A river, lake and reservoir water quality collaborative data management method and system based on field activities

Abstract

A collaborative data management method and system for river, lake, and reservoir water quality based on field activities optimizes the entire process from sample collection to data analysis through modules for demand matching information management, compliance information management, test data management, and data review and storage. This improves the efficiency and accessibility of field observations, lowers the professional threshold, and enhances collaboration and participation among all participants. The demand matching information management module facilitates effective connection between researchers and field participants; the compliance information management module ensures the professional compliance of equipment, standards, and personnel; the test data management module standardizes the collection and uploading of field and laboratory data; and the data review and storage module strengthens the scientific review, quality control, and evaluation of data. This invention improves the efficiency and data quality of scientific research activities and provides strong technical support and data management solutions for field water quality monitoring and other activities through a systematic approach.

Description

Technical Field

[0001] This invention relates to the field of water quality monitoring technology, and in particular to a multi-role collaborative environmental monitoring method, specifically a collaborative data management method and system for river, lake and reservoir water quality based on field activities. Background Technology

[0002] With increasing public awareness of water resource protection, the demand for monitoring water quality in rivers, lakes, and reservoirs is becoming increasingly urgent, requiring more comprehensive and real-time data to understand water quality conditions and trends. Due to the unique nature of field activities, water quality monitoring in the field faces numerous challenges, especially in scientific research. Field observation activities, particularly water quality monitoring, often involve multiple roles and complex operational procedures. Traditional scientific research activities typically rely on researchers to independently complete all work, including professional activities (such as data analysis and test standard development) and non-professional activities (such as sample collection and on-site measurement). However, this approach has the following significant limitations:

[0003] 1. Low efficiency of field observation:

[0004] When researchers conduct field observations of rivers, lakes, and reservoirs, insufficient understanding of the destination often leads to low efficiency in water quality observation, sample collection, and other operations, resulting in low work efficiency or waste of resources.

[0005] 2. Lack of professionalism among outdoor enthusiasts:

[0006] Current technologies often lack sufficient support for non-professional activities. Field participants, lacking the necessary expertise and skills, may be unable to effectively participate in research activities or complete related tasks. This not only affects the efficiency and quality of research activities but may also threaten the safety and health of field participants.

[0007] 3. Difficulty in managing research data:

[0008] Inadequate data management is another drawback of existing technologies. In scientific research, the collection, processing, review, and storage of data often lack unified standards and processes, resulting in inconsistent data quality. Furthermore, data security and privacy protection are also at risk, potentially leading to data leaks or misuse.

[0009] 4. Uneven participation between researchers and field participants:

[0010] In traditional scientific research activities, the division of roles is often not clearly defined. Researchers often need to handle both specialized and non-specialized activities simultaneously, which not only increases their workload but may also affect the quality and efficiency of specialized activities. At the same time, field participants often lack the specialized knowledge to directly participate in specialized activities, leading to a waste of resources.

[0011] 5. Unclear division of roles:

[0012] In traditional scientific research activities, the division of roles is often not clearly defined. Researchers often need to handle both specialized and non-specialized activities simultaneously, which not only increases their workload but may also affect the quality and efficiency of specialized activities. At the same time, field participants often lack the specialized knowledge to directly participate in specialized activities, leading to a waste of resources.

[0013] 6. Inconsistent technical standards:

[0014] The lack of unified technical standards is a common problem in scientific research. Different research teams or institutions may adopt different technical methods and standards, leading to data inconsistencies and difficulties in data sharing. This not only increases the difficulty of data management but may also affect the credibility and comparability of research results.

[0015] 7. Inadequate data management:

[0016] Inadequate data management is another drawback of existing technologies. In scientific research, the collection, processing, review, and storage of data often lack unified standards and processes, resulting in inconsistent data quality. Furthermore, data security and privacy protection are also at risk, potentially leading to data leaks or misuse.

[0017] 8. Lack of an effective coordination mechanism:

[0018] In research activities involving multiple roles and teams, the lack of effective coordination mechanisms is also a problem. Communication barriers or information asymmetry may exist between different roles, leading to low work efficiency or wasted resources. In addition, the lack of unified command and scheduling may also result in the inability to effectively control the work progress.

[0019] 9. Insufficient support for non-professional activities:

[0020] Current technologies often lack sufficient support for non-professional activities. Field participants, lacking the necessary expertise and skills, may be unable to effectively participate in research activities or complete related tasks. This not only affects the efficiency and quality of research activities but may also threaten the safety and health of field participants.

[0021] 10. Slow pace of technological updates:

[0022] With the continuous advancement of science and technology, new technologies and methods are constantly emerging. However, existing technologies often have a slow update cycle, making it difficult to apply new technologies in a timely manner to improve the efficiency and quality of scientific research activities. This may not only cause research teams to fall behind in competition, but also affect the practicality and innovativeness of research results.

[0023] In summary, existing technologies have significant shortcomings in areas such as role division, technical standards, data management, coordination mechanisms, support for non-professional activities, and the speed of technology updates. To overcome these shortcomings and improve the efficiency and quality of scientific research activities, it is necessary to continuously explore new technical methods and solutions. Summary of the Invention

[0024] To address the aforementioned problems, this invention proposes a collaborative data management system for river, lake, and reservoir water quality based on field activities. This system integrates the technical advantages of researchers with the local advantages of familiarity with terrain among field participants, thereby increasing the efficiency of research activities.

[0025] A collaborative data management method for river, lake, and reservoir water quality based on field activities is proposed. Based on the different roles of the participants in the monitoring activities, the methods are divided into three categories: researchers, field participants, and data managers. Water quality monitoring activities are also divided into professional activities and non-professional activities.

[0026] In a computer system, perform the following steps:

[0027] 1) Demand matching information management is carried out through the demand matching information management module; it allows data managers to assist researchers in defining and recording the needs of professional and non-professional activities in water quality monitoring, allows researchers to determine the detailed needs of non-professional activities, and allows field participants to provide relevant field activity information for demand matching.

[0028] 2) Compliance information is managed through the compliance information management module, which allows researchers and field participants to jointly verify the compliance of sampling equipment, testing standards, and personnel training.

[0029] 3) Test data is managed through the test data management module; it allows field participants to upload on-site measurement data and sample preservation and transportation information, and allows researchers to upload laboratory test data, test standards and methods, and laboratory instrument and equipment information.

[0030] 4) Construct a data review and storage module to conduct data review and storage management; it allows data managers to conduct scientific compliance and consistency reviews of collected data, integrate, clean and store the reviewed data, and evaluate and classify the data.

[0031] Furthermore, the demand matching information management includes a data manager assisting researchers in sorting out professional and non-professional activities within their research activities. Researchers fill in the professional and non-professional activities in the system according to module prompts, and subsequent matching work is carried out for non-professional activities. First, researchers divide the non-professional activities in water quality monitoring into sampling location, sampling time, on-site measurement, and sample collection demand information reporting stages, and generate reports. Then, field participants fill in the field activity location, field activity time, field sampling intention, and field carrying space information according to the demand information submitted by researchers, and submit it to the system. Next, the information management module distributes the forms provided by field participants to researchers for comparison. After confirmation by researchers, the demand matching information management module is completed.

[0032] Furthermore, the compliance information management module includes an equipment compliance unit, a standard compliance unit, and a personnel compliance unit. After completing the first step of the requirements matching information management module, this compliance information management module manages the compliance information for equipment, standards, and personnel. First, after both parties confirm the requirements, the researchers and field participants confirm the sampling equipment. The research manager submits the requirements for on-site sampling equipment and uploads the information to the on-site sampling equipment management system in the equipment compliance unit. At the same time, the research manager submits the requirements for on-site sample preservation and transportation. After confirmation by the sampling participants, the equipment compliance unit is completed, and the process moves to the standard compliance unit. Next, the researchers send relevant documents on on-site field testing standards and sample preservation standards to the field participants. After the field participants submit proof of their training and the researchers confirm it, the process moves to the personnel compliance unit of this module. Finally, the researchers conduct online training and examinations. Once the field participants' assessments reach a certain level, they are considered to be in compliance.

[0033] Furthermore, the test data management module includes a field data management unit and a laboratory data management unit. The field data management unit is maintained by field participants, and the laboratory data management unit is maintained by researchers. First, field participants input and upload relevant information through the auxiliary information, field measurement information, and saved transportation information in the field data management unit. Then, researchers input and upload relevant information through the laboratory testing standards and methods, laboratory instrument and equipment information, and laboratory data management information in the laboratory data management unit.

[0034] Furthermore, the data review and storage module includes a data review unit, a data storage unit, and a data evaluation unit, maintained by the data manager. First, the data manager reviews the scientific compliance and data consistency of the data obtained from the monitoring activities based on the prompts from the data review unit, i.e., whether the sample collection and data production process conforms to scientific standards and whether the data meets relevant data verification requirements. Next, a data quality audit module is embedded in the data storage unit, setting relevant data quality requirements. After joint review by the data manager and researchers, the data storage unit is considered passed. Finally, in the data evaluation unit, the data is stored and retrieved in a hierarchical manner, and a preliminary evaluation of the target area is performed.

[0035] Furthermore, in the data evaluation unit, "sites" represents sampling points and "temperature" represents water temperature. The SQL query statement SELECT * FROM sites WHERE temperature>12; is used to find sampling points with a water temperature greater than 12 degrees Celsius. Similarly, the water temperature situation in the target area is analyzed.

[0036] This also includes calculating relevant water quality indices, the formula for which is:

[0037] ,

[0038] In the formula, WQI—water quality index, W i —The weight value of the pollutant, q i —Concentration of pollutants, n—Number of types of water quality parameters.

[0039] A collaborative data management system for river, lake, and reservoir water quality based on field activities includes the following modules:

[0040] The demand matching information management module is used for demand matching information management. It allows data managers to assist researchers in defining and recording the needs of professional and non-professional activities in water quality monitoring, allows researchers to determine the detailed needs of non-professional activities, and allows field participants to provide relevant field activity information for demand matching.

[0041] The compliance information management module is used for compliance information management; it allows researchers and field participants to jointly verify the compliance of sampling equipment, testing standards, and personnel training.

[0042] The test data management module is used for test data management; it allows field participants to upload field measurement data and sample preservation and transportation information, and allows researchers to upload laboratory test data, test standards and methods, and laboratory instrument and equipment information.

[0043] The data review and storage module is used for data review and storage management. It allows data managers to conduct scientific compliance and consistency reviews of collected data, integrate, clean and store the reviewed data, and evaluate and classify the data.

[0044] Furthermore:

[0045] The demand matching information management module includes a scientific research sampling demand unit and a field planning unit. The scientific research sampling demand unit sets and manages information such as sampling location, sampling time, on-site measurement, and sample collection demand. The field planning unit sets and manages information such as field activity location, field activity time, field sampling intention, and field carrying space information.

[0046] The compliance information management module includes an equipment compliance unit, a standard compliance unit, and a personnel compliance unit. The equipment compliance unit sets up and manages the requirements for on-site sampling equipment and the requirements for on-site sample preservation and transportation. The standard compliance unit sets up on-site field testing standards and related sample preservation standards. The personnel compliance unit sets up online training and examinations and assessment information for field participants.

[0047] The test data management module includes a field data management unit and a laboratory data management unit. The field data management unit sets up management auxiliary information, field measurement information, and saves transportation information. The laboratory data management unit sets up management of laboratory test standards and methods, laboratory instrument and equipment information, and laboratory data management information.

[0048] The data review and storage module includes a data review unit, a data storage unit, and a data evaluation unit. The data review unit manages the scientific compliance and consistency of the acquired data. The data storage unit manages the data system review and manual review information. The data evaluation unit manages the hierarchical storage and retrieval of data and provides preliminary evaluation information for different regions.

[0049] A computer program product includes a computer program that, when executed by a processor, implements the aforementioned collaborative data management method for river, lake, and reservoir water quality based on field activities.

[0050] This invention proposes a collaborative data management method and system for river, lake, and reservoir water quality based on field activities. Through an innovative modular system, it optimizes the entire process from sample collection to data analysis, improving the efficiency and accessibility of field observations, lowering the professional threshold, and enhancing collaboration and participation among all stakeholders. The demand matching information management module enables effective connection between researchers and field participants; the compliance information management module ensures the professional compliance of equipment, standards, and personnel; the test data management module standardizes the collection and uploading of field and laboratory data; and the data review and storage module strengthens the scientific review, quality control, and evaluation of data. Overall, this invention not only improves the efficiency and data quality of scientific research activities but also provides strong technical support and data management solutions for the development of the scientific research field through a systematic approach.

[0051] Compared with existing technologies, this invention solves the following problems by breaking down scientific research activities into professional and non-professional activities and providing corresponding tools and support for both; and by providing a comprehensive data management system to help researchers manage, analyze and share data more conveniently, thereby improving the efficiency and quality of scientific research activities.

[0052] 1. The problem of low efficiency in field observation:

[0053] When conducting field observations of rivers, lakes, and reservoirs, researchers often experience low efficiency in water quality monitoring and sample collection due to insufficient understanding of the destination. This invention aims to provide an efficient data management system to help researchers complete various tasks more quickly and accurately during field observations.

[0054] 2. Insufficient efficiency in the sample collection process:

[0055] Traditional sample collection methods can be limited by various factors, such as geographical location, environmental conditions, and personnel skills, leading to low collection efficiency. This invention optimizes the sample collection process, improves collection efficiency, and ensures that the collected samples are representative and accurate.

[0056] 3. Low accessibility for field observation:

[0057] When conducting field observations in remote or hard-to-reach locations, researchers may face accessibility issues. This invention improves the accessibility of field observations by introducing advanced data management and communication technologies, enabling researchers to more easily access and monitor target areas.

[0058] 4. Lack of professionalism among outdoor enthusiasts:

[0059] Field participants may lack the necessary scientific knowledge and skills, leading to inefficiency or inaccurate task completion when participating in scientific research. This invention aims to lower the professional barrier by providing a simple and easy-to-use interface and tools, enabling non-professional field participants to effectively participate in scientific research activities.

[0060] 5. Difficulty in managing research data:

[0061] In scientific research, a large amount of data needs to be collected, organized, and analyzed. Traditional methods can be inefficient and error-prone. This invention provides a comprehensive data management system to help researchers manage, analyze, and share data more conveniently, thereby improving the efficiency and quality of scientific research activities.

[0062] 6. Uneven participation between researchers and field participants:

[0063] In scientific research activities, the participation of researchers and field participants may be uneven. This invention aims to increase the participation of both groups by breaking down scientific research activities into professional and non-professional activities and providing corresponding tools and support for both, thereby leveraging the strengths of all parties to jointly promote the progress of scientific research.

[0064] In summary, this invention addresses the problems of low efficiency in field observation, insufficient efficiency in sample collection, limited accessibility to field observation, lack of professionalism among field personnel, difficulties in data management for scientific research activities, and uneven participation between researchers and field personnel. By providing an efficient data management system and corresponding support tools, this invention will significantly improve the efficiency and quality of scientific research activities. Attached Figure Description

[0065] Figure 1 This is a schematic diagram of the demand matching information management module in an embodiment of the present invention.

[0066] Figure 2 This is a schematic diagram of the compliance information management module in an embodiment of the present invention.

[0067] Figure 3 This is a schematic diagram of the test data management module in an embodiment of the present invention.

[0068] Figure 4 This is a schematic diagram of the data review and storage module in an embodiment of the present invention.

[0069] Figure 5 This is a schematic diagram of a collaborative data management system for river, lake, and reservoir water quality based on field activities, according to an embodiment of the present invention. Detailed Implementation

[0070] The present invention patent will be further described below with reference to the accompanying drawings:

[0071] See Figures 1 to 5 This invention discloses a collaborative data management method for river, lake, and reservoir water quality based on field activities. It categorizes participants in monitoring activities into three roles: researchers, field participants, and data managers. Water quality monitoring activities are further divided into two categories: irreplaceable, primarily intellectually demanding professional activities, and replaceable, primarily physically demanding non-professional activities. The invention aims to combine the technical advantages of researchers with the local advantages of familiarity with terrain among field participants to increase the efficiency of research activities. The following steps are performed in a computer system:

[0072] S1. Through the demand matching information management module, demand matching information management is carried out; it allows data managers to assist researchers in defining and recording the needs of professional and non-professional activities in water quality monitoring, allows researchers to determine the detailed needs of non-professional activities, and allows field participants to provide relevant field activity information for demand matching.

[0073] S2. Compliance information management is conducted through the compliance information management module, which allows researchers and field participants to jointly verify the compliance of sampling equipment, testing standards, and personnel training.

[0074] S3. Test data management is performed through the test data management module; it allows field participants to upload on-site measurement data and sample preservation and transportation information, and allows researchers to upload laboratory test data, test standards and methods, and laboratory instrument and equipment information.

[0075] S4. Construct a data review and storage module to conduct data review and storage management; it allows data managers to conduct scientific compliance and consistency reviews of collected data, integrate, clean and store the reviewed data, and evaluate and classify the data.

[0076] Through the coordinated operation of the four modules described above, this invention enables efficient collaboration and data management for activities such as field water quality monitoring, improving the efficiency and data quality of scientific research while ensuring data accuracy and reliability. Through these modular steps, this invention optimizes the data collection, review, and application processes, thereby enhancing the efficiency and quality of scientific research.

[0077] In some specific embodiments:

[0078] See Figure 1In step S1, the demand matching information management module assists data managers in sorting out the professional and non-professional activities of the research activities. Researchers fill in the professional and non-professional activities in the system according to the module prompts, and subsequent matching work is carried out for the non-professional activities. First, researchers divide the non-professional activities in water quality monitoring into demand information reporting stages such as sampling location, sampling time, on-site measurement, and sample collection, and generate reports. Then, field participants fill in information such as field activity location, field activity time, field sampling intention, and field carrying space according to the demand information submitted by researchers, and submit it to the system. Next, the information management module distributes the forms provided by field participants to researchers for comparison. After confirmation by researchers, the demand matching information management module is completed, and the process proceeds to step S2.

[0079] See Figure 2 In step S2, the conformity information management module consists of three units: equipment conformity unit, standard conformity unit, and personnel conformity unit. After completing the first step of the requirement matching information management module, this module manages information regarding the conformity of equipment, standards, and personnel. First, after both parties confirm the requirements, the researchers and field participants confirm the sampling equipment. The research manager submits the requirements for on-site sampling equipment and uploads the information to the on-site sampling equipment management system in the equipment conformity unit. Simultaneously, the research manager submits the requirements for on-site sample preservation and transportation, which are then confirmed by the sampling participants. The equipment compliance unit is completed, and the process moves to the standard compliance unit. Next, the researcher sends relevant documents on field testing standards and sample preservation standards to the field participant. After the field participant submits proof of completion and the researcher confirms it, the standard compliance unit is completed, and the participant moves to the personnel compliance unit of this module. Finally, the researcher conducts online training and examinations. Once the field participant's assessment reaches a certain level, the personnel compliance unit is considered complete. At this point, the equipment compliance unit, standard compliance unit, and personnel compliance unit are all completed, and the compliance information management module is considered complete, proceeding to step three (S3).

[0080] See Figure 3In step S3, the test data management module consists of two units: a field data management unit and a laboratory data management unit. The field data management unit is mainly maintained by field participants, while the laboratory data management unit is mainly maintained by researchers. Within this module, firstly, field participants input and upload information using the auxiliary information, field measurement information, and saved transportation information in the field data management unit of the test data management module of this invention. Next, researchers input and upload information using the laboratory testing standards and methods, laboratory instrument and equipment information, and laboratory data management information in the laboratory data management unit of the test data management module of this invention. At this point, the test data management module is complete, and the process proceeds to step S4.

[0081] See Figure 4 Step S4, the data review and storage module consists of three units: a data review unit, a data storage unit, and a data evaluation unit. All three units are maintained by the data manager. First, the data manager reviews the scientific compliance and consistency of the data obtained from the monitoring activities based on the prompts from the data review unit. This includes verifying whether the sample collection and data production processes conform to scientific standards and whether the data meets relevant data verification requirements. Passing the data review unit means the data has passed. Next, in the data storage unit, a data quality audit module is embedded in the system. Relevant data quality requirements are set, and after joint review by the data manager and researchers, passing the audit means the data has passed. Finally, in the data evaluation unit, the data manager develops relevant algorithms for hierarchical storage and retrieval of the data. Simultaneously, the data manager develops certain algorithms to conduct a preliminary evaluation of the region. At this point, the data review and storage module is completed, and the four-step process of this invention is finished.

[0082] In some specific embodiments, the data evaluation unit uses "sites" to represent sampling points and "temperature" to represent water temperature. The SQL query statement SELECT * FROM sites WHERE temperature>12; is used to find sampling points with a water temperature greater than 12 degrees Celsius, which can be used to analyze the water temperature of the area.

[0083] Its characteristic is that it can calculate relevant water quality indices, and the formula for calculating the water quality index is:

[0084] ,

[0085] In the formula, WQI—water quality index, W i —The weight value of the pollutant, q i —Concentration of pollutants, n—Number of types of water quality parameters; for example, when calculating the water quality index of pH, set W. i=1, pH1=8, pH2=7, the water quality index of pH is .

[0086] The advantages of this invention's technological innovation are:

[0087] 1. Efficient data management system: The core of this invention lies in its efficient data management system, which can optimize the data collection, processing and analysis process for researchers during field observations, and significantly improve work efficiency.

[0088] 2. Optimize the sample collection process: By introducing advanced data management and communication technologies, this invention can optimize the sample collection process, ensure that the collected samples are representative and accurate, and improve the quality of scientific research activities.

[0089] 3. Improve accessibility of field observation: For remote or hard-to-reach observation sites, this invention improves the accessibility of field observation through data management and communication technologies, enabling researchers to more easily access and monitor target areas.

[0090] 4. Lowering the professional threshold: This invention provides a simple and easy-to-use interface and tools, lowering the professional threshold for participating in scientific research activities, so that non-professional field participants can also effectively participate in scientific research activities.

[0091] 5. Enhance participation and collaboration: By breaking down scientific research activities into professional and non-professional activities and providing corresponding tools and support for both, this invention aims to enhance the participation of researchers and field participants, mobilize the strengths of all parties, and jointly promote the progress of scientific research activities.

[0092] To make the technical problems, technical solutions, and beneficial effects of the embodiments of the present invention clearer, the present invention will be further described in detail. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0093] See Figures 1 to 5 A collaborative data management system for river, lake, and reservoir water quality based on field activities includes the following modules:

[0094] The demand matching information management module is used for demand matching information management. It allows data managers to assist researchers in defining and recording the needs of both professional and non-professional water quality monitoring activities. It also allows researchers to determine the detailed needs of non-professional activities and allows field participants to provide relevant field activity information for demand matching. (See also...) Figure 1Specifically, it includes a scientific research sampling requirement unit and a field planning unit. The scientific research sampling requirement unit sets up and manages the sampling location, sampling time, on-site measurement, sample collection and other requirement information; the field planning unit sets up and manages the field activity location, field activity time, field sampling intention, and field carrying space information.

[0095] The compliance information management module is used for compliance information management; it allows researchers and field participants to jointly verify the compliance of sampling equipment, testing standards, and personnel training. See also... Figure 2 Specifically, it includes three units: equipment compliance unit, standard compliance unit, and personnel compliance unit. These units respectively set up requirements for managing on-site sampling equipment, requirements for on-site sample preservation and transportation, on-site field testing standards and related sample preservation standards, online training and examinations, and assessment information for field participants.

[0096] The test data management module is used for test data management; it allows field participants to upload on-site measurement data and sample preservation and transportation information, and allows researchers to upload laboratory test data, test standards and methods, and laboratory instrument and equipment information. (See also...) Figure 3 Specifically, it includes a field data management unit and a laboratory data management unit. The field data management unit sets up management auxiliary information, field measurement information, and saves transportation information. The laboratory data management unit sets up management laboratory testing standards and methods, laboratory instrument and equipment information, and laboratory data management information.

[0097] The data review and storage module is used for data review and storage management. It allows data managers to review the scientific compliance and consistency of collected data, integrate, clean, and store the reviewed data, and evaluate and classify the data. See also Figure 4 Specifically, it includes a data review unit, a data entry unit, and a data evaluation unit. The data review unit manages the scientific compliance and consistency of the acquired data. The data entry unit manages the data system review and manual review information. The data evaluation unit manages the hierarchical storage and retrieval of data and provides preliminary evaluation information for different regions.

[0098] Example

[0099] I. System Deployment and Initialization

[0100] 1. Hardware preparation: Deploy the necessary servers, storage devices, network equipment, etc., to ensure the system can run stably.

[0101] 2. Software Installation: Install the software for the Intelligent Scientific Research Collaboration and Data Management System, including the database, middleware, application server, etc.

[0102] 3. System Configuration: Configure various system parameters, such as user permissions, data format, and communication protocol, to ensure that the system can operate according to the predetermined rules.

[0103] 4. User Registration and Authorization: Register accounts for researchers, field participants, and data managers, and assign corresponding permissions and roles.

[0104] II. System Usage Flow

[0105] 1. Demand Matching Information Management Module

[0106] Researchers log in to the system and enter the demand matching information management module.

[0107] The system records the professional and non-professional activities of scientific research activities and generates a requirements report.

[0108] Outdoor enthusiasts log into the system, fill in the relevant information in the required report, and submit it.

[0109] The information management module compares the information provided by field participants with the needs of scientific researchers, and completes the matching of needs after confirmation.

[0110] 2. Compliance Information Management Module

[0111] After the needs matching is completed, researchers and field participants enter the compliance information management module.

[0112] Equipment Compliance Unit: Researchers propose on-site sampling equipment requirements, and field personnel confirm equipment compliance.

[0113] Standard compliance unit: Researchers send relevant test standards and save standard documents, field participants study and submit proof, and researchers confirm.

[0114] Personnel Compliance Unit: Researchers conduct online training and examinations for field participants, and those who pass the examination complete the personnel compliance unit.

[0115] 3. Test Data Management Module

[0116] Field participants enter the field data management unit, input and upload field data, such as auxiliary information, field measurement information, and saved transportation information.

[0117] Researchers can access the laboratory data management unit to input and upload laboratory data, such as testing standards and methods, instrument and equipment information, and laboratory data.

[0118] 4. Data review and entry module

[0119] Data managers enter the data review unit to review the scientific compliance and consistency of the data, ensuring its accuracy and reliability.

[0120] After data review, the data enters the data storage unit. The system, in conjunction with the joint review by data managers and researchers, processes the data for storage.

[0121] After the data is entered into the database, the data manager enters the data evaluation unit, where relevant algorithms are developed to classify, store, and retrieve the data, and to conduct a preliminary evaluation of the region.

[0122] III. System Maintenance and Optimization

[0123] 1. Data Backup and Recovery: Regularly back up system data to ensure data security. In the event of data loss or corruption, timely recovery is possible.

[0124] 2. System Updates and Upgrades: Based on user feedback and market demands, update and upgrade the system to optimize its functions and performance.

[0125] 3. Technical support and training: Provide users with technical support and training services to ensure that users can use the system proficiently and make full use of its various functions.

[0126] IV. User Feedback and Continuous Improvement

[0127] 1. Collect user feedback: Regularly collect user feedback on system usage to understand user needs and opinions.

[0128] 2. Analyze user feedback: Organize and analyze the collected user feedback to identify problems in the system and directions for improvement.

[0129] 3. Continuous system improvement: Based on user feedback and analysis results, continuously improve and optimize the system to enhance its stability and user experience.

[0130] This invention also provides a storage medium for storing a computer program, which, when executed, performs at least the methods described above.

[0131] This invention also provides a control device, including a processor and a storage medium for storing a computer program; wherein the processor executes the computer program by performing at least the method described above.

[0132] This invention also provides a processor that executes a computer program, at least performing the methods described above.

[0133] The storage medium can be implemented by any type of non-volatile storage device, or a combination thereof. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM); the magnetic surface memory can be a disk drive or magnetic tape drive. The storage media described in the embodiments of this invention are intended to include, but are not limited to, these and any other suitable types of memory.

[0134] In the several embodiments provided by this invention, it should be understood that the disclosed systems and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0135] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0136] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0137] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0138] Alternatively, if the integrated units of this invention are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this invention, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROM, RAM, magnetic disks, or optical disks.

[0139] The methods disclosed in the several method embodiments provided by this invention can be arbitrarily combined without conflict to obtain new method embodiments.

[0140] The features disclosed in the several product embodiments provided by this invention can be arbitrarily combined without conflict to obtain new product embodiments.

[0141] The features disclosed in the several method or device embodiments provided by the present invention can be arbitrarily combined without conflict to obtain new method or device embodiments.

[0142] The above description, in conjunction with specific preferred embodiments, provides a further detailed explanation of the present invention. It should not be construed that the specific implementation of the present invention is limited to these descriptions. For those skilled in the art, various equivalent substitutions or obvious modifications can be made without departing from the concept of the present invention, and all such modifications, achieving the same performance or application, should be considered within the scope of protection of the present invention.

Claims

1. A collaborative data management method for river, lake, and reservoir water quality based on field activities, characterized in that: Based on the different participants in the monitoring activities, roles are divided into three categories: researchers, field participants, and data managers; water quality monitoring activities are also divided into professional and non-professional activities. In a computer system, perform the following steps: 1) Demand matching information management is carried out through the demand matching information management module; it allows data managers to assist researchers in defining and recording the needs of professional and non-professional activities in water quality monitoring, allows researchers to determine the detailed needs of non-professional activities, and allows field participants to provide relevant field activity information for demand matching. 2) Compliance information management is conducted through a compliance information management module. This module allows researchers and field participants to jointly confirm the compliance of sampling equipment, testing standards, and personnel training. The compliance information management module includes an equipment compliance unit, a standard compliance unit, and a personnel compliance unit. After completing the first step of the requirements matching information management module, this module manages the compliance information for equipment, standards, and personnel. First, after both parties confirm the requirements, researchers and field participants manage the confirmation of sampling equipment. The research manager submits the requirements for on-site sampling equipment and uploads the information to the on-site sampling equipment management system in the equipment compliance unit. Simultaneously, the research manager submits requirements for on-site sample preservation and transportation. After confirmation by the sampling participants, the equipment compliance unit is completed, and the process moves to the standard compliance unit. Next, the researchers send relevant documents regarding on-site field testing standards and sample preservation standards to the field participants. After the field participants submit proof of training, and after confirmation by the researchers, the process moves to the personnel compliance unit of this module. Finally, the researchers conduct online training and examinations. Once the field participants achieve a certain assessment score, they are considered to be in compliance. 3) Manage test data through the test data management module; It allows field participants to upload on-site measurement data and sample preservation and transportation information, and allows researchers to upload laboratory test data, test standards and methods, and laboratory instrument and equipment information; 4) Data review and warehousing management are carried out through the data review and warehousing module; It allows data managers to conduct scientific compliance and consistency reviews of the collected data, integrate, clean and store the reviewed data, and evaluate and classify the data.

2. The method for collaborative data management of river, lake, and reservoir water quality based on field activities as described in claim 1, characterized in that: The demand matching information management includes a process where data managers assist researchers in categorizing their research activities into professional and non-professional activities. Researchers then fill in the information for each activity in the system as prompted by the module. Subsequent matching work is then carried out for non-professional activities. First, researchers categorize non-professional activities in water quality monitoring into sampling location, sampling time, on-site measurement, and sample collection demand information reporting stages, generating reports. Next, field participants, based on the research researchers' demands, fill in the corresponding information, including field activity location, field activity time, field sampling intention, and field carrying space information, and submit it to the system. Finally, the information management module distributes the forms provided by the field participants to the researchers for comparison. After confirmation by the researchers, the demand matching information management module is complete.

3. The method for collaborative data management of river, lake, and reservoir water quality based on field activities as described in claim 1, characterized in that: The test data management module includes a field data management unit and a laboratory data management unit. The field data management unit is maintained by field participants, and the laboratory data management unit is maintained by researchers. First, field participants input and upload relevant information through the auxiliary information, field measurement information, and saved transportation information in the field data management unit. Then, researchers input and upload relevant information through the laboratory test standards and methods, laboratory instrument and equipment information, and laboratory data management information in the laboratory data management unit.

4. The method for collaborative data management of river, lake, and reservoir water quality based on field activities as described in claim 1, characterized in that: The data review and storage module includes a data review unit, a data storage unit, and a data evaluation unit, maintained by the data manager. First, the data manager reviews the scientific compliance and consistency of the data obtained from the monitoring activities based on prompts from the data review unit, i.e., whether the sample collection and data production process conforms to scientific standards and whether the data meets relevant data verification requirements. Next, a data quality audit module is embedded in the data storage unit, setting relevant data quality requirements. After joint review by the data manager and researchers, the data is considered passed when it passes the data storage unit. Finally, the data evaluation unit performs hierarchical storage and retrieval of the data, and simultaneously conducts a preliminary evaluation of the target area.

5. The method for collaborative data management of river, lake, and reservoir water quality based on field activities as described in claim 4, characterized in that: In the data evaluation unit, "sites" represents sampling points and "temperature" represents water temperature. The SQL query statement SELECT * FROM sites WHERE temperature>12; is used to find sampling points with a water temperature greater than 12 degrees Celsius. Similarly, the water temperature of the target area is analyzed.

6. The method for collaborative data management of river, lake, and reservoir water quality based on field activities as described in claim 4, characterized in that: This also includes calculating relevant water quality indices, the formula for which is: ； In the formula, Water quality index Pollutant weight values, The concentration of pollutants, The types and number of water quality parameters.

7. A collaborative data management system for river, lake, and reservoir water quality based on field activities, used to implement the method described in any one of claims 1 to 6, characterized in that, Includes the following modules: The demand matching information management module is used for demand matching information management. It allows data managers to assist researchers in defining and recording the needs of professional and non-professional activities in water quality monitoring, allows researchers to determine the detailed needs of non-professional activities, and allows field participants to provide relevant field activity information for demand matching. The compliance information management module is used for compliance information management; it allows researchers and field participants to jointly confirm the compliance of sampling equipment, testing standards, and personnel training. The compliance information management module includes an equipment compliance unit, a standard compliance unit, and a personnel compliance unit. The equipment compliance unit sets up and manages the requirements for on-site sampling equipment and the requirements for on-site sample preservation and transportation. The standard compliance unit sets up on-site field testing standards and related sample preservation standards. The personnel compliance unit sets up online training and examinations, as well as assessment information for field participants. The test data management module is used for test data management. It allows field participants to upload on-site measurement data and sample preservation and transportation information, and allows researchers to upload laboratory test data, test standards and methods, and laboratory instrument and equipment information; The data review and warehousing module is used for data review and warehousing management. It allows data managers to conduct scientific compliance and consistency reviews of the collected data, integrate, clean and store the reviewed data, and evaluate and classify the data.

8. The collaborative data management system for river, lake, and reservoir water quality based on field activities as described in claim 7, characterized in that, The demand matching information management module includes a scientific research sampling demand unit and a field planning unit. The scientific research sampling demand unit sets and manages information such as sampling location, sampling time, on-site measurement, and sample collection demand. The field planning unit sets and manages information such as field activity location, field activity time, field sampling intention, and field carrying space information. The test data management module includes a field data management unit and a laboratory data management unit. The field data management unit sets up management auxiliary information, field measurement information, and saves transportation information. The laboratory data management unit sets up management of laboratory test standards and methods, laboratory instrument and equipment information, and laboratory data management information. The data review and storage module includes a data review unit, a data storage unit, and a data evaluation unit. The data review unit manages the scientific compliance and consistency of the acquired data. The data storage unit manages the data system review and manual review information. The data evaluation unit manages the hierarchical storage and retrieval of data and provides preliminary evaluation information for different regions.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the collaborative data management method for river, lake and reservoir water quality based on field activities as described in any one of claims 1-6.

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