Full-period management method for environmental samples

Abstract

The invention relates to the field of management, in particular to a full-period management method for environmental samples. The method comprises the following steps: collecting an environmental sample, recording collection parameters, verifying the integrity of the collection parameters, and uploading the collection parameters to a cloud database in real time; through a sample storage and environment dynamic degradation risk scheduling algorithm, management of environment samples in storage and analysis stages is optimized. The problem of parameter loss caused by manual operation or equipment failure in the traditional acquisition process is solved; acquisition parameters are low in uploading speed and easy to make mistakes; the influence of storage environment fluctuation on environmental sample degradation is ignored; and the analysis sequence is unreasonable due to the adoption of the static priority.

Description

Technical Field

[0001] The present invention relates to the field of management, and particularly to a full-cycle management method for environmental samples. Background Art

[0002] With the improvement of global environmental awareness, the technologies and methods of environmental monitoring are becoming increasingly refined, scientific, and standardized. For each link in the processes of environmental sample collection, processing, storage, transportation, analysis, and result feedback, strict compliance with standardized procedures is required. Any mistake in any link may affect the accuracy of the final analysis results and the effectiveness of environmental monitoring work.

[0003] Traditional environmental sample management methods often rely on manual records and traditional database management systems, which are prone to omissions and errors, and have a large workload for updating and maintenance, making it difficult to achieve real-time monitoring and management; currently, the traceability of environmental samples is poor, and it is difficult to track the entire sample life cycle, resulting in the inability to quickly locate the source of problems when inaccurate or non-compliant analysis results occur.

[0004] The full-cycle management of environmental samples is an important link in environmental monitoring work. With the continuous development of technologies, environmental sample management methods are also continuously evolving towards informatization, intelligence, and automation. The full-cycle management method of environmental samples provides a solid technical foundation in ensuring the representativeness, reliability, traceability of environmental samples, and improving management efficiency. In the future, with the continuous application of new technologies, environmental sample management will be more efficient and accurate, and will provide more powerful data support and decision-making basis for the global environmental protection cause.

[0005] However, the above full-cycle management method for environmental samples still has the following problems: parameter loss due to human operation or equipment failure during the traditional collection process; slow and error-prone upload speed of collection parameters; ignoring the impact of storage environment fluctuations on the degradation of environmental samples; and unreasonable analysis order caused by static priorities. Summary of the Invention

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

[0007] A full-cycle management method for environmental samples of the present invention specifically includes the following technical solutions: A full-cycle management method for environmental samples includes the following steps: S1. Collect environmental samples, record the collection parameters, verify the integrity of the collection parameters, and upload the collection parameters to the cloud database in real time; S2. Introduce the sample storage and environmental dynamic degradation risk scheduling algorithm. Based on the dynamic degradation rate and time interval, generate a function that decays over time, integrate it over the future time window, quantify the cumulative degradation effect within the future time window, predict the degradation risk of the environmental sample within the future time window, obtain the degradation risk prediction value of the environmental sample, and optimize the management of the environmental sample during the storage and analysis phases.

[0008] Preferably, S1 specifically includes: Verify the collection parameters and set the existence flag of the collection parameters; based on the existence flag of the collection parameters, calculate the integrity score of the collection parameters through product operation to verify the integrity of the collection parameters.

[0009] Preferably, S1 specifically includes: When the integrity verification fails, generate an environmental sample warning notice, display the specific missing collection parameters, take remedial measures according to the environmental sample warning notice, upload the supplemented collection parameters after review, and record the supplementation process.

[0010] Preferably, S2 specifically includes: The dynamic degradation rate is calculated by introducing a correction factor and combining it with a preset reference degradation rate.

[0011] Preferably, S2 specifically includes: The correction factor is calculated by introducing an environmental impact weight and combining it with a storage environment deviation factor.

[0012] Preferably, S2 specifically includes: Quantify the deviation between the actual storage environment parameters and the preset ideal storage environment parameters, and perform weighted normalization on the deviation to obtain the storage environment deviation factor.

[0013] Preferably, S2 specifically includes: Based on the degradation risk prediction value of the environmental sample, introduce the analysis task priority of the environmental sample to generate the dynamic scheduling index of the environmental sample; sort the environmental samples according to the dynamic scheduling index of the environmental sample to optimize the resource management during the storage and analysis phases.

[0014] The beneficial effects of the technical solution of the present invention are: 1. Automatically record key parameters such as collection time, location, temperature, and humidity through standardized Internet of Things devices (such as GPS locators, temperature and humidity sensors, clock modules), 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 collection parameter one by one, verify the parameter integrity to ensure that missing or incorrect data can be detected in a timely manner, trigger the supplement process and record detailed logs, effectively reducing the risk of environmental samples being invalid due to missing collection parameters, ensuring the reliability and compliance of environmental samples, and improving the accuracy of subsequent analysis.

[0015] 2. Introduce a sample storage and environmental dynamic degradation risk scheduling algorithm to monitor the storage environment in real time, predict the degradation risk of environmental samples within a future time window, combine with the analysis task priority, generate a dynamic scheduling index for environmental samples, which is used to optimize the analysis order of environmental samples and adjust the storage conditions. Compared with traditional fixed degradation rate or static priority methods, dynamically adjust the degradation rate according to real-time environmental deviations, which can accurately reflect the trend of environmental sample degradation loss, thus effectively avoiding 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.

[0016] 3. The dynamic scheduling index of environmental samples generated by comprehensively considering degradation risk and task urgency provides a scientific basis for priority ranking of environmental sample analysis. Environmental samples with high priority can enter the analysis process earlier to ensure the timely completion of urgent tasks, while taking into account the protection of environmental sample quality, effectively balancing task efficiency and sample quality, and improving the overall execution efficiency of analysis tasks. Brief Description of the Drawings

[0017] Figure 1 It is a flowchart of a full-cycle management method for an environmental sample described in the present invention. Detailed Embodiments

[0018] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the intended invention purpose, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0019] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which the present invention belongs.

[0020] The following specifically describes the specific solution of a full-cycle management method for environmental samples provided by the present invention in conjunction with the accompanying drawings.

[0021] Refer to the attached Figure 1 , which shows a flowchart of a full-cycle management method for environmental samples provided by an embodiment of the present invention. The method includes the following steps: 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; On-site staff are equipped with standardized Internet of Things devices, including a GPS locator (recording longitude and latitude), a temperature and humidity sensor (recording environmental temperature and humidity), and a clock module (recording the collection time). The standardized Internet of Things devices are pre-calibrated to ensure compliance with national standards (such as the accuracy of the temperature sensor is ±0.5°C); according to the monitoring task (such as water quality monitoring), environmental samples are collected at a designated location (such as a river sampling point) using a standard container (such as a sterile sampling bottle); key parameters (collection parameters) are automatically recorded through the standardized Internet of Things devices, including collection time, location, temperature, humidity, etc.; the collection parameters are uploaded to the cloud database in real time through the 4G / 5G network, and a unique identification code is assigned; check whether each collection parameter is successfully recorded one by one. Specifically, for each collection parameter, it is verified through logical operations. If the collection parameter exists and the format is correct, it is marked as "existent", and the existence flag value of the collection parameter is set to "1". If the collection parameter is missing (such as the location data is empty due to the loss of GPS signal) or the format is incorrect, it is marked as "missing", and the existence flag value of the collection parameter is set to "0"; calculate the integrity score of the collection parameters through product operations. If the integrity score of the collection parameters is "1", it means that all collection parameters are marked as "existent", and the collection parameters of the environmental samples pass the integrity verification. If the integrity score of the collection parameters is "0", it means that there are collection parameters marked as "missing", and the supplementary process is triggered; The calculation formula for integrity verification is:

[0022] Wherein, represents the integrity score of the collection parameters of the th environmental sample; represents the mathematical product operator, indicating that the existence flags of all collection parameters from to are multiplied; represents the total number of collection parameters; represents the th environmental sample and the th collection parameter's existence flag. The collection parameters include time, location, temperature, humidity, etc.; If it is verified that collection parameters are missing, an environmental sample warning notice will be automatically generated, and the specific missing collection parameters will be displayed. The staff needs to take remedial measures according to the environmental sample warning notice, such as using a backup handheld GPS device to re-record the location, or supplementing environmental data through the backup thermometer and hygrometer in the laboratory. The supplemented collection parameters need to be reviewed by the on-site person in charge, and after confirming the accuracy, they will be uploaded. A detailed log of the supplementation process will be recorded, including the supplementation time, the operator, and the method, to ensure compliance.

[0023] S2. Optimize the management of environmental samples during the storage and analysis phases through the sample storage and environmental dynamic degradation risk scheduling algorithm; To address the problem of inefficient resource allocation caused by fixed degradation rates or static priorities in traditional methods, optimize the management of environmental samples during the storage and analysis phases through the sample storage and environmental dynamic degradation risk scheduling algorithm; The sample storage and environmental dynamic degradation risk scheduling algorithm optimizes the management of environmental samples during the storage and analysis phases by predicting the degradation risk of environmental samples within a future time window through real-time monitoring of the storage environment, combining the analysis task priorities, generating a dynamic scheduling index for environmental samples, and guiding the prioritization of environmental sample analysis tasks and the emergency adjustment of storage conditions to ensure the quality of environmental samples and task efficiency; Predicting the degradation risk of environmental samples within a future time window, i.e., the predicted value of the degradation risk, is achieved by integrating the dynamic degradation rate over time. The integration process considers the entire period from the current time to the future time window and calculates the cumulative effect of the mass loss of environmental samples due to degradation. Specifically, based on the dynamic degradation rate and the time interval (the difference between the current time and the future time point), a function that decays over time is generated to reflect the decreasing trend of the degradation risk over time. The dynamic degradation rate is calculated based on the environmental factor impact model in environmental science through the benchmark degradation rate and the correction factor, and is used to reflect the impact of the real-time storage environment on the degradation rate of environmental samples. The correction factor is jointly determined by the storage environment deviation factor and the environmental impact weight. The environmental impact weight is used to adjust the degree of influence of the storage environment deviation on the degradation rate of environmental samples. The storage environment deviation factor quantifies the deviation between the actual storage environment parameters (such as temperature, humidity, light intensity, etc.) and the ideal storage environment parameters, and reflects the overall storage environment deviation degree through weighted normalization. When the storage environment conditions are ideal, the storage environment deviation factor is 0, and the dynamic degradation rate is equal to the benchmark degradation rate. If the environmental deviation is large, the dynamic degradation rate increases significantly, which can reflect the exacerbation of the degradation risk of environmental samples; Based on the predicted value of the degradation risk, introduce the analysis task priority of environmental samples 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 and directly affects the urgency of environmental sample analysis; The calculation formula for the dynamic scheduling index of environmental samples is as follows:

[0024] Wherein, represents the dynamic scheduling index of the th environmental sample, which is used to guide the prioritization of analysis tasks and the emergency adjustment of storage conditions; represents the weight coefficient of the degradation risk prediction value, which is used to balance the contributions of the degradation risk prediction value and the analysis task priority in the dynamic scheduling index of environmental samples, emphasizing the importance of the degradation risk prediction value, and needs to satisfy , which is set by the expert experience method, and the value range is ; is the degradation risk prediction value of the environmental sample, which represents the cumulative effect of predicting the dynamic degradation risk of the environmental sample within the future time window, represents the length of the future time window, which is set according to the expert experience method; represents the current time; represents the degradation state of the environmental sample at time reflected by the exponential decay function, which is used to simulate the exponential decay behavior of the environmental sample degrading over time to ensure that the dynamic degradation rate can reflect the impact of environmental fluctuations; represents the th environmental sample at time The dynamic degradation rate changes with time and the storage environment, and the calculation formula is:

[0025] Wherein, represents the baseline degradation rate of the th environmental sample, which is the degradation rate of the environmental sample type under ideal storage conditions and is obtained through the expert experience method; represents the correction factor at time ; represents the environmental impact weight, which is used to reflect the influence weight of the storage environment deviation on the degradation rate and is obtained through experiments. The value range is ; represents the storage environment deviation factor at time , which can reflect the degree to which the real-time storage environment deviates from the ideal storage environment and is used to dynamically adjust the degradation rate to reflect the actual impact of the storage environment on the degradation of environmental samples. The calculation formula for the storage environment deviation factor is:

[0026] Represents the total number of storage environment parameters, including temperature, humidity, light intensity, etc., which are set according to specific circumstances and are not limited here; Represents the weight of the th storage environment parameter, used to reflect the influence degree of the storage environment parameter on the degradation of environmental samples, obtained through experiments, and the value range is ; Represents the actual value of the th storage environment parameter at time Represents the ideal value of the th storage environment parameter, set according to the expert experience method; And respectively represent the maximum and minimum values of the th storage environment parameter, from the existing database; Represents the weight coefficient of the analysis task priority, used to balance the contribution of the analysis task priority in the dynamic scheduling index of environmental samples, to ensure that urgent tasks are processed first, meeting , obtained through the expert experience method, and the value range is ; Represents the analysis task priority of the th environmental sample, which comes from the urgency of the analysis task, is set according to expert experience, can reflect the importance of the analysis task, and affects the analysis order of environmental samples. The larger the value of the analysis task priority, the more urgent the analysis task; Sort environmental samples according to the dynamic scheduling index of environmental samples. The higher the dynamic scheduling index of environmental samples, the earlier they enter the analysis process; The resource management of the storage and analysis stages is optimized through the sample storage and environmental dynamic degradation risk scheduling algorithm, significantly improving the intelligent level of environmental sample management, reducing the risk of environmental sample degradation, and ensuring the efficient execution of analysis tasks.

[0027] In summary, a full-cycle management method for environmental samples is completed.

[0028] The sequence of invention embodiments is only for description and does not represent the superiority or inferiority of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0029] Each embodiment in this specification is described in a progressive manner. The same or similar parts between each embodiment can be referred to each other, and the key points of each embodiment are to illustrate the differences from other embodiments.

[0030] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included within the protection scope of the present invention.

Claims

1. A full-cycle management method for environmental samples, characterized in that, It includes the following steps: 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; S2. Introduce the sample storage and environmental dynamic degradation risk scheduling algorithm, generate a function that decays over time based on the dynamic degradation rate and time interval, integrate the future time window, quantify the cumulative degradation effect within the future time window, predict the degradation risk of the environmental sample within the future time window, obtain the degradation risk prediction value of the environmental sample, and optimize the management of the environmental sample during the storage and analysis phases.

2. The full-cycle management method for an environmental sample according to claim 1, wherein The S1 specifically includes: Verify the collection parameters and set the existence flag of the collection parameters; based on the existence flag of the collection parameters, calculate the integrity score of the collection parameters through multiplication operations to verify the integrity of the collection parameters.

3. The full-cycle management method for an environmental sample according to claim 2, characterized in that, The S1 specifically includes: When the integrity verification fails, generate an environmental sample warning notice, display the specific missing collection parameters, take remedial measures according to the environmental sample warning notice, upload the supplemented collection parameters after review, and record the supplementation process.

4. The full-cycle management method for an environmental sample according to claim 1, wherein The S2 specifically includes: The dynamic degradation rate is obtained by introducing a correction factor and combining it with a preset reference degradation rate.

5. The full-cycle management method for an environmental sample according to claim 4, wherein The S2 specifically includes: The correction factor is obtained by introducing an environmental impact weight and combining it with a storage environment deviation factor.

6. The full-cycle management method for an environmental sample according to claim 5, characterized in that, The S2 specifically includes: Quantify the deviation between the actual storage environment parameters and the preset ideal storage environment parameters, and perform weighted normalization on the deviation to obtain the storage environment deviation factor.

7. The full-cycle management method for an environmental sample according to claim 6, characterized in that, The S2 specifically includes: Based on the degradation risk prediction value of the environmental sample, introduce the analysis task priority of the environmental sample to generate the dynamic scheduling index of the environmental sample; sort the environmental samples according to the dynamic scheduling index of the environmental sample to optimize the resource management during the storage and analysis phases.

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