A sample storage device and method for ecological environment monitoring

Abstract

The application provides a sample storage device and method for ecological environment monitoring, multi-stage characteristic identification is performed on the degradation rate of an ecological environment sample, a stage state characteristic of the ecological environment sample is obtained, a sample stability index under a current storage condition is determined according to the stage state characteristic and a performance degradation coefficient in each sealing data, a quality decay characteristic of the ecological environment sample is determined according to the stability index and the effective component content of the ecological environment sample, a balanced preservation condition for maintaining the sample effectiveness of the ecological environment sample is predicted based on the quality decay characteristic, the balanced preservation condition is compared with each external environment parameter collected by an intelligent sensor in multiple stages, an environment deviation value of each temperature and humidity control node is obtained, and the storage state of the ecological environment sample under visual monitoring is classified and determined based on each environment deviation value. Based on the above scheme, the environment classification regulation based on state prediction can be realized.

Description

Technical Field

[0001] This application relates to the field of environmental monitoring technology, and more specifically, to a sample storage device and method for ecological and environmental monitoring. Background Technology

[0002] Ecological and environmental monitoring is the process of systematically observing, measuring, and analyzing environmental elements such as air, water, soil, and organisms to assess their quality, trends, and the impact of human activities. It comprehensively utilizes physical, chemical, and biological technologies to obtain basic data on environmental quality, aiming to provide a scientific basis for environmental protection, ecological restoration, policy making, and sustainable development. The monitoring targets include pollutant concentrations, ecological indicators, and environmental risk factors.

[0003] Current environmental monitoring technologies rely on independently deployed temperature and humidity sensors to perform point-based measurements inside storage facilities or equipment. Their control logic typically uses preset fixed thresholds for on / off adjustments, reflecting only the macroscopic climatic conditions of the physical space. They cannot establish a quantitative relationship between environmental fluctuations and the degradation process of specific active components or pollutants within the sample. Because different chemical components exhibit significantly different sensitivities to temperature and humidity, and degradation processes often possess nonlinear time-varying characteristics, simply meeting physical parameter standards does not equate to maintaining sample chemical stability. This results in monitoring systems failing to provide early warnings of gradual sample degradation caused by the accumulation of minor environmental fluctuations, and failing to offer differentiated preservation strategies based on the specific degradation pathways of different samples. Therefore, how to achieve state-predictive-based tiered environmental control to improve the effectiveness of long-term sample preservation has become a challenge for the industry. Summary of the Invention

[0004] This application provides a sample storage device and method for ecological environment monitoring, which can realize environmental hierarchical control based on state prediction, thereby improving the effectiveness of long-term sample preservation.

[0005] Firstly, this application provides a method for storing samples for ecological and environmental monitoring, including: The collected ecological environment samples are preprocessed and their initial state information is marked. Based on the initial state information and the preset storage conditions, multiple temperature and humidity control nodes are marked in the storage environment. Multi-source sensors are deployed at each temperature and humidity control node to collect the sealing data of the sample container and external environmental parameters. Based on various external environmental parameters, the degradation rate of ecological environment samples is identified in multiple stages to obtain the stage state characteristics of ecological environment samples. Based on the stage state characteristics and the performance degradation coefficient in the sealing data, the sample stability index under the current storage conditions is determined. Based on the stability index and the effective component content of the ecological environment sample, the quality degradation trend during long-term storage is fitted to obtain the quality degradation characteristics of the ecological environment sample. Based on the quality degradation characteristics, the equilibrium storage conditions for maintaining the sample effectiveness of the ecological environment sample are predicted. By retrieving real-time external environmental parameters from each temperature and humidity control node using intelligent sensors, the balanced preservation conditions and the external environmental parameters collected by the intelligent sensors are compared in multiple stages to obtain the environmental deviation value of each temperature and humidity control node. Based on the environmental deviation value, the preservation status of the ecological environment sample under visual monitoring is graded and determined, thereby generating a storage optimization strategy.

[0006] In some embodiments, marking multiple temperature and humidity control nodes within the storage environment based on the initial state information and preset storage conditions specifically includes: The initial state information is gradient mapped with a preset storage condition library to obtain a temperature and humidity gradient distribution map of the internal storage environment. In the temperature and humidity gradient distribution map, identify the potential risk areas that have the greatest impact on sample stability and the typical areas that best represent the overall environmental state, and then obtain multiple candidate control nodes. Spatial coverage optimization of all candidate control nodes was performed by considering the physical placement of the sample containers and the airflow path, resulting in multiple temperature and humidity control nodes.

[0007] In some embodiments, multi-stage feature identification of the degradation rate of ecological environment samples based on various external environmental parameters is performed to obtain the staged state characteristics of the ecological environment samples, specifically including: All external environmental parameters were serialized to obtain the degradation rate curves of the ecological environment samples.

[0008] A sliding window analysis was performed on the degradation rate curve to identify the time inflection point where the degradation rate changed significantly. By statistically analyzing the correlation strength of degradation rates within each time period, the phased state characteristics of the ecological environment samples can be obtained.

[0009] In some embodiments, determining the sample stability index under the current storage conditions based on the staged state characteristics and the performance degradation coefficient in the sealing data specifically includes: Extract the performance degradation coefficient under the current storage conditions from the sealing data; The current time stage's phased state characteristics are fused with the performance degradation coefficient to obtain the sample stability index under the current storage conditions.

[0010] In some embodiments, the quality degradation trend of the ecological environment sample during long-term storage is fitted based on the stability index and the effective component content of the ecological environment sample to obtain the quality degradation characteristics of the ecological environment sample, specifically including: With the time series as the horizontal axis and the content of effective ingredients obtained from periodic detection as the vertical axis data points, the decay curve of the content of effective ingredients is obtained; The stability index is used as a covariate of the decay curve for piecewise fitting to obtain the quality decay curve of the ecological environment sample. The quality degradation characteristics of the ecological environment samples were extracted from the quality degradation curve.

[0011] In some embodiments, the equilibrium preservation conditions for predicting the sample validity of ecological environment samples based on the quality degradation characteristics specifically include: Determine the quantitative criteria for sample validity and set physical constraints for adjusting storage conditions; Based on the aforementioned quality degradation characteristics, the sample validity of maintaining ecological environment samples is deduced through reverse inference, resulting in the equilibrium preservation conditions for maintaining the sample validity of ecological environment samples.

[0012] In some embodiments, real-time external environmental parameters of each temperature and humidity control node are retrieved by intelligent sensors, and the equilibrium preservation conditions and the various external environmental parameters collected by the intelligent sensors are compared in multiple stages to obtain the environmental deviation value of each temperature and humidity control node. Specifically, this includes: For each temperature and humidity control node, the equilibrium preservation conditions are compared with the external environmental parameters of the temperature and humidity control node collected by the intelligent sensor in stages to obtain the average deviation and deviation fluctuation of the temperature and humidity control node in each time stage. The environmental deviation values ​​of the temperature and humidity control nodes are determined by all average deviations and deviation fluctuations, and then the environmental deviation values ​​of each temperature and humidity control node are obtained.

[0013] Secondly, this application provides a sample storage system for ecological environment monitoring, comprising: The acquisition module is used to preprocess the collected ecological environment samples and identify their initial state information. Based on the initial state information and preset storage conditions, multiple temperature and humidity control nodes are marked in the storage environment, and multi-source sensors are deployed at each temperature and humidity control node to collect the sealing data of the sample container and external environmental parameters. The processing module is used to identify the multi-stage characteristics of the degradation rate of ecological environment samples based on various external environmental parameters, obtain the stage state characteristics of ecological environment samples, and determine the sample stability index under the current storage conditions based on the stage state characteristics and the performance degradation coefficient in the sealing data. The processing module is also used to perform trend fitting on the quality degradation trend during long-term storage based on the stability index and the effective component content of the ecological environment sample, to obtain the quality degradation characteristics of the ecological environment sample, and to predict the balanced storage conditions for maintaining the sample effectiveness of the ecological environment sample based on the quality degradation characteristics. The execution module is used to retrieve real-time external environmental parameters of each temperature and humidity control node through intelligent sensors, compare the balanced preservation conditions with the external environmental parameters collected by the intelligent sensors in multiple stages to obtain the environmental deviation value of each temperature and humidity control node, and classify the preservation status of the ecological environment sample under visual monitoring based on the environmental deviation value, thereby generating a storage optimization strategy.

[0014] Thirdly, this application provides a computer device, which includes a memory and a processor. The memory is used to store a computer program, and the processor is used to call and run the computer program from the memory, so that the computer device performs the above-described method for storing samples for ecological and environmental monitoring.

[0015] Fourthly, this application provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-described method for storing samples for ecological environment monitoring.

[0016] The technical solutions provided by the embodiments disclosed in this application have the following beneficial effects: This application provides a sample storage device and method for ecological environment monitoring. The collected ecological environment samples are pre-processed and their initial state information is identified. Based on the initial state information and preset storage conditions, multiple temperature and humidity control nodes are marked within the storage environment. Multi-source sensors are deployed at each temperature and humidity control node to collect the sealing data of the sample container and external environmental parameters. Based on these external environmental parameters, the degradation rate of the ecological environment samples is identified through multi-stage features to obtain the staged state characteristics of the ecological environment samples. The sample stability index under the current storage conditions is determined based on the staged state characteristics and the performance degradation coefficient in the sealing data. The quality decay trend during long-term storage is trend-fitted based on the stability index and the effective component content of the ecological environment samples to obtain the quality decay characteristics of the ecological environment samples. Based on these quality decay characteristics, balanced storage conditions for maintaining the sample effectiveness of the ecological environment samples are predicted. Real-time external environmental parameters of each temperature and humidity control node are retrieved through intelligent sensors. The balanced storage conditions and the external environmental parameters collected by the intelligent sensors are compared in multiple stages to obtain the environmental deviation values ​​of each temperature and humidity control node. Based on these environmental deviation values, the storage status of the ecological environment samples under visual monitoring is graded and determined, thereby generating a storage optimization strategy.

[0017] Therefore, in this application, real-time external environmental parameters of each temperature and humidity control node are retrieved by intelligent sensors. The balanced storage conditions and the external environmental parameters collected by the intelligent sensors are compared in multiple stages to obtain the environmental deviation values ​​of each temperature and humidity control node. Based on these environmental deviation values, the storage status of the ecological environment sample under visual monitoring is graded and determined, thereby generating a storage optimization strategy. Firstly, by determining the sample stability index, a dynamic health quantification score of the sample under the current storage environment can be obtained. This sample stability index integrates the characteristics of the environment-driven degradation process with the decay data of the container's sealing performance, thus coupling and evaluating the sample's inherent chemical or biological instability and the risk of external packaging failure. This transforms discrete environmental monitoring data into a technology that can directly determine whether the sample is in a stable storage state. Based on this, a logical hub from passive monitoring to active intervention is realized. Then, by determining the equilibrium preservation conditions, the theoretical environmental target value for optimal sample preservation can be dynamically calculated. This target value is obtained by inversely solving the optimal solution of the model under physical constraints based on the current and predicted decay kinetic characteristics of the sample. The theoretical optimal value is adaptively adjusted according to the actual state of the sample and historical environmental data, mapping the ultimate goal of maintaining sample effectiveness to a specific environmental control target. This ensures that subsequent environmental deviation calculations and state classification judgments are no longer based on simple comparisons of fixed standards, but on directed and precise control based on dynamic optimal targets, thereby ensuring that the generated optimization strategy always serves to extend the effective preservation period of the sample. In summary, based on the above scheme, environmental classification control based on state prediction can be realized, thereby improving the effectiveness of long-term sample preservation. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is an exemplary flowchart illustrating a method for storing samples for ecological and environmental monitoring according to some embodiments of this application; Figure 2 This is a flowchart illustrating the process of determining quality degradation characteristics according to some embodiments of this application; Figure 3 This is a schematic diagram of the structure of a sample storage system for ecological environment monitoring according to some embodiments of this application; Figure 4This is a schematic diagram of the structure of a computer device for implementing a sample storage method for ecological environment monitoring, according to some embodiments of this application. Detailed Implementation

[0020] To better understand the technical solution of this application, the technical solution of this application will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0021] refer to Figure 1 The figure is an exemplary flowchart of a method for storing samples for ecological and environmental monitoring according to some embodiments of this application. The method for storing samples for ecological and environmental monitoring mainly includes the following steps: In step 101, the collected ecological environment samples are preprocessed and their initial state information is marked. Based on the initial state information and the preset storage conditions, multiple temperature and humidity control nodes are marked in the storage environment, and multi-source sensors are deployed at each temperature and humidity control node to collect the sealing data of the sample container and external environmental parameters.

[0022] It should be noted that, in this application, ecological environment samples are substances to be tested that reflect the environmental quality and ecological characteristics of a designated area; initial state information is a set of data used to record and quantify the basic physical, chemical and biological properties of the samples at the moment of completion of pretreatment.

[0023] In practice, a standardized pretreatment process is first executed based on the sample type (e.g., soil, water, plant leaves) and the preset analytical objectives (e.g., determining heavy metal content, organic pollutant concentration, and microbial community structure). For solid samples such as soil, pretreatment includes removing gravel and plant debris, air drying, grinding, and sieving to obtain homogeneous samples of a specified particle size. For liquid samples such as water, pretreatment may include filtration to remove suspended particles and adding specified chemical preservatives to inhibit microbial activity or chemical reactions. For biological samples, pretreatment may involve washing, cutting specified tissues, and rapid cryofixation. All operations are performed in a clean environment using inert tools and containers to prevent cross-contamination and sample denaturation. After physical pretreatment, the samples must be identified and their initial state information collected. Each sample container is assigned a globally unique QR code or RFID tag as a unique sample identification number. The initial state information associated with this number is collected and recorded, including: the sample's physical morphology (color, texture), precise mass, volume, baseline concentration of initial key components, as well as the collection time, location, pretreatment method, and operator. The baseline concentration can be obtained through rapid on-site testing or preliminary laboratory testing, such as the pH value and conductivity of water samples, and the moisture content and organic matter content of soil samples.

[0024] In some embodiments, marking multiple temperature and humidity control nodes within the storage environment based on the initial state information and preset storage conditions can be achieved using the following steps: The initial state information is gradient mapped with a preset storage condition library to obtain a temperature and humidity gradient distribution map of the internal storage environment. In the temperature and humidity gradient distribution map, identify the potential risk areas that have the greatest impact on sample stability and the typical areas that best represent the overall environmental state, and then obtain multiple candidate control nodes. Spatial coverage optimization of all candidate control nodes was performed by considering the physical placement of the sample containers and the airflow path, resulting in multiple temperature and humidity control nodes.

[0025] It should be noted that, in this application, the storage condition library is used to store the recommended data set for different types of ecological environment samples; the temperature and humidity gradient distribution map is used to visualize the spatial distribution model at each theoretical location point inside the storage space; the potential risk area is used to indicate the theoretical area in the temperature and humidity gradient distribution map that is most likely to deviate from the sample's tolerance range; the typical area is used to represent the theoretical area representing the overall average state of the storage environment; the candidate control node is used to initially identify the set of central location points of all potential risk areas and typical areas that require key attention; and the temperature and humidity control node is the actual physical location point used to perform real-time environmental parameter acquisition and monitoring.

[0026] In specific implementation, firstly, the initial state information is gradient-mapped with a preset storage condition library to obtain the temperature and humidity gradient distribution map of the internal storage environment. This can be achieved by establishing a preset storage condition library. This library is classified and stored according to the physicochemical properties of the samples. For example, for water samples containing volatile organic compounds, the storage conditions are "4 degrees Celsius ± 0.5 degrees Celsius, protected from light"; for soil samples used for microbial analysis, the conditions are "-20 degrees Celsius freezing, avoiding repeated freeze-thaw cycles". The initial state information of the samples recorded in step one, especially the sample type and key components, is used as a query key to match the corresponding optimal temperature and humidity settings and their allowable upper and lower limits of deviation from the condition library. Based on the three-dimensional model of the physical structure of the storage space, the system matches the ideal storage conditions (target temperature and humidity values) matched for each sample container location in the previous step with the... The allowable deviation range is assigned as the "attribute value" of the location point. Considering that the conditions at different locations may be different, a spatial interpolation algorithm can be used to perform smooth calculations in the entire three-dimensional model space to generate a continuous field covering the entire space, where each theoretical point has corresponding suggested control temperature and humidity data. This process generates a temperature and humidity gradient distribution map. Then, in the temperature and humidity gradient distribution map, the potential risk areas that have the greatest impact on sample stability and the typical areas that best represent the overall environmental state are identified, thereby obtaining multiple candidate control nodes. This can be achieved by identifying regions and obtaining candidate control nodes. On the generated map, the system automatically performs regional analysis. The identification criteria for potential risk areas are: the recommended control value at the center point of the area (e.g., the low temperature zone) is close to the limit boundary of the sample's tolerance range in that area. The identification criteria for typical areas are: the recommended control value at the center point of the area (e.g., the center of the constant temperature zone) is closest to the statistical average of the recommended control values ​​of all points in the entire map. The geometric center points of all identified potential risk areas and typical areas are extracted to form a preliminary list of candidate control nodes. Finally, the spatial coverage of all candidate control nodes is optimized by considering the physical placement of the sample container and the air circulation path. Multiple temperature and humidity control nodes can be implemented in the following way: the arrangement of candidate control nodes is optimized by considering the actual physical structure of the sample cabinet, such as the air circulation path formed by the shelf position, vents, and door gaps. The optimization principles are: prioritize retaining nodes upstream, downstream, and in dead zones along the airflow path to ensure effective monitoring of airflow impact; merge candidate nodes that are too close in space and have similar environmental attributes; ensure that the remaining nodes after optimization are evenly distributed in three-dimensional space and can effectively represent the entire environment. This optimization process can be accomplished by calculating the coverage and representativeness of nodes to all locations in space. The optimized list of temperature and humidity control nodes with specific three-dimensional coordinates and their deployment basis will serve as the formal scheme to guide subsequent sensor installation and environmental monitoring spatial framework.

[0027] In some embodiments, the collection of sample container sealing data and external environmental parameters at each temperature and humidity control node can be achieved as follows: First, according to the determined temperature and humidity control node layout plan, a sensing device is installed and calibrated at the physical location of each node. Each node is equipped with an integrated temperature and humidity sensor to measure external environmental parameters. Simultaneously, in the typical or risk area represented by each node, at least one representative sample container is selected, and a micro-pressure sensor or gas permeation sensor is attached or integrated at its seal (e.g., bottle cap, sealing gasket) to indirectly monitor the container's sealing performance. The specific data collection process is performed periodically and automatically. In each set collection cycle (e.g., every ten minutes), the system instructs all nodes' sensors to operate synchronously. The temperature and humidity sensors directly read and record the instantaneous values ​​of air temperature and relative humidity at the current location of the node. For sealing data, the micro-pressure sensor measures the rate of change of the small pressure difference inside and outside the container caused by temperature changes or gas escape; the gas permeation sensor monitors the permeation concentration of a specified tracer gas (e.g., nitrogen) through the sealing material. The raw electrical signals measured by the sensors are converted into quantitative indicators characterizing sealing performance degradation using a standard algorithm. Examples include "pressure maintenance coefficient" or "gas permeability"; all collected data, including node number, timestamp, temperature value, humidity value, and corresponding airtightness quantitative indicators, are transmitted in real time and stored in a central database. The data undergoes preliminary verification, such as removing obvious outliers caused by transient interference. The external environmental parameter sequence and airtightness data sequence, which are recorded continuously in time and strictly correspond to each temperature and humidity control node and have undergone preliminary verification, are used together as a monitoring dataset to assess the stability of the storage environment and the integrity of the container. Among them, the airtightness data is a sequence of measured values ​​used to quantify the change over time in the ability of the sealing device of the sample container to prevent the exchange of substances between the internal sample and the external environment under the temperature and humidity environment. The external environmental parameters are used to directly describe the physical state of the local space where the temperature and humidity control node is located and serve as the core indicators for assessing whether the storage environment meets the preset preservation conditions. These mainly refer to the air temperature value and relative humidity value of the node.

[0028] In step 102, the degradation rate of the ecological environment sample is identified in multiple stages based on various external environmental parameters to obtain the staged state characteristics of the ecological environment sample. The sample stability index under the current storage conditions is determined based on the staged state characteristics and the performance degradation coefficient in the sealing data.

[0029] In some embodiments, the multi-stage feature identification of the degradation rate of ecological environment samples based on various external environmental parameters, to obtain the staged state characteristics of the ecological environment samples, can be achieved through the following steps: All external environmental parameters were serialized to obtain the degradation rate curves of the ecological environment samples.

[0030] A sliding window analysis was performed on the degradation rate curve to identify the time inflection point where the degradation rate changed significantly. By statistically analyzing the correlation strength of degradation rates within each time period, the phased state characteristics of the ecological environment samples can be obtained.

[0031] It should be noted that, in this application, the degradation rate curve is a continuous mathematical function graph that reflects the instantaneous rate change trend of the concentration of the target effective component in the ecological environment sample over time; the time inflection point is a specified time point used to identify the directional change of the trend of the degradation rate curve; the correlation strength statistic is a mathematical indicator used to quantify the degree of linear or nonlinear correlation between the degradation rate and its main driving environmental factors within each time stage; and the stage state characteristics are a set of multi-dimensional attributes used to describe the stability, rate level, and strength of correlation with the external environment of the degradation behavior of the ecological environment sample within a specified storage stage.

[0032] In practice, firstly, all external environmental parameters are serialized to obtain the degradation rate curve of the ecological environment sample. This can be achieved in the following way: based on the preset sample degradation kinetic model and the influence factor function of the environment on degradation, the serialized external environmental parameters (mainly the time series of temperature and humidity) are used as input variables. For most chemical reactions or microbial degradation processes, the rate constant often follows the Arrhenius equation and is exponentially related to temperature. Humidity may be a reactant or a key factor affecting microbial activity. The system substitutes the measured temperature and humidity data at each time point into the influencing factor function to calculate the theoretical degradation rate constant corresponding to that moment. Then, it multiplies this rate constant by the current concentration of the active ingredient in the sample (calculated iteratively from the initial concentration and cumulative degradation amount) to obtain the instantaneous degradation rate at that moment. The instantaneous degradation rate values ​​of all time points are connected in chronological order to generate a degradation rate curve characterizing the dynamic changes in the degradation process. Then, a sliding window analysis is performed on the degradation rate curve to identify the time inflection point where the degradation rate changes significantly. This can be achieved by using a sliding window analysis method to identify time inflection points. An analysis window of a certain length (default 24 hours) is set and slides along the time axis of the degradation rate curve. At each window position, the local linear regression slope or higher-order derivative of the data within the window is calculated. When the sign of the slope of multiple consecutive windows changes stably (e.g., from positive to negative), or the absolute value of its derivative exceeds a preset significance threshold, the system determines that there is a significant degradation rate near the center of that region. At a given inflection point, all identified inflection points divide the entire storage timeline into several consecutive time stages. Finally, the correlation strength statistics of degradation rates within each time stage are statistically analyzed to obtain the stage-specific state characteristics of the ecological environment sample. This can be achieved in the following way: Within each divided time stage, two key calculations are performed: First, the average and standard deviation of the degradation rate within that stage are calculated to describe the average degradation rate level and fluctuation stability. Second, the correlation coefficient or mutual information entropy between the degradation rate sequence within that stage and the main environmental factors (such as temperature sequence) during the same period are calculated as correlation strength statistics to quantify the strength of the impact of environmental fluctuations on the degradation process. The set of average rate level, fluctuation stability, and correlation strength statistics corresponding to each time stage is used as the stage-specific state characteristics describing the degradation behavior of the sample in that stage. The set of stage-specific state characteristic data with clearly defined time stages and quantified degradation behavior characteristics is used as the analysis result to evaluate the stability evolution of the sample in different storage periods.

[0033] In some embodiments, determining the sample stability index under the current storage conditions based on the staged state characteristics and the performance degradation coefficient in the sealing data can be achieved by the following steps: Extract the performance degradation coefficient under the current storage conditions from the sealing data; The current time stage's phased state characteristics are fused with the performance degradation coefficient to obtain the sample stability index under the current storage conditions.

[0034] It should be noted that, in this application, the performance degradation coefficient is a dimensionless quantitative index that characterizes the degree of decline in the actual sealing performance of the sample container relative to its brand new or initial sealing performance within the current monitoring period, and its value range is usually set between 0 and 1; the sample stability index is a quantitative score used to evaluate the ability of an ecological environment sample to maintain its initial effective component content and physicochemical properties unchanged within a given actual storage environment and container state during a specified time period.

[0035] In specific implementation, firstly, extracting the performance degradation coefficient under the current storage conditions from the sealing data can be achieved in the following way: retrieve all sealing data collected within the current time period, such as the continuously monitored attenuation rate of the pressure difference between the inside and outside of the container or the specified gas permeability, using the baseline measurement value of the container in its initial intact state as a reference, calculate the ratio of the average value (or the 95th percentile, to consider the worst case) of the sealing measurement values ​​within the current time period to the initial baseline value, and normalize this ratio to obtain the performance degradation coefficient. For example, if the current average permeability is 1.5 times the initial value, after a specified function mapping (such as using a reciprocal or exponential decay model), the performance degradation coefficient may be calculated as 0.67, indicating that the sealing performance has maintained approximately 67% of the initial state. Then, feature fusion of the stage-specific state characteristics of the current time period with the performance degradation coefficient to obtain the sample stability index under the current storage conditions can be achieved in the following way: using a preset weighted fusion model. The model's input consists of two parts: the first part is the stage-specific state characteristics at the current time stage, including three sub-features: average degradation rate level, degradation rate fluctuation stability, and correlation strength with environmental factors; the second part is the performance degradation coefficient. The four input features are standardized to eliminate the influence of dimensions. Each feature is assigned a weight value according to a pre-determined weighting scheme based on experiments or expert knowledge. For example, the average degradation rate level is given the highest weight because it directly reflects the rate of change; the performance degradation coefficient is given a significant weight because it determines the degree of influence of external disturbances. The sample stability index is calculated by multiplying these four standardized feature values ​​by their corresponding weights and then summing them. This sample stability index is a comprehensive score; a higher score indicates better overall stability of the sample at the current stage. This single quantitative score, which integrates internal degradation dynamics and external sealing protection information, serves as an authoritative evaluation basis for judging the overall stability of the sample at the current specified time stage and under actual storage conditions.

[0036] In step 103, the quality degradation trend during long-term storage is fitted based on the stability index and the effective component content of the ecological environment sample to obtain the quality degradation characteristics of the ecological environment sample. Based on the quality degradation characteristics, the equilibrium preservation conditions for maintaining the sample effectiveness of the ecological environment sample are predicted.

[0037] In some embodiments, the quality degradation trend during long-term storage is fitted based on the stability index and the effective component content of the ecological environment sample to obtain the quality degradation characteristics of the ecological environment sample, with reference to... Figure 2 The diagram is a flowchart illustrating the process of determining quality degradation characteristics in some embodiments of this application. In this embodiment, the determination of quality degradation characteristics can be achieved using the following steps: In step 1031, the effective ingredient content obtained by periodic detection is used as the vertical axis data point with the time series as the horizontal axis to obtain the decay curve of the effective ingredient content; In step 1032, the stability index is used as a covariate of the decay curve for piecewise fitting to obtain the quality decay curve of the ecological environment sample. In step 1033, the quality degradation characteristics of the ecological environment sample are extracted from the quality degradation curve.

[0038] It should be noted that, in this application, the decay curve is a line graph connecting raw data points used to show the change of the measured concentration or content of the target effective component in the ecological environment sample over the storage time; the quality decay curve is a fitted function curve used to describe and predict the overall trend of the content of the effective component in the ecological environment sample changing over time; and the quality decay characteristics are a set of parameters characterizing the rate of deterioration, deterioration mode, and degree of influence of stability indicators of the effective component during the storage process.

[0039] In specific implementation, firstly, using the time series as the horizontal axis and the content of effective components obtained from periodic testing as the vertical axis data points, the decay curve of the effective component content can be obtained in the following way: The quantitative detection results of the target effective components (such as the concentration of a certain organic pollutant, the activity of a certain enzyme) obtained from laboratory analysis of samples at each preset periodic testing time point (e.g., day 0, day 7, day 30, day 90) are collected. The testing time points are used as the horizontal axis, and the corresponding measured values ​​of the effective component content are used as the vertical axis. All data points are plotted in the coordinate system, and all data points are connected sequentially with line segments according to time order, forming a line graph that intuitively reflects the change of effective component content over time, called the decay curve. Then, the stability index is used as a covariate of the decay curve for piecewise fitting. The quality decay curve of the ecological environment sample can be obtained in the following way: The sample stability index corresponding to each testing time interval is introduced into the model as a core covariate. For example, a function with time as the independent variable and the stability index as the adjustment coefficient can be used, i.e., effective component... Degradation content = initial content × exp(-(rate constant adjusted by stability index) × time); the rate constant adjusted by stability index means that the degradation rate constant is no longer a fixed value, but scales proportionally with the dynamic changes of the sample stability index; the model parameters are estimated by regression algorithm (such as nonlinear least squares method) to minimize the overall deviation between the fitted curve and all measured data points. The resulting smooth and continuous mathematical curve is the quality degradation curve, which reflects the combined effects of time factor and sample dynamic stability; finally, the quality degradation characteristics of the ecological environment sample can be extracted from the quality degradation curve in the following way: after the mathematical expression of the fitted quality degradation curve, the system automatically extracts a set of preset key parameters, including: the overall degradation rate constant of the curve, the degradation model exponent representing the linear or exponential degradation mode, and the sensitivity coefficient representing the degradation rate change caused by each unit change in the sample stability index. The three together constitute the core feature set describing the quality degradation behavior of the sample, and this core feature set is used as the quality degradation characteristics.

[0040] In some embodiments, the equilibrium preservation conditions for maintaining the sample validity of ecological environment samples based on the quality degradation characteristics can be achieved by the following steps: Determine the quantitative criteria for sample validity and set physical constraints for adjusting storage conditions; Based on the aforementioned quality degradation characteristics, the sample validity of maintaining ecological environment samples is deduced through reverse inference, resulting in the equilibrium preservation conditions for maintaining the sample validity of ecological environment samples.

[0041] It should be noted that, in this application, the quantitative standard for sample validity is used to accurately define and judge whether ecological and environmental samples can still meet the requirements of subsequent analysis and testing at the minimum target effective component content or the maximum allowable degradation percentage after storage; the physical constraints of storage condition adjustment are used to limit the theoretical upper and lower limits and technical feasibility boundaries that parameters such as temperature and humidity in the storage environment can be achieved in the actual adjustment process, such as the minimum controllable temperature of the refrigeration equipment and the humidity adjustment range of the dehumidification system; reverse deduction is used to calculate the mathematical model solution process of the initial or process conditions (such as temperature and humidity setpoints) required to achieve the desired final goal (maintaining sample validity) in reverse order; and the equilibrium storage conditions are used to define the theoretically optimal set of temperature and humidity control target values ​​dynamically calculated to achieve the longest effective storage period or the lowest quality decay rate of the sample under the dual premise of meeting the quantitative standard for sample validity and the actual physical constraints.

[0042] In practice, the first step is to determine the quantitative standards for sample validity and set physical constraints for adjusting storage conditions. This can be achieved by: for a given ecological environment sample and its intended testing purpose, the validity threshold is predefined by standards or the user. For example, for a soil heavy metal sample used in legal arbitration, the validity standard might be set as "the extractable concentration of the target heavy metal (such as cadmium) must not be lost by more than 5% of its initial value during storage." Combined with the hardware specifications of the storage equipment (such as a smart sample cabinet), physical constraints for adjusting storage conditions are determined, for example, the temperature controllable range of the equipment is -20°C to +25°C, and the relative humidity controllable range is 20%RH to 60%RH. These constraints collectively constitute the boundary conditions for subsequent optimization. Then, based on the quality decay characteristics, the sample validity of maintaining ecological environment samples is deduced in reverse. The equilibrium preservation conditions for maintaining the sample validity of ecological environment samples can be achieved in the following way: using the quality decay characteristics as the core mathematical model, the goal is to find a set (or more) of specified environmental parameters (such as constant temperature T and humidity H) such that when the sample is stored under these conditions, the time it takes for its quality decay curve to reach the validity quantification standard threshold (e.g., the effective component content drops to 95% of the initial value) is maximized (i.e., the effective preservation period). This is a parameter optimization problem within the physical constraint boundary, targeting the "maximization of preservation period" of the quality decay mathematical model. In practice, numerical iterative algorithms (such as gradient descent or genetic algorithms) are typically used to search in the two-dimensional or higher-dimensional solution space composed of temperature and humidity. The algorithm evaluates the effective storage period predicted by substituting each candidate temperature and humidity combination (e.g., T=4°C, H=40%RH) into the decay model, and continuously adjusts the candidate solutions in the direction that can extend the storage period, until it finds the set or multiple sets of temperature and humidity settings that can make the predicted storage period the longest within the physical constraints. The optimal settings obtained by solving are the balanced storage conditions.

[0043] In step 104, real-time external environmental parameters of each temperature and humidity control node are retrieved by intelligent sensors. The balanced preservation conditions and the external environmental parameters collected by the intelligent sensors are compared in multiple stages to obtain the environmental deviation value of each temperature and humidity control node. Based on the environmental deviation value, the preservation status of the ecological environment sample under visual monitoring is graded and determined, and then a storage optimization strategy is generated.

[0044] In some embodiments, the real-time external environmental parameters of each temperature and humidity control node are retrieved by intelligent sensors, and the balance preservation conditions and the various external environmental parameters collected by the intelligent sensors are compared in multiple stages to obtain the environmental deviation value of each temperature and humidity control node. This can be achieved by the following steps: For each temperature and humidity control node, the equilibrium preservation conditions are compared with the external environmental parameters of the temperature and humidity control node collected by the intelligent sensor in stages to obtain the average deviation and deviation fluctuation of the temperature and humidity control node in each time stage. The environmental deviation values ​​of the temperature and humidity control nodes are determined by all average deviations and deviation fluctuations, and then the environmental deviation values ​​of each temperature and humidity control node are obtained.

[0045] It should be noted that, in this application, the average deviation is a statistic used to quantify the degree of deviation between the actual monitored values ​​of the temperature and humidity control node and the theoretical target values ​​set for the equilibrium preservation conditions within a single time period; the deviation fluctuation is a statistic used to quantify the instability and dispersion of the deviation between the actual monitored values ​​of the temperature and humidity control node and the theoretical target values ​​set for the equilibrium preservation conditions within a single time period; and the environmental deviation value is the final quantitative score used to evaluate the degree of conformity between the environmental control quality and the equilibrium preservation condition target of a single temperature and humidity control node throughout the entire monitoring cycle.

[0046] In specific implementation, firstly, for each temperature and humidity control node, the equilibrium preservation conditions are compared with the external environmental parameters of the temperature and humidity control node collected by the intelligent sensor in stages to obtain the average deviation and deviation fluctuation of the temperature and humidity control node in each time stage. This can be achieved in the following way: according to the identified storage stage (e.g., "initial stabilization period", "intermediate fluctuation period"), the long-term series of external environmental parameters (temperature and humidity) collected by each temperature and humidity control node are divided into corresponding data segments; for the temperature data sequence and humidity data sequence in each time stage, the following calculations are performed respectively: the measured temperature value at each time point in the stage is subtracted from the theoretical target temperature value set in the equilibrium preservation conditions to obtain a series of temperature deviation values, and then the average value of the absolute values ​​of these deviations is calculated to obtain the "average temperature deviation" of the node in this stage. At the same time, the standard deviation of these deviations is calculated to obtain the "temperature deviation fluctuation". The same calculation is performed on the humidity data to obtain the "average humidity deviation" and "humidity deviation fluctuation". The above method yields four basic deviation statistics at each node at each time stage. Then, the environmental deviation values ​​of the temperature and humidity control nodes are determined using all average deviations and deviation fluctuations. The environmental deviation values ​​for each temperature and humidity control node can be achieved by integrating the node's performance across all stages of the entire monitoring cycle. The system pre-assigns a weighting coefficient to each basic deviation statistic (e.g., "average temperature deviation"), reflecting the statistic's importance in the overall evaluation. For example, the weight of "average temperature deviation" can be set to be greater than the weight of "humidity deviation fluctuation." Next, a linear weighted method is adopted: all basic deviation statistics calculated for each node at each time stage (e.g., a total of M stages, 4 statistics per stage, and a total of 4M values) are standardized to eliminate dimensions; then, each standardized value is multiplied by its corresponding preset weight and summed; the calculated weighted sum is the environmental deviation value of the temperature and humidity control node. This environmental deviation value is a comprehensive index. The lower the value, the better the actual environment of the node conforms to the equilibrium preservation conditions as a whole, and the more stable the control. The comprehensive weighted score, which integrates the deviations of temperature and humidity in both concentration and dispersion at multiple stages, is used as the final benchmark for quantitatively evaluating the overall conformity between the actual environmental state of each temperature and humidity control node and the theoretical optimal target.

[0047] In some embodiments, the classification and determination of the preservation status of ecological environment samples under visual monitoring based on various environmental deviation values, and the generation of storage optimization strategies, can be achieved in the following manner: Based on the calculated environmental deviation values ​​of each temperature and humidity control node, the preservation status of ecological environment samples under visual monitoring is classified and determined, and then a storage optimization strategy is generated. First, a preset classification threshold for environmental deviation values ​​is established: the node area with an environmental deviation value less than or equal to the first threshold is classified as a "stable zone," indicating that the environment in this area highly meets the conditions for balanced preservation; the area with an environmental deviation value greater than the first threshold but less than or equal to the second threshold is classified as a "zone of concern," indicating that there is an acceptable slight deviation; the area with an environmental deviation value greater than the second threshold is classified as a "risk zone," indicating that there is a significant deviation in environmental control. On the visual monitoring interface, the system highlights the corresponding nodes and the physical space areas they represent with different colors (such as green, yellow, and red). Subsequently, differentiated storage optimization strategies were generated for different classification results: for the "stable zone", the strategy was to maintain the existing control parameters and review them periodically; for the "zone of concern", the strategy was to automatically fine-tune the output of the air supply or temperature control equipment in the zone and shorten the re-inspection cycle of the samples in the zone; for the "risk zone", the strategy was to immediately trigger an alarm, recommend that the samples in the zone be temporarily transferred to a backup stable storage location, and arrange for equipment maintenance or calibration. The storage optimization strategy instruction set, which combines spatial status classification and specific intervention measures, serves as the final operational guideline for guiding the proactive maintenance of storage facilities and sample preservation.

[0048] In another aspect, in some embodiments, this application provides a sample storage system for ecological environment monitoring, with reference to... Figure 3 The figure is a schematic diagram of the structure of an ecological environment monitoring sample storage system according to some embodiments of this application. The ecological environment monitoring sample storage system includes: a data acquisition module 201, a processing module 202, and an execution module 203, which are described below: The acquisition module 201 in this application is mainly used to preprocess the collected ecological environment samples and mark their initial state information. According to the initial state information and the preset storage conditions, multiple temperature and humidity control nodes are marked in the storage environment, and multi-source sensors are deployed at each temperature and humidity control node to collect the sealing data of the sample container and external environmental parameters. Processing module 202 in this application is used to identify the multi-stage characteristics of the degradation rate of ecological environment samples based on various external environmental parameters, obtain the stage state characteristics of ecological environment samples, and determine the sample stability index under the current storage conditions based on the stage state characteristics and the performance degradation coefficient in the sealing data. It should be noted that the processing module 202 is also used to perform trend fitting on the quality degradation trend during long-term storage based on the stability index and the effective component content of the ecological environment sample, to obtain the quality degradation characteristics of the ecological environment sample, and to predict the balanced storage conditions for maintaining the sample effectiveness of the ecological environment sample based on the quality degradation characteristics. The execution module 203 in this application is mainly used to retrieve the real-time external environmental parameters of each temperature and humidity control node through intelligent sensors, compare the balanced storage conditions and the various external environmental parameters collected by the intelligent sensors in multiple stages to obtain the environmental deviation value of each temperature and humidity control node, and classify and determine the storage status of the ecological environment sample under visual monitoring based on the environmental deviation value, thereby generating a storage optimization strategy.

[0049] The foregoing has detailed examples of the sample storage device and method for ecological environment monitoring provided in the embodiments of this application. It is understood that, in order to achieve the above functions, the corresponding device includes hardware structures and / or software modules corresponding to the execution of each function. Those skilled in the art should readily recognize that, based on the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein, this application can be implemented in hardware or a combination of hardware and computer software. Whether a function is executed by hardware or by computer software driving hardware depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specified application, but such implementation should not be considered beyond the scope of this application.

[0050] In some embodiments, this application also provides a computer device, the computer device including a memory and a processor, the memory for storing a computer program, and the processor for calling and running the computer program from the memory, so that the computer device performs the above-described method for storing samples for ecological environment monitoring.

[0051] In some embodiments, reference Figure 4 The dashed lines in the figure indicate that the unit or module is optional. This figure is a schematic diagram of the structure of a computer device for implementing a sample storage method for ecological environment monitoring according to an embodiment of this application. The sample storage method for ecological environment monitoring described in the above embodiments can be achieved through… Figure 4 The computer device shown is used to implement this, and the computer device includes at least one processor 301, a memory 302 and at least one communication unit 305. The computer device may be a terminal device, a server or a chip.

[0052] Processor 301 can be a general-purpose processor or a special-purpose processor. For example, processor 301 can be a central processing unit (CPU), which can be used to control computer devices, execute software programs, and process data from software programs. The computer device may also include a communication unit 305 for inputting (receiving) and outputting (transmitting) signals.

[0053] For example, the computer device may be a chip, and the communication unit 305 may be the input and / or output circuit of the chip, or the communication unit 305 may be the communication interface of the chip, which may be a component of a terminal device, network device or other device.

[0054] For example, the computer device may be a terminal device or a server, and the communication unit 305 may be a transceiver of the terminal device or the server, or the communication unit 305 may be a transceiver circuit of the terminal device or the server.

[0055] The computer device may include one or more memories 302 storing a program 304. The program 304 can be executed by a processor 301 to generate instructions 303, causing the processor 301 to execute the method described in the above method embodiments according to the instructions 303. Optionally, the memory 302 may also store data (such as a target audit model). Optionally, the processor 301 may also read data stored in the memory 302, which may be stored at the same storage address as the program 304, or it may be stored at a different storage address than the program 304.

[0056] The processor 301 and memory 302 can be configured separately or integrated together, for example, integrated on the system on chip (SOC) of the terminal device.

[0057] It should be understood that each step of the above method embodiment can be completed by hardware logic circuits or software instructions in the processor 301. The processor 301 can be a CPU, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, such as discrete gates, transistor logic devices, or discrete hardware components.

[0058] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0059] For example, in some embodiments, this application also provides a computer-readable storage medium storing instructions or code that, when executed on a computer, cause the computer to implement the above-described method for storing samples for ecological environment monitoring.

[0060] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0061] Obviously, those skilled in the art can make various modifications and variations to this application without departing from the spirit and scope of this application. Therefore, if such modifications and variations fall within the scope of the claims of this application and their equivalents, this application also intends to include such modifications and variations.

Claims

1. A method for storing samples for ecological and environmental monitoring, characterized in that, Includes the following steps: The collected ecological environment samples are preprocessed and their initial state information is marked. Based on the initial state information and the preset storage conditions, multiple temperature and humidity control nodes are marked in the storage environment. Multi-source sensors are deployed at each temperature and humidity control node to collect the sealing data of the sample container and external environmental parameters. Based on various external environmental parameters, the degradation rate of ecological environment samples is identified in multiple stages to obtain the stage state characteristics of ecological environment samples. Based on the stage state characteristics and the performance degradation coefficient in the sealing data, the sample stability index under the current storage conditions is determined. Based on the stability index and the effective component content of the ecological environment sample, the quality degradation trend during long-term storage is fitted to obtain the quality degradation characteristics of the ecological environment sample. Based on the quality degradation characteristics, the equilibrium storage conditions for maintaining the sample effectiveness of the ecological environment sample are predicted. By retrieving real-time external environmental parameters from each temperature and humidity control node using intelligent sensors, the balanced preservation conditions and the external environmental parameters collected by the intelligent sensors are compared in multiple stages to obtain the environmental deviation value of each temperature and humidity control node. Based on the environmental deviation value, the preservation status of the ecological environment sample under visual monitoring is graded and determined, thereby generating a storage optimization strategy.

2. The method as described in claim 1, characterized in that, Based on the initial state information and preset storage conditions, multiple temperature and humidity control nodes are marked within the storage environment, specifically including: The initial state information is gradient mapped with a preset storage condition library to obtain a temperature and humidity gradient distribution map of the internal storage environment. In the temperature and humidity gradient distribution map, identify the potential risk areas that have the greatest impact on sample stability and the typical areas that best represent the overall environmental state, and then obtain multiple candidate control nodes. Spatial coverage optimization of all candidate control nodes was performed by considering the physical placement of the sample containers and the airflow path, resulting in multiple temperature and humidity control nodes.

3. The method as described in claim 1, characterized in that, Based on various external environmental parameters, multi-stage feature identification of the degradation rate of ecological environment samples was performed to obtain the stage-specific state characteristics of the ecological environment samples, which specifically include: All external environmental parameters were serialized to obtain the degradation rate curves of the ecological environment samples. A sliding window analysis was performed on the degradation rate curve to identify the time inflection point where the degradation rate changed significantly. By statistically analyzing the correlation strength of degradation rates within each time period, the phased state characteristics of the ecological environment samples can be obtained.

4. The method as described in claim 1, characterized in that, Determining the sample stability index under the current storage conditions based on the aforementioned stage-specific characteristics and the performance degradation coefficient in the sealing data specifically includes: Extract the performance degradation coefficient under the current storage conditions from the sealing data; The current time stage's phased state characteristics are fused with the performance degradation coefficient to obtain the sample stability index under the current storage conditions.

5. The method as described in claim 1, characterized in that, Based on the stability index and the effective component content of the ecological environment samples, a trend fitting was performed on the quality degradation trend during long-term storage to obtain the specific characteristics of quality degradation of the ecological environment samples, including: With the time series as the horizontal axis and the content of effective ingredients obtained from periodic detection as the vertical axis data points, the decay curve of the content of effective ingredients is obtained; The stability index is used as a covariate of the decay curve for piecewise fitting to obtain the quality decay curve of the ecological environment sample. The quality degradation characteristics of the ecological environment samples were extracted from the quality degradation curve.

6. The method as described in claim 1, characterized in that, The equilibrium preservation conditions for maintaining the effectiveness of ecological environment samples, based on the aforementioned quality degradation characteristics, specifically include: Determine the quantitative criteria for sample validity and set physical constraints for adjusting storage conditions; Based on the aforementioned quality degradation characteristics, the sample validity of maintaining ecological environment samples is deduced through reverse inference, resulting in the equilibrium preservation conditions for maintaining the sample validity of ecological environment samples.

7. The method as described in claim 1, characterized in that, By retrieving real-time external environmental parameters from each temperature and humidity control node using intelligent sensors, and comparing the equilibrium preservation conditions with the various external environmental parameters collected by the intelligent sensors in multiple stages, the environmental deviation values ​​of each temperature and humidity control node are obtained, specifically including: For each temperature and humidity control node, the equilibrium preservation conditions are compared with the external environmental parameters of the temperature and humidity control node collected by the intelligent sensor in stages to obtain the average deviation and deviation fluctuation of the temperature and humidity control node in each time stage. The environmental deviation values ​​of the temperature and humidity control nodes are determined by all average deviations and deviation fluctuations, and then the environmental deviation values ​​of each temperature and humidity control node are obtained.

8. A sample storage system for ecological environment monitoring, comprising: The acquisition module is used to preprocess the collected ecological environment samples and identify their initial state information. Based on the initial state information and preset storage conditions, multiple temperature and humidity control nodes are marked in the storage environment, and multi-source sensors are deployed at each temperature and humidity control node to collect the sealing data of the sample container and external environmental parameters. The processing module is used to identify the multi-stage characteristics of the degradation rate of ecological environment samples based on various external environmental parameters, obtain the stage state characteristics of ecological environment samples, and determine the sample stability index under the current storage conditions based on the stage state characteristics and the performance degradation coefficient in the sealing data. The processing module is also used to perform trend fitting on the quality degradation trend during long-term storage based on the stability index and the effective component content of the ecological environment sample, to obtain the quality degradation characteristics of the ecological environment sample, and to predict the balanced storage conditions for maintaining the sample effectiveness of the ecological environment sample based on the quality degradation characteristics. The execution module is used to retrieve real-time external environmental parameters of each temperature and humidity control node through intelligent sensors, compare the balanced preservation conditions with the external environmental parameters collected by the intelligent sensors in multiple stages to obtain the environmental deviation value of each temperature and humidity control node, and classify the preservation status of the ecological environment sample under visual monitoring based on the environmental deviation value, thereby generating a storage optimization strategy.

9. A computer device, characterized in that, The computer device includes a memory and a processor. The memory is used to store computer programs, and the processor is used to call and run the computer programs from the memory, so that the computer device performs the method for storing samples for ecological and environmental monitoring as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores instructions or code that, when executed on a computer, cause the computer to implement the sample storage method for ecological and environmental monitoring as described in any one of claims 1 to 7.

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