.

[0067] Furthermore, the dynamic risk assessment module includes a risk calculation unit, a heat map generation unit, and an early warning triggering unit;

[0068] The risk calculation unit is used to calculate the risk value of temperature and humidity imbalance in each area based on the current temperature and humidity data, equipment operating parameters and operation behavior data.

[0069] The heat map generation unit is used to map the temperature and humidity imbalance risk values ​​of each area to the spatial coordinates of the cold storage, generate a real-time dynamic risk heat map, and display it with color coding according to the preset risk level.

[0070] The early warning triggering unit is used to predict the temperature and humidity imbalance trend within a future time window based on a real-time dynamic risk heat map and historical temperature and humidity imbalance risk values, and generate a regional risk early warning signal.

[0071] Furthermore, the collaborative control module includes a status monitoring unit and a collaborative scheduling unit;

[0072] The status monitoring unit is used to monitor the operating status parameters of the main refrigeration unit in real time, determine the operating status of the main refrigeration unit through a fault diagnosis algorithm, and generate equipment status assessment results.

[0073] The collaborative scheduling unit is used to generate a preload start command based on the regional risk warning signal and equipment status assessment results, control the standby refrigeration unit to execute the preload strategy, and realize intelligent scheduling of the main refrigeration unit and the standby refrigeration unit when the main refrigeration unit fails.

[0074] Furthermore, the blockchain evidence storage module includes a data upload unit and a traceability query unit;

[0075] The data uplink unit is used to store temperature and humidity data, equipment operating parameters, operation behavior data, real-time dynamic risk heat map, regional risk warning signals and corresponding collaborative scheduling instructions in the blockchain network after hash encryption.

[0076] The traceability query unit is used to provide blockchain-based data traceability query services to ensure the immutability and verifiability of monitoring data and control decisions.

[0077] Furthermore, the system also includes an energy-saving control module, which is configured with energy-saving control logic, including:

[0078] Low-risk areas are identified based on real-time dynamic risk heat maps. Refrigeration equipment in low-risk areas is operated in an intermittent mode to reduce energy consumption. These low-risk areas are divided according to preset risk levels.

[0079] Acquire equipment operating parameters and historical energy efficiency data, calculate the real-time energy efficiency ratio (COP) of each chiller unit, sort the COP values ​​in descending order, and prioritize the chiller unit with the highest COP value as the main chiller unit.

[0080] Historical operational data is extracted from the blockchain evidence storage module, and the risk threshold and risk value weight coefficient are optimized through reinforcement learning algorithms.

[0081] Example 2:

[0082] Please see Figure 2 The present invention provides an embodiment of a configuration strategy for a temperature and humidity redundancy monitoring and energy-saving control system for a pharmaceutical cold storage, applied to the temperature and humidity redundancy monitoring and energy-saving control system of the pharmaceutical cold storage, the configuration strategy including:

[0083] S1: Real-time collection of temperature and humidity data, equipment operating parameters and operational behavior data in various areas of the cold storage, generating a multi-dimensional monitoring data set;

[0084] Specifically, to ensure that the temperature and humidity environment in all areas of the cold storage remains within the specified range and to guarantee the efficient operation of the refrigeration equipment, this embodiment generates a multi-dimensional monitoring data set by collecting multiple data dimensions from different areas within the cold storage in real time. The multi-dimensional monitoring data set includes temperature and humidity information for each area, equipment operating parameters such as the power, pressure, and flow rate of the cold storage refrigeration unit, and operational behavior data such as the frequency of goods entering and leaving the premises and the duration of door and window openings. Through the aggregation and analysis of this multi-dimensional monitoring data set, the status of the environment and equipment within the cold storage can be comprehensively assessed.

[0085] S2: Calculate the temperature and humidity imbalance risk value of each area based on the multi-dimensional monitoring data set, construct a real-time dynamic risk heat map, and generate regional risk warning signals based on the real-time dynamic risk heat map;

[0086] Specifically, in order to monitor the temperature and humidity changes in the cold storage in real time and predict possible temperature and humidity imbalances, this embodiment analyzes the multi-dimensional monitoring data set from the data acquisition module, calculates the temperature and humidity imbalance risk value of each area, and constructs a real-time dynamic risk heat map based on the temperature and humidity imbalance risk value, so as to identify and warn of possible temperature and humidity imbalance risks in a timely manner.

[0087] In this embodiment, the calculation of the temperature and humidity imbalance risk value involves multiple data sources and analysis steps. First, by performing time-series analysis on the real-time temperature and humidity data of each region, the rate of change of temperature and humidity in each region is calculated, and the temperature and humidity fluctuations are smoothed to remove noise caused by short-term fluctuations. Furthermore, by combining equipment operating parameters and operational behavior data, the rate of change of temperature and humidity, equipment operating parameters, and operational behavior data are weighted and summed according to preset risk weight coefficients to generate the temperature and humidity imbalance risk value for each region.

[0088] S3: Based on the regional risk warning signal, coordinate the main refrigeration unit and the standby refrigeration unit through a pre-loading strategy, wherein the pre-loading strategy includes controlling the standby refrigeration unit to enter a low-power standby mode when the regional risk warning signal is received.

[0089] Specifically, based on regional risk warning signals, a pre-loading strategy can be used to coordinate the main and standby refrigeration units to optimize energy efficiency and ensure stable temperature and humidity within the cold storage. In practical applications, the standby refrigeration units will not operate at full power; instead, they will be intelligently scheduled according to the actual needs of the cold storage environment to save energy and avoid unnecessary energy waste.

[0090] In this embodiment, the pre-loading strategy includes the standby refrigeration unit automatically entering a low-power standby mode after receiving a regional risk warning signal. In low-power standby mode, the standby refrigeration unit maintains a low-power operating state, preserving essential equipment functions such as lubricating oil circulation and refrigerant pressure control, without needing to perform refrigeration work. Through this pre-loading strategy, the standby refrigeration unit not only reduces energy consumption but also minimizes equipment wear, thereby extending the equipment's lifespan.

[0091] Furthermore, in the event of a malfunction in the main refrigeration unit or excessive workload, the standby refrigeration unit will respond rapidly within a preset time and switch to full-power refrigeration mode to ensure that the temperature and humidity of the cold storage are maintained within the set safe range. To ensure a smooth switching process, in this embodiment, key operating parameters of the main refrigeration unit are monitored in real time. Once an abnormal operating status of the main refrigeration unit is detected, the standby refrigeration unit will start up and operate at full power within a preset time to ensure the normal operation of the cold storage.

[0092] S4: Record multi-dimensional monitoring data sets, real-time dynamic risk heat maps, regional risk warning signals and corresponding collaborative scheduling instructions through blockchain evidence storage;

[0093] In this embodiment, blockchain technology is used to ensure the immutability and traceability of all monitoring data and scheduling instructions in cold storage management, thereby improving data security and transparency. The blockchain records multiple data layers, including temperature and humidity data, equipment operating parameters, operational behavior data, real-time dynamic risk heat maps, regional risk warning signals, and collaborative scheduling instructions, to ensure that every aspect of the cold storage environment and equipment operation process can be traced and verified.

[0094] S5: Based on the blockchain evidence, energy-saving control is performed on each refrigeration unit, and the preset risk threshold and risk value weight coefficient are optimized through reinforcement learning algorithm.

[0095] In this embodiment, blockchain not only ensures the immutability and traceability of temperature and humidity monitoring data, risk assessments, and scheduling instructions for the cold storage, but also provides strong data support for energy-saving control. Specifically, by storing the operating data, risk assessment results, and historical operation records of the refrigeration units in the cold storage, intelligent energy-saving control of the refrigeration units can be achieved. Furthermore, by dynamically optimizing the risk threshold and risk value weighting coefficient through reinforcement learning algorithms, the overall energy efficiency and stability can be improved.

[0096] The specific steps of S2 are as follows:

[0097] S2.1: Calculate the risk value of temperature and humidity imbalance in each area based on current temperature and humidity data, equipment operating parameters, and operational behavior data;

[0098] Specifically, to accurately assess the potential risk of temperature and humidity imbalance in different areas of a cold storage facility under complex operating conditions, it is necessary to comprehensively consider the coupled effects of environmental factors, equipment factors, and human operational factors. Single-dimensional data is often insufficient to reflect the true stability of the regional environment. Therefore, this embodiment introduces multi-dimensional indicators in the risk assessment process. By comprehensively calculating real-time temperature and humidity changes, refrigeration unit operational stability, and regional activity, the risk of temperature and humidity imbalance in different areas of the cold storage facility can be more objectively characterized.

[0099] The specific steps of S2.1 are as follows:

[0100] S2.1.1: Acquire real-time temperature and humidity data, equipment operating parameters, and operational behavior data for each area of ​​the cold storage;

[0101] Specifically, to accurately assess the risk of temperature and humidity imbalance within cold storage facilities, it is essential to efficiently collect real-time data from different areas. Environmental conditions in different areas can fluctuate due to variations in spatial location, refrigeration equipment, and operational behavior. Therefore, acquiring real-time temperature and humidity data, equipment operating parameters, and operational behavior data from each area is a crucial prerequisite for comprehensive monitoring. By collecting data from multiple dimensions within the cold storage facility in real time, a comprehensive understanding of the internal environment and equipment operation can be achieved.

[0102] S2.1.2: Perform time-series analysis on the real-time temperature and humidity data to calculate the rate of change of temperature and humidity;

[0103] Specifically, in order to accurately identify the temperature and humidity fluctuation trends in different areas of the cold storage, it is necessary to perform time-series analysis on real-time temperature and humidity data to calculate the rate of change of temperature and humidity. Since temperature and humidity changes have obvious time-series characteristics, simple instantaneous data points are insufficient to reflect the long-term fluctuation trend of the environment. Therefore, it is necessary to serialize the data at each moment and perform periodic analysis to more effectively capture the dynamic characteristics of temperature and humidity changes.

[0104] S2.1.3: Based on the equipment operating parameters, extract the equipment vibration frequency characteristics and energy consumption fluctuation characteristics, and calculate the equipment health status through preset operating stability indicators;

[0105] Specifically, to ensure the normal operation of cold storage equipment and promptly identify potential failure risks, it is necessary to monitor and analyze key characteristics generated during equipment operation in real time. In this embodiment, the focus is on two dimensions: vibration frequency characteristics and energy consumption fluctuation characteristics. By extracting and calculating these characteristics, the health of the equipment is assessed, thus providing a basis for assessing the risk of temperature and humidity imbalance. Changes in equipment health often reflect the operational stability of the equipment, allowing for early identification of potential failures or performance degradation.

[0106] S2.1.4: Based on the operational behavior data, calculate the frequency of goods entering and exiting and the duration of door opening within a fixed time window, and generate an area activity index;

[0107] Specifically, in actual operation, the entry and exit of goods directly increases the frequency of heat exchange between the cold storage and the external environment, thereby accelerating the fluctuations in temperature and humidity inside the storage. Furthermore, the longer the storage door is open, the greater the total amount of external air intrusion, and the more significant the impact on the stability of temperature and humidity in the local area. Therefore, combining these two statistical analyses can effectively characterize the intensity of operational disturbances in a cold storage facility within a given time window.

[0108] In this embodiment, the operational behavior data is first processed by time segmentation. Specifically, within a preset fixed time window (e.g., 5 minutes, 10 minutes, or 30 minutes), the number of goods entering and exiting and the cumulative door opening time are counted. Then, a comprehensive regional activity index is generated using normalization and weighted summation. For example, the frequency of goods entering and exiting can be standardized to the ratio of entries and exits per unit time, and the door opening duration is proportionalized in seconds or minutes. These are then weighted and summed using set weight parameters to form the regional activity index within that time window.

[0109] S2.1.5: The temperature and humidity change rate, equipment health and regional activity index are weighted and summed using preset risk value weighting coefficients to generate a regional temperature and humidity imbalance risk value.

[0110] Specifically, to comprehensively assess the risk of temperature and humidity imbalance in various areas of the cold storage, this embodiment introduces a weighted summation method, taking into account the combined effects of temperature and humidity change rate, equipment health, and area activity index. By assigning preset risk value weight coefficients to each indicator, the focus of risk assessment can be dynamically adjusted according to the relative importance of each factor in the stability of the cold storage environment, thereby achieving precise control of the cold storage environment.

[0111] S2.2: Map the temperature and humidity imbalance risk values ​​of each area to the spatial coordinates of the cold storage, generate a real-time dynamic risk heat map, and display it with color coding according to the preset risk level;

[0112] Specifically, to clearly display the risk of temperature and humidity imbalance in different areas of the cold storage and help staff quickly identify key areas requiring attention, this embodiment maps the risk values ​​of temperature and humidity imbalance in each area onto the spatial coordinate system of the cold storage, generating a real-time dynamic risk heat map. The dynamic risk heat map can intuitively reflect the degree of risk of temperature and humidity imbalance in different areas and provide an intuitive basis for management decisions.

[0113] In this embodiment, the temperature and humidity imbalance risk value represents the temperature and humidity stability level of each area at the current time point. The temperature and humidity imbalance risk value is correlated with the spatial coordinates of the cold storage, and the location of each area is marked on a dynamic risk heat map using the spatial coordinate axes. The generation process of the dynamic risk heat map depends on the planar layout of the cold storage. The temperature and humidity imbalance risk value of each area is mapped to three-dimensional spatial coordinates through interpolation or other suitable mapping algorithms to ensure that the generated dynamic risk heat map is consistent with the actual layout of the cold storage.

[0114] Specifically, to ensure the heatmap clearly presents the risk distribution of temperature and humidity imbalances, the risk values ​​are color-coded according to preset risk levels. For example, low-risk areas may be displayed in green or blue, while high-risk areas are displayed in red or orange, creating a significant color difference. The color coding threshold is set according to actual needs and can be automatically adjusted using real-time data to ensure the heatmap accurately reflects the current risk status of the cold storage.

[0115] S2.3: Based on real-time dynamic risk heat map and historical temperature and humidity imbalance risk values, predict the temperature and humidity imbalance trend within the future time window and generate regional risk warning signals;

[0116] Specifically, to effectively predict and provide early warnings of temperature and humidity imbalance trends in cold storage facilities, this embodiment combines real-time dynamic risk heat maps and historical temperature and humidity imbalance risk values. A time-series prediction model is constructed to accurately predict the temperature and humidity imbalance risk values ​​for each area within a future time window. In this process, an LSTM neural network algorithm is used to analyze historical data and establish a data-driven time-series prediction model, thereby providing real-time and accurate early warning information for cold storage management.

[0117] The specific steps of S2.3 are as follows:

[0118] S2.3.1: Based on the real-time dynamic risk heat map and historical temperature and humidity imbalance risk values, the temperature and humidity imbalance risk values ​​of each region within the future time window are predicted using the LSTM neural network algorithm;

[0119] Specifically, in order to achieve dynamic prediction of the risk of temperature and humidity imbalance in various areas of cold storage, this embodiment is based on real-time dynamic risk heat map and historical temperature and humidity imbalance risk values. By learning from historical data, the LSTM neural network algorithm is used to capture the long-term dependence of temperature and humidity changes, predict the risk of temperature and humidity imbalance that may occur in future time windows, and thus provide effective early warning information for cold storage management.

[0120] In this embodiment, the first step is to collect and organize multidimensional data from dynamic risk heat maps and historical temperature and humidity imbalance risk values. This multidimensional data reflects the real-time temperature and humidity change rates, equipment operating parameters, and operational behavior data for each region. By organizing this multidimensional data into a time series, the LSTM neural network algorithm can analyze the temperature and humidity imbalance risk at each point in time and learn trends and periodic changes.

[0121] The LSTM neural network algorithm, through its unique structure, can effectively capture temperature and humidity variation patterns over long periods and consider time dependence in the prediction process. Through multiple rounds of training, the internal parameters of the LSTM neural network algorithm are continuously optimized, gradually improving prediction accuracy. During training, historical temperature and humidity imbalance risk values ​​are used as input to the LSTM neural network algorithm to predict the risk of temperature and humidity imbalance in various regions within future time windows.

[0122] S2.3.2: When the temperature and humidity imbalance risk value in any area exceeds the preset risk threshold, a regional risk warning signal is generated.

[0123] Specifically, in the temperature and humidity control logic of cold storage, the calculation and monitoring of temperature and humidity imbalance risk values ​​is a core part of ensuring the safety of the cold storage environment. To promptly detect potential temperature and humidity problems, this embodiment sets a preset risk threshold for temperature and humidity imbalance risk values ​​based on a dynamic risk heat map and historical temperature and humidity imbalance risk values. When the temperature and humidity imbalance risk value of a certain area exceeds the preset risk threshold, an area risk warning signal will be automatically generated, prompting management personnel to take corresponding measures to prevent further temperature and humidity imbalance.

[0124] Please see Figure 3 The schematic diagram of the working mode of the temperature and humidity redundancy monitoring and energy-saving control system for pharmaceutical cold storage according to an embodiment of the present invention, and the specific steps of S3 are as follows:

[0125] S3.1: Monitor the operating status parameters of the main refrigeration unit in real time, determine the operating status of the main refrigeration unit through fault diagnosis algorithm, and generate equipment status assessment results;

[0126] Specifically, in this embodiment, to ensure the normal operation and stability of the main refrigeration unit in the cold storage system, it is necessary to monitor various operating status parameters of the main refrigeration unit in real time. These operating status parameters include compressor current, refrigerant pressure, outlet air temperature, condensing pressure, vibration frequency, and operating indicators of other key components. By continuously tracking the operating status parameters, the operating status of the main refrigeration unit can be obtained in real time, thereby promptly identifying potential faults or anomalies.

[0127] S3.2: Based on the regional risk warning signal and equipment status assessment results, generate a preload start command, control the standby refrigeration unit to execute the preload strategy, and realize intelligent scheduling of the main refrigeration unit and the standby refrigeration unit when the main refrigeration unit fails.

[0128] Specifically, in order to ensure that the temperature and humidity of the cold storage environment remain within a stable range, this embodiment uses intelligent scheduling of the main refrigeration unit and the standby refrigeration unit. Based on the regional risk warning signal and equipment status assessment results, it automatically generates a preload start command, controls the standby refrigeration unit to execute the preload strategy, and realizes intelligent switching between the main refrigeration unit and the standby refrigeration unit when the main refrigeration unit fails, so as to ensure that the refrigeration capacity in the cold storage is continuous and uninterrupted.

[0129] The specific steps of S3.2 are as follows:

[0130] S3.2.1: Obtain regional risk warning signals and generate preload startup instructions;

[0131] Specifically, in this embodiment, the temperature and humidity changes in each area of ​​the cold storage are monitored in real time, and a dynamic risk heat map is generated. Based on this heat map, a regional risk warning signal is issued. When the risk of temperature and humidity imbalance in a certain area exceeds a preset safe range, a pre-load start-up command is automatically generated based on the regional risk warning signal. The purpose of the pre-load start-up command is to activate the standby refrigeration unit in advance and adjust it to a low-power standby mode to cope with potential temperature and humidity imbalance risks and ensure the stability of the cold storage's temperature and humidity.

[0132] S3.2.2: According to the preload start command, control the standby compressor to operate at a preset minimum operating frequency to maintain stable lubricating oil circulation and refrigerant pressure;

[0133] In this embodiment, to ensure that the standby refrigeration unit can quickly resume normal refrigeration operation in the event of a failure in the main refrigeration unit, a pre-loading strategy keeps the standby compressor in a low-power standby state. Upon receiving the pre-loading start command, the standby compressor begins to operate at the lowest operating frequency. At this time, the compressor speed is low, maintaining only the stability of lubricating oil circulation and refrigerant pressure. This ensures that the refrigerant always maintains a certain level of fluidity, thereby avoiding start-up failure due to refrigerant stagnation when switching to full-power refrigeration.

[0134] S3.2.3: Keep the evaporator fan off so that the standby refrigeration unit remains in a low-power standby mode when not in refrigeration operation.

[0135] In this embodiment, to further reduce the energy consumption of the standby refrigeration unit in standby mode, the evaporator fan is designed to remain off. Evaporator fans are typically used to promote the circulation of cool air and maintain the required temperature and humidity control; however, activating the evaporator fan when the standby refrigeration unit is in non-cooling standby mode leads to unnecessary energy consumption and mechanical wear. Therefore, this embodiment further optimizes energy efficiency by turning off the evaporator fan, ensuring that the standby refrigeration unit operates only at the lowest power consumption level.

[0136] Specifically, when the standby chiller unit is executing a preload strategy, the low-speed operation of the compressor is sufficient to maintain the basic functions of the system, such as lubricating oil circulation and refrigerant pressure stability. In this state, the operation of the evaporator fan is considered non-essential; the main function of the evaporator fan is to enhance cooling efficiency through air circulation. However, when the standby chiller unit is in low-power standby mode, the goal is to maximize energy efficiency rather than actively cooling.

[0137] The specific steps of S3.2.3 are as follows:

[0138] S3.2.3.1: The key operating parameters of the main refrigeration unit are monitored in real time through the status monitoring unit. The key operating parameters include compressor current, refrigerant pressure, outlet air temperature and vibration frequency.

[0139] In this embodiment, the key operating parameters of the main refrigeration unit include compressor current, refrigerant pressure, outlet air temperature, and vibration frequency. These key operating parameters directly affect the normal operation and fault diagnosis of the refrigeration unit. Therefore, to ensure the stability of the main refrigeration unit and to respond promptly to potential faults, the condition monitoring unit monitors the key operating parameters in real time, promptly detects operational anomalies, and responds accordingly.

[0140] Specifically, compressor current, as the main power source of the refrigeration unit, directly reflects the compressor's operating status. Monitoring compressor current helps identify potential electrical faults or load anomalies, preventing equipment damage due to overload or abnormal current. Refrigerant pressure is another key parameter, reflecting the refrigerant's flow state. Excessively high or low pressure can affect refrigeration efficiency and even lead to system failure. Monitoring outlet air temperature helps understand the cooling effect and the actual operating efficiency of the equipment. Abnormal changes in outlet air temperature indicate abnormalities in the condensation or evaporation process of the main refrigeration unit. Further diagnosis can determine whether it is insufficient heat exchange, restricted refrigerant flow, or other faults. Excessively high vibration frequencies usually indicate internal mechanical problems, such as wear, loosening, or imbalance of the compressor or other rotating parts. By monitoring vibration frequency in real time, measures can be taken before mechanical failures occur, thus avoiding serious damage.

[0141] S3.2.3.2: When any critical operating parameter of the main refrigeration unit exceeds the preset normal operating range, the main refrigeration unit will be marked as faulty and an emergency switchover command will be generated immediately.

[0142] In this embodiment, to ensure the main refrigeration unit operates stably and efficiently, key operating parameters are monitored in real time. If any of these key operating parameters exceeds the preset normal range, the main refrigeration unit is immediately marked as faulty, and an emergency switching mechanism is triggered to prevent temperature and humidity control failure due to main refrigeration unit malfunction or performance degradation, which could affect the safety of items inside the cold storage.

[0143] Specifically, a high compressor current may indicate that the compressor is overloaded or has an electrical fault; abnormal refrigerant pressure may indicate a refrigerant leak or system blockage; an outlet air temperature exceeding the expected range usually means reduced cooling efficiency, possibly due to a condenser or evaporator malfunction; abnormal changes in vibration frequency may be a precursor to mechanical failure, such as compressor imbalance or loose components. Any abnormality in any critical operating parameter will affect the normal operation of the refrigeration unit.

[0144] For example, when a critical operating parameter is detected to be outside the normal range, the main refrigeration unit will first be marked as faulty, indicating that it may not be able to continue operating stably. Immediately afterwards, an emergency switchover command is generated, instructing the standby refrigeration unit to start and take over the refrigeration task. At this time, the standby refrigeration unit will quickly switch to full-power refrigeration mode according to the preset control logic, ensuring that the temperature and humidity inside the cold storage are maintained within a safe range.

[0145] S3.2.3.3: According to the emergency switching command, control the standby refrigeration unit in the preload state to switch to full power refrigeration operation mode within a preset time, and at the same time shut down the main refrigeration unit;

[0146] In this embodiment, the emergency switchover command is generated based on abnormal critical operating parameters of the main refrigeration unit, aiming to ensure the continuous stability of temperature and humidity in the cold storage. When the main refrigeration unit malfunctions or malfunctions, the standby refrigeration unit will switch to full-power refrigeration operation mode in the shortest possible time according to a preset pre-loading strategy, taking over the refrigeration task.

[0147] Specifically, upon receiving an emergency switchover command, the standby refrigeration unit is activated and, following a predetermined control procedure, rapidly transitions from low-power standby mode to full-power refrigeration mode within a preset time. During this process, parameters such as the standby refrigeration unit's operating frequency, refrigerant pressure, and fan speed are adjusted to the operating conditions required for normal refrigeration mode, ensuring it can fully take over the refrigeration load of the main refrigeration unit and guaranteeing that the environmental conditions inside the cold storage remain unaffected.

[0148] S3.2.3.4: After the main refrigeration unit is troubleshooted, control the main refrigeration unit to enter a low-power standby mode.

[0149] Specifically, after a malfunction in the main refrigeration unit and subsequent troubleshooting and repair, the unit automatically switches to a low-power standby mode to ensure energy efficiency and system stability in the cold storage. This low-power standby mode aims to ensure the main refrigeration unit can quickly respond to subsequent cooling demands while remaining in standby mode, while minimizing energy consumption.

[0150] In low-power standby mode, the main refrigeration unit will maintain a minimum operating level, focusing primarily on maintaining basic equipment operations such as lubricating oil circulation and refrigerant pressure control, but will not perform refrigeration work. Meanwhile, the standby refrigeration unit will continue operating at full power to ensure stable temperature and humidity in the cold storage.

[0151] Furthermore, the low-power standby mode not only protects equipment and maintains system stability, but also plays a crucial role in the intelligent scheduling of the main and standby chiller units. When the standby chiller unit is running at full power, the main chiller unit in standby mode can serve as a readily switchable backup resource, enabling dynamic coordination between the main and standby chiller units.

[0152] The specific steps for S4 are as follows:

[0153] S4.1: The collected temperature and humidity data, equipment operating parameters and operation behavior data are packaged in batches at preset time intervals to generate basic data blocks;

[0154] In this embodiment, to ensure the integrity and consistency of cold storage environment and equipment operation data during the evidence storage process, after collecting temperature and humidity data, equipment operating parameters, and operational behavior data, the collected data is automatically aggregated and organized at preset time intervals, and basic data blocks are generated through batch packaging. The generation of basic data blocks not only solidifies the original monitoring data in time slices but also unifies and structures the encoding of different types of data through internal formatting processing, ensuring that the data can be identified and quickly retrieved during on-chain and traceability queries. Unlike traditional single-point recording methods, this embodiment uses batch packaging, which effectively reduces storage and computational overhead caused by frequent writes, while avoiding data link redundancy caused by overly fragmented individual records.

[0155] Furthermore, a redundancy verification mechanism is introduced during the batch packaging process to compare and correct data collected from multiple sources within the same time period, ensuring the accuracy and tamper resistance of the basic data blocks during generation. For example, when there is a timestamp discrepancy between temperature and humidity sensor data and equipment operating parameters, a synchronization correction algorithm is used to align the temperature and humidity sensor data to ensure the consistency of various information within the basic data block in the time dimension.

[0156] S4.2: Record real-time dynamic risk heat maps and regional risk early warning signals, and generate risk assessment records;

[0157] In this embodiment, to ensure the traceability and verifiability of risk changes during cold storage operation, after generating a real-time dynamic risk heatmap and regional risk warning signals, these are recorded in a structured format to form a risk assessment record. This risk assessment record not only includes the temperature and humidity imbalance risk values ​​and their spatial distribution for each region within the current time window, but also incorporates risk level labels and timestamp information generated by the warning triggering unit, achieving a complete traceability of the risk status. Unlike simple instantaneous alarms, this embodiment uses periodic snapshots to solidify the overall distribution of the real-time dynamic risk heatmap, while dynamically linking the warning triggering status of each region. This allows the risk assessment record to reflect both the macro-level situation and accurately locate local anomalies.

[0158] S4.3: Record the triggering conditions, execution content, and execution time for each scheduling instruction to form a decision chain;

[0159] Specifically, when generating scheduling instructions, not only is the execution content of the instruction itself recorded, but the corresponding triggering conditions and execution time are also included in the record, forming a complete decision chain. The decision chain is equivalent to a dynamic logical trajectory that closely connects the triggering of risk signals, the formulation of scheduling strategies, and the sequence of execution actions, enabling accurate reconstruction of the environmental state and control logic at the time of the backtracking query.

[0160] Furthermore, triggering conditions typically originate from risk assessment records and equipment status assessment results. For example, when the risk of temperature and humidity imbalance in a certain area exceeds a threshold or the operating parameters of the main chiller unit are abnormal, a triggering event will be generated immediately and the corresponding scheduling logic will be initiated. The execution content clearly defines the type of action involved in the instruction, such as the standby chiller unit entering a low-power standby mode, switching to full-power operation, or adjusting the operating frequency to optimize energy efficiency. The execution time is timestamped using a high-precision clock system to ensure that the order of each instruction in the chain can be strictly verified. By organically combining the three types of information—triggering conditions, execution content, and execution time—the resulting data chain is not only a simple record of scheduling behavior but also reflects the decision-making basis and logical flow under different scenarios, clearly demonstrating the entire chain process from risk discovery to response and handling.

[0161] S4.4: Hash the aforementioned basic data block, risk assessment record, and decision chain using the SHA-256 algorithm to generate a digital fingerprint;

[0162] Specifically, after generating the basic data blocks, risk assessment records, and decision-making chains, they are all hashed and encrypted using the SHA-256 algorithm to obtain digital fingerprints that correspond one-to-one with the original data. This digital fingerprint serves as a unique identifier for the data, enabling verification of integrity and authenticity without revealing the original content.

[0163] Furthermore, the SHA-256 algorithm possesses strong collision resistance and one-wayness, meaning that even minor data alterations will result in significant differences in the generated digital fingerprints, ensuring that any tampering can be quickly identified. In practical applications, basic data blocks packaged at different time periods are first concatenated and serialized with real-time generated risk assessment records and decision chains according to preset encoding rules. This is then input into the SHA-256 encryption unit to generate a fixed-length hash value. This hash value serves as the digital fingerprint of the corresponding data set, used for on-chain evidence storage and rapid verification. When external verification of historical data is required, the data to be verified is simply processed again using the same algorithm and compared with the stored digital fingerprint to determine whether the data retains its original state.

[0164] S4.5: Combine the digital fingerprint and timestamp and upload them to the blockchain network, and obtain the blockchain transaction hash value as a proof of evidence;

[0165] In this embodiment, to achieve end-to-end reliable evidence storage of cold storage operation data, after generating a digital fingerprint, it is further bound to a high-precision timestamp to form a unique and time-sensitive combined identifier. This combined identifier not only reflects the integrity of the data content but also clarifies the order of data generation and time-limited boundaries through the introduction of the timestamp, thereby ensuring the traceability and legal validity of the data when recorded on the blockchain. The digital fingerprint and timestamp are submitted to the blockchain network as an integrated data packet, and consensus and on-chain operations are completed through distributed nodes. The corresponding transaction hash value is obtained during the block generation process. This transaction hash value serves as the unique evidence storage certificate at the blockchain level, ensuring that any basic data block, risk assessment record, or scheduling decision chain can be quickly retrieved and compared in subsequent query and verification stages. Due to the immutability and network-wide consensus characteristics of the blockchain, even if external attempts are made to modify the original data, it is impossible to generate an evidence storage result consistent with the on-chain hash value, thereby effectively preventing data forgery and tampering.

[0166] S4.6: Establish a mapping index between the local database and the blockchain network to enable fast querying and verification.

[0167] In this embodiment, to balance the security of blockchain-based evidence storage with efficiency in practical applications, a bidirectional mapping index between the local database and the blockchain network is established simultaneously with data upload to the blockchain, thereby enabling rapid data querying and verification. While blockchain guarantees data immutability and traceability, its distributed storage and consensus mechanisms often result in low data retrieval efficiency, making it unsuitable for frequent real-time scenarios. Therefore, this embodiment stores basic data blocks, risk assessment records, and the original content of the decision chain in the local database, and establishes an index link between the local database and the corresponding transaction hash values ​​and timestamps on the blockchain. This allows for faster local retrieval during queries, and data consistency verification is achieved by comparing the blockchain's evidence hash.

[0168] Specifically, when generating each piece of data for storage, the corresponding digital fingerprint and blockchain transaction hash are simultaneously recorded in the local database, forming a one-to-one index table. When users or regulators need to verify the cold storage operation data for a certain period, they can directly call the local database for quick location and cross-verify the data using the storage hash value returned by the blockchain network, thus completing the query and verification in milliseconds and avoiding the latency issues caused by large-scale on-chain traversal.

[0169] The specific steps for S5 are as follows:

[0170] S5.1: Identify low-risk areas in real time based on dynamic risk heat maps, and adopt intermittent operation mode for refrigeration equipment in low-risk areas to reduce energy consumption;

[0171] In this embodiment, to reduce overall energy consumption while ensuring the stability of temperature and humidity in the cold storage, the risk level of each area is identified in real time based on a dynamic risk heat map, and low-risk areas are prioritized for energy-saving control. Low-risk areas typically refer to storage spaces with minimal temperature and humidity fluctuations, stable equipment operation, and minimal external operational interference. These areas are relatively less sensitive to environmental conditions, so it is not necessary to maintain the refrigeration equipment at full power continuously to keep it within safe threshold ranges. After identifying a low-risk area, control commands for intermittent operation are dynamically generated, causing the corresponding refrigeration equipment to operate alternately with a preset start-stop cycle, effectively reducing energy consumption without affecting overall temperature and humidity compliance. The intermittent operation mode is not fixed but adaptively adjusted based on historical risk assessment records and real-time data feedback. For example, when the area maintains a low-risk state for an extended period, the shutdown interval can be appropriately extended to further save energy; conversely, when environmental disturbance signals increase or the risk value of temperature and humidity imbalance shows an upward trend, the intermittent cycle will be shortened to ensure that the risk is not amplified by the energy-saving strategy.

[0172] S5.2: Obtain equipment operating parameters and historical energy efficiency data, calculate the real-time energy efficiency ratio (COP) value of each chiller unit, sort the COP values ​​in descending order, and prioritize scheduling the chiller unit with the highest COP value as the main chiller unit.

[0173] Specifically, to maximize energy efficiency while meeting the temperature and humidity stability requirements of cold storage, the energy efficiency ratio (EER) of each refrigeration unit is dynamically calculated based on real-time collected equipment operating parameters and historical energy efficiency data. The EER is then used as a key indicator for prioritizing the scheduling of refrigeration units. The Coefficient of Performance (COP) reflects the cooling capacity produced per unit of energy consumption by the refrigeration unit and is a core parameter for measuring equipment operating efficiency. By acquiring real-time operating parameters such as current, voltage, refrigerant pressure, and outlet air temperature of the refrigeration units, and combining this with historical energy efficiency curves and an operating condition database, the energy efficiency level of each refrigeration unit under current load conditions can be accurately assessed. When multiple refrigeration units are available simultaneously, the unit with the higher COP will be prioritized as the primary refrigeration unit, while units with relatively lower COP will enter standby or low-power standby mode, achieving overall energy efficiency optimization.

[0174] S5.3: Extract historical operation data from the blockchain evidence storage module and optimize the risk threshold and risk value weight coefficient through reinforcement learning algorithm.

[0175] In this embodiment, to further enhance the adaptability and intelligence of energy-saving control, encrypted historical operating data is extracted from the blockchain notarization module during the energy-saving control process and used as training samples and feedback for the reinforcement learning algorithm. Blockchain notarization ensures the authenticity and immutability of historical operating data, giving the sample data high credibility and preventing bias in learning results due to missing or tampered data. The reinforcement learning algorithm gradually adjusts the threshold parameters by comparing the relationship between risk threshold settings, risk value weight allocation, and the actual energy efficiency performance of the cold storage at different operating stages, thereby achieving a dynamic balance between risk control and energy consumption optimization.

[0176] For example, when a reinforcement learning algorithm discovers in multiple iterations that the risk threshold setting for a certain area is too conservative, leading to frequent start-ups and shutdowns of refrigeration equipment and increased energy consumption, it automatically relaxes the risk threshold for that area while ensuring safety. Conversely, when historical operating data shows that a certain type of risk signal has a low weight in the weight allocation, easily causing temperature and humidity fluctuations, the reinforcement learning algorithm will increase the weight of this type of risk signal to enhance its influence in risk assessment. Through optimization via reinforcement learning, the algorithm can continuously evolve its own control logic, so that the risk threshold and weight coefficients no longer rely on fixed empirical settings, but are adaptively updated according to the dynamic changes in the cold storage operating environment, equipment status, and operational behavior. This allows it to maintain the optimal solution for both stable temperature and humidity control and energy utilization efficiency during long-term operation.

[0177] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A redundant monitoring and energy-saving control system for temperature and humidity in a pharmaceutical cold storage facility, characterized in that, The system includes: The data acquisition module is used to collect temperature and humidity data, equipment operating parameters and operational behavior data in various areas of the cold storage in real time, and generate a multi-dimensional monitoring data set; The dynamic risk assessment module is used to calculate the risk value of temperature and humidity imbalance in each area based on the multi-dimensional monitoring data set, construct a real-time dynamic risk heat map, and generate regional risk warning signals based on the real-time dynamic risk heat map. The collaborative control module is used to coordinate the main refrigeration unit and the standby refrigeration unit according to the regional risk warning signal through a pre-loading strategy, wherein the pre-loading strategy includes controlling the standby refrigeration unit to enter a preset low-power standby mode when the regional risk warning signal is received. The blockchain evidence storage module is used to record multi-dimensional monitoring data sets, real-time dynamic risk heat maps, regional risk early warning signals, and corresponding collaborative scheduling instructions. The dynamic risk assessment module includes a risk calculation unit and a heatmap generation unit, comprising: The risk calculation unit is used to calculate the risk value of temperature and humidity imbalance in each area based on the current temperature and humidity data, equipment operating parameters and operation behavior data. The heat map generation unit is used to map the risk values ​​of temperature and humidity imbalance in each area to the spatial coordinates of the cold storage, generate a real-time dynamic risk heat map, and display it in color according to the preset risk level. The risk calculation unit is configured with risk value calculation logic, which includes: Acquire real-time temperature and humidity data, equipment operating parameters, and operational behavior data for each area of ​​the cold storage; Perform time-series analysis on the real-time temperature and humidity data to calculate the rate of change of temperature and humidity; Based on the equipment operating parameters, the vibration frequency characteristics and energy consumption fluctuation characteristics of the equipment are extracted, and the equipment health is calculated through preset operating stability indicators. Based on the operational behavior data, the frequency of goods entering and exiting and the duration of door opening within a fixed time window are statistically analyzed to generate an area activity index. The temperature and humidity change rate, equipment health, and regional activity index are weighted and summed using preset risk value weighting coefficients to generate a regional temperature and humidity imbalance risk value.

2. The temperature and humidity redundant monitoring and energy-saving control system for pharmaceutical cold storage according to claim 1, characterized in that, The dynamic risk assessment module also includes an early warning triggering unit, comprising: The early warning triggering unit is used to predict the temperature and humidity imbalance trend within a future time window based on a real-time dynamic risk heat map and historical temperature and humidity imbalance risk values, and generate a regional risk early warning signal.

3. The temperature and humidity redundant monitoring and energy-saving control system for pharmaceutical cold storage according to claim 2, characterized in that, The warning triggering unit is configured with warning triggering logic, which includes: Based on the real-time dynamic risk heat map and historical temperature and humidity imbalance risk values, the LSTM neural network algorithm is used to predict the temperature and humidity imbalance risk values ​​of each region within the future time window. When the risk value of temperature and humidity imbalance in any area exceeds the preset risk threshold, a regional risk warning signal is generated.

4. The temperature and humidity redundant monitoring and energy-saving control system for pharmaceutical cold storage according to claim 1, characterized in that, The collaborative control module includes a status monitoring unit and a collaborative scheduling unit, comprising: The status monitoring unit is used to monitor the operating status parameters of the main refrigeration unit in real time, determine the operating status of the main refrigeration unit through a fault diagnosis algorithm, and generate equipment status assessment results. The collaborative scheduling unit is used to generate a preload start command based on the regional risk warning signal and equipment status assessment results, control the standby refrigeration unit to execute the preload strategy, and realize intelligent scheduling of the main refrigeration unit and the standby refrigeration unit when the main refrigeration unit fails.

5. The temperature and humidity redundant monitoring and energy-saving control system for pharmaceutical cold storage according to claim 4, characterized in that, The collaborative scheduling unit is configured with preloading control logic, which includes: Obtain regional risk warning signals and generate preloaded startup instructions; According to the preload start command, control the standby compressor to operate at a preset minimum operating frequency; Keep the evaporator fan off so that the standby refrigeration unit remains in a preset low-power standby mode when not in refrigeration operation.

6. The temperature and humidity redundant monitoring and energy-saving control system for pharmaceutical cold storage according to claim 5, characterized in that, The preloading control logic is also configured with intelligent scheduling sub-logic, which includes: The key operating parameters of the main refrigeration unit are monitored in real time by the status monitoring unit. These key operating parameters include compressor current, refrigerant pressure, outlet air temperature, and vibration frequency. When any critical operating parameter of the main chiller unit exceeds the preset operating stability threshold, the main chiller unit will be marked as faulty and an emergency switchover command will be generated. According to the emergency switching command, the standby refrigeration unit in the preload state is controlled to switch to full-power refrigeration operation mode within a preset time, while the main refrigeration unit is shut down. After the main refrigeration unit is troubleshooted, the main refrigeration unit is controlled to enter a preset low-power standby mode.

7. The temperature and humidity redundant monitoring and energy-saving control system for pharmaceutical cold storage according to claim 1, characterized in that, The blockchain evidence storage module includes a data upload unit and a traceability query unit, comprising: The data on-chain unit is used to store the multi-dimensional monitoring data set, real-time dynamic risk heat map, regional risk warning signal and corresponding collaborative scheduling instructions to the blockchain network after hash encryption. The traceability query unit is used to provide blockchain-based data traceability query services.

8. The temperature and humidity redundant monitoring and energy-saving control system for pharmaceutical cold storage according to claim 7, characterized in that, The data upload unit is configured with blockchain storage logic, which includes: The collected temperature and humidity data, equipment operating parameters, and operational behavior data are packaged in batches at preset time intervals to generate basic data blocks; Record real-time dynamic risk heat maps and regional risk early warning signals, and generate risk assessment records; For each scheduling instruction, the triggering conditions, execution content, and execution time are recorded to form a decision chain; The basic data blocks, risk assessment records, and decision-making chains are hashed and encrypted using the SHA-256 algorithm to generate digital fingerprints. The digital fingerprint and timestamp are combined and uploaded to the blockchain network, and the blockchain transaction hash value is obtained as a proof of evidence. Establish a mapping index between the local database and the blockchain network for querying and verification.

9. The temperature and humidity redundant monitoring and energy-saving control system for pharmaceutical cold storage according to claim 1, characterized in that, The system also includes an energy-saving control module, which is configured with energy-saving control logic, including: Low-risk areas are identified based on real-time dynamic risk heat maps, and refrigeration equipment in low-risk areas is operated in an intermittent mode. These low-risk areas are divided according to preset risk levels. Acquire equipment operating parameters and historical energy efficiency data, calculate the real-time energy efficiency ratio (COP) of each chiller unit, sort the COP values ​​in descending order, and prioritize the chiller unit with the highest COP value as the main chiller unit. Historical operational data is extracted from the blockchain evidence storage module, and the risk threshold and risk value weight coefficient are optimized through reinforcement learning algorithms.

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