A biological sample storage environment safety evaluation and early warning method

Abstract

The application discloses a kind of biological sample storage environment safety assessment early warning method, comprising: according to the correlation degree between initial risk condition assessment cerebrospinal fluid viscosity and pipe wall liquid film thickness, identify pipe wall adhesion force change trend;According to the increase degree of liquid film thickness when cerebrospinal fluid viscosity rises, confirm the cumulative thickness of pipe opening liquid accumulation after pipe wall adhesion force is enhanced, according to the cumulative thickness of pipe opening liquid accumulation and pipe wall adhesion force change trend assessment pipe opening liquid accumulation rising rate and cumulative volume, determine the risk accumulation level of storage environment;When pipe opening accumulation volume exceeds preset early warning limit, touch biological sample storage safety early warning signal;Extract high-risk time period range from time period accumulation risk prediction result, strengthen liquid level sampling density in high-risk time period range.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for safety assessment and early warning of biological sample storage environments. Background Technology

[0002] In the field of biosample storage, safety assessment and early warning mechanisms are particularly crucial, as they directly impact sample integrity and the reliability of experimental results. Cerebrospinal fluid (CSF), as an important biological sample, has a significant impact on medical research and clinical diagnosis due to the safety of its storage environment. Ensuring no leakage or contamination during storage is a vital step in guaranteeing research quality and patient safety. However, current storage safety assessments generally suffer from insufficient attention to the dynamic changes in liquid properties. Many methods often overlook the complex behavior of liquids within storage containers, especially when dealing with a special fluid like CSF, failing to fully consider the potential risks to storage safety posed by changes in its physical properties. This neglect results in early warning mechanisms lacking specificity in responding to emergencies and struggling to effectively identify hidden dangers.

[0003] Focusing on specific technical challenges, changes in cerebrospinal fluid (CSF) viscosity become a core factor affecting storage safety. Viscosity not only determines the flow rate of the liquid within the container but also directly affects its adhesion to the container walls and the degree of accumulation in specific areas. While increased viscosity slows liquid flow, theoretically reducing the risk of spillage, in reality, due to enhanced adhesion, the thickness of the film formed on the container walls increases significantly. Especially when the container is tilted or repositioned, abnormal accumulation may occur near the tube opening. If this accumulation is not detected in time, it can create a safety hazard. Taking CSF storage in glass tubes as an example, during routine operations, if the storage device tilts due to handling or adjustment and then returns to an upright position, the high-viscosity CSF will form a thick liquid film at the tube opening, causing the local liquid volume to exceed safe limits. This phenomenon is often overlooked in routine inspections because traditional assessment methods lack in-depth consideration of the relationship between viscosity changes and liquid distribution, and fail to dynamically capture subtle changes in the liquid within the container.

[0004] Therefore, how to identify the impact of changes in cerebrospinal fluid viscosity on the liquid distribution within the container in real time during storage, and accurately determine whether the liquid accumulation in the tube opening area has reached the risk threshold, has become a key issue in ensuring the safety of biological sample storage. Summary of the Invention

[0005] In view of the shortcomings of the existing technology, the purpose of this invention is to provide a method for safety assessment and early warning of biological sample storage environment, which solves the problem that the existing technology lacks in-depth consideration of the relationship between viscosity change and liquid distribution, and fails to dynamically capture subtle changes of liquid in the container.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] This invention provides a method for safety assessment and early warning of biological sample storage environments, comprising:

[0008] Real-time monitoring of cerebrospinal fluid viscosity, obtaining fluid film thickness on the tube wall, fluid film sliding speed and accumulation volume at the tube opening, and identifying initial risk conditions of abnormal fluid adhesion and accumulation in the storage environment;

[0009] Assess the correlation between cerebrospinal fluid viscosity and fluid film thickness on the vessel wall based on the initial risk profile, and identify trends in vessel wall adhesion.

[0010] The increase in cerebrospinal fluid film thickness was assessed based on historical data of changes in cerebrospinal fluid thickness, and the cumulative thickness of fluid accumulation at the tube opening was confirmed after the adhesion of the tube wall was enhanced.

[0011] The rate of increase and volume of liquid accumulation at the pipe opening are assessed based on the cumulative thickness of the liquid accumulation at the pipe opening and the trend of changes in the adhesion to the pipe wall, thereby determining the risk accumulation level of the storage environment.

[0012] When the accumulated volume at the tube opening exceeds the preset warning limit, a biological sample storage safety warning signal is triggered.

[0013] The network logs when the biological sample storage safety warning signal is triggered are obtained. The viscosity of cerebrospinal fluid, the thickness of the fluid film and the volume of fluid accumulation at the tube opening are analyzed in the logs to obtain the characteristics of abnormal viscosity fluctuations. Based on the characteristics of abnormal viscosity fluctuations and the risk accumulation level, the probability of increase and the rate of development of fluid accumulation at the tube opening in the future period are assessed to obtain the accumulation risk prediction results for the period.

[0014] Extract high-risk time periods from the accumulated risk prediction results over time periods, and increase the liquid level sampling density within these high-risk time periods.

[0015] Furthermore, the real-time monitoring of cerebrospinal fluid viscosity, obtaining the thickness of the fluid film on the tube wall, the velocity of the fluid film's descent, and the accumulation volume at the tube opening, and identifying the initial risk conditions of abnormal fluid adhesion and accumulation in the storage environment, includes:

[0016] The cerebrospinal fluid is continuously scanned using an optical refractive sensor to obtain data on changes in the refractive index of the fluid. Based on the calibrated relationship curve between refractive index and viscosity, the real-time viscosity value of the cerebrospinal fluid is determined.

[0017] The laser ranging module using the optical refraction sensor performs multi-point measurements along the vertical direction of the pipe wall to obtain distance data from the pipe wall surface to the liquid surface. The distance data of each measurement point is subtracted from the reference distance when the pipe is empty to obtain the liquid film thickness value at each point. A liquid film thickness distribution map of the pipe wall is generated based on the liquid film thickness values ​​at each point. If the liquid film thickness exceeds the preset first threshold, it is marked as an adhesion abnormality.

[0018] Based on the continuous sampling data of the liquid film edge position in the liquid film thickness distribution map, the position difference between adjacent sampling times is calculated, and the liquid film sliding speed is obtained by the ratio of the position difference to the sampling time interval.

[0019] The liquid level at the pipe opening is obtained by ultrasonic liquid level detection, and the liquid accumulation volume at the pipe opening is calculated by combining the inner diameter of the pipe opening.

[0020] The viscosity value, adhesion anomaly status, liquid film sliding speed, and pipe orifice accumulation volume are input into a preset risk assessment matrix to obtain the initial risk status level.

[0021] Furthermore, the assessment of the correlation between cerebrospinal fluid viscosity and vessel wall fluid film thickness based on the initial risk status, and the identification of trends in vessel wall adhesion, includes:

[0022] The cerebrospinal fluid viscosity sequence and the vascular wall fluid film thickness sequence are extracted from the initial risk status. The linear correlation between the two sequences is calculated using the Pearson correlation coefficient. If the correlation coefficient exceeds the preset second threshold, it is determined that there is a strong correlation between the two, and the correlation degree value is recorded.

[0023] The sampling frequency is determined based on the correlation degree value. Liquid film thickness change data within a continuous time period is obtained according to the sampling frequency. The thickness change rate is obtained by calculating the ratio of the thickness difference between adjacent moments to the time interval. At the same time, the viscosity change rate at the corresponding moment is obtained. The ratio of the thickness change rate to the viscosity change rate is used as the adhesion sensitivity coefficient.

[0024] By fitting the trend of the adhesion sensitivity coefficient over time, a sequence of adhesion change trend data is formed. The least squares method is used to fit the linear equation of the sequence, and the slope of the fitted line is obtained as the rate of adhesion change. The trend of pipe wall adhesion change is determined according to the sign of the slope.

[0025] Furthermore, the assessment of the increase in cerebrospinal fluid film thickness based on historical data of cerebrospinal fluid thickness changes, and the confirmation of the cumulative thickness of fluid accumulation at the tube opening after enhanced adhesion to the tube wall, includes:

[0026] The sampling records of the most recent monitoring period in the historical data of liquid film thickness change are obtained. The liquid film thickness data corresponding to the change of viscosity value from low to high are extracted. The increase in liquid film thickness for each unit increase in viscosity is calculated. The relationship curve between viscosity increment and thickness increment is fitted by linear regression method to obtain the liquid film thickness increase coefficient.

[0027] Based on the liquid film thickness amplification coefficient, when an increase in cerebrospinal fluid viscosity is detected, the current viscosity increase is multiplied by the amplification coefficient to calculate the expected liquid film thickness increment. The expected liquid film thickness increment is then added to the original liquid film thickness of the tube wall to obtain the total liquid film thickness after the tube wall adhesion is enhanced.

[0028] The reduction in the cross-sectional area of ​​the pipe opening is calculated based on the total thickness of the liquid film. The volumetric flow rate of the liquid at the pipe opening per unit time is determined in combination with the liquid inflow velocity. The accumulation volume is calculated based on the volumetric flow rate and time. The accumulation volume is then converted into a vertical height value to determine the cumulative thickness of the liquid accumulation at the pipe opening.

[0029] Furthermore, the assessment of the rate of increase and cumulative volume of liquid accumulation at the pipe opening based on the cumulative thickness of the liquid accumulation and the changing trend of the pipe wall adhesion, and the determination of the risk accumulation level of the storage environment, includes:

[0030] Obtain the cumulative thickness time series data of liquid accumulation at the pipe opening, and calculate the instantaneous rate of increase by the ratio of the thickness difference between adjacent sampling points to the time interval;

[0031] The slope value of the trend of pipe wall adhesion is extracted and normalized. An acceleration factor is set according to the sign of the slope. The adjusted rise rate is obtained by multiplying the instantaneous rise rate by the acceleration factor.

[0032] The expected cumulative increment is calculated based on the adjusted rate of ascent and the preset time window, and the current cumulative thickness is added to the expected cumulative increment to obtain the total cumulative magnitude.

[0033] Clustering algorithms are used to group historical cumulative data to determine boundary values. The cumulative level is determined by comparing the total cumulative level with the boundary values. At the same time, the ratio of the adjusted rise rate to the preset rate threshold is calculated as the rate coefficient. The risk score is located through a preset risk lookup table. The risk accumulation level of the storage environment is determined based on the interval in which the risk score is located.

[0034] Furthermore, the triggering of a biological sample storage safety warning signal when the accumulated volume at the tube opening exceeds a preset warning limit includes:

[0035] The system acquires the accumulated volume data at the pipe opening in real time and compares it with the preset warning limit. If the accumulated volume exceeds the warning limit, a warning trigger command is generated.

[0036] According to the warning trigger command, the warning signal output is initiated, and the current timestamp, accumulated volume value and the difference exceeding the limit are encoded to output a biological sample storage safety warning signal.

[0037] Furthermore, the process involves acquiring network logs when a biological sample storage safety warning signal is triggered, analyzing cerebrospinal fluid viscosity, fluid film thickness, and tube orifice accumulation volume in the logs to obtain abnormal viscosity fluctuation characteristics. Based on these characteristics and the risk accumulation level, the probability and rate of increase in fluid accumulation at the tube orifice in the future are assessed to obtain a time-period accumulation risk prediction result, including:

[0038] Obtain the network log file when the biological sample storage safety warning signal is triggered, extract the time series data of cerebrospinal fluid viscosity value, liquid film thickness value and tube orifice accumulation volume value in the monitoring cycle before the warning from the log record, calculate the difference of viscosity value at adjacent time points, and count the number and amplitude range of positive and negative changes of the difference to obtain the viscosity abnormal fluctuation characteristics;

[0039] The degree of viscosity instability is determined based on the number of changes in the abnormal viscosity fluctuation characteristics, and the current risk accumulation level is obtained. A weighting coefficient is set based on the risk level and the number of changes.

[0040] The weighting coefficients are used to adjust the historical accumulation rate data. The growth trajectory of liquid accumulation at the pipe opening in the future period is calculated by the moving average method. The slope of the growth trajectory is used as the development rate. The ratio of the number of time points when the trajectory exceeds the safety limit to the total number of time points is calculated to obtain the probability of increase.

[0041] A two-dimensional lookup table is constructed using the rise probability and growth rate. The corresponding risk value is located in the lookup table according to the rise probability and growth rate. The risk value is then associated with the prediction period information to obtain the prediction result of the cumulative risk during the period.

[0042] Furthermore, the step of extracting high-risk time periods from the accumulated risk prediction results and increasing the liquid level sampling density within these high-risk time periods includes:

[0043] The risk value sequence is read from the cumulative risk prediction results of the time period. The risk value is compared with the preset high risk threshold. If the risk value exceeds the threshold, the time period is marked as a high risk time period. Temporally adjacent high risk time periods are merged into a continuous interval to obtain the range of high risk time periods.

[0044] The maximum value within the high-risk period range is selected as the risk peak. The density enhancement factor is calculated using the ratio of the risk peak to the high-risk threshold. The original sampling interval is divided by the density enhancement factor to obtain a new sampling interval.

[0045] The sampling frequency of liquid level in the high-risk period is adjusted using the new sampling interval. While keeping the sampling interval of other periods unchanged, the sampling time of the high-risk period is set according to the new sampling interval.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] This invention discloses a method for safety assessment and early warning of biological sample storage environment. By real-time monitoring of cerebrospinal fluid viscosity, tube wall liquid film thickness, liquid film sliding speed, and tube orifice accumulation volume, it identifies the initial risk status of abnormal liquid adhesion and accumulation in the storage environment. Based on this, it assesses the correlation and trend of cerebrospinal fluid viscosity, tube wall liquid film thickness, and adhesion. Furthermore, it combines historical liquid film thickness data to quantify the increase in liquid film thickness when viscosity increases, confirming the cumulative thickness of liquid accumulation at the tube orifice after enhanced adhesion. Thus, it comprehensively assesses the rate of increase, cumulative magnitude, and risk accumulation level of liquid accumulation at the tube orifice. When the accumulation volume exceeds the warning threshold, it immediately triggers a biological sample storage safety warning. Simultaneously, it analyzes the network log at the trigger time to extract abnormal viscosity fluctuation characteristics, and combines the risk level to predict the probability and development speed of liquid accumulation at the tube orifice in the future period, obtaining the accumulation risk prediction result for the period. For high-risk periods, it increases the liquid level sampling density, realizing closed-loop management from real-time monitoring, risk assessment to warning triggering and prediction optimization. This effectively prevents the risk of contamination or leakage caused by abnormal liquid adhesion and accumulation during cerebrospinal fluid sample storage, ensuring the safety of biological sample storage. Attached Figure Description

[0048] Figure 1 This is a flowchart of a method for safety assessment and early warning of biological sample storage environment according to the present invention.

[0049] Figure 2 This is a schematic diagram of a biological sample storage environment safety assessment and early warning method according to the present invention.

[0050] Figure 3 This is another schematic diagram of a biological sample storage environment safety assessment and early warning method according to the present invention. Detailed Implementation

[0051] The present invention will now be described in detail through specific embodiments:

[0052] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0053] like Figures 1-3 This embodiment of a biological sample storage environment safety assessment and early warning method may specifically include:

[0054] S101. Real-time monitoring of cerebrospinal fluid viscosity, obtaining fluid film thickness on the tube wall, fluid film sliding speed and accumulation volume at the tube opening, and identifying initial risk conditions of abnormal fluid adhesion and accumulation in the storage environment.

[0055] Continuous scanning of cerebrospinal fluid (CSF) using an optical refractive sensor acquires data on changes in the liquid's refractive index. Based on the calibrated relationship curve between refractive index and viscosity, the real-time viscosity of the CSF is determined. A laser ranging module using the optical refractive sensor performs multi-point measurements along the vertical direction of the tube wall to acquire distance data from the tube wall surface to the liquid surface. Subtracting the baseline distance for an empty tube from the distance data at each measurement point yields the liquid film thickness at each point. A liquid film thickness distribution map is generated based on these values. If the liquid film thickness exceeds a preset first threshold, it is marked as an adhesion anomaly. Based on continuous sampling data at the liquid film edge position in the thickness distribution map, the position difference between adjacent sampling times is calculated. The ratio of the position difference to the sampling time interval yields the liquid film's downward velocity. Ultrasonic liquid level detection at the tube opening is used to obtain the liquid level height at the opening. Combined with the tube opening's inner diameter, the liquid accumulation volume at the opening is calculated. The viscosity value, adhesion anomaly status, downward velocity, and accumulation volume are input into a preset risk assessment matrix to obtain an initial risk level.

[0056] In one embodiment, real-time monitoring of cerebrospinal fluid (CSF) viscosity is achieved using an optical refractive sensor. The sensor emits a beam of light of a specific wavelength into the CSF within a storage tube. As the beam passes through liquids of different viscosities, its refraction angle changes accordingly. A photodetector built into the sensor receives the refracted light signal, converts it into an electrical signal, and calculates the real-time viscosity value of the CSF using a pre-calibrated refractive index versus viscosity curve. This calibration curve is obtained through optical measurements of CSF samples with different known viscosities, ensuring the accuracy of the viscosity measurement.

[0057] Specifically, the laser ranging module has multiple measuring points arranged vertically along the pipe wall, with each measuring point spaced 2 millimeters apart. A laser beam is emitted from the outer wall of the pipe, penetrates the glass pipe wall, and reaches the liquid surface. The distance from the pipe wall to the liquid surface is calculated based on the laser's round-trip time. By comparing the real-time measured distance with a reference distance in an empty pipe state, the liquid film thickness value at each measuring point is obtained. These discrete thickness values ​​are used to generate a continuous liquid film thickness distribution map on the pipe wall using an interpolation algorithm, visually reflecting the distribution of the liquid film on the pipe wall.

[0058] For example, when the thickness of the liquid film in a certain area exceeds a preset first threshold, it indicates that abnormal liquid adhesion has occurred in that area. This abnormal adhesion usually occurs during the process of the tube wall returning to upright after tilting, where high-viscosity cerebrospinal fluid forms a thicker liquid film layer on the tube wall due to increased adhesion.

[0059] Preferably, the liquid film sliding velocity is calculated using a continuous sampling method, recording the liquid film edge position data every 100 milliseconds. The instantaneous sliding velocity is obtained by dividing the position difference between two adjacent samples by the time interval. The ultrasonic liquid level detection device at the pipe opening emits ultrasonic waves vertically downwards, determines the liquid level height based on the echo time, and calculates the liquid accumulation volume in the pipe opening area by combining this with the pipe opening inner diameter parameter.

[0060] In one possible implementation, a risk assessment matrix is ​​pre-built and stored in the system. This matrix takes viscosity value, adhesion anomaly state, descent velocity, and accumulation volume as four input parameters, calculates a comprehensive risk value through weighted averages, and maps the risk value to three risk levels: low, medium, and high. When multiple parameters show abnormalities simultaneously, the risk level increases accordingly, providing a basis for subsequent safety warnings.

[0061] S102. Assess the correlation between cerebrospinal fluid viscosity and fluid film thickness on the vessel wall based on the initial risk status, and identify the trend of changes in vessel wall adhesion.

[0062] Cerebrospinal fluid viscosity and vessel wall film thickness sequences are extracted from the initial risk profile. The Pearson correlation coefficient is used to calculate the linear correlation between the two sequences. If the correlation coefficient exceeds a preset second threshold, a strong correlation is identified, and the correlation strength is recorded. A sampling frequency is determined based on this correlation strength, and film thickness variation data are acquired over consecutive time periods at this frequency. The ratio of the thickness difference between adjacent moments to the time interval is calculated to obtain the thickness change rate. Simultaneously, the viscosity change rate at the corresponding moment is obtained. The ratio of the thickness change rate to the viscosity change rate is used as the adhesion sensitivity coefficient, reflecting the influence of viscosity changes on film thickness. By fitting the adhesion sensitivity coefficient over time, an adhesion change trend sequence is formed. The least squares method is used to fit the linear equation of this sequence, and the slope of the fitted line is used as the adhesion change rate. A positive slope indicates an upward trend, while a negative slope indicates a downward trend, thus determining the vessel wall adhesion change trend.

[0063] In one embodiment, a cerebrospinal fluid viscosity sequence and a vessel wall fluid film thickness sequence are extracted from initial risk status data. The viscosity sequence includes viscosity data points collected at preset intervals within a preset monitoring period, and the thickness sequence consists of fluid film thickness measurements at corresponding times. The linear correlation between the two sequences is calculated using the Pearson correlation coefficient formula, which ranges from -1 to 1. A strong correlation is indicated when the absolute value of the coefficient is greater than 0.7.

[0064] Specifically, the calculation of the adhesion sensitivity coefficient involves analyzing the relationship between the rate of change in thickness and the rate of change in viscosity. When the viscosity of cerebrospinal fluid increases, the adhesion of the liquid to the tube wall increases accordingly, leading to an increase in the thickness of the liquid film. The adhesion sensitivity coefficient is obtained by calculating the change in thickness caused by a unit change in viscosity. This coefficient reflects the degree of influence of viscosity on the ability to form a liquid film; a larger coefficient indicates a more significant impact of viscosity changes on the liquid film thickness. In actual storage, when the viscosity of cerebrospinal fluid increases due to temperature changes or changes in composition, the sensitivity coefficient can quantitatively assess the magnitude of changes in liquid adhesion on the tube wall. By fitting the trend of the adhesion sensitivity coefficient at multiple time points, the overall direction of change in the adhesion force on the tube wall can be determined.

[0065] It should be noted that the correlation level value is used to dynamically adjust the subsequent sampling strategy. When the correlation level is high, the sampling frequency is increased to once every 5 minutes; when the correlation level is low, the sampling frequency is decreased to once every 15 minutes, thus achieving reasonable resource allocation.

[0066] Preferably, when calculating the estimated adhesion force to the pipe wall, the adhesion response coefficient is multiplied by the viscosity value at the current moment to obtain a value reflecting the actual adhesion strength. This estimated value changes over time to form continuous time series data.

[0067] For example, when using the least squares method to perform linear fitting on the adhesion time series, data points from the most recent two hours are selected for calculation. The slope of the fitted line represents the rate of change of adhesion; a positive slope indicates an upward trend, while a negative slope indicates a downward trend. By judging the trend, storage managers can predict the future direction of liquid adhesion on the pipe walls and take corresponding preventative measures.

[0068] S103. Evaluate the degree of increase in liquid film thickness when cerebrospinal fluid viscosity increases based on historical data of liquid film thickness changes, and confirm the cumulative thickness of liquid accumulation at the tube opening after the adhesion of the tube wall is enhanced.

[0069] The sampling records of the most recent monitoring period are obtained from the historical data on liquid film thickness changes. Liquid film thickness data corresponding to changes in viscosity from low to high are extracted, and the increase in liquid film thickness for each unit increase in viscosity is calculated. A linear regression method is used to fit the relationship curve between viscosity increment and thickness increment to obtain the liquid film thickness amplification coefficient. Based on this coefficient, when an increase in cerebrospinal fluid viscosity is detected, the current viscosity increase is multiplied by the coefficient to calculate the expected liquid film thickness increment. This expected increment is added to the original liquid film thickness on the tube wall to obtain the total liquid film thickness after enhanced adhesion. Using this total liquid film thickness data, the flow cross-sectional area of ​​the liquid at the tube opening is calculated based on the reduction in effective radial space within the tube due to the increased liquid film thickness. The product of the flow cross-sectional area and the liquid inflow velocity is used to obtain the liquid volumetric flow rate at the tube opening per unit time. Multiplying the flow rate by time yields the liquid accumulation volume, which is then divided by the tube opening cross-sectional area to convert it into a vertical height value, determining the cumulative thickness of the liquid accumulation at the tube opening.

[0070] In one embodiment, by retrieving the historical liquid film thickness data from the most recent monitoring period in the stored database, data segments showing an increasing viscosity trend are selected.

[0071] Specifically, when a positive viscosity difference is detected between two adjacent time points, the corresponding liquid film thickness value is extracted, forming a data pair of viscosity increment and thickness increment. A linear regression method is used to fit these data pairs, and the slope of the regression line is the liquid film thickness increase coefficient, which characterizes the degree of liquid film thickness response caused by a unit change in viscosity.

[0072] It should be noted that the coefficient of increase in liquid film thickness reflects the influence of changes in the physical properties of cerebrospinal fluid on the adhesion characteristics of the vessel wall. When the protein content in the cerebrospinal fluid increases or the temperature decreases, leading to an increase in viscosity, the cohesive forces between liquid molecules strengthen, making it easier for the liquid to form a stable liquid film layer on the vessel wall surface. Statistical analysis of historical data can quantify this influence relationship, enabling the prediction of future changes in liquid film thickness.

[0073] Preferably, when calculating the total thickness of the liquid film after the enhanced adhesion to the pipe wall, the difference between the current viscosity value and the reference viscosity value is first obtained, and then this difference is multiplied by the amplification coefficient to obtain the expected increase in liquid film thickness. The incremental value is then added to the original liquid film thickness on the pipe wall to obtain new total liquid film thickness data.

[0074] For example, the formation mechanism of liquid accumulation at the pipe orifice is closely related to changes in the flow cross-section. As the liquid film on the pipe wall thickens, the effective radial space available for liquid flow within the pipe decreases accordingly. According to the fluid continuity equation, the reduction in the flow cross-section leads to a decrease in local flow velocity, resulting in liquid accumulation in the orifice region. By calculating the flow cross-sectional area after the liquid film thickens and combining it with the liquid inflow velocity at the inlet, the volumetric flow rate through that cross-section per unit time can be determined. Multiplying this volumetric flow rate by the accumulation time yields the accumulated liquid volume, which, when divided by the cross-sectional area of ​​the pipe orifice, converts it into the accumulated thickness value in the vertical direction.

[0075] In one possible implementation, when the accumulated thickness exceeds a preset safety threshold, it indicates a risk of liquid spillage in the pipe opening area. At this time, a corresponding early warning mechanism is triggered to remind the operator to take intervention measures.

[0076] S104. Evaluate the rate of increase and volume of liquid accumulation at the pipe opening based on the cumulative thickness of liquid accumulation and the trend of changes in pipe wall adhesion, and determine the risk accumulation level of the storage environment.

[0077] The process involves acquiring time-series data of the accumulated thickness of liquid at the pipe inlet, calculating the ratio of the thickness difference between adjacent sampling points to the time interval to obtain the instantaneous rate of increase, and simultaneously extracting the slope value of the pipe wall adhesion trend. The slope value is then normalized. If the adhesion shows an upward trend, an acceleration factor is set based on the ratio of the absolute value of the slope to the baseline slope; if it shows a downward trend, an acceleration factor is also set. The instantaneous rate of increase is multiplied by the acceleration factor to obtain the adjusted rate of increase. Based on the adjusted rate of increase, it is multiplied by a preset time window to obtain the expected future cumulative increment. The current accumulated thickness is added to the expected cumulative increment to obtain the total cumulative magnitude. A clustering algorithm is used to group the historical cumulative magnitude data, determining the boundary values ​​for low, medium, and high cumulative magnitudes. The total cumulative magnitude is compared with the boundary values ​​to determine the cumulative level. Simultaneously, the ratio of the adjusted rate of increase to a preset rate threshold is obtained as a rate coefficient. A risk lookup table is pre-constructed, where each element corresponds to a risk score under the combination of the cumulative level and the rate coefficient. The risk score is located in the risk lookup table by the accumulated level and rate coefficient. The risk level is determined according to the preset range in which the risk score is located. If the score is in the low range, it is determined to be a low risk level. If the score is in the middle range, it is determined to be a medium risk level. If the score is in the high range, it is determined to be a high risk level, thus determining the risk accumulation level of the storage environment.

[0078] In one embodiment, the cumulative thickness time series data of the liquid accumulation at the pipe opening is obtained through continuous monitoring, with the liquid thickness value at the pipe opening recorded every 5 minutes. When calculating the instantaneous rate of rise, the thickness data of two adjacent sampling points are extracted, and the thickness difference is divided by the time interval to obtain the average rate of rise for that period. Simultaneously, the slope value of the adhesion change trend is obtained from the pipe wall adhesion monitoring module, which reflects the rate of change of adhesion over time.

[0079] Specifically, the acceleration factor is set based on the mechanism by which changes in adhesion affect the rate of liquid accumulation. When the adhesion to the pipe wall increases, it means that the degree of liquid adhesion to the pipe wall is continuously enhanced, causing more liquid to remain in the pipe opening area and accelerating the accumulation process. In this case, a baseline slope value needs to be determined first. This baseline slope represents the typical rate of change of adhesion under normal conditions and is obtained through historical data statistics. Dividing the absolute value of the current adhesion slope by the baseline slope yields the dimensionless acceleration factor, which reflects the relative degree of adhesion enhancement. Conversely, when the adhesion decreases, the degree of liquid adhesion weakens, and its impact on the accumulation rate is smaller. Therefore, the acceleration factor is set to 1, indicating that the baseline rate of increase is maintained. By multiplying the instantaneous rate of increase by the acceleration factor, an adjusted rate of increase that considers the influence of adhesion is obtained. This rate more accurately reflects the actual dynamics of liquid accumulation.

[0080] It should be noted that the calculation of the expected cumulative increment uses a linear extrapolation method. The adjusted rate of increase is regarded as the average rate over a future period, multiplied by a preset time window length to obtain the expected cumulative increment within that period. The time window is determined based on the actual needs of storage management, and is usually set to 2 to 6 hours. The current cumulative thickness is added to the expected cumulative increment to obtain the total cumulative magnitude, which represents the maximum cumulative level that may be reached in the future under the current trend. Preferably, the K-means clustering algorithm is used to perform statistical analysis on the historical cumulative magnitude data. The input of the clustering algorithm is the set of cumulative magnitude values ​​recorded in the past 30 days, and the output is three cluster centers and their corresponding sample allocation results. In the algorithm implementation process, three cluster centers are first randomly initialized, and then the following steps are iteratively executed: calculate the distance from each sample point to each cluster center, assign the sample to the nearest cluster center, update each cluster center to the mean of its samples, and repeat the above process until the cluster centers no longer change significantly. The three cluster centers obtained finally represent the typical values ​​of low, medium, and high cumulative levels, respectively, and the midpoint between adjacent cluster centers is used as the boundary value for level division. This data-driven approach can adapt to the characteristics of different storage environments, avoiding the limitations of manually setting fixed thresholds.

[0081] For example, the risk lookup table is constructed by considering both the cumulative level and the rate coefficient. The cumulative level reflects the absolute level of liquid accumulation, while the rate coefficient reflects the dynamic trend of accumulation. The lookup table uses a two-dimensional matrix, with rows corresponding to the three cumulative levels and columns corresponding to different ranges of the rate coefficient. Each matrix element stores a pre-calculated risk score, which is derived through statistical analysis of historical accident data. When the cumulative level is high and the rate coefficient is large, it indicates that the liquid accumulation has reached a dangerous level and is still growing rapidly, corresponding to the highest risk score. Conversely, when the cumulative level is low and the rate coefficient is small, the risk score is the lowest.

[0082] In one possible implementation, the rate coefficient calculation involves normalization. The adjusted rate of ascent is divided by a preset rate threshold to obtain a dimensionless rate coefficient. The rate threshold, determined based on the storage container specifications and typical characteristics of cerebrospinal fluid, represents the maximum permissible rate of ascent under normal conditions. A rate coefficient greater than 1 indicates that the rate of ascent exceeds the normal level, while a coefficient less than 1 indicates that it is within a safe range. Furthermore, the risk level is determined using an interval division method. The risk score range is divided into three non-overlapping intervals, corresponding to low, medium, and high risk levels, respectively. The boundary values ​​of the intervals are determined through risk tolerance analysis, considering a balance between storage security requirements and management costs.

[0083] For example, when the accumulation level is medium and the rate coefficient is 1.5, the corresponding risk score of 65 is located in the risk lookup table. This score falls within the medium-risk range, and the system determines that the current storage environment is at a medium-risk level. At this time, although the liquid accumulation has not yet reached a dangerous level, the rate of increase is relatively fast, requiring increased monitoring frequency and preparation of emergency measures.

[0084] Understandably, this multi-dimensional risk assessment method can comprehensively reflect the security status of the storage environment. Accumulated thickness provides static risk information, while the adhesion change trend and rate of increase reflect dynamic development trends. The combination of both enables accurate risk identification and tiered management.

[0085] S105. When the accumulated volume at the tube opening exceeds the preset warning limit, a biological sample storage safety warning signal is triggered.

[0086] The system acquires the accumulated volume data at the tube opening in real time and compares it with a preset warning threshold. If the accumulated volume exceeds the warning threshold, a warning trigger command is generated. Based on the warning trigger command, a warning signal is output, encoding the current timestamp, the accumulated volume value, and the difference exceeding the threshold to output a biological sample storage safety warning signal.

[0087] In one embodiment, the accumulated volume data at the pipe opening is collected every 30 seconds by a liquid level sensor, which converts the detected liquid level height into a volume value. A preset warning threshold is determined based on the storage tube's capacity and a safety factor, typically set to 80% of the pipe opening capacity.

[0088] Specifically, the comparison process employs a real-time judgment mechanism. When the accumulated volume data exceeds the warning threshold, a warning trigger command is immediately generated, which includes a trigger flag and the current volume data.

[0089] It should be noted that the encoding of the warning signal includes three key pieces of information: a timestamp recording the exact time the warning occurred, an accumulated volume value reflecting the current actual liquid level, and a difference exceeding the limit indicating the degree of danger. This information is combined according to a predetermined format to form a complete warning signal data packet.

[0090] Preferably, the biological sample storage safety warning signal is output simultaneously through both audible and visual alarm devices and network communication, achieving dual protection of local reminders and remote notifications.

[0091] S106. Obtain the network log when the biological sample storage safety warning signal is triggered, analyze the cerebrospinal fluid viscosity, liquid film thickness and tube orifice accumulation volume in the log, obtain the viscosity abnormal fluctuation characteristics, and assess the probability and speed of increase of tube orifice liquid accumulation in the future period based on the viscosity abnormal fluctuation characteristics and risk accumulation level, and obtain the period accumulation risk prediction results.

[0092] The network log file triggered when the biological sample storage safety warning signal is obtained is used to extract time-series data of cerebrospinal fluid viscosity, fluid film thickness, and orifice accumulation volume from the monitoring period prior to the warning. The difference in viscosity values ​​between adjacent time points is calculated, and the number and range of positive and negative changes in the difference are statistically analyzed to obtain viscosity anomaly fluctuation characteristics. The degree of viscosity instability is determined based on the number of changes in the viscosity anomaly fluctuation characteristics, and the current risk accumulation level is obtained. If the risk level is high and the number of changes exceeds a preset threshold, a weighting coefficient is set as the ratio of the number of changes to the threshold; if the risk level is medium or low, the weighting coefficient is set to 1. The historical accumulation rate data is adjusted using the weighting coefficient, and the growth trajectory of orifice fluid accumulation in the future period is calculated using the moving average method. The slope of the growth trajectory is used as the development rate, and the ratio of the number of time points where the trajectory exceeds the safety limit to the total number of time points is statistically analyzed to obtain the probability of increase. A two-dimensional lookup table is constructed using the rise probability and development speed. Each element in the table corresponds to a risk value under different combinations of probability and speed intervals. The corresponding risk value is located in the lookup table based on the rise probability and development speed. The risk value is then associated with the prediction period information to obtain the cumulative risk prediction result for the period.

[0093] In one embodiment, the network log file uses a structured storage format, with each log record containing four fields: timestamp, device identifier, monitoring parameter name, and parameter value. When a biological sample storage safety warning signal is triggered, the system automatically marks the log position at that moment and traces back a complete monitoring cycle. The length of the monitoring cycle is set according to the stability requirements of the storage environment, typically between 3 and 7 days. From the traced log data, cerebrospinal fluid viscosity, fluid film thickness, and tube orifice accumulation volume are extracted according to the parameter name, forming three independent time series.

[0094] Specifically, the identification process for abnormal viscosity fluctuations involves difference calculation and statistical analysis. For a viscosity time series, the difference between each adjacent moment is calculated, i.e., the viscosity value at the later moment minus the viscosity value at the previous moment. These differences reflect the instantaneous changes in viscosity. A positive difference indicates an increase in viscosity; a negative difference indicates a decrease in viscosity. The number of positive and negative differences throughout the entire monitoring period is counted, reflecting the frequency of viscosity changes. Simultaneously, the absolute value range of all differences is calculated to determine the amplitude characteristics of viscosity fluctuations. Combining the number of changes and the amplitude range forms a characteristic vector of abnormal viscosity fluctuations.

[0095] It should be noted that the assessment of viscosity instability is based on a comparison of the number of changes with a preset threshold. When the composition of cerebrospinal fluid is stable, the viscosity value should remain relatively constant with few changes. A significant increase in the number of changes indicates that the cerebrospinal fluid may be affected by temperature fluctuations, protein precipitation, or microbial activity, leading to physical instability. This instability increases the risk of fluid accumulation at the orifice.

[0096] Preferably, the weighting coefficient is set using a tiered processing mechanism. When the risk accumulation level is high, it indicates that the storage environment is in a dangerous state, and any abnormal fluctuations in viscosity will amplify the risk. Therefore, the ratio of the number of changes to the threshold is used as the weighting coefficient, and a coefficient greater than 1 indicates that the risk is amplified. When the risk level is medium or low, the storage environment is relatively safe, and the impact of viscosity fluctuations is small. The weighting coefficient is set to 1 to maintain the basic prediction calculation.

[0097] For example, the application process of the moving average method includes two steps: window setting and trajectory calculation. Accumulation velocity data from several recent time points are selected. These velocity data have been adjusted with weighting coefficients to reflect the actual accumulation trend after considering the influence of viscosity. The size of the moving window is set, typically 5 to 10 data points. The velocity values ​​within the window are averaged to obtain the smoothed velocity value at that moment. As time progresses, the window slides forward, continuously calculating new average values ​​to form a continuous velocity curve. Based on this curve, the cumulative growth in future periods is calculated through numerical integration, obtaining the predicted trajectory of liquid accumulation at the pipe opening. The slope of the trajectory represents the development speed, characterizing the rate of accumulation. By comparing the intersections of the predicted trajectory with the safety limit line, the number of time points exceeding the limit is counted, and this number is divided by the total number of predicted time points to obtain the probability of increase.

[0098] In one possible implementation, the two-dimensional lookup table is constructed based on statistical analysis of historical data. Data on the probability of escalation and the rate of development before past liquid spills are collected. The probability is divided into low, medium, and high intervals, and the rate is also divided into slow, medium, and fast intervals, forming a 3×3 grid structure. Each grid cell corresponds to a probability-rate combination, and a corresponding risk value is assigned based on the frequency of actual accidents under that combination. The risk value uses a scale from 0 to 100, with higher values ​​indicating higher risk. Further, based on the currently calculated probability of escalation and the rate of development, the corresponding grid cell is located in the two-dimensional lookup table, and the risk value of that cell is read. The risk value is then associated with the predicted time period information, including the predicted start and end times.

[0099] For example, when the probability of increase is 0.7 and the development speed is 2 millimeters per hour, the high-probability-fast-speed combination is located in the lookup table, corresponding to a risk value of 85. Combined with the prediction period of the next 6 hours, a complete time-period cumulative risk prediction result is formed.

[0100] Understandably, this prediction provides a quantitative basis for storage management decisions, helping operators to take early intervention measures to avoid liquid spill accidents.

[0101] S107. Extract the high-risk time period range from the accumulated risk prediction results over time periods, and increase the liquid level sampling density within the high-risk time period range.

[0102] The risk value sequence is read from the accumulated risk prediction results over a time period. Each risk value is compared to a preset high-risk threshold. If a risk value exceeds the threshold, the time period is marked as a high-risk period. Temporally adjacent high-risk periods are merged into a continuous interval to obtain the high-risk period range. Based on all risk values ​​within the high-risk period range, the maximum value is selected as the risk peak value. The ratio of the risk peak value to the high-risk threshold is used as the density enhancement factor. The original sampling interval is divided by this density enhancement factor to obtain a new sampling interval. Using this new sampling interval, the liquid level sampling frequency within the high-risk period range is adjusted. While keeping the sampling intervals for other time periods unchanged, the sampling time for the high-risk period is set according to the new sampling interval, thereby enhancing the liquid level sampling density.

[0103] In one embodiment, the accumulated risk prediction results over a period of time are stored in time series format, with each data point containing a timestamp and a corresponding risk value. When reading the risk value sequence, each risk value is traversed in chronological order and compared with a preset high-risk threshold. The high-risk threshold is determined based on historical accident statistics. By performing percentile analysis on the historical risk value sequence, quantiles that can effectively distinguish between normal and abnormal states are selected as the threshold, ensuring that the threshold setting is neither too sensitive to cause false alarms nor too lenient to miss real risks.

[0104] Specifically, the merging of high-risk periods follows the principle of temporal continuity. When the time interval between two adjacent high-risk periods is less than a preset merging threshold (i.e., twice the original sampling interval), these two periods are considered as a continuous high-risk period range. This merging threshold is typically set to twice the original sampling interval to avoid excessive segmentation of periods due to short-term fluctuations in risk values. Through this merging process, the resulting high-risk period range is more stable, facilitating subsequent adjustments to the sampling density.

[0105] It should be noted that the density enhancement factor is calculated based on a quantitative assessment of the risk level. Within the high-risk period, all risk values ​​are iterated through, and the maximum value is identified as the risk peak. This peak represents the most dangerous state within that period. The density enhancement factor is the ratio obtained by dividing the risk peak by the high-risk threshold; a higher value indicates a higher risk and requires more intensive monitoring.

[0106] Preferably, the new sampling interval is obtained by dividing the original sampling interval by the density enhancement factor. If the original sampling interval is 10 minutes and the density enhancement factor is 2, then the new sampling interval is 5 minutes. This linear adjustment method ensures that the sampling density is proportional to the risk level.

[0107] For example, dynamic adjustment of sampling density is achieved by modifying the sampling task scheduling table. The system pre-establishes and maintains a sampling time table, recording the initial sampling time for each time period. For high-risk time periods, the sampling time points are recalculated according to the new sampling interval, replacing the original sparse sampling points. The sampling times for other time periods remain unchanged, achieving targeted density enhancement.

[0108] In one possible implementation, once the high-risk period is determined, the system automatically sends a configuration update command to the liquid level sampling equipment, modifying the sampling task scheduling table to adjust the sampling frequency for that period. This dynamic adjustment mechanism can acquire more detailed monitoring data when the risk is high, improving the ability to perceive dangerous conditions, while maintaining a normal sampling frequency when the risk is low, thus making reasonable use of system resources.

[0109] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the concept of this application. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

[0110] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A method for safety assessment and early warning of biological sample storage environment, characterized in that, include: Real-time monitoring of cerebrospinal fluid viscosity, obtaining fluid film thickness on the tube wall, fluid film sliding speed and accumulation volume at the tube opening, and identifying initial risk conditions of abnormal fluid adhesion and accumulation in the storage environment; Assess the correlation between cerebrospinal fluid viscosity and fluid film thickness on the vessel wall based on the initial risk profile, and identify trends in vessel wall adhesion. The increase in cerebrospinal fluid film thickness was assessed based on historical data of changes in cerebrospinal fluid thickness, and the cumulative thickness of fluid accumulation at the tube opening was confirmed after the adhesion of the tube wall was enhanced. The rate of increase and volume of liquid accumulation at the pipe opening are assessed based on the cumulative thickness of the liquid accumulation at the pipe opening and the trend of changes in the adhesion to the pipe wall, thereby determining the risk accumulation level of the storage environment. When the accumulated volume at the tube opening exceeds the preset warning limit, a biological sample storage safety warning signal is triggered. The network logs when the biological sample storage safety warning signal is triggered are obtained. The viscosity of cerebrospinal fluid, the thickness of the fluid film and the volume of fluid accumulation at the tube opening are analyzed in the logs to obtain the characteristics of abnormal viscosity fluctuations. Based on the characteristics of abnormal viscosity fluctuations and the risk accumulation level, the probability of increase and the rate of development of fluid accumulation at the tube opening in the future period are assessed to obtain the accumulation risk prediction results for the period. Extract high-risk time periods from the accumulated risk prediction results over time periods, and increase the liquid level sampling density within these high-risk time periods.

2. The method for safety assessment and early warning of biological sample storage environment according to claim 1, characterized in that, The real-time monitoring of cerebrospinal fluid viscosity, obtaining the thickness of the fluid film on the tube wall, the velocity of the fluid film's descent, and the accumulation volume at the tube opening, and identifying initial risk conditions of abnormal fluid adhesion and accumulation in the storage environment, includes: The cerebrospinal fluid is continuously scanned using an optical refractive sensor to obtain data on changes in the refractive index of the fluid. Based on the calibrated relationship curve between refractive index and viscosity, the real-time viscosity value of the cerebrospinal fluid is determined. The laser ranging module using the optical refraction sensor performs multi-point measurements along the vertical direction of the pipe wall to obtain distance data from the pipe wall surface to the liquid surface. The distance data of each measurement point is subtracted from the reference distance when the pipe is empty to obtain the liquid film thickness value at each point. A liquid film thickness distribution map of the pipe wall is generated based on the liquid film thickness values ​​at each point. If the liquid film thickness exceeds the preset first threshold, it is marked as an adhesion abnormality. Based on the continuous sampling data of the liquid film edge position in the liquid film thickness distribution map, the position difference between adjacent sampling times is calculated, and the liquid film sliding speed is obtained by the ratio of the position difference to the sampling time interval. The liquid level at the pipe opening is obtained by ultrasonic liquid level detection, and the liquid accumulation volume at the pipe opening is calculated by combining the inner diameter of the pipe opening. The viscosity value, adhesion anomaly status, liquid film sliding speed, and pipe orifice accumulation volume are input into a preset risk assessment matrix to obtain the initial risk status level.

3. The method for safety assessment and early warning of biological sample storage environment according to claim 1, characterized in that, The assessment of the correlation between cerebrospinal fluid viscosity and vessel wall film thickness based on initial risk status, and the identification of trends in vessel wall adhesion, include: The cerebrospinal fluid viscosity sequence and the vascular wall fluid film thickness sequence are extracted from the initial risk status. The linear correlation between the two sequences is calculated using the Pearson correlation coefficient. If the correlation coefficient exceeds the preset second threshold, it is determined that there is a strong correlation between the two, and the correlation degree value is recorded. The sampling frequency is determined based on the correlation degree value. Liquid film thickness change data within a continuous time period is obtained according to the sampling frequency. The thickness change rate is obtained by calculating the ratio of the thickness difference between adjacent moments to the time interval. At the same time, the viscosity change rate at the corresponding moment is obtained. The ratio of the thickness change rate to the viscosity change rate is used as the adhesion sensitivity coefficient. By fitting the trend of the adhesion sensitivity coefficient over time, a sequence of adhesion change trend data is formed. The least squares method is used to fit the linear equation of the sequence, and the slope of the fitted line is obtained as the rate of adhesion change. The trend of pipe wall adhesion change is determined according to the sign of the slope.

4. The method for safety assessment and early warning of biological sample storage environment according to claim 1, characterized in that, The assessment of the increase in cerebrospinal fluid film thickness based on historical data of cerebrospinal fluid thickness changes, and the confirmation of the cumulative thickness of fluid accumulation at the tube opening after enhanced adhesion to the tube wall, includes: The sampling records of the most recent monitoring period in the historical data of liquid film thickness change are obtained. The liquid film thickness data corresponding to the change of viscosity value from low to high are extracted. The increase in liquid film thickness for each unit increase in viscosity is calculated. The relationship curve between viscosity increment and thickness increment is fitted by linear regression method to obtain the liquid film thickness increase coefficient. Based on the liquid film thickness amplification coefficient, when an increase in cerebrospinal fluid viscosity is detected, the current viscosity increase is multiplied by the amplification coefficient to calculate the expected liquid film thickness increment. The expected liquid film thickness increment is then added to the original liquid film thickness of the tube wall to obtain the total liquid film thickness after the tube wall adhesion is enhanced. The reduction in the cross-sectional area of ​​the pipe opening is calculated based on the total thickness of the liquid film. The volumetric flow rate of the liquid at the pipe opening per unit time is determined in combination with the liquid inflow velocity. The accumulation volume is calculated based on the volumetric flow rate and time. The accumulation volume is then converted into a vertical height value to determine the cumulative thickness of the liquid accumulation at the pipe opening.

5. The method for safety assessment and early warning of biological sample storage environment according to claim 1, characterized in that, The method of assessing the rate of increase and cumulative volume of liquid accumulation at the pipe orifice based on the cumulative thickness of the accumulated liquid and the changing trend of the adhesion to the pipe wall, and determining the risk accumulation level of the storage environment, includes: Obtain the cumulative thickness time series data of liquid accumulation at the pipe opening, and calculate the instantaneous rate of increase by the ratio of the thickness difference between adjacent sampling points to the time interval; The slope value of the trend of pipe wall adhesion is extracted and normalized. An acceleration factor is set according to the sign of the slope. The adjusted rise rate is obtained by multiplying the instantaneous rise rate by the acceleration factor. The expected cumulative increment is calculated based on the adjusted rate of ascent and the preset time window, and the current cumulative thickness is added to the expected cumulative increment to obtain the total cumulative magnitude. Clustering algorithms are used to group historical cumulative data to determine boundary values. The cumulative level is determined by comparing the total cumulative level with the boundary values. At the same time, the ratio of the adjusted rise rate to the preset rate threshold is calculated as the rate coefficient. The risk score is located through a preset risk lookup table. The risk accumulation level of the storage environment is determined based on the interval in which the risk score is located.

6. The method for safety assessment and early warning of biological sample storage environment according to claim 1, characterized in that, When the accumulated volume at the tube opening exceeds a preset warning limit, a biological sample storage safety warning signal is triggered, including: The system acquires the accumulated volume data at the pipe opening in real time and compares it with the preset warning limit. If the accumulated volume exceeds the warning limit, a warning trigger command is generated. According to the warning trigger command, the warning signal output is initiated, and the current timestamp, accumulated volume value and the difference exceeding the limit are encoded to output a biological sample storage safety warning signal.

7. The method for safety assessment and early warning of biological sample storage environment according to claim 1, characterized in that, The process involves acquiring network logs when a biological sample storage safety warning signal is triggered, analyzing cerebrospinal fluid viscosity, fluid film thickness, and tube orifice accumulation volume in the logs to obtain abnormal viscosity fluctuation characteristics. Based on these characteristics and the risk accumulation level, the probability and rate of increase in tube orifice fluid accumulation in the future are assessed to obtain a time-period accumulation risk prediction result, including: Obtain the network log file when the biological sample storage safety warning signal is triggered, extract the time series data of cerebrospinal fluid viscosity value, liquid film thickness value and tube orifice accumulation volume value in the monitoring cycle before the warning from the log record, calculate the difference of viscosity value at adjacent time points, and count the number and amplitude range of positive and negative changes of the difference to obtain the viscosity abnormal fluctuation characteristics; The degree of viscosity instability is determined based on the number of changes in the abnormal viscosity fluctuation characteristics, and the current risk accumulation level is obtained. A weighting coefficient is set based on the risk level and the number of changes. The weighting coefficients are used to adjust the historical accumulation rate data. The growth trajectory of liquid accumulation at the pipe opening in the future period is calculated by the moving average method. The slope of the growth trajectory is used as the development rate. The ratio of the number of time points when the trajectory exceeds the safety limit to the total number of time points is calculated to obtain the probability of increase. A two-dimensional lookup table is constructed using the rise probability and growth rate. The corresponding risk value is located in the lookup table according to the rise probability and growth rate. The risk value is then associated with the prediction period information to obtain the prediction result of the cumulative risk during the period.

8. The method for safety assessment and early warning of biological sample storage environment according to claim 1, characterized in that, The step of extracting high-risk time periods from the accumulated risk prediction results and increasing the liquid level sampling density within these high-risk time periods includes: The risk value sequence is read from the cumulative risk prediction results of the time period. The risk value is compared with the preset high risk threshold. If the risk value exceeds the threshold, the time period is marked as a high risk time period. Temporally adjacent high risk time periods are merged into a continuous interval to obtain the range of high risk time periods. The maximum value within the high-risk period range is selected as the risk peak. The density enhancement factor is calculated using the ratio of the risk peak to the high-risk threshold. The original sampling interval is divided by the density enhancement factor to obtain a new sampling interval. The sampling frequency of liquid level in the high-risk period is adjusted using the new sampling interval. While keeping the sampling interval of other periods unchanged, the sampling time of the high-risk period is set according to the new sampling interval.

Images