Biological sample library management method based on integration of intelligent early warning and emergency treatment

Abstract

The invention discloses a biological sample library management method based on integration of intelligent early warning and emergency treatment. According to the method, through data acquisition, an intelligent early warning algorithm and an emergency response mechanism, the temperature and humidity environment in a biological sample library is monitored in real time, and emergency processing is triggered when abnormity is detected. Through multi-level threshold comparison and a self-adaptive algorithm, the system can automatically adjust the temperature and humidity according to environmental changes, and the stability and safety of a sample storage environment are ensured. And specifically, through a dual-system identification strategy and a CO2 dry ice backup refrigeration mechanism, the flexibility and reliability of emergency treatment are improved. According to the method, the automation level and response efficiency of biological sample library management can be remarkably improved, and long-term storage and safety of samples are ensured. The method has the advantages of quick response, automatic adjustment, efficient optimization and the like, and can be widely applied to the environment monitoring fields of biological sample libraries, medical storage and the like.

Description

Technical Field

[0001] This invention relates to the field of environmental monitoring and emergency response technology, specifically to a biobank management method based on the integration of intelligent early warning and emergency response. Background Technology

[0002] Biobanks, as important medical and research facilities, undertake the long-term storage of various biological samples (such as blood, tissues, and DNA). These biological samples play a crucial role in disease diagnosis, drug development, and other fields. Therefore, ensuring the stability of sample quality, especially the precise control of temperature and humidity, is key to guaranteeing the long-term stable storage of samples.

[0003] However, existing biobank management methods generally rely on manual operation or basic temperature and humidity monitoring equipment, which have several shortcomings:

[0004] Failure to respond promptly to environmental fluctuations: Traditional biobank management systems often rely on periodic manual checks or simple alarm systems. When temperature and humidity fluctuate, they cannot react immediately, leading to unstable sample storage conditions and potentially affecting sample quality. Lack of automatic adjustment mechanisms: Many existing systems rely on static temperature and humidity control equipment, unable to adjust in real time according to environmental changes. This necessitates manual intervention to adjust during environmental fluctuations, wasting time and increasing management complexity. Delayed data recording and analysis: Many traditional systems only have basic temperature and humidity recording functions, lacking real-time data analysis and feedback mechanisms. Managers cannot optimize management strategies or conduct effective data analysis in a timely manner, missing opportunities to improve management. Lack of intelligent early warning functions: Most existing technologies rely on simple threshold monitoring, unable to flexibly adjust thresholds or response strategies according to actual environmental changes. The lack of intelligent emergency response mechanisms results in an inability to respond quickly and accurately to emergencies. With the increasing scale and management needs of biobanks, traditional management methods can no longer meet the demands of modern biobanks for precise control, intelligent response, and efficient management. Therefore, this invention proposes a biobank management method based on integrated intelligent early warning and emergency response. Summary of the Invention

[0005] The purpose of this invention is to provide a biobank management method based on intelligent early warning and emergency response, which has the advantages of automated monitoring, intelligent early warning and efficient emergency response, and solves the problems of slow equipment failure response and poor flexibility of early warning system in the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a biobank management method based on integrated intelligent early warning and emergency response, comprising the following steps:

[0007] S1. Environmental Data Acquisition and Preprocessing: Real-time acquisition of temperature T env and humidity H env The data is then subjected to Gaussian filtering for noise reduction, and normalized to the [0,1] range using a standardization formula to generate standardized data I. in S2, Intelligent Early Warning and Anomaly Detection: Utilizing intelligent early warning algorithms based on standardized data I in Perform a threshold comparison when T env >T max ·f(T env H env ) or H env >H max ·f(T env H env When ), a warning signal W is generated. alert It outputs the warning level L; S3, Emergency Response and Adjustment: Response to the warning signal W alert If W alert =1, activate emergency response measures, set temperature T env Adjust to 5°C to 7°C, humidity H env Adjust to 40% to 50%; S4, Data recording and processing result archiving: Record temperature T env and humidity H env Data, early warning signals W alert In addition to emergency response procedures, reports are generated and archived; S5, System Optimization and Feedback Adjustment: Based on historical data and feedback information, the temperature threshold T in the early warning system is adjusted using system optimization algorithms. max and humidity threshold H max Optimize response time; S6, Result Output: Output quantization terms, including at least the warning signal W. alert Response time t response Temperature and humidity values ​​T max and H max ;t response ≤5 seconds is the condition for achievement; S7, Dual System Identification and Emergency Cooling Mechanism: The system automatically identifies the equipment type. When the equipment is a self-cascaded dual system, it triggers the start of both cooling systems; when the equipment is a binary cascaded system, the CO2 dry ice backup cooling system is activated first, and rapid cooling is achieved by releasing CO2 dry ice.

[0008] Preferably, S1 includes: S1.1, real-time acquisition of temperature T env and humidity H env Sampling frequency f s ∈[1,10]Hz, acquire real-time environmental data; S1.2, process the collected temperature and humidity data T env and humidity H envApply a Gaussian filter with a standard deviation σ ∈ [0.6, 1.2] to obtain the denoised data. and

[0009] S1.3, denoise the data and Standardize the process using the standardized formula: Generate standardized data I in (t)=[[x T (t),x H (t)].

[0010] Preferably, S2 includes: S2.1: using an adaptive function f(x) T ,x H ), calculate the dynamic adjustment factor for temperature and humidity thresholds, the function form is: Where α1, α2 ∈ [0, 1] are weighting coefficients adjusted using historical data to adjust the influence of temperature and humidity on the warning threshold; x T and x H The standardized temperature and humidity data are derived from the sensor data collected in step S1, and are generated through Gaussian filtering for noise reduction and standardization. Tmin ,x Tmax ,x Hmin ,xH max The minimum and maximum standardized thresholds for temperature and humidity are derived from experimental settings or historical data.

[0011] S2.2, Based on the dynamic adjustment factor f(x) T ,x H Calculate the dynamic threshold: Θ T =T max ·f(x T ,x H ),Θ H =H max ·f(x T ,x H ), where T max and H max The maximum temperature and humidity thresholds set for the system; S2.3, using standardized data x T and x H Calculate the risk ratio And generate an early warning signal W alert : If R(t) > 1, an early warning is triggered, and W is generated. alert =1, otherwise W alert=0; S2.4, calculate the warning level L(t), and classify it by normalizing the risk ratio R(t): L(t) = [3·clip(R(t)-1,0,1)], where clip(R(t)-1,0,1) ensures that the risk ratio is normalized to the range of [0,1], and outputs the warning level.

[0012] Preferably, S3 includes: S3.1, responding to the early warning signal W alert If W alert =1, then dynamic adjustment of temperature and humidity will be performed. H env ← in, The target temperature and humidity range set for the system; if W alert =0, then the current temperature and humidity data remain unchanged; S3.2, based on the warning signal W alert Temperature and humidity data T env H env The magnitude of temperature and humidity changes is dynamically adjusted, and the adjustment intensity is controlled by adaptive coefficients γ1 and γ2: ΔT env =γ1·(T env -T target ),ΔH env =γ2·(H env -H target ), where T target and H target These represent the target temperature and humidity values, respectively; γ1 and γ2 are adaptive coefficients that dynamically adjust the temperature and humidity variation based on the magnitude of the temperature and humidity deviation; ΔT env The temperature adjustment range represents the change from the current temperature T. env To target temperature T target The change in ΔH is dynamically controlled by the adaptive coefficient γ1; env The adjustment range of humidity indicates the change from the current humidity H. env To target humidity H target The change in temperature and humidity is dynamically controlled by the adaptive coefficient γ2; S3.3, after dynamically adjusting the temperature and humidity, the adjusted temperature and humidity value T is... env H env Perform target interval clamping to ensure and Avoid over- or under-adjusting the system.

[0013] Preferably, S4 includes: S4.1, recording temperature T env and humidity H env Data, early warning signals W alert And the adjustment value ΔT during emergency response. env ΔH envAmong them, for each time point when temperature and humidity data are collected, the current temperature and humidity state T is recorded. env (t),H env (t), and the corresponding warning signal and adjustment value; calculate the response time t. response To generate early warning signal W alert To the temperature and humidity value T env and H env Time to reach the target range; S4.2 Generate and store a report file. The report includes the following: Temperature and humidity data records: including the temperature and humidity values ​​T at each time point. env H env Warning signal record: each triggered warning signal W alert Warning level L; Emergency response process record: including temperature and humidity adjustment amount ΔT env ΔH env And the final adjustment results; response time record: calculate and record the time t for each emergency response. response To make it meet the response time requirement t response ≤5 seconds; S4.3, archive the report and output it to the management system for subsequent monitoring and decision support; the report file format is structured data, and it is exported to a commonly used file format.

[0014] Preferably, S5 includes the following steps: S5.1, t response >5 seconds, based on historical data and feedback information, the threshold T of the early warning system is dynamically adjusted using a system optimization algorithm. max and H max T max ←T max -β1·(T env -T target )·f(T env H env ), H max ←H max -β2·(H env -H target )·f(T env H env ), where β1 and β2 are adjustment coefficients learned from historical data, f(T) env H env () is the adaptive function in S2.1, which adjusts T based on the dynamic adjustment factor calculated by the interaction of temperature and humidity. max and H max Optimize system response; S5.2, continuously optimize temperature and humidity thresholds in the early warning system based on historical data and feedback information; after each emergency response, the system dynamically adjusts and updates the T based on changes in temperature and humidity. max and Hmax This enables the system to cope with environmental fluctuations.

[0015] Preferably, S6 includes: S6.1, output quantization term, which includes at least the warning signal W. alert Response time t response Temperature and humidity threshold T max and H max ;t response ≤5 seconds is the condition for achievement; S6.2, through the minimum closed-loop operation condition, the collected data I in The data is transmitted to S2 and continuously monitored and adjusted through the following closed-loop process: I in →S2W alert →S3(T env H env →S4t response →S5(T max H max →S2, until t is satisfied. response ≤5 seconds.

[0016] Preferably, S7 includes:

[0017] S7.1 Dual System Identification Strategy: The system automatically identifies the type of biobank equipment, including self-cascading dual systems and binary cascading systems. For self-cascading dual systems, when abnormal temperature or humidity is detected, the system automatically activates two cooling systems: a single-compressor cooling system and a CO2 dry ice backup cooling system. For binary cascading systems, when the system detects a temperature deviation from the set value, the CO2 dry ice backup cooling system is activated first to cool the system and prevent further temperature increases. S7.2 CO2 Dry Ice Backup Cooling Mechanism: When the system identifies the equipment as a binary cascading system and triggers a warning signal, the system activates the backup cooling mechanism by releasing CO2 dry ice. This process uses a temperature monitoring sensor to monitor temperature changes in real time, ensuring the temperature quickly returns to the set range. env ∈[[5℃,7℃];S7.3、Equipment Status and User Push Notification: The system monitors the equipment status in real time and pushes alarm information, emergency handling measures, and equipment status feedback to the user terminal after an alarm signal is triggered. The user can obtain the equipment operation status in real time through the terminal interface and take response actions based on the system feedback.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The present invention realizes real-time monitoring and anomaly detection of the temperature and humidity of the biobank environment through intelligent early warning algorithm and data acquisition, responds to environmental changes in a timely manner, and improves the automation and accuracy of environmental control.

[0019] 2. Through a dual-system identification strategy, this invention can automatically identify the type of biobank equipment and activate different emergency cooling mechanisms for different types of equipment to deal with equipment failures or abnormal temperature and humidity.

[0020] 3. The emergency response mechanism of this invention activates the CO2 dry ice backup cooling system to ensure that the temperature can still be maintained within the set range in the event of equipment failure, thereby avoiding sample loss due to abnormal temperature.

[0021] 4. The intelligent early warning system of the present invention is optimized through historical data and feedback information to ensure that the system continuously adapts to environmental changes and provides more accurate response decisions. Attached Figure Description

[0022] Figure 1 This is a flowchart of the data acquisition and processing process of the present invention;

[0023] Figure 2 This is a diagram of the dual-system identification and emergency cooling mechanism of the present invention;

[0024] Figure 3 This is a flowchart of the intelligent early warning and emergency response process of the present invention;

[0025] Figure 4 This is a flowchart of the closed-loop control system of the present invention. Detailed Implementation

[0026] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0027] Please see Figures 1 to 4 This invention provides a biobank management method integrating intelligent early warning and emergency response, aiming to ensure the safe storage of samples within the biobank through intelligent temperature and humidity control, anomaly detection, and emergency response mechanisms. The method constitutes a complete closed-loop control system through the following steps: real-time acquisition and processing of environmental data, intelligent early warning and anomaly detection, emergency response and temperature and humidity regulation, data recording and report generation, and system optimization and feedback adjustment. Each step is closely linked to ensure that when environmental anomalies occur, the system can automatically trigger early warnings and take emergency measures, maximizing the protection of the quality of biological samples.

[0028] The technical approach of this invention is based on real-time acquisition of temperature and humidity data within a biobank using sensors, followed by preprocessing of the data through Gaussian filtering and standardization. The preprocessed data is then input into an intelligent early warning system. This system analyzes the environmental data in real time and calculates dynamic thresholds using an adaptive function to trigger early warning signals. The generation of early warning signals is not only based on threshold comparisons but also considers the impact of environmental changes on the warning level, ensuring timely responses to anomalies of varying degrees.

[0029] Upon detecting a warning signal, the system activates the corresponding emergency response mechanism. The emergency response includes dynamic adjustment of temperature and humidity and switching of the refrigeration system to ensure that the temperature and humidity within the biobank are maintained within the set range. To enhance system stability, this invention also introduces a dual-system identification strategy, which can activate the corresponding refrigeration system based on the equipment type (self-cascading dual system or binary cascading system), including a single-compressor refrigeration system and a CO2 dry ice backup refrigeration system, ensuring that the system can continue to operate even if some equipment fails.

[0030] Step-by-step coordination and closed-loop control, environmental data acquisition and preprocessing: Real-time acquisition of environmental data (including temperature T) within the biobank using temperature and humidity sensors. env and humidity H env The collected data is first processed by Gaussian filtering to remove noise, and then normalized to the range [0,1] using a standardization formula. This process effectively removes external environmental interference and improves the accuracy and reliability of the data.

[0031] Intelligent Early Warning and Anomaly Detection: After data preprocessing, standardized data is input into the intelligent early warning system. The system calculates dynamic temperature and humidity thresholds using an adaptive function and compares the current environmental data with preset thresholds. If the data exceeds the set range, the system generates an early warning signal and calculates the warning level based on the risk ratio, thereby triggering the corresponding emergency response.

[0032] Emergency Response and Adjustment: Once a warning signal is detected, the system will activate corresponding emergency response measures, adjusting the temperature and humidity to the target range. The temperature adjustment range is dynamically calculated using an adaptive coefficient γ1, and the humidity adjustment range is dynamically calculated using γ2. The system uses automatic control equipment to precisely adjust the temperature and humidity, ensuring that they are restored to the preset standards in the shortest possible time.

[0033] Data recording and processing result archiving: All temperature and humidity data, early warning signals, and emergency response processes will be recorded in real time and generated into structured reports. These reports include detailed environmental data, early warning levels, adjustment amounts, and response times, facilitating subsequent data analysis, decision support, and system optimization.

[0034] System optimization and feedback adjustment: Based on historical data and feedback information, the system will continuously optimize the temperature and humidity thresholds T in the early warning system. max and H max This improves response speed and adjustment accuracy. Through continuous learning and adjustment, the system can adapt to environmental changes, optimize management strategies, and ensure more precise temperature and humidity control of the sample library.

[0035] Output Results: The system outputs quantitative results in real time, including warning signals, response time, temperature and humidity thresholds, for reference by managers and decision-making systems. Through continuous monitoring and feedback adjustments, the system ensures that temperature and humidity remain within a safe range and dynamically optimizes based on real-time data.

[0036] Dual-system identification and emergency cooling mechanism: The system can automatically identify the type of biobank equipment, mainly divided into self-cascading dual systems and binary cascading systems. For self-cascading dual systems, when abnormal temperature or humidity is detected, the system automatically activates two cooling systems: a single-compressor cooling system and a CO2 dry ice backup cooling system. For binary cascading systems, when the system detects a temperature deviation from the set value, it prioritizes activating the CO2 dry ice backup cooling system, rapidly cooling the system by releasing CO2 dry ice to prevent further temperature increases. CO2 dry ice backup cooling mechanism: When the system identifies the equipment as a binary cascading system and triggers an alarm signal, it activates the backup cooling mechanism by releasing CO2 dry ice. This process uses a temperature monitoring sensor to monitor temperature changes in real time, ensuring the temperature quickly returns to the set range. Equipment status and user push notifications: The system monitors the equipment status in real time and pushes alarm information, implemented emergency handling measures, and equipment status feedback to the user terminal after an alarm signal is triggered. Users can obtain real-time equipment operation information through the terminal interface and take necessary response actions based on system feedback to ensure sample safety and continuous system operation.

[0037] The S1 environmental data acquisition and preprocessing includes: S1.1 real-time acquisition of temperature T env and humidity H env In this embodiment, temperature T env and humidity H env Data is collected in real time using high-precision digital temperature and humidity sensors (DHT22 or SHT30) installed within the biobank. Sensors are positioned in different areas of the biobank to ensure the representativeness and coverage of the collected data. The specific steps are as follows:

[0038] Sensor Selection: Choose a high-precision and stable temperature and humidity sensor to ensure data accuracy during long-term use. The DHT22 sensor is recommended, with a temperature measurement range of -40℃ to +80℃ and a humidity measurement range of 0% to 100% RH. Sampling Frequency: Set the system sampling frequency to f. s The sampling frequency is ∈[1,10]Hz, meaning it collects data 1 to 10 times per second. The sampling frequency can be adjusted according to environmental fluctuations to ensure the system reflects temperature and humidity changes in real time under normal operating conditions. Data accuracy: Temperature acquisition accuracy is 0.1℃, and humidity acquisition accuracy is 1% RH, ensuring high data precision. The sensor is connected to a microcontroller (Arduino or Raspberry Pi), and the data collected by the sensor is input to the data processing module for further processing.

[0039] S1.2 for the collected temperature and humidity data T env and humidity H env To eliminate noise that may be introduced during the data acquisition process, Gaussian filtering is applied in this implementation method for data denoising. The specific steps are as follows:

[0040] Gaussian filter settings: The standard deviation σ of the Gaussian filter is set to 0.8. This value was determined based on experimental data and can effectively smooth the signal and remove low-frequency noise. The formula for the Gaussian filter is as follows: Where t represents each temperature and humidity data point collected, μ represents the mean of the data, and σ represents the standard deviation; Filtering effect: Gaussian filtering can smooth the data, reduce instantaneous data fluctuations caused by environmental changes, and ensure that the data is more stable and reliable.

[0041] S1.3 Standardizes the denoised data T and H. Standardization transforms the data into a fixed range of values, which helps in better comparison and calculation of different data types (temperature and humidity) in subsequent early warning and control algorithms. The specific steps are as follows:

[0042] Standardization formula: Use a standardization formula to normalize data to the range [0,1]. Wherein: T env (t) and H env (t) represents the collected temperature and humidity data; T max and T min H max and H min x represents the maximum and minimum values ​​of temperature and humidity, respectively. T and x H To standardize the temperature and humidity data, ensure that the temperature and humidity data are normalized to the range of [0,1].

[0043] The standardized data processing method allows temperature and humidity data to be input into subsequent intelligent early warning and control algorithms without units and with a uniform scale, avoiding algorithm deviations caused by different data scales. The result of standardization is the consistency of temperature and humidity data when comparing different environmental changes.

[0044] In practice, the standardization process is usually implemented using existing tools, including StandardScaler provided by the scikit-learn library in Python or the normalize function in Matlab.

[0045] Among them, S2 intelligent early warning and anomaly detection includes: S2.1 using an adaptive function f(x) T ,x H ), calculate the dynamic adjustment factor for temperature and humidity thresholds, and design the adaptive function: In this embodiment, intelligent early warning and anomaly detection are based on the adaptive function f(x) T ,x H The adaptive function calculates the dynamically adjusted temperature and humidity thresholds by combining temperature x. T and humidity x H The standardized value is used to adjust the impact of environmental data on the early warning threshold. The function has the following form: α1 and α2 are weighting coefficients adjusted using historical data to adjust the weighting of temperature and humidity on the warning threshold, with values ​​ranging from [0,1]; T env and H env These are the current ambient temperature and humidity, respectively; T min ,T max H min H max The minimum and maximum standardized thresholds for temperature and humidity are derived from experimental settings or historical data.

[0046] Adjustment factor calculation: This function uses standardized temperature and humidity data T env and H env The system's threshold is dynamically adjusted. The adjustment factor is based on the relative difference between the current ambient temperature and humidity and the preset maximum and minimum values, thus reflecting the impact of changes in the current environment.

[0047] Gaussian filtering for noise reduction and normalization: in calculating f(x) T ,x H Before processing, the collected temperature and humidity data are first subjected to Gaussian filtering to remove noise, ensuring data accuracy. After filtering, the data is transformed to the [0,1] range using a standardization formula to obtain standardized data x. T and x H To ensure data consistency.

[0048] S2.2 Based on the dynamic adjustment factor f(x)T ,x H Calculate the dynamic threshold. The dynamic threshold calculation is based on the dynamic adjustment factor f(x) calculated in step S2.1. T ,x H The system applies this factor to the calculation of temperature and humidity thresholds. The dynamic thresholds for temperature and humidity are automatically adjusted based on ambient temperature and humidity data: Θ T =T max ·f(x T ,x H ),Θ H =H max ·f(x T ,x H ), where T max and H max The maximum temperature and humidity threshold set for the system; f(x) T ,x H ) is an adaptive function that reflects the impact of environmental changes on the temperature and humidity control thresholds.

[0049] Dynamic adjustment: This dynamic threshold calculation can be adjusted based on real-time data, avoiding the limitations of static threshold settings. Through feedback from real-time temperature and humidity data, the system can dynamically adjust its response according to environmental fluctuations.

[0050] S2.3 uses standardized data x T and x H Calculate the risk ratio R(t) and generate an early warning signal W. alert

[0051] Risk ratio calculation: using standardized temperature and humidity data x T and x H Calculate the environmental risk ratio R(t). The risk ratio reflects the relationship between the current environmental state and a set threshold. Where Θ T and Θ H x is the dynamic threshold calculated in step S2.2. T and x H The data obtained in step S1.3 is standardized. By calculating R(t), the system can quantify the degree of anomaly in the current environment.

[0052] Generate an early warning signal: Based on the calculated risk ratio R(t), the system determines whether to trigger an early warning signal W. alert If R(t) > 1, an early warning is triggered, and W is generated. alert =1, otherwise W alert =0 indicates that the current environment is normal.

[0053] S2.4 Calculate the warning level L(t). Classification is performed by normalizing the risk ratio R(t). Warning level calculation: The system calculates the warning level L(t) based on the risk ratio R(t) using the following formula: L(t) = [3·clip(R(t)-1,0,1)], where clip(R(t)-1,0,1) ensures the risk ratio is normalized to the range [0,1], ensuring the warning level L(t) is within the range [0,3], representing different levels of warning. Explanation of the clip function: The clip function limits the numerical range, ensuring that the value of R(t)-1 is always within the [0,1] interval, avoiding excessively large or small outliers, and ensuring the stability of the warning level calculation. Warning level output: Through the output of the warning level L(t), the system can further take different emergency response measures to ensure that the biobank's temperature and humidity control capabilities under abnormal environmental conditions are adjusted in a timely manner.

[0054] The S2 intelligent early warning and anomaly detection section details how dynamic temperature and humidity thresholds are calculated using an adaptive function, and how a risk ratio R(t) is generated based on standardized data to trigger an early warning signal and output the warning level. The entire process involves dynamic threshold calculation, risk assessment, and early warning level classification, ensuring the system can automatically adjust its response strategy based on real-time environmental data.

[0055] The S3 emergency response and control includes: S3.1 response warning signal W alert In this embodiment, the first task of step S3 is based on the warning signal W. alert The value of W determines whether to initiate the temperature and humidity control process. The specific steps are as follows: If W... alert =1 indicates that the system has detected abnormal temperature and humidity, triggering an emergency response. The system will dynamically adjust the ambient temperature and humidity according to the preset target temperature and humidity range.

[0056] in, The target temperature and humidity range set for the system;

[0057] W alert =0 indicates that the environment is within a safe range and the system does not need to adjust the temperature and humidity.

[0058] Temperature and humidity control range: Temperature control range: T env Adjust to 5°C to 7°C; Humidity adjustment range: H env It will be adjusted to 40% to 50%.

[0059] S3.2 Based on early warning signal W alert Temperature and humidity data T env H env

[0060] According to the warning signal W in S3.1 alert The system will initiate dynamic temperature and humidity adjustment under the following conditions:

[0061] Temperature and humidity adjustment range calculation: If W alert =1 indicates that the environmental data deviates from the target range, and the system activates a dynamic adjustment mechanism. The dynamic adjustment amplitude is calculated using adaptive coefficients γ1 and γ2: ΔT env =γ1·(T env -T target ),ΔH env =γ2·(H env -H target ), where: γ1 and γ2 are adaptive coefficients, which dynamically adjust the variation range of temperature and humidity based on the magnitude of the temperature and humidity deviation; T target and H target These are the preset target temperature and humidity; ΔT env The temperature adjustment range represents the change from the current temperature T. env To target temperature T target The change in ΔH is dynamically controlled by the adaptive coefficient γ1; env The adjustment range of humidity indicates the change from the current humidity H. env To target humidity H target The change in is dynamically controlled by the adaptive coefficient γ2;

[0062] Adjustment process: Using an adaptive coefficient, the system adjusts the temperature and humidity based on the deviation between the current environment and the target environment. If the deviation between the current temperature and humidity and the target values ​​is large, the adjustment intensity is increased; if the deviation is small, the adjustment intensity is decreased to avoid over-adjustment.

[0063] S3.3 After dynamically adjusting temperature and humidity, the target range is clamped: After temperature and humidity adjustment, the system will ensure that the adjusted temperature and humidity values ​​T are clamped. env and H env All are within the target range: and

[0064] Through clamping operation, the system ensures that temperature and humidity values ​​do not exceed the target range, avoiding over- or under-adjustment and ensuring environmental stability. It also ensures the accuracy of temperature and humidity regulation: the system uses real-time feedback and adjustment to ensure that the temperature and humidity remain within the specified range after each adjustment. If the temperature and humidity still do not reach the target values ​​after adjustment, the system will adjust again until the environmental parameters stabilize within the target range.

[0065] S4 Data Recording and Processing Result Archiving

[0066] S4.1 Record temperature Tenv and humidity H env Data, early warning signals W alert And the adjustment value ΔT during emergency response. env ΔH env .

[0067] Temperature and humidity data recording: After each data collection, the system will record the current temperature and humidity data (T). env and H env Record the data. Record the temperature and humidity values ​​at each time point t. The recording frequency is the same as the sampling frequency to ensure the real-time nature and comprehensiveness of the data.

[0068] Temperature and humidity data are stored in a timestamp and corresponding data value format, which facilitates subsequent analysis and backtracking.

[0069] Data storage location: You can choose to store the data in a local database (such as MySQL or SQLite) or in the cloud for remote access and management.

[0070] Warning signal recording: Each time a warning is triggered, the system will record the warning signal W. alert And the reasons for its triggering. The warning signal record includes the following: Warning signal W alert The value (1 indicates a warning triggered, 0 indicates normal); the warning level L(t) is determined based on the calculated risk ratio R(t). Temperature and humidity adjustment record: After each temperature and humidity adjustment, the system records the adjustment range ΔT of temperature and humidity. env ΔH env These adjustments reflect the temperature and humidity correction measures taken by the system in response to warning signals.

[0071] Record the changes in temperature and humidity, as well as the adjusted actual temperature and humidity values, to ensure data integrity and transparency.

[0072] S4.2 generates and stores report files, which include the following:

[0073] Temperature and humidity data recording: The report should include temperature and humidity data (T) for each time point. env and H env This involves recording changes in environmental parameters at different points in time, which facilitates subsequent analysis of trends in temperature and humidity fluctuations.

[0074] Warning signal record: each triggered warning signal W alert Both the warning level L(t) and the warning level should be recorded in the report. The report should detail the specific circumstances under which each warning is triggered, including the warning level and the handling measures.

[0075] Emergency Response Process Record: Record the temperature and humidity adjustment measures taken during the emergency response process and their effects, specifically including the changes in temperature and humidity ΔT. env ΔH env And the adjusted temperature and humidity values.

[0076] Response time record: The report should also record the time (t) of each emergency response. response This refers to the time from when the warning signal is triggered to when the temperature and humidity have been adjusted and stabilized within the target range. This time should meet the response time requirement, i.e., t response S4.3 archives the report within 5 seconds and outputs it to the management system for subsequent monitoring and decision support.

[0077] Report format: The generated report file should be in a structured data format (such as JSON, XML or CSV) to ensure that the data can be stored and transmitted efficiently, facilitating subsequent data analysis and decision support.

[0078] Data export format: The report file should support commonly used data formats, such as CSV, to facilitate system export and subsequent analysis. The data in the report can be used by managers, decision-makers, and technical personnel to further optimize the management strategy of the sample database.

[0079] Output to Management System: The generated report files will be output to the biobank's management system for subsequent monitoring, analysis, and decision support by relevant personnel. This system should have real-time monitoring capabilities and be able to receive data from various sensors, providing real-time display of temperature, humidity, and early warning information.

[0080] S5 system optimization and feedback adjustment, S5.1 when t response When the time exceeds 5 seconds, based on historical data and feedback information, the system optimization algorithm is used to adjust the temperature and humidity threshold T in the early warning system. max and H max

[0081] Optimization and adjustment conditions: When the emergency response time t response If the time exceeds 5 seconds, the system will initiate an optimization and adjustment process. By analyzing historical data and feedback information, the system adjusts the warning threshold T based on dynamically changing temperature and humidity data. max and H max This is to optimize system response time.

[0082] Optimization Algorithm and Feedback Mechanism: The system uses historical data and feedback information to calculate two adjustment factors, β1 and β2, for dynamically adjusting the temperature and humidity thresholds: T max ←T max -β1·(T env -T target )·f(T env Henv ), H max ←H max -β2·(H env -H target )·f(T env H env ), where β1 and β2 are adjustment coefficients learned from historical data, reflecting the impact of temperature and humidity deviations on threshold adjustment;

[0083] f(T env H env () is the adaptive function in S2.1, a dynamic adjustment factor calculated based on the interaction of temperature and humidity, which adjusts T according to the current environmental data. max and H max Optimize system response.

[0084] Calculation of adjustment factors: The values ​​of β1 and β2 are obtained through analysis and feedback learning of historical data, and are used to quantify the impact of temperature and humidity on the response of the early warning system;

[0085] The impact of temperature and humidity changes on threshold adjustment is dynamically adjusted to ensure that the system can respond quickly and make appropriate adjustments when temperature and humidity fluctuate significantly.

[0086] Feedback mechanism: By learning from historical data, the system can automatically adjust temperature and humidity thresholds according to environmental changes, optimizing response time t. response Ensure that the temperature and humidity are within the specified range.

[0087] S5.2 Based on historical data and feedback information, continuously optimize the temperature and humidity thresholds in the early warning system.

[0088] Continuous optimization process: The system continuously optimizes the temperature and humidity thresholds T in the early warning system by periodically analyzing historical data and real-time feedback. max and H max The optimization process ensures that after each emergency response, the system can dynamically adjust and update the warning thresholds based on environmental changes, reducing latency and improving response efficiency.

[0089] Dynamic threshold adjustment: After each emergency response, the system automatically adjusts the temperature and humidity thresholds based on the feedback of temperature and humidity changes. Through an adaptive algorithm, the system optimizes the magnitude of temperature and humidity changes to ensure that temperature and humidity adjustments reach the target range in the shortest possible time.

[0090] Early warning system update: The system will adjust the T based on historical data. max and H max Verification will be conducted to ensure their applicability in practical operation and further enhance the reliability of the early warning system.

[0091] Real-time feedback mechanism: The system monitors changes in temperature and humidity and provides feedback on the adjustment results to ensure that each early warning system adjustment reflects the actual environmental needs, thereby maximizing the system's efficiency and stability.

[0092] S6 Output: S6.1 Output quantization terms, including at least the warning signal W. alert Response time t response Temperature and humidity threshold T max and H max ;t response The condition for achieving this is ≤5 seconds;

[0093] Warning signal output: The quantization result output by the system includes the warning signal W. alert This signal indicates the presence of environmental anomalies and the level of warning. The warning signal is generated based on a comparison of the temperature and humidity data detected by the system with corresponding thresholds. When the detected temperature and humidity exceed the preset thresholds, the warning signal is triggered, indicating that the system needs to initiate an emergency response.

[0094] Response time output: The system will also output the response time t. response This refers to the time from the generation of the warning signal to the temperature and humidity values ​​reaching the target range. This data is used to evaluate the system's response speed in emergency situations, ensuring the system's rapid effectiveness. Response time t response The system requirements need to be met, namely t response ≤5 seconds.

[0095] Temperature and humidity threshold output: The system outputs the current ambient temperature and humidity threshold T. max and H max These two data points are key parameters for system operation. Using these thresholds, administrators can monitor the set temperature and humidity ranges in real time and adjust the system's early warning strategies as needed.

[0096] S6.2 uses the minimum closed-loop operating conditions to collect data I. in Transmitted to S2, and continuously monitored and adjusted through the following closed-loop process: I in →S2W alert →S3(T env H env →S4t response →S5(T max H max →S2, until t is satisfied. response ≤5 seconds.

[0097] Closed-loop control process: To ensure that temperature and humidity remain within the preset target range, the system adopts a closed-loop control mechanism. Through the triggering and feedback of each warning signal, the system can continuously adjust itself to ensure that temperature and humidity parameters recover to the target range in the shortest possible time and optimize emergency response time. The specific process is as follows: Data transmission: Each time data is collected, I in (Including standardized temperature and humidity data x) T and x H The data will be passed to step S2 for further processing. Emergency response: If the temperature and humidity data exceed the threshold, an early warning signal W will be issued. alert This will trigger the dynamic temperature and humidity adjustment process in S3. The system adjusts the temperature and humidity based on the current environmental data until they return to the target range. Response time monitoring: The system calculates the time t for each emergency response. response Ensure it meets the response time requirements. If the response time exceeds the set threshold t... response If the time is ≤5 seconds, the system will continue to adjust until the requirements are met. System optimization feedback: After each response, the system will transmit the adjusted temperature and humidity results to S5 to optimize the temperature and humidity threshold T in the early warning system. max and H max This ensures faster and more accurate future responses. The ultimate goal: This closed-loop control process continuously cycles until the system response time t... response The system meets the set requirements, namely, restoring the temperature and humidity to the target range within 5 seconds. This process can be adjusted and fed back in real time to ensure that the temperature and humidity within the biobank are always maintained at an optimal level.

[0098] The S6 output section details how quantified results are output, including warning signals, response times, and temperature and humidity thresholds. Simultaneously, the system uses a closed-loop control process for real-time monitoring and adjustment, ensuring that temperature and humidity return to the target range in the shortest possible time, ultimately achieving optimized environmental control.

[0099] S7 Dual System Identification and Emergency Cooling Mechanism, S7.1 Dual System Identification Strategy, Automatic Equipment Type Identification: The system has the function of automatically identifying the type of biobank equipment. Equipment types mainly include self-cascading dual systems and binary cascading systems. Based on real-time environmental data collected by the system (such as temperature T...), env and humidity H env The system will automatically determine the current equipment type and trigger the corresponding cooling mechanism. Self-cascading dual system: When the system detects abnormal temperature and humidity, it will automatically activate two cooling systems, including a single-compressor cooling system and a CO2 dry ice backup cooling system, ensuring that if one system fails, the other continues to operate, thus maintaining the stability of the biobank environment.

[0100] Binary cascade system: When the system detects that the device is a binary cascade system, it prioritizes activating the CO2 dry ice backup cooling system and rapidly cools the sample by releasing CO2 dry ice. The CO2 dry ice system can quickly reduce the temperature, prevent it from rising further, and ensure sample safety.

[0101] S7.2 CO2 Dry Ice Backup Cooling Mechanism: Triggering an Alarm and Activating Backup Cooling: When the system detects that the equipment is a binary cascade system and detects abnormal temperature and humidity, the system activates the cooling mechanism through the CO2 dry ice backup cooling system. This mechanism rapidly cools the system by releasing CO2 dry ice to ensure that the temperature quickly returns to the set range. In case of abnormal temperature and humidity, the system will trigger an alarm signal and select to activate the CO2 dry ice system based on the temperature deviation. The system will control the cooling process by adjusting the amount of CO2 dry ice released according to the set cooling rate. The specific implementation method of cooling by releasing CO2 dry ice is as follows: CO2 dry ice is released into the designated cooling area, utilizing the low-temperature characteristics of CO2 dry ice to rapidly absorb heat and quickly reduce the temperature inside the chamber.

[0102] Cooling range: The goal of a CO2 dry ice system is to regulate the temperature to the target range: T env ∈[5℃, 7℃]. When the temperature reaches the target value, the system automatically stops releasing dry ice to avoid excessive cooling.

[0103] S7.3 Device Status and User Push Notifications: The system monitors device status in real time to ensure that temperature and humidity remain within preset ranges. When an alarm signal is triggered, the system automatically activates the corresponding emergency cooling system based on the different device types and sends relevant alarm information to the user terminal. When W... alert When the value is 1, it indicates abnormal temperature and humidity in the equipment. The system activates the emergency cooling system and pushes alarm information to the user via the terminal. The system pushes alarm information and executed handling measures in real time through the user terminal interface, ensuring that users can promptly grasp the equipment status and take necessary actions based on system feedback. User terminal operation: Users can view the equipment status, current temperature and humidity, warning signals, and corresponding handling measures through the terminal. The terminal interface not only provides real-time alarm information but also allows users to manually intervene or confirm automatically executed measures, ensuring more flexible and efficient management of the biobank.

[0104] To further clarify the specific applications and effects of the present invention, the technical solution of the present invention is illustrated through the following embodiments. These embodiments demonstrate, through practical operation, the application effects of the present invention in biobank management compared with traditional technologies, focusing on the analysis of various technical indicators, performance differences, and the advantages of optimized response time and system stability.

[0105] Background: With the increasing demand for biological samples in the medical and scientific research fields, biobanks play a crucial role in the long-term preservation and management of various biological samples (such as blood, tissues, and DNA). The quality of biological samples directly affects key areas such as disease diagnosis and drug development; therefore, the stability of the biobank environment (especially temperature and humidity control) is paramount. Traditional biobank management methods mainly rely on manual monitoring and adjustments. This approach suffers from slow response times and untimely operations, making it susceptible to environmental fluctuations that adversely affect sample quality.

[0106] The purpose of this embodiment is to combine environmental monitoring and emergency response systems by adopting an integrated intelligent early warning and emergency response technology solution, so as to achieve real-time monitoring and automatic adjustment of temperature and humidity, ensuring that environmental conditions are kept within the range most suitable for sample storage at all times, thereby improving management efficiency and reducing the possibility of human error.

[0107] Through innovative early warning mechanisms and emergency response strategies, the system can ensure immediate response when environmental parameters deviate from the set range, and restore temperature and humidity to the target range through automated means. A dual-system identification and emergency cooling mechanism is introduced to ensure the continuous stability of the sample library environment in the event of abnormal temperature and humidity through a highly efficient cooling system (including a single-compressor refrigeration system and a CO2 dry ice backup refrigeration system).

[0108] Innovations: By combining real-time data acquisition with intelligent algorithms, the system dynamically adjusts temperature and humidity thresholds to optimize response time; it introduces a CO2 dry ice refrigeration mechanism as an emergency backup plan to ensure stable ambient temperature even when equipment malfunctions; the system can automatically identify equipment types (such as self-cascading dual systems and binary cascading systems) and activate the corresponding refrigeration systems to ensure efficient system response.

[0109] Application Scenarios: This technology is suitable for various biobanks, especially for the management of biological samples requiring long-term preservation and high stability. This invention can also be applied to other environments requiring precise temperature and humidity control, such as high-end laboratories, temperature-controlled warehouses, and food storage facilities.

[0110] In order to fully demonstrate the advantages of the present invention over the prior art, we designed the following comparative experiment and compared the differences between the present invention and the traditional management system in terms of environmental stability, response time, and temperature and humidity control accuracy through detailed data tables.

[0111]

[0112]

[0113] Response time: Traditional systems rely on manual intervention or periodic checks, resulting in long response times; this invention significantly shortens response time through an intelligent early warning system and automated emergency response.

[0114] Temperature and humidity stability: Traditional systems rely on static equipment for adjustment, which is prone to temperature and humidity fluctuations; this invention maintains temperature and humidity within the set range through dynamic adjustment and real-time monitoring.

[0115] Equipment failure recovery time: Traditional systems require manual intervention after equipment failure, resulting in a long recovery time; the dual-system design and CO2 dry ice backup refrigeration system of this invention can respond quickly and ensure continuous stability.

[0116] System automation level: Traditional systems mostly rely on manual operation, while this invention achieves fully automated management, greatly improving operational efficiency and accuracy.

[0117] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A biological sample bank management method based on intelligent early warning and emergency treatment integration, characterized in that, Comprising the following steps: S1, environmental data collection and preprocessing: real-time collection of temperature T env and humidity H env , and Gaussian filter denoising processing is performed on the data, the data is normalized to the range of [0, 1] using a standardization formula, and standardized data I in is generated; S2, intelligent early warning and anomaly detection: using intelligent early warning algorithm, based on standardized data I in Threshold comparison is performed, when T env >T max ·f(T env ,H env ) or H env >H max ·f(T env ,H env ), a warning signal W alert is generated, and a warning level L is output. S3, emergency response and regulation: in response to the warning signal W alert , if W alert = 1, start emergency response measures, adjust the temperature T env to 5 to 7 °C, adjust the humidity H env to 40 to 50 %. S4, Data logging and processing results archiving: record temperature T env and humidity H env Data, warning signal W alert and emergency handling procedure, generate and archive report; S5, System optimization and feedback adjustment: Based on historical data and feedback information, use system optimization algorithm to adjust the temperature threshold T in the early warning system max and humidity threshold H max , optimize the response time; S6, result output: output the quantification item, at least including the warning signal W alert Response time t response , temperature and humidity value T max and H max ; t response ≤ 5 seconds as the condition is met; S7, dual system identification and emergency refrigeration mechanism: the system automatically identifies the type of equipment, when the equipment is a self-lift dual system, trigger the start of two sets of refrigeration system; when the equipment is a binary cascade system, preferentially start the CO2 dry ice backup refrigeration system, through the release of CO2 dry ice for rapid cooling.

2. The biological sample bank management method based on intelligent early warning and emergency treatment integration according to claim 1, characterized in that, The S1 comprises: S1.1, collect temperature T in real time env and humidity H env , sampling frequency f s ∈[1, 10] Hz, get real-time environmental data; S1.2, the collected temperature and humidity data T env and humidity H env Apply Gaussian filter, filter standard deviation σ ∈ [0.6, 1.2], get the denoised data and S1.3, the de-noised data and standardization processing is performed, using a standardization formula: standardized data I is generated in (t) = [x T (t), x H (t)]. 3.The biological sample bank management method based on intelligent early warning and emergency treatment integration of claim 1, characterized in that, The S2 comprises: S2.1: using an adaptive function f(x T ,x H ), calculate the temperature and humidity threshold dynamic adjustment factor, the function form is: wherein, α1, α2 ∈ [0, 1] are the weighting coefficients adjusted by historical data, adjusting the influence weight of temperature and humidity on the early warning threshold; x T and x H The standardized temperature and humidity data are generated by Gaussian filtering and standardization processing from the sensor collected data in step S1. Min and Max normalized threshold for temperature and humidity, derived from experimental setup or historical data; S2.2, Based on the dynamic adjustment factor f(x) T ,x H Calculate the dynamic threshold: Θ T =T max ·f(x T ,x H ),Θ H =H max ·f(x T ,x H ), where T max and H max The maximum temperature and humidity threshold set for the system; S2.3, using standardized data x T and x H , calculate risk ratio and generate warning signal W alert : if R(t) > 1, trigger a warning, generate W alert = 1, otherwise W alert = 0; S2.4, calculate the warning level L(t), classify by the normalization of risk ratio R(t): L(t)=[3·clip(R(t)-1,0,1)], where clip(R(t)-1,0,1) ensures that the risk ratio is normalized to the range [0,1], and the output warning level.

4. The intelligent early warning and emergency treatment integrated biological sample bank management method according to claim 1, characterized in that, The S3 comprises: S3.1, in response to the warning signal W alert , if W alert = 1, dynamic adjustment of temperature and humidity is performed wherein, is the target temperature and humidity range set by the system; if W alert = 0, the current temperature and humidity data remains unchanged; S3.2, based on the early warning signal W alert and temperature and humidity data T env , H env The dynamic adjustment of the temperature and humidity variation amplitude, through the adaptive coefficients γ1 and γ2 control the adjustment intensity: ΔT env = γ1·(T env -T target ), ΔH env = γ2·(H env -H target ), where T target and H target are the target temperature and humidity values, γ1 and γ2 are adaptive coefficients, based on the size of the temperature and humidity deviation dynamic adjustment of temperature and humidity variation amplitude; ΔT env is the adjustment amplitude of the temperature, indicating the change from the current temperature T env to the target temperature T target , which is dynamically controlled by the adaptive coefficient γ1; ΔH env is the adjustment amplitude of the humidity, indicating the change from the current humidity H env to the target humidity H target , which is dynamically controlled by the adaptive coefficient γ2; S3.3, after dynamically adjusting the temperature and humidity, the adjusted temperature and humidity value T env , H env is clamped in the target interval to ensure and avoid system over-adjustment or under-adjustment.

5. The intelligent early warning and emergency treatment integrated-based management method of biological sample banks according to claim 1, characterized in that, The S4 comprises: S4.1, record temperature T env and humidity H env data, warning signal W alert and adjustment value ΔT during emergency handling env , ΔH env ; Among them, the temperature and humidity data is collected every time point, record the current temperature and humidity state T env (t), H env (t), and the corresponding early warning signal and adjustment value; The response time t is calculated response To generate a warning signal W alert To the temperature and humidity values T env And H env To reach the target range of time; S4.2, generate and store a report file, the report including the following contents: Temperature and humidity data record: includes temperature and humidity values T at each time point env , H env ; Warning signal record: the warning signal W triggered each time alert and the warning level L; Emergency handling procedure record: including temperature and humidity adjustment amount ΔT env , ΔH env and final adjustment result; Response time record: Calculate and record the time t for each emergency response response to meet the response time requirements t response ≤ 5 seconds; S4.3, archive the report and output to the management system for subsequent monitoring and decision support; the format of the report file is structured data, exported to a commonly used file format.

6. The intelligent early warning and emergency treatment integrated biological sample bank management method according to claim 1, characterized in that, The S5 comprises the following steps: S5.1, t response >5 seconds, based on historical data and feedback information, use system optimization algorithm to dynamically adjust the threshold T of the early warning system max and H max , T max ←T max -β1·(T env -T target )·f(T env ,H env ), H max ←H max -β2·(H env -H target )·f(T env ,H env ), where β1 and β2 are adjustment coefficients learned from historical data, f(T env ,H env ) is an adaptive function in S2.1, a dynamic adjustment factor calculated according to the interaction of temperature and humidity, adjusting T max and H max Optimize the response of the system; S5.2, According to historical data and feedback information, continuously optimize the temperature and humidity threshold in the early warning system; the system dynamically adjusts the updated T max and H max to make the system respond to environmental fluctuations.

7. The intelligent early warning and emergency treatment integrated-based management method of biological sample banks according to claim 1, characterized in that, The S6 comprises: S6.1 outputting a quantification item, at least comprising a warning signal W alert , response time t response , temperature and humidity threshold T max and H max ; t response ≤ 5 seconds is a condition for achievement; S6.2, Collect data I by minimum closed loop operating conditions in Pass to S2 and continuously monitor and adjust by the following closed loop process: in → S2W alert → S3(T env , H env ) → S4t response → S5(T max , H max ) → S2 until t response ≤ 5 seconds. 8.The biological sample bank management method based on intelligent early warning and emergency treatment integration of claim 1, characterized in that, The S7 comprises: S7.1, dual system identification strategy: the system automatically identifies the type of biological sample bank equipment, including self-lift dual system and binary cascade system; For self-lift dual system equipment, when temperature or humidity anomalies are detected, the system automatically starts two sets of refrigeration system, single compressor refrigeration and CO2 dry ice backup refrigeration respectively; For binary cascade system equipment, when the system detects that the temperature deviates from the set value, preferentially start the CO2 dry ice backup refrigeration system, use CO2 dry ice for cooling to avoid further temperature rise; S7.2, CO2 dry ice backup refrigeration mechanism: when the system identifies that the device is a binary cascade system and triggers a warning signal, the system starts the backup refrigeration mechanism by releasing CO2 dry ice; this process monitors the temperature change in real time through the temperature monitoring sensor to ensure that the temperature quickly returns to the set range: T env ∈ [5℃, 7℃]; S7.3, equipment state and user push notification: the system monitors the equipment state in real time, and after the warning signal is triggered, the alarm information, the executed emergency treatment measures and the equipment state feedback are pushed to the user terminal, the user obtains the equipment operation through the terminal interface in real time, and can take response operation according to the system feedback.

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