A method for full-cycle management of environmental samples

Abstract

This invention relates to the field of management, and more particularly to a method for the full-cycle management of environmental samples. The method includes: collecting environmental samples and recording collection parameters, verifying the integrity of the collection parameters, and uploading the collection parameters to a cloud database in real time; optimizing the management of environmental samples during the storage and analysis stages through a sample storage and environmental dynamic degradation risk scheduling algorithm. This method solves the problems of parameter loss due to human error or equipment malfunction during traditional collection processes; slow and error-prone parameter uploading speeds; ignoring the impact of storage environment fluctuations on environmental sample degradation; and unreasonable analysis order due to the use of static priorities.

Description

Technical Field

[0001] This invention relates to the field of management, and more particularly to a method for the full lifecycle management of environmental samples. Background Technology

[0002] With increasing global environmental awareness, environmental monitoring technologies and methods are becoming increasingly sophisticated, scientific, and standardized. Every step in the process—from environmental sample collection, processing, storage, transportation, analysis, and result feedback—requires strict adherence to standardized procedures. Errors in any step can affect the accuracy of the final analysis results and the effectiveness of environmental monitoring efforts.

[0003] Traditional environmental sample management methods often rely on manual recording and traditional database management systems, which are prone to omissions and errors, and require a large amount of work for updating and maintenance, making it difficult to achieve real-time monitoring and management. Currently, the traceability of environmental samples is poor, making it difficult to track the entire sample lifecycle. As a result, it is impossible to quickly locate the source of the problem when inaccurate analysis results or non-compliance with standards occur.

[0004] The full lifecycle management of environmental samples is a crucial aspect of environmental monitoring. With continuous technological advancements, environmental sample management methods are evolving towards informatization, intelligentization, and automation. Full lifecycle management of environmental samples provides a solid technological foundation for ensuring the representativeness, reliability, and traceability of environmental samples, as well as improving management efficiency. In the future, with the continuous application of new technologies, environmental sample management will become more efficient and precise, providing stronger data support and decision-making basis for global environmental protection efforts.

[0005] However, the above-mentioned full-cycle management method for environmental samples still has the following problems: parameters are missing due to human operation or equipment failure during traditional collection; the upload speed of collected parameters is slow and prone to errors; the impact of storage environment fluctuations on the degradation of environmental samples is ignored; and the use of static priority leads to unreasonable analysis order. Summary of the Invention

[0006] This invention provides a full-cycle management method for environmental samples to solve the problems of parameter loss due to human operation or equipment failure in traditional collection processes; slow and error-prone upload speed of collected parameters; ignoring the impact of storage environment fluctuations on environmental sample degradation; and unreasonable analysis order caused by the use of static priority.

[0007] The present invention provides a method for full-cycle management of environmental samples, specifically comprising the following technical solutions:

[0008] A method for full-cycle management of environmental samples, comprising the following steps:

[0009] S1. Collect environmental samples and record the collection parameters, verify the integrity of the collection parameters, and upload the collection parameters to the cloud database in real time;

[0010] S2. An algorithm for scheduling the dynamic degradation risk of environmental samples is introduced. Based on the dynamic degradation rate and time interval, a function that decays over time is generated. The algorithm is then integrated over future time windows to quantify the cumulative degradation effect within the future time windows. This predicts the degradation risk of environmental samples within future time windows, yields the predicted degradation risk value of environmental samples, and optimizes the management of environmental samples during the storage and analysis stages.

[0011] Preferably, S1 specifically includes:

[0012] The collected parameters are verified, and a flag indicating their existence is set. Based on the flag, the integrity score of the collected parameters is calculated through a product operation, and the integrity of the collected parameters is verified.

[0013] Preferably, S1 specifically includes:

[0014] When the integrity verification fails, an environmental sample warning notification is generated, showing the specific missing collection parameters. Remedial measures are then taken based on the environmental sample warning notification, and the supplemented collection parameters are uploaded after review, with the supplementation process recorded.

[0015] Preferably, S2 specifically includes:

[0016] The dynamic degradation rate is calculated by introducing a correction factor and combining it with a preset baseline degradation rate.

[0017] Preferably, S2 specifically includes:

[0018] The correction factor is calculated by introducing environmental impact weights and combining them with storage environment deviation factors.

[0019] Preferably, S2 specifically includes:

[0020] By quantifying the deviation between the actual storage environment parameters and the preset ideal storage environment parameters, and then performing weighted normalization on the deviation, the storage environment deviation factor is obtained.

[0021] Preferably, S2 specifically includes:

[0022] Based on the predicted degradation risk of environmental samples, the analysis task priority of environmental samples is introduced to generate a dynamic scheduling index for environmental samples; environmental samples are sorted according to the dynamic scheduling index to optimize resource management in the storage and analysis stages.

[0023] The beneficial effects of the technical solution of the present invention are:

[0024] 1. Standardized IoT devices (such as GPS locators, temperature and humidity sensors, and clock modules) automatically record key parameters such as collection time, location, temperature, and humidity, and upload them to the cloud database in real time, assigning a unique identification code to ensure the traceability of environmental samples. By checking the recording status of each collected parameter one by one, the integrity of the parameters is verified, ensuring that missing or erroneous data can be detected in a timely manner, triggering the supplementation process and recording detailed logs. This effectively reduces the risk of environmental samples being invalid due to missing collected parameters, ensuring the reliability and compliance of environmental samples, and improving the accuracy of subsequent analysis.

[0025] 2. An algorithm for dynamic degradation risk scheduling of sample storage and environment is introduced to monitor the storage environment in real time, predict the degradation risk of environmental samples in future time windows, and generate a dynamic scheduling index for environmental samples based on the priority of analysis tasks. This index is used to optimize the analysis order of environmental samples and adjust storage conditions. Compared with traditional fixed degradation rate or static priority methods, this method dynamically adjusts the degradation rate according to real-time environmental deviations, which can accurately reflect the trend of degradation loss of environmental samples. This effectively avoids the degradation risk of environmental samples caused by environmental fluctuations. The dynamic management method significantly improves the safety of environmental sample storage and optimizes the allocation of laboratory resources.

[0026] 3. By comprehensively considering the degradation risk and task urgency of the generated environmental samples, a dynamic scheduling index is provided to give environmental samples a scientific basis for prioritization. High-priority environmental samples can enter the analysis process earlier to ensure that urgent tasks are completed in a timely manner, while also taking into account the protection of environmental sample quality. This effectively balances task efficiency and sample quality and improves the overall execution efficiency of the analysis task. Attached Figure Description

[0027] Figure 1 This is a flowchart of a full-cycle management method for environmental samples according to the present invention. Detailed Implementation

[0028] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0030] The following description, in conjunction with the accompanying drawings, details a specific scheme for a full-cycle management method for environmental samples provided by the present invention.

[0031] See attached document Figure 1 The diagram illustrates a flowchart of a full-cycle management method for environmental samples according to an embodiment of the present invention, which includes the following steps:

[0032] S1. Collect environmental samples and record the collection parameters, verify the integrity of the collection parameters, and upload the collection parameters to the cloud database in real time;

[0033] On-site staff are equipped with standardized IoT devices, including GPS locators (to record latitude and longitude), temperature and humidity sensors (to record ambient temperature and humidity), and clock modules (to record collection time). These standardized IoT devices are pre-calibrated to ensure compliance with national standards (e.g., temperature sensor accuracy ±0.5℃). Based on the monitoring task (e.g., water quality monitoring), environmental samples are collected at designated locations (e.g., river sampling points) using standard containers (e.g., sterile sampling bottles). Key parameters (collection parameters), including collection time, location, temperature, and humidity, are automatically recorded using the standardized IoT devices. These collected parameters are uploaded to a cloud database in real time via 4G / 5G networks and assigned a unique identifier. Each collected sample is then checked individually. Whether the parameters were successfully recorded is verified through logical operations. Specifically, for each collected parameter, it is verified by logical operations. If the collected parameter exists and is in the correct format, it is marked as "existing" and the existence flag value of the collected parameter is set to "1". If the collected parameter is missing (e.g., GPS signal loss resulting in empty location data) or has an incorrect format, it is marked as "missing" and the existence flag value of the collected parameter is set to "0". The completeness score of the collected parameters is calculated by multiplication operations. If the completeness score of the collected parameters is "1", it means that all collected parameters are marked as "existing" and the collected parameters of environmental samples pass the completeness verification. If the completeness score of the collected parameters is "0", it means that some collected parameters are marked as "missing", triggering the supplementation process.

[0034] The formula for calculating integrity verification is:

[0035]

[0036] in, Indicates the first The integrity score of the collection parameters of each environmental sample; This represents the mathematical product operator, indicating that the product will be generated from... arrive Multiply the presence flags of all collected parameters; Indicates the total number of parameters collected; Indicates the first The environmental sample number The presence of a collection parameter is indicated by the collection parameters, which include time, location, temperature, humidity, etc.

[0037] If the verification reveals missing collection parameters, an environmental sample warning notification will be automatically generated, displaying the specific missing collection parameters. Staff must take remedial measures based on the environmental sample warning notification, such as using a backup handheld GPS device to re-record the location, or supplementing the environmental data using a backup thermometer and hygrometer in the laboratory. The supplemented collection parameters must be reviewed by the on-site supervisor to confirm their accuracy before being uploaded. A detailed log must be kept of the supplementation process, including the supplementation time, operators, and methods, to ensure compliance.

[0038] S2. The management of environmental samples during the storage and analysis stages is optimized through a sample storage and environmental dynamic degradation risk scheduling algorithm.

[0039] To address the inefficient resource allocation caused by fixed degradation rates or static priorities in traditional methods, a sample storage and environmental dynamic degradation risk scheduling algorithm is used to optimize the management of environmental samples during the storage and analysis stages.

[0040] The sample storage and environmental dynamic degradation risk scheduling algorithm monitors the storage environment in real time, predicts the degradation risk of environmental samples in future time windows, and generates a dynamic scheduling index for environmental samples by combining the priority of analysis tasks. This guides the priority ranking of environmental sample analysis tasks and the emergency adjustment of storage conditions to ensure the quality of environmental samples and the efficiency of tasks.

[0041] The predicted degradation risk of environmental samples within a future time window, i.e., the predicted value of degradation risk, is achieved by integrating the dynamic degradation rate over time. This integration process considers the entire period from the current time to the future time window, calculating the cumulative effect of environmental sample mass loss due to degradation. Specifically, based on the dynamic degradation rate and the time interval (the difference between the current time and a future time point), a function that decays over time is generated to reflect the decreasing trend of degradation risk over time. The dynamic degradation rate is calculated using a baseline degradation rate and a correction factor based on an environmental factor impact model in environmental science, reflecting the impact of the real-time storage environment on the degradation rate of environmental samples. The correction factor is jointly determined by a storage environment deviation factor and an environmental impact weight. The environmental impact weight is used to adjust the degree of influence of storage environment deviation on the degradation rate of environmental samples. The storage environment deviation factor quantifies the deviation between actual storage environment parameters (such as temperature, humidity, and light intensity) and ideal storage environment parameters, and reflects the overall degree of storage environment deviation through weighted normalization. If the storage environment conditions are ideal, the storage environment deviation factor is 0, and the dynamic degradation rate equals the baseline degradation rate. If the environmental deviation is large, the dynamic degradation rate increases significantly, reflecting the aggravation of the degradation risk of environmental samples.

[0042] Based on the predicted value of degradation risk, the analysis task priority of environmental samples is introduced to generate a dynamic scheduling index for environmental samples; the analysis task priority of environmental samples is determined by the urgency of the analysis task, which directly affects the urgency of environmental sample analysis.

[0043] The formula for calculating the dynamic scheduling index of environmental samples is:

[0044]

[0045] in, Indicates the first The dynamic scheduling index of each environmental sample is used to guide the prioritization of analysis tasks and the emergency adjustment of storage conditions. The weighting coefficient representing the predicted degradation risk value is used to balance the contribution of the predicted degradation risk value and the priority of analytical tasks to the dynamic scheduling index of environmental samples, emphasizing the importance of the predicted degradation risk value, and must meet the following requirements. The value range is set using expert experience. ; It is the predicted degradation risk value of environmental samples, representing the cumulative effect of the dynamic degradation risk of environmental samples within a predicted future time window. This indicates the length of the future time window, set based on expert experience. Indicates the current time; This indicates that the exponential decay function reflects the environmental sample's degradation over time. The degradation state is used to simulate the exponential decay behavior of environmental samples over time, so as to ensure that the dynamic degradation rate can reflect the impact of environmental fluctuations. Indicates the first An environmental sample at time The dynamic degradation rate over time And changes in the storage environment, the calculation formula is:

[0046]

[0047] in, Indicates the first The baseline degradation rate for each environmental sample is obtained through expert experience, based on the degradation rate of the environmental sample type under ideal storage conditions. Indicates time Correction factor; The environmental impact weight, used to reflect the influence of storage environment deviations on the degradation rate, was obtained experimentally and its value range is [value missing]. ; Indicates time The storage environment deviation factor reflects the degree to which the real-time storage environment deviates from the ideal storage environment. It is used to dynamically adjust the degradation rate and reflects the actual impact of the storage environment on the degradation of environmental samples. The formula for calculating the storage environment deviation factor is:

[0048]

[0049] This represents the total number of storage environment parameters, including temperature, humidity, light intensity, etc. These parameters should be set according to specific circumstances and are not limited here. Indicates the first The weights of each storage environment parameter are used to reflect the degree of influence of the storage environment parameter on the degradation of environmental samples. These weights were obtained experimentally and their values ​​range from [value range missing]. ; Indicates time No. The actual values ​​of each storage environment parameter; Indicates the first The ideal values ​​for each storage environment parameter are set based on expert experience. and They represent the first The maximum and minimum values ​​of each storage environment parameter are derived from an existing database;

[0050] The weighting coefficients representing the priority of analytical tasks are used to balance the contribution of analytical task priorities to the dynamic scheduling index of environmental samples, ensuring that urgent tasks are given priority and meeting the requirements. The value range was obtained through expert experience and is as follows: ; Indicates the first The priority of an environmental sample analysis task is determined by the urgency of the analysis task and is set based on expert experience. It reflects the importance of the analysis task and affects the analysis order of environmental samples. The higher the priority value of the analysis task, the more urgent the analysis task.

[0051] Environmental samples are sorted according to their dynamic scheduling index. The higher the dynamic scheduling index of an environmental sample, the earlier it enters the analysis process.

[0052] By optimizing resource management during the storage and analysis phases through a sample storage and environmental dynamic degradation risk scheduling algorithm, the level of intelligence in environmental sample management was significantly improved, the risk of environmental sample degradation was reduced, and the efficient execution of analysis tasks was ensured.

[0053] In summary, a method for the full lifecycle management of environmental samples has been developed.

[0054] The order of the embodiments is for illustrative purposes only and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are possible or may be advantageous.

[0055] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0056] 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 the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A method for full-cycle management of environmental samples, characterized in that, Includes the following steps: S1. Collect environmental samples and record the collection parameters. Verify the integrity of the collection parameters by calculating the integrity score of the collection parameters of the environmental samples, and upload the collection parameters to the cloud database in real time. S2. An algorithm for dynamic degradation risk scheduling of sample storage and environment is introduced. Based on the dynamic degradation rate and time interval, a function that decays over time is generated, and the cumulative degradation effect within the future time window is quantified by integrating the function over the future time window. This predicts the degradation risk of environmental samples within the future time window, yielding a predicted degradation risk value for the environmental samples. Based on this predicted degradation risk value, the analysis task priority of the environmental samples is introduced, generating a dynamic scheduling index for the environmental samples. The management of environmental samples during storage and analysis is optimized based on this dynamic scheduling index. The dynamic degradation rate is calculated by introducing a correction factor and a preset baseline degradation rate. The specific formula for the dynamic scheduling index of environmental samples is: ； in, Indicates the first Dynamic scheduling index of an environmental sample; The weighting coefficients represent the predicted degradation risk values; It is the predicted value of degradation risk for environmental samples; Indicates the length of the future time window; Indicates the current time; Indicates the first An environmental sample at time The dynamic degradation rate; Weighting coefficients representing the priority of the analysis tasks; Indicates the first Prioritize the analysis tasks for each environmental sample; Environmental samples are sorted according to a dynamic scheduling index to optimize resource management during storage and analysis.

2. The method for full-cycle management of environmental samples according to claim 1, characterized in that, S1 specifically includes: The collected parameters are verified, and a flag indicating their existence is set. Based on the flag indicating their existence, the integrity score of the collected parameters is calculated through a product operation.

3. The method for full-cycle management of environmental samples according to claim 2, characterized in that, S1 specifically includes: When the integrity verification fails, an environmental sample warning notification is generated, showing the specific missing collection parameters. Remedial measures are then taken based on the environmental sample warning notification, and the supplemented collection parameters are uploaded after review, with the supplementation process recorded.

4. The method for full-cycle management of environmental samples according to claim 1, characterized in that, S2 specifically includes: The correction factor is calculated by introducing environmental impact weights and combining them with storage environment deviation factors.

5. The method for full-cycle management of environmental samples according to claim 4, characterized in that, S2 specifically includes: By quantifying the deviation between the actual storage environment parameters and the preset ideal storage environment parameters, and then performing weighted normalization on the deviation, the storage environment deviation factor is obtained.

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