Information acquisition and tracing method of ozone-consuming substance gas sample

Abstract

The invention discloses an ozone depleting substance gas sample information acquisition and tracing method. Belongs to the technical field of environment monitoring, and realizes information acquisition, abnormity prevention and control and intelligent tracing in the whole atmospheric ODS sampling process through automatic sampling plan pushing, sampling process real-time error-proofing verification, key operation image evidence, information encryption, packaging and uploading and LIMS (laboratory information management) seamless connection. All sampling information, operation logs and evidence information are automatically archived, and efficient retrieval and backtracking based on multi-dimensional conditions such as sample numbers, time and positions are supported. According to the invention, the accuracy, integrity and traceability of sample collection information are obviously improved, the informatization and intelligence level of environment monitoring management can be effectively improved, and the method is suitable for atmosphere ODS sampling work in environment monitoring and related fields.

Description

Technical Field

[0001] This invention belongs to the field of environmental monitoring technology, and more specifically relates to a method for collecting and tracing information on ozone-depleting gas samples. Background Technology

[0002] Ozone-depleting substances (ODS) are mostly synthetic halogenated compounds and are the core contributors to stratospheric ozone depletion. They are also the primary targets of the Montreal Protocol on Substances that Deplete the Ozone Layer. Furthermore, most ODS also have a significant greenhouse effect, exacerbating global warming. Therefore, the management of ODS is crucial not only for ozone layer protection but also for addressing climate change, making it a key element in the coordinated advancement of both ozone layer protection and climate change response.

[0003] Monitoring ozone-depleting substances (ODS) in the atmosphere is a crucial means and core approach to supporting the scientific management of ODS. Sample collection is a fundamental and core step in environmental monitoring, directly determining the accuracy, representativeness, and reliability of the monitoring data. To ensure sample representativeness, a constant-current integration method is commonly used to collect atmospheric ODS samples. During sampling, a series of relevant information must be recorded in detail according to the needs of sample testing and data analysis, such as the sampling container serial number, sampling start / end time, sampling latitude and longitude, sampling altitude, sampling container pressure, and meteorological parameters (wind direction, wind speed, humidity, air pressure, and temperature, etc.).

[0004] Currently, information recording during atmospheric ODS sample collection still relies on paper labels and forms for manual recording. This method suffers from problems such as error-proneness and inefficiency (manual entry is prone to errors and omissions, and cannot be automated), poor traceability (inability to share information in a timely manner, and difficulty in restoring lost or unclear records), and data silos (making it difficult to extract necessary information for subsequent analysis). Furthermore, given the numerous and complex steps involved in the sampling process, manual sampling is prone to errors in operation or omissions of important steps, such as forgetting to open or close the sampling tank valve at the start or end of sampling, leading to sample invalidation and affecting the overall effectiveness of the sampling. Summary of the Invention

[0005] This invention establishes a method for collecting and tracing information on atmospheric samples containing ozone-depleting substances (ODS). Through multiple means such as intelligent reminders, error-proof verification, and image evidence, it realizes information collection, anomaly prevention and intelligent tracing throughout the entire ODS sampling process, which can significantly improve the accuracy, completeness and traceability of sample collection information.

[0006] To achieve the above objectives, the present invention employs the following technical solution: the method comprises: Before on-site sampling, the sampling plan and task prompts are automatically pushed out, and operators receive task and information guidance through mobile terminals or on-site equipment; During on-site sampling, real-time error prevention verification and evidence verification are triggered. The form's built-in logic verification engine automatically verifies parameter ranges, required fields, etc. When an anomaly is detected, a pop-up warning is displayed and data submission is blocked. For critical operations such as reading tank pressure at the end of sampling and opening / closing tank valves at the start / end of sampling, automatic prompts are given, requiring photos or videos to be provided as evidence, and the current spatiotemporal coordinate information is automatically bound. After on-site sampling, the system communicates with the Laboratory Information Management System (LIMS) via API interface. The sampling information, including form data, environmental parameters, and supporting documents, is automatically packaged and encrypted on the local device and transmitted to the server, then pushed to the LIMS backend. It supports quick search and traceability queries based on keywords. The database stores all sampling-related operation logs, including time, location, operator, verification results, and supporting documents. Users can retrieve complete historical sampling information by defining query conditions.

[0007] In one approach, the real-time error prevention verification is implemented by integrating a logic verification engine into the form. This engine can automatically determine the range and reasonableness of all sampled parameters. Only after all verification items pass can the operator proceed to the next step. In case of abnormal parameters or missing required fields, timely prompts and warnings are given to prevent erroneous information from flowing into subsequent stages, thereby improving the reliability of the sampled information.

[0008] In one approach, key operational nodes require sampling personnel to take photos or record videos using mobile terminals. At the same time, the system automatically acquires and binds the corresponding GPS location information and accurate timestamps. All images and supporting materials correspond one-to-one with the sampling form information, ensuring that each key step is supported by visual evidence and enabling full traceability of sampling information throughout the entire process.

[0009] In one approach, after sampling is completed, all collected information (including form data, environmental parameters, image evidence, spatiotemporal tags, etc.) is automatically packaged locally. Then, an encryption algorithm is used to protect data security. The data is connected and pushed to the Laboratory Information Management System (LIMS) via an API interface. After receiving the data, the backend server automatically archives it and associates it with the unique sample number, providing a complete dataset for laboratory testing and data analysis.

[0010] In one solution, all log information is stored in a database. The log content includes detailed information for each operation, including operation time, geographical location, operator identity, verification results, and relevant supporting materials. Users can efficiently search using keywords such as sample ID, time range, and geographical scope, and can quickly locate a specific historical sample and all related information, which can greatly improve data management efficiency.

[0011] In one approach, the traceability query incorporates a Dynamic Trace Path Optimization (DTO) algorithm. Based on user-defined keywords or filtering conditions, it dynamically filters all relevant logs and sampling information in the database, and visually displays the entire information flow and abnormal nodes from the start to the end of sampling, achieving second-level source tracing and anomaly localization. The beneficial effects of this invention are: This invention constructs a complete digital closed-loop management method for ODS atmospheric sampling. Through integrated closed-loop control of sampling plan delivery, task execution, real-time error prevention and verification, key operation verification, encrypted data upload, archiving, and efficient traceability, it effectively avoids problems such as operational oversights, data loss, information silos, and traceability difficulties during the sampling process. It enhances the standardization and transparency of the sampling process, ensuring the authenticity and integrity of the sampling information. Simultaneously, the adaptive priority-optimized traceability algorithm enables second-level retrieval and anomaly tracing in large-scale data environments, significantly improving the efficiency of data retrieval and tracking. This effectively promotes the informatization and intelligentization of ODS atmospheric sampling, possessing significant application value and promising prospects for widespread adoption. Attached Figure Description

[0012] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram of the specific method of the present invention. Detailed Implementation

[0013] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the present invention more thorough and complete.

[0014] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. To facilitate understanding, the invention will now be described more fully with reference to the accompanying drawings, which illustrate typical embodiments of the invention. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the invention more thorough and complete.

[0015] like Figure 1 and Figure 2 As shown, a method for collecting and tracing information on ozone-depleting gas samples includes the following specific steps: Step 1: Initial setup and electronic form creation. Before sampling begins, create and configure the electronic form.

[0016] The form template can predefine some sampling parameters (such as sampling purpose, type, etc.) and embed logical rules (such as parameter range restrictions, mandatory locking of required fields). Operators can log in through various terminal devices (such as tablets or smartphones), the form automatically loads the preset template, and administrators can flexibly adjust field rules to adapt to different sampling tasks.

[0017] First, the electronic form needs to be created and configured. During implementation, administrators will pre-set and design form templates based on different sampling needs through electronic form management. In the templates, some sampling parameters, such as sampling purpose, type, and sampling object, are pre-defined as fields, with detailed parameter requirements set for each. For example, for the sampling type field, its selection range can be limited, allowing only specified options to be filled in or selected. Simultaneously, logical rules are embedded for some key parameters, such as setting upper and lower limits for input values, specifying required fields and forcibly locking them, ensuring that operators cannot skip these necessary information during subsequent data entry. After the form template design is completed, it is stored and published to mobile or web platforms, ensuring that sampling personnel can easily access it through various terminal devices. Before on-site sampling, operators automatically load the corresponding form template after logging in, avoiding manual searching and improving sampling preparation efficiency. To adapt to changes in requirements such as differences in different on-site sampling environments, administrators can also flexibly adjust field content and validation rules at any time through the backend interface. For example, when the sampling task changes or new parameters need to be added, the administrator can update the form template in real time and immediately synchronize it to all terminal devices, ensuring that the form always remains consistent with the latest task requirements. This process reduces human error and data omissions caused by inaccurate information filling from the source, further ensuring the efficiency and standardization of the sampling work.

[0018] Step 2: Sampling Initiation and Automatic Data Capture. After the sampling personnel begin on-site sampling, core sampling information can be automatically captured.

[0019] The device automatically records spatiotemporal information such as sampling time and latitude / longitude coordinates through its sensors (such as GPS positioning modules). Simultaneously, it supports wireless communication (such as Bluetooth or Wi-Fi) with external devices like on-site meteorological parameter measuring instruments to read environmental data such as temperature, humidity, and wind speed in real time and automatically fill them into electronic forms. This improves on-site sampling efficiency, ensures the accuracy of sampling information, and provides a reliable data foundation for subsequent analysis.

[0020] When the sampling personnel arrive at the designated sampling site and initiate the sampling process, various sensors or measuring instruments built into or externally connected to the device are automatically called. First, the GPS positioning module is used to obtain the longitude and latitude coordinates of the current location in real time, and the exact sampling time at the site is automatically recorded. All these key spatio-temporal information is strictly bound to the sample form to be collected, without manual input, effectively preventing omissions and errors caused by manual entry.

[0021] Meanwhile, real-time interconnection is achieved with external environmental parameter detection devices such as meteorological parameter measuring instruments equipped at the site through wireless communication. Through this process, environmental parameters such as temperature, humidity, and wind speed are continuously or periodically collected and synchronously written into the corresponding fields of the relevant form. The entire data capture process is highly automated. The sampling personnel only need to complete simple operations according to the prompts to automatically obtain and archive important sampling information, greatly improving the on-site sampling efficiency and the accuracy of information acquisition. Through automatic information capture, a solid data foundation is provided for subsequent data processing and result analysis, and at the same time, human errors caused by information omission or input errors are significantly reduced.

[0022] Step 3: Real-time error prevention verification and corroboration operations. During the sampling process, a real-time error prevention mechanism is triggered to intercept anomalies through active verification to ensure the quality of information collection.

[0023] The form is built with a logic verification engine (such as parameter range checking and mandatory item verification). When an anomaly is detected (such as exceeding a reasonable range), a warning is popped up and data submission is blocked; key operations (such as reading the sampling tank pressure and opening / closing the tank valve at the start / end of sampling) require taking photos or recording videos for corroboration, and the current spatio-temporal coordinates (such as GPS location and time) are automatically bound. The operator must complete all verification items before proceeding to the next step. Error data is intercepted in real time to ensure the authenticity and accuracy of information; the photos / videos for corroboration provide visual evidence for subsequent anomaly analysis.

[0024] During the actual sampling process, the real-time error prevention verification mechanism is continuously started and executed to ensure the standardization and accuracy of data. Whenever the sampling personnel enter new data in the electronic form, the logic verification engine embedded in the form automatically checks various parameters. This verification process usually proceeds based on preset parameter ranges, mandatory item settings, and logical relationships. For example, for parameters such as sampling environment temperature (set as T), humidity (H), and wind speed (V), reasonable data intervals are set according to the required data units: When the actual input value meets the above intervals, it is determined to be qualified; otherwise, a warning prompt is automatically popped up and the submission of form data is blocked until the data is adjusted to be compliant. In addition, for records such as sampling tank pressure ( Key points such as valve switching operations require not only the input of corresponding numerical values ​​(e.g., ...) Sampling personnel also need to use terminal devices to take photos or record short videos on site.

[0025] All image data is automatically annotated with the current GPS location information (represented by L) and a timestamp (represented by t), achieving a strong binding between supporting documents and sampling operations. Taking this process as an example, the supporting data for sampling a key operation can be described as a tuple (...). (I) represents the image file, L represents the latitude and longitude coordinates, and t represents the operation time. This set of data corresponds one-to-one with the form entries. The sampling process is only allowed to proceed to the next step after all verification items have passed and all supporting materials for the key operations have been collected.

[0026] Through the aforementioned real-time error prevention verification and visual evidence mechanism, inaccurate information caused by improper filling or omissions in the process can be effectively prevented. At the same time, a traceable and verifiable chain of evidence for all sampling operations is formed, providing scientific and rigorous technical support for subsequent data analysis and quality verification.

[0027] Step 4: Automatic data integration and communication. After sampling is completed, all information is automatically integrated and pushed.

[0028] Communicating with LIMS (Laboratory Information Management) via API, sampling information (including form data, environmental parameters, and supporting documents) is automatically packaged on the local device and then encrypted before being transmitted to the server. In case of data anomalies, sampling-related information can be quickly retrieved by ID. After sampling is completed, the system automatically enters the data integration and communication phase, comprehensively integrating all data generated during the sampling process. Through these functions, the digital management of atmospheric ODS sampling information is promoted, providing a complete dataset for laboratory analysis. First, mobile terminals or field devices intelligently categorize and package various types of information, including data entered in electronic forms, automatically captured environmental parameters, and supporting documents such as photos or videos generated throughout the process. In the background, sampling items are identified and indexed using a unified data structure, and unique sampling IDs (e.g., Sample_ID) are assigned for easy retrieval later. After packaging, the information package is encrypted using a built-in data encryption algorithm to ensure the security and confidentiality of information transmission. Subsequently, seamless integration with Laboratory Information Management (LIMS) is achieved through an API interface, automatically pushing all collected data to the server. The server-side LIMS platform automatically archives the received data based on the sampling ID and establishes information connectivity with laboratory analysis and other processes.

[0029] The entire transmission process requires no manual intervention, greatly improving data flow efficiency and significantly reducing the risk of sampled information loss or data mismatch. Furthermore, when subsequent anomaly analysis or result verification is needed, simply entering the sample ID allows for rapid retrieval and display of complete sampling information, including original form content, spatiotemporal information, environmental parameters, and full-process imagery. This automated integration and communication mechanism promotes the digital and systematic management of atmospheric ODS sampling information and provides a solid and reliable technical guarantee for laboratory analysis and quality assurance.

[0030] Step 5: Data archiving and traceability support. The database stores all operation logs (including time, location, operator, verification results, and supporting documents), supporting traceability queries and quick keyword retrieval.

[0031] Users can input query criteria (such as sample ID or time range) to retrieve complete historical sampling information, which efficiently supports sampling information query and anomaly analysis, and promotes the digitalization of sampling information management.

[0032] After sampling, the data archiving and traceability support phase begins. All sampling-related information and operation logs are automatically archived into a dedicated database. Each sampling operation, including form completion, parameter collection, error-proofing verification, and uploading of supporting materials, generates detailed operation logs in real time. Each log entry is stored as a five-tuple (Log_ID, OP, T, L, R), where Log_ID is the unique log identifier, OP is the operator number, T is the operation timestamp, L is the GPS location at the time of sampling, and R is the index of the verification result and associated supporting documents. Furthermore, all sampling information and logs are bound to the sample ID (Sample_ID) via a hash chain index, ensuring data immutability and full traceability.

[0033] A Dynamic Trace Optimization (DTO) algorithm is introduced. Based on user-input keywords (such as sample ID S, time interval [t1, t2], or geographical range [L1, L2]), this algorithm first generates a multi-dimensional filtering function F(S, [t1, t2], [L1, L2]) to initially screen the vast archived logs and sampling information, forming a set N containing all operation nodes that meet the conditions. To further improve retrieval speed and the intuitiveness of traceability, the DTO algorithm maps the node set N to a weighted directed graph G=(V,E,W), where node V corresponds to a specific operation or data point, edge E represents the temporal and spatial sequence of log events, and weight W represents the time interval of the events (…). ), spatial distance ( ), Operational Level ( ) and the degree of abnormality (β), calculated using the following formula: ,in , These are dynamically adjusted weighting parameters used to adapt to the time or space sensitivity requirements of different scenarios.

[0034] During a traceback query, the DTO algorithm automatically starts from the initial node associated with the user's search criteria and traverses the operation chain backwards along the most weighted traceback path, prioritizing the display of nodes associated with anomalies, warnings, or insufficient evidence. The traceback path is returned in real time after the user enters their search criteria. This enables all relevant sampling steps to be presented efficiently according to causal and anomaly priorities. Through this innovative dynamic optimization tracing algorithm, not only can massive amounts of historical sampling information be retrieved in seconds, but key anomalies and operational details can also be intuitively identified, greatly enhancing the intelligence of sampling information management and data reliability, providing solid data support for subsequent data analysis and quality control.

[0035] The implementation steps of the DT0 algorithm are as follows: S501: Receive user-input search criteria, including parameters such as sample number, time interval, and geographical range. Based on these inputs, dynamically generate a multi-dimensional filtering function and apply it to the underlying archived logs and sampling information database. Through spatial and inverted index mechanisms, efficiently filter massive amounts of data to obtain a set of operation nodes that meet the preliminary criteria.

[0036] S502. Based on these operation nodes, a directed graph structure is constructed according to the time sequence and operational logic in the actual business process. Each node represents a specific sampling, transfer, or related operation event, and nodes are connected by directed edges according to causal or process sequence. Furthermore, a weight is assigned to each edge, with the weight value comprehensively considering four indicators: time interval between events, spatial distance, operation level, and anomaly degree. The time interval is measured by a standardized function to measure the sequential closeness between events; spatial distance quantifies the geographical migration range of samples or personnel; the operation level is set according to the importance of the process; and the anomaly degree is automatically assessed based on historical rules or intelligent models. The weight coefficients of each parameter can be dynamically adjusted according to the actual scenario to meet different traceability needs.

[0037] S503. Starting from the node corresponding to the user's search criteria, an optimal path algorithm (such as Dijkstra's algorithm or A*) is used to perform a reverse traversal on the directed weighted graph. The algorithm automatically finds the tracing path with the highest weight, the strongest risk indication, or the highest anomaly cluster. During the traversal, priority is given to path nodes with high anomaly scores, high operation levels, and strong temporal and spatial information consistency, ensuring that the tracing chain covers the entire operation process while prioritizing the display of key risks and anomalies.

[0038] After obtaining the optimal tracing path, the algorithm organizes and visualizes it in causal order, marking all key events with a timeline and operation flowchart, and highlighting nodes with anomalies, warnings, or insufficient evidence. Users can quickly view the context of each operation detail and accurately locate the problematic step. The entire process has adaptive parameter capabilities, allowing administrators to adjust weight parameters in real time based on changes in the sampling site, scenario requirements, or historical tracing results, dynamically optimizing the algorithm's response performance.

[0039] Example: The user enters "Sample ID=2014A12, Time: 2024-05-01 to 2024-05-07, Geographic range: latitude and longitude range"; The system automatically selects the complete set of relevant sampling, flow, and detection (node ​​set N); Construct a directed graph G and assign weights to each edge (time interval, spatial distance, operation level, anomaly degree). The DTO performs a reverse traversal with optimal weights to automatically identify and visualize the path chain with the most concentrated anomaly alerts. Users can clearly see the causal chain and key abnormal nodes of the entire sampling process, which helps with subsequent intervention and quality control.

[0040] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program, which can be stored in a computer-readable storage medium. When executed, the program can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0041] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or make equivalent substitutions for some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for collecting and tracing information on ozone-depleting gas samples, characterized in that: The method includes: Before on-site sampling, the sampling plan and task prompts are automatically pushed out, and operators receive task and information guidance through mobile terminals or on-site equipment; During on-site sampling, the real-time error prevention verification and evidence operation function is triggered. The form's built-in logic verification engine automatically verifies the parameter range and required fields. When an anomaly is detected, a pop-up warning is displayed and data submission is blocked. For critical operations such as opening and closing tank valves and recording tank pressure at the start / end of sampling, an automatic pop-up reminder is displayed. After confirming the operation, the subsequent steps are entered. Sampling personnel are required to take photos or record videos as evidence and the current spatiotemporal coordinate information is automatically bound. After on-site sampling, the system communicates with the Laboratory Information Management System (LIMS) via API interface. It automatically packages and encrypts information including form data, environmental parameters, and supporting documents on the local device and transmits it to the server, which then pushes it to the LIMS backend. It supports quick search and traceability queries based on keywords. The database stores all sampling-related operation logs, including time, location, operator, verification results, and supporting documents. Users can retrieve complete historical sampling information by defining query conditions.

2. The method for collecting and tracing information on ozone-depleting gas samples according to claim 1, characterized in that: The real-time error prevention verification is implemented by integrating a logic verification engine into the form. It automatically performs range and reasonableness judgment on all sampling parameters. Only after all verification items pass can the operator proceed to the next step. In case of abnormal parameters or missing required fields, clear warnings are given in a timely manner to prevent erroneous information from flowing into subsequent stages and improve the accuracy of sampling information entry and collection.

3. The method for collecting and tracing information on ozone-depleting gas samples according to claim 1, characterized in that: Key operational nodes require sampling personnel to take photos or record videos using mobile terminals to automatically obtain and bind the GPS location information and accurate timestamp at the time of the operation. All images and supporting materials correspond one-to-one with the information in the sampling form, ensuring that each key step is supported by visual evidence and achieving full traceability of data throughout the entire process. Automatic pop-up prompts are set at key operation nodes, requiring the completion and confirmation of specified actions before proceeding to the next step. This mandatory verification mechanism ensures that key operations are carried out effectively, providing a guarantee for the validity of sampling from the process level.

4. The method for collecting and tracing information on ozone-depleting gas samples according to claim 1, characterized in that: After sampling is completed, all sampling information, including form data, environmental parameters, image evidence, and spatiotemporal labels, is automatically packaged locally. Then, an encryption algorithm is used to protect data security. The data is connected and pushed to the Laboratory Information Management System (LIMS) via API interface. After receiving the data, the backend server automatically archives it and associates it with the unique sample number, providing a complete dataset for laboratory testing and subsequent data analysis.

5. The method for collecting and tracing information on ozone-depleting gas samples according to claim 1, characterized in that: All log information is stored in the database. The log content contains detailed information for each operation, including: operation time, geographical location, operator identity, verification results, and relevant supporting materials. Users can quickly locate a historical sample and all related information by searching by sample ID, time range, and geographical range keywords, which can greatly improve data management efficiency.

6. The method for collecting and tracing information on ozone-depleting gas samples according to claim 1, characterized in that: The aforementioned traceability query introduces the Dynamic Traceability Path Optimization (DTO) algorithm; based on user-defined keywords or filtering conditions, it dynamically filters all relevant logs and sampling information in the database, and visually displays the entire information flow and abnormal nodes from the start to the end of sampling, thereby achieving source tracing and anomaly location.

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