A mobile terminal-based conductivity measurement and data management method

Abstract

The present application relates to a kind of conductivity measurement and data management method based on mobile terminal, belong to conductivity measurement technical field.Therein, the method includes: S1 adaptive establishment terminal and the communication connection of measuring equipment, complete equipment identification, interface matching and parameter check configuration, generate equipment parameter configuration information;S2 constructs conductivity measurement evaluation model, fusion multi-dimensional feature completes original data acquisition and intelligent quality check, filters output effective measurement data;S3 is combined with electrode historical use and calibration data calculation electrode health degree and remaining service life, builds hierarchical data management mechanism, forms structured measurement data archives;S4 relies on archives data to carry out trend analysis, realizes abnormal early warning and standardized report generation, while combining feedback data iterative optimization evaluation model.The automation, intelligent control of conductivity measurement whole process is realized, effectively improves measurement data precision and reliability.

Description

Technical Field

[0001] This invention belongs to the field of conductivity measurement technology, specifically relating to a conductivity measurement and data management method based on a mobile terminal. Background Technology

[0002] Electrical conductivity is a core indicator reflecting the ion content and purity of a solution. It is widely used in many fields such as water environment monitoring, industrial production quality control, food testing, and scientific research experiments. The accuracy, validity, and standardization of data management directly affect the reliability of test results and the implementation of industry quality control standards. At present, most conductivity measuring equipment on the market is an independent fixed instrument with poor intelligence and mobile adaptability, resulting in many technical defects in actual field testing operations.

[0003] First, traditional measurement equipment uses a fixed and singular communication method, which cannot adapt to the complex and ever-changing field testing environment. It is difficult to adaptively match the communication channel according to the actual transmission requirements, resulting in poor equipment connectivity and an inability to automatically match the corresponding operating interface and measurement method for each equipment model. The parameter configuration process is cumbersome, requires a high degree of manual intervention, and has low testing adaptation efficiency. Second, existing measurement technologies can only complete the simple acquisition and transmission of raw data. There is no specific conductivity data quality assessment mechanism. The measurement process is highly susceptible to factors such as temperature fluctuations, electrode response attenuation, and environmental signal interference, generating a large amount of abnormal and invalid data that cannot be automatically identified and removed, leading to insufficient accuracy of the measurement results.

[0004] Meanwhile, conductivity electrodes, as core sensing consumables, will experience performance degradation and response lag with long-term use. Current technologies mainly rely on manual periodic calibration and experience to judge the electrode status, which cannot combine historical measurement data and calibration records to quantify the electrode health and remaining service life in real time. This can easily lead to inaccurate measurements due to untimely replacement of aging electrodes, or waste of resources due to premature replacement of electrodes before they are aged. In addition, the data management mode of traditional detection systems is extensive and uniform, and does not implement differentiated and dynamic management based on differences in data quality. All data is stored uniformly and managed with uniform access, resulting in insufficient protection of high-quality and effective data, low-quality and redundant data occupying storage resources, and poor data traceability.

[0005] Currently, the limited number of measurement solutions that integrate with mobile terminals only achieve basic data display and transmission functions. They have not formed a complete closed-loop system encompassing parameter adaptive configuration, intelligent data verification, electrode status monitoring, hierarchical data management, data analysis and early warning, and model iterative optimization. As a result, the overall level of intelligence is low and the data utilization rate is poor, failing to meet the current needs for high-precision, standardized, intelligent, and collaborative conductivity field testing and data management. Summary of the Invention

[0006] To address the aforementioned problems in the existing technology, this invention provides a method for conductivity measurement and data management based on a mobile terminal. The objective of this invention can be achieved through the following technical solution: include: S1: The mobile terminal scans multiple communication channels and automatically selects the communication method to establish a connection with the measuring equipment based on the environmental signal strength and transmission requirements; it automatically identifies the equipment model and functional characteristics, loads the matching operation interface and measurement method; it configures the measurement parameters and verifies their rationality before synchronizing them to the measuring equipment and generating equipment parameter configuration information. S2: Based on the equipment parameter configuration information, a conductivity measurement and evaluation model is constructed; by integrating data fluctuation characteristics, temperature change patterns, and electrode response characteristics, the measuring equipment performs continuous measurement and transmits the raw data to the mobile terminal; the conductivity measurement and evaluation model is used to verify the quality of the raw data and output valid measurement data and quality evaluation results. S3: Based on the quality assessment results and combined with historical electrode usage and calibration data, calculate electrode health and remaining service life in real time; establish a dynamic data management mechanism based on quality level, allocate different storage periods and access permissions according to data reliability, and form a structured measurement data archive; S4: Based on the measurement data archive, perform classification statistics and trend analysis to generate abnormal early warning information and customized measurement reports; through the cloud system, perform multi-user hierarchical permission management and collaborative work, receive user feedback and continuously optimize the conductivity measurement and evaluation model.

[0007] Specifically, the selection process for establishing a connection between the communication method and the measuring device is as follows: The mobile terminal scans the communication channels in a preset order and collects three core indicators in real time: signal strength, transmission delay, and packet loss rate of each channel. Based on the real-time data transmission volume and transmission latency requirements of the current measurement task, each communication method is scored, and the communication method with the highest score is selected to initiate a connection request to the measurement device. If the connection fails, it automatically switches to the second-best communication method to retry. After two-way authentication, an encrypted communication connection is established.

[0008] Specifically, the loading process for the matching operation interface and measurement method is as follows: The mobile terminal extracts the device identifier and hardware version information returned by the measuring device, and retrieves the resource package that matches the device identifier and version from the pre-stored device resource set; It automatically parses the interface layout files and interaction logic files in the resource package, loads the operation interface that matches the device screen size and operating habits, and imports the measurement process, parameter range and operation specifications of the corresponding device.

[0009] Specifically, the process for generating the device parameter configuration information is as follows: Based on the identified measurement device information, the mobile terminal automatically retrieves the historical parameter configuration information of the corresponding device and pre-fills it into the operation interface; The mobile terminal performs multi-parameter correlation verification in real time, analyzes the logical matching relationship between parameters, dynamically prompts abnormal parameter combinations and provides optimization suggestions; After all parameters have been verified, all parameters are encrypted, packaged, and synchronized to the measuring device. At the same time, device parameter configuration information is generated and stored in the temporary cache area of ​​the mobile terminal.

[0010] Specifically, the conductivity measurement and evaluation model includes a fusion layer, a mapping layer, and an output layer, and its construction process is as follows: The total input consists of standardized data fluctuation characteristics, temperature change characteristics, and electrode response characteristics, while the total output consists of the quality score and quality level corresponding to each piece of raw data. The fusion layer performs cross-dimensional integration processing on the three types of input features, eliminating the differences in dimensionality and distribution bias between different features; The mapping layer learns the nonlinear mapping relationship between features and measurement quality, and extracts deep features that can characterize data quality. The output layer performs weighted calculations on the deep features and outputs the final quality assessment result, using historical labeled data for initial training.

[0011] Specifically, the process by which the measuring device performs continuous measurement and transmits the raw data to the mobile terminal is as follows: After receiving the synchronized parameter configuration information, the measuring equipment automatically performs self-test and electrode initialization. After the self-test passes, the continuous measurement process is started. The conductivity of the solution is obtained at a preset sampling interval. The analog signal is converted into a digital signal and then subjected to preliminary filtering. A timestamp and device identifier are added to each set of data. The data is then transmitted to the mobile terminal in real time through the established communication connection.

[0012] Specifically, the output process for the effective measurement data and quality assessment results is as follows: The mobile terminal inputs the received raw data into the conductivity measurement and evaluation model. The conductivity measurement and evaluation model extracts and analyzes multi-dimensional features of the data and outputs the quality score and quality level corresponding to each data point. Data with quality scores below a preset threshold are marked as outliers and removed. Data with quality scores that meet preset requirements are retained as valid measurement data. The quality assessment result corresponding to each valid data point is output.

[0013] Specifically, the calculation process for the electrode health and remaining service life is as follows: Extract multiple sets of quality assessment results from the conductivity measurement and evaluation model, and statistically analyze the variation of the deviation rate and fluctuation amplitude over time. The data were matched with the cumulative usage time, cumulative number of measurements, and performance recovery data after each calibration to establish a quantitative correlation between changes in quality assessment indicators and the degree of electrode performance degradation. Based on the quantified correspondence, the current performance degradation of the electrode is calculated, and then the electrode health value is obtained. The remaining usable time is then calculated by combining the standard service life curve of the electrode.

[0014] Specifically, the establishment process of the dynamic data management mechanism based on quality levels is as follows: Based on the quality assessment results, all measurement data are divided into multiple different quality levels. Taking into account the reliability of the measurement data and its business importance, corresponding differentiated management rules are formulated for each quality level. Different storage periods and access permissions are assigned to data of different quality levels. At the same time, a dynamic quality level adjustment mechanism is built to automatically update the data quality level and corresponding management strategy based on subsequent verification results.

[0015] Specifically, the process of forming the structured measurement data archive is as follows: Establish a one-to-one correspondence between valid measurement data and corresponding quality assessment results, electrode health information, measurement environment information and operator information to form a single complete measurement record unit; All measurement record units are categorized into layers based on four dimensions: measurement time, measurement location, measurement task type, and equipment model. A standardized data structure is adopted to organize various types of information in a unified manner, a multi-level rapid retrieval system is constructed, and each record is stored in a structured database after integrity verification, forming a traceable, searchable and analyzable structured measurement data archive.

[0016] Specifically, the generation process of the abnormal warning information and the customized measurement report is as follows: Perform time series trend analysis on historical data in structured measurement data archives to identify abnormal fluctuations, continuous increases or decreases, and other abnormal change patterns in the data; When an abnormal trend affecting measurement accuracy or equipment safety is detected, an early warning message containing the abnormal location and cause is automatically generated and pushed to the mobile terminal. A measurement report with corresponding content modules and display style is generated according to the pre-configured report parameters.

[0017] Specifically, the optimization process of the conductivity measurement and evaluation model is as follows: The system collects confirmation and correction feedback information on measurement results and quality assessment results, extracts correctly labeled data from the feedback and organizes it into incremental training samples, retaining the original core parameters and basic feature extraction capabilities of the conductivity measurement and assessment model. The conductivity measurement and evaluation model was fine-tuned by adjusting the feature weights and quality judgment thresholds, and the optimized performance was verified using a validation set.

[0018] The beneficial effects of this invention are as follows: (1) By setting up a mobile terminal multi-channel adaptive connection adaptation, automatic device identification and matching, and a hierarchical conductivity measurement and evaluation model, it can automatically select the best communication method to establish device connection according to the on-site environmental signal and transmission requirements, automatically adapt to the corresponding operation interface and measurement method, simplify the parameter configuration process and reduce the degree of manual intervention; relying on the model structure of the fusion layer, mapping layer and output layer to integrate multiple features to carry out data quality verification, accurately identify and eliminate abnormal measurement data, effectively suppress the measurement error caused by environmental interference and electrode performance decay, and at the same time combine the historical use and calibration data of the electrode to evaluate the health status and remaining service life in real time, avoiding measurement inaccuracies or waste of consumables caused by manual experience judgment; (2) By setting up differentiated data management based on quality level, constructing structured measurement data archives, and cloud-based data analysis and model iteration processes, it is possible to configure differentiated storage cycles and access permissions according to data credibility, change the traditional extensive unified data management mode, reduce redundant data resource occupation and strengthen the security protection of high-quality data, and form a traceable and organizable structured measurement data archive; at the same time, it can carry out statistical trend analysis on archive data to realize automatic early warning of anomalies, support the output of customized measurement reports, realize multi-user collaborative operation based on cloud-based hierarchical permissions, and continuously optimize the measurement evaluation model based on user feedback, forming an intelligent closed loop for the entire conductivity measurement process, and meeting the needs of high-precision and standardized on-site testing and data management. Attached Figure Description

[0019] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0020] Figure 1 This is a flowchart illustrating a mobile terminal-based conductivity measurement and data management method according to the present invention. Figure 2 This is a data flow diagram of a mobile terminal-based conductivity measurement and data management method according to the present invention. Detailed Implementation

[0021] To further illustrate the technical means and effects of the present invention in achieving its intended purpose, the following detailed description of the specific implementation methods, structures, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided.

[0022] Please see Figure 1-2 A method for conductivity measurement and data management based on mobile terminals; include: S1: The mobile terminal scans multiple communication channels and automatically selects the communication method to establish a connection with the measuring equipment based on the environmental signal strength and transmission requirements; it automatically identifies the equipment model and functional characteristics, loads the matching operation interface and measurement method; it configures the measurement parameters and verifies their rationality before synchronizing them to the measuring equipment and generating equipment parameter configuration information. S2: Based on the equipment parameter configuration information, a conductivity measurement and evaluation model is constructed; by integrating data fluctuation characteristics, temperature change patterns, and electrode response characteristics, the measuring equipment performs continuous measurement and transmits the raw data to the mobile terminal; the conductivity measurement and evaluation model is used to verify the quality of the raw data and output valid measurement data and quality evaluation results. S3: Based on the quality assessment results and combined with historical electrode usage and calibration data, calculate electrode health and remaining service life in real time; establish a dynamic data management mechanism based on quality level, allocate different storage periods and access permissions according to data reliability, and form a structured measurement data archive; S4: Based on the measurement data archive, perform classification statistics and trend analysis to generate abnormal early warning information and customized measurement reports; through the cloud system, perform multi-user hierarchical permission management and collaborative work, receive user feedback and continuously optimize the conductivity measurement and evaluation model.

[0023] In this embodiment, the communication method refers to the standardized communication technology used to establish a bidirectional data transmission link between the mobile terminal and the conductivity measurement device. It can be automatically switched according to the field environment to ensure data transmission stability. Specifically, it includes: Bluetooth communication, Wi-Fi communication, near-field communication, and USB wired communication. Measuring equipment refers to specialized testing instruments that can acquire solution conductivity signals and perform analog-to-digital conversion. They can connect wirelessly or wiredly with mobile terminals to complete data interaction. Specifically, they include: portable conductivity meters, benchtop conductivity analyzers, handheld conductivity detectors, and multi-parameter water quality analyzers with integrated conductivity measurement functions. Equipment model and functional characteristics refer to the set of information used to uniquely identify the identity of the measuring equipment and describe its technical capabilities. It is the core basis for mobile terminals to automatically match the operation interface and parameter templates. Specifically, it includes: unique equipment identification code, hardware version number, software version number, supported measurement modes, conductivity measurement range, sampling frequency range, temperature compensation range, and electrode type compatibility. Measurement parameters refer to configurable parameters used to control the measurement process and ensure the accuracy of the measurement results. They can be automatically pre-filled by the mobile terminal and undergo multi-dimensional rationality verification. Specifically, they include: measurement mode, sampling interval, total measurement duration, temperature compensation coefficient, calibration coefficient, and data filtering intensity. Data fluctuation characteristics refer to the statistical characteristics that reflect the stability of raw conductivity measurement data over time. They are one of the important indicators for conductivity measurement evaluation models to judge data quality. Specifically, they include: standard deviation, range, coefficient of variation, average difference between adjacent data points, and proportion of data that exceeds the normal range. Temperature change pattern refers to the characteristics of solution temperature change over time during the measurement process. It is used to correct the systematic deviation of conductivity measurement results caused by temperature. Specifically, it includes: average temperature, rate of temperature change, temperature fluctuation amplitude, temperature extreme value, and correlation coefficient between temperature and conductivity. Electrode response characteristics refer to the ability of a conductivity electrode to respond to changes in the concentration of ions in a solution, which directly affects the accuracy and real-time performance of measurement data. Specifically, these characteristics include: electrode response time, response slope, zero drift, repeatability error, and temperature coefficient. Raw data refers to the basic data that the measuring device directly collects and transmits to the mobile terminal without quality assessment. It is the original input for all subsequent data processing and analysis work. Specifically, it includes: conductivity value, synchronously collected temperature value, data collection timestamp, unique identifier of the measuring device, and current measurement parameter snapshot. Electrode historical usage and calibration data refers to a data set that records the usage status and calibration status of the electrode throughout its entire life cycle. It is used to assess the electrode health and predict its remaining service life in real time. Specifically, it includes: the first time the electrode was used, the cumulative usage time, the cumulative number of measurements, the calibration time, the measurement error before calibration, the measurement error after calibration, and the calibration environment conditions. Quality assessment data refers to the quality evaluation results output by the conductivity measurement and assessment model after analyzing the raw data. It is the core basis for data hierarchical management and electrode status monitoring, and specifically includes: the quality score, quality level identifier, abnormal data type marker, and data reliability coefficient corresponding to each piece of raw data.

[0024] Specifically, the selection process for establishing a connection between the communication method and the measuring device is as follows: The mobile terminal scans the communication channels in a preset order and collects three core indicators in real time: signal strength, transmission delay, and packet loss rate of each channel. Based on the real-time data transmission volume and transmission latency requirements of the current measurement task, each communication method is scored, and the communication method with the highest score is selected to initiate a connection request to the measurement device. If the connection fails, it automatically switches to the second-best communication method to retry. After two-way authentication, an encrypted communication connection is established.

[0025] Specifically, the loading process for the matching operation interface and measurement method is as follows: The mobile terminal extracts the device identifier and hardware version information returned by the measuring device, and retrieves the resource package that matches the device identifier and version from the pre-stored device resource set; It automatically parses the interface layout files and interaction logic files in the resource package, loads the operation interface that matches the device screen size and operating habits, and imports the measurement process, parameter range and operation specifications of the corresponding device.

[0026] Specifically, the process for generating the device parameter configuration information is as follows: Based on the identified measurement device information, the mobile terminal automatically retrieves the historical parameter configuration information of the corresponding device and pre-fills it into the operation interface; The mobile terminal performs multi-parameter correlation verification in real time, analyzes the logical matching relationship between parameters, dynamically prompts abnormal parameter combinations and provides optimization suggestions; After all parameters have been verified, all parameters are encrypted, packaged, and synchronized to the measuring device. At the same time, device parameter configuration information is generated and stored in the temporary cache area of ​​the mobile terminal.

[0027] Specifically, the conductivity measurement and evaluation model includes a fusion layer, a mapping layer, and an output layer, and its construction process is as follows: The total input consists of standardized data fluctuation characteristics, temperature change characteristics, and electrode response characteristics, while the total output consists of the quality score and quality level corresponding to each piece of raw data. The fusion layer performs cross-dimensional integration processing on the three types of input features, eliminating the differences in dimensionality and distribution bias between different features; The mapping layer learns the nonlinear mapping relationship between features and measurement quality, and extracts deep features that can characterize data quality. The output layer performs weighted calculations on the deep features and outputs the final quality assessment result, using historical labeled data for initial training.

[0028] Specifically, the process by which the measuring device performs continuous measurement and transmits the raw data to the mobile terminal is as follows: After receiving the synchronized parameter configuration information, the measuring equipment automatically performs self-test and electrode initialization. After the self-test passes, the continuous measurement process is started. The conductivity of the solution is acquired at a preset sampling interval. The analog signal is converted into a digital signal and then subjected to preliminary filtering. A timestamp and device identifier are added to each set of data. The data is then transmitted to the mobile terminal in real time through the established communication connection.

[0029] Specifically, the output process for the effective measurement data and quality assessment results is as follows: The mobile terminal inputs the received raw data into the conductivity measurement and evaluation model. The conductivity measurement and evaluation model extracts and analyzes multi-dimensional features of the data and outputs the quality score and quality level corresponding to each data point. Data with quality scores below a preset threshold are marked as outliers and removed. Data with quality scores that meet preset requirements are retained as valid measurement data. The quality assessment result corresponding to each valid data point is output.

[0030] Specifically, the calculation process for the electrode health and remaining service life is as follows: Extract multiple sets of quality assessment results from the conductivity measurement and evaluation model, and statistically analyze the variation of the deviation rate and fluctuation amplitude over time. The data were matched with the cumulative usage time, cumulative number of measurements, and performance recovery data after each calibration to establish a quantitative correlation between changes in quality assessment indicators and the degree of electrode performance degradation. Based on the quantified correspondence, the current performance degradation of the electrode is calculated, and then the electrode health value is obtained. The remaining usable time is then calculated by combining the standard service life curve of the electrode.

[0031] Specifically, the establishment process of the dynamic data management mechanism based on quality levels is as follows: Based on the quality assessment results, all measurement data are divided into multiple different quality levels. Taking into account the reliability of the measurement data and its business importance, corresponding differentiated management rules are formulated for each quality level. Different storage periods and access permissions are assigned to data of different quality levels. At the same time, a dynamic quality level adjustment mechanism is built to automatically update the data quality level and corresponding management strategy based on subsequent verification results.

[0032] Specifically, the process of forming the structured measurement data archive is as follows: Establish a one-to-one correspondence between valid measurement data and corresponding quality assessment results, electrode health information, measurement environment information and operator information to form a single complete measurement record unit; All measurement record units are categorized into layers based on four dimensions: measurement time, measurement location, measurement task type, and equipment model. A standardized data structure is adopted to organize various types of information in a unified manner, a multi-level rapid retrieval system is constructed, and each record is stored in a structured database after integrity verification, forming a traceable, searchable and analyzable structured measurement data archive.

[0033] Specifically, the generation process of the abnormal warning information and the customized measurement report is as follows: Perform time series trend analysis on historical data in structured measurement data archives to identify abnormal fluctuations, continuous increases or decreases, and other abnormal change patterns in the data; When an abnormal trend affecting measurement accuracy or equipment safety is detected, an early warning message containing the abnormal location and cause is automatically generated and pushed to the mobile terminal. A measurement report with corresponding content modules and display style is generated according to the pre-configured report parameters.

[0034] Specifically, the optimization process of the conductivity measurement and evaluation model is as follows: The system collects confirmation and correction feedback information on measurement results and quality assessment results, extracts correctly labeled data from the feedback and organizes it into incremental training samples, retaining the original core parameters and basic feature extraction capabilities of the conductivity measurement and assessment model. The conductivity measurement and evaluation model was fine-tuned by adjusting the feature weights and quality judgment thresholds, and the optimized performance was verified using a validation set.

[0035] In this embodiment, the specific selection process for establishing a connection between the communication method and the measuring device is as follows: The mobile terminal scans the communication channels in the preset order of Bluetooth 5.0, Wi-Fi 2.4GHz, and USB Type-C, and collects three core indicators of each channel in real time: signal strength, transmission delay, and packet loss rate. Combined with the current continuous measurement task's transmission volume of 1 set of data per second and the latency requirement of ≤50ms, the communication methods are comprehensively scored. The communication method with the highest score is selected to initiate a connection request to the portable conductivity meter. If the connection fails, it automatically switches to the second-best communication method to retry. After completing two-way authentication, an encrypted communication connection is established using the AES-128 encryption algorithm.

[0036] In this embodiment, the specific process of generating device parameter configuration information is as follows: Based on the identified portable conductivity meter information, the mobile terminal automatically retrieves the historical parameter configuration information of the device used for surface water monitoring last time and pre-fills it into the operation interface. The pre-filled parameters include continuous measurement mode, 10s sampling interval, 30min total measurement duration, 25℃ standard temperature compensation coefficient, 0.5 level calibration coefficient, and medium intensity data filtering. During the user's parameter adjustment process, multi-parameter correlation verification is performed in real time. When it is detected that the parameter combination of sampling interval of 0.1s and measurement duration of 120min will cause the data volume to exceed the device storage capacity, the parameter conflict is dynamically prompted and optimization suggestions are given. After all parameters are verified, all parameters are encrypted and packaged using CRC32 verification and synchronized to the measurement device. At the same time, device parameter configuration information containing the device's unique identifier, complete parameter content, and configuration timestamp is generated and stored in the temporary cache area of ​​the mobile terminal.

[0037] In this embodiment, the specific construction process of the conductivity measurement and evaluation model is as follows: A conductivity measurement and evaluation model comprising a fusion layer, a mapping layer, and an output layer is constructed for the platinum black conductivity electrode used in conjunction with the model. The total input of the model consists of standardized data fluctuation characteristics, temperature change characteristics, and electrode response characteristics. The total output consists of a quality score of 0-100 points and four quality levels (excellent, good, medium, and poor) corresponding to each original data point. The fusion layer uses a normalization method to perform cross-dimensional integration processing on the three types of input features to eliminate dimensional differences and distribution biases between different features. The mapping layer uses a three-layer fully connected neural network to learn the nonlinear mapping relationship between features and measurement quality to extract deep features. The output layer uses the Softmax activation function to perform weighted calculation on the deep features and output the final quality evaluation result. Initial training is performed using 5000 sets of labeled data accumulated in the laboratory, and the initial accuracy of the model reaches 92.3%.

[0038] In this embodiment, the specific process of the measuring device performing continuous measurement and transmitting the raw data to the mobile terminal is as follows: After receiving the synchronized parameter configuration information, the portable conductivity meter automatically performs device circuit self-test and platinum black conductivity electrode polarization initialization. After the self-test passes, the continuous measurement process is started according to the preset 10s sampling interval. The conductivity analog signal of the solution is collected through the electrode, converted into a digital signal by a 16-bit analog-to-digital converter, and then subjected to 3-point median filtering. A timestamp accurate to milliseconds and a unique device identifier are added to each set of data. The data is transmitted to the mobile terminal in real time through the established encrypted communication connection. During the transmission process, an integrity check is performed every 100 sets of data.

[0039] In this embodiment, the specific output process of valid measurement data and quality assessment results is as follows: The mobile terminal inputs the received raw data into the conductivity measurement and assessment model. The model extracts the data fluctuation characteristics, temperature change characteristics, and electrode response characteristics of each data point in sequence and performs standardization processing. After completing feature analysis through the fusion layer and mapping layer, the model outputs the corresponding quality score and quality level. Data with a quality score lower than 50 is marked as abnormal data and removed. Data with a quality score ≥ 50 is retained as valid measurement data. At the same time, the corresponding quality assessment result and explanation of the reason for the abnormality are attached to each valid data point.

[0040] In this embodiment, the specific calculation process for electrode health and remaining service life is as follows: 1200 sets of quality assessment results from 30 consecutive days were extracted from the conductivity measurement and evaluation model. The data deviation rate of the platinum black conductivity electrode increased from the initial 0.8% to 3.2%, and the fluctuation range increased from the initial 0.5 μS / cm to 2.1 μS / cm. These data were then matched with the electrode's cumulative usage time of 1200 hours, cumulative measurement count of 8500 times, and performance recovery data after 3 calibrations. A quantitative correspondence between the changes in quality assessment indicators and the degree of electrode performance degradation was established. Based on this quantitative correspondence, the current performance degradation of the electrode was calculated to be 35%, and the health score was 65. Combined with the curve of the standard service life of the platinum black conductivity electrode (3000 hours), the remaining usable time was estimated to be approximately 1800 hours.

[0041] In this embodiment, the specific process of establishing a dynamic data management mechanism based on quality levels is as follows: All measurement data are divided into four quality levels according to the quality assessment results: a quality score ≥90 indicates Level 1 high-precision data, 70-89 indicates Level 2 valid data, 50-69 indicates Level 3 reference data, and <50 indicates Level 4 invalid data. Differentiated management rules are formulated based on the business importance of environmental monitoring. Level 1 data is permanently stored and accessed only by senior engineers; Level 2 data is stored for 5 years and accessed only by testing personnel; Level 3 data is stored for 1 year and accessed only by ordinary users; and Level 4 data is automatically deleted. Simultaneously, a dynamic adjustment mechanism for quality levels is constructed. When subsequent laboratory standard method verification finds that the actual deviation of a certain Level 2 data is less than 0.5%, it is automatically upgraded to Level 1 data, and the corresponding storage period and access permissions are adjusted.

[0042] In this embodiment, the specific process of forming a structured measurement data archive is as follows: A one-to-one correspondence is established between valid measurement data and corresponding quality assessment results, electrode health information, measurement environment temperature and humidity information, and operator ID, forming a single complete measurement record unit. All measurement record units are hierarchically categorized according to four dimensions: measurement time, measurement location, measurement task type, and equipment model. A standardized JSON data structure is used to uniformly organize various types of information, constructing a fast retrieval system including a main index and three-level sub-indexes. After verifying the field integrity and logical consistency of each record, it is stored in a relational database, forming a traceable, searchable, and analyzable structured measurement data archive.

[0043] In this embodiment, taking the daily water quality monitoring scenario of a wastewater discharge outlet in an industrial park as an example, the specific process is as follows: S represents the communication channel signal strength, T represents the communication transmission delay, L represents the communication packet loss rate, D represents the data transmission volume of the measurement task, Y represents the measurement task delay requirement, F1 represents the data fluctuation characteristics, F2 represents the temperature change pattern, F3 represents the electrode response characteristics, Q represents the data quality score, G represents the data quality level, U represents the cumulative electrode usage time, N represents the cumulative number of electrode measurements, C represents the electrode calibration data from previous cycles, H represents the electrode health value, R represents the remaining electrode lifespan, P represents the data storage period, and A represents the data access permission. The specific implementation process of S1 is as follows: The mobile terminal scans multiple communication channels and collects three indicators for each channel: signal strength S, transmission delay T, and packet loss rate L. Combined with the data transmission volume D and latency requirement Y of the current measurement task, the communication methods are comprehensively scored, and the communication method with the highest score is selected to establish an encrypted connection with the measurement equipment. The device model and functional characteristics are automatically identified, and the matching operation interface and measurement process are loaded. The historical best parameter configuration template is automatically retrieved and pre-filled into the interface. Multi-parameter correlation verification is performed in real time, and unreasonable parameter combinations are prompted and optimization suggestions are given. After all parameters are verified, they are synchronized to the measurement equipment to generate device parameter configuration information containing device identification, parameter content, and configuration time. The specific implementation process of S2 is as follows: Based on the device parameter configuration information, a conductivity measurement and evaluation model including a fusion layer, a mapping layer, and an output layer is constructed. The model inputs are data fluctuation characteristics F1, temperature change law F2, and electrode response characteristics F3. The outputs are the quality score Q and quality level G corresponding to each piece of raw data. After the measurement device completes self-test and electrode initialization, it performs continuous measurement according to the preset sampling interval. The collected raw conductivity data and synchronous temperature data are timestamped and then transmitted to the mobile terminal. The conductivity measurement and evaluation model performs multi-dimensional quality verification on the raw data, removes data with quality scores below the threshold, and outputs valid measurement data and corresponding quality evaluation results. The specific implementation process of S3 is as follows: Extract the quality assessment results within a continuous time period, combine the cumulative usage time U of the electrode, the cumulative number of measurements N, and the calibration data C of each time, establish a quantitative correspondence between the changes in quality assessment indicators and the degree of electrode performance degradation, and calculate the current health value H and remaining service life R of the electrode; divide the measurement data into different levels according to the quality level G, assign corresponding storage period P and access permission A to the data of each level, and establish a dynamic adjustment mechanism for the quality level; establish a one-to-one correspondence between the effective measurement data and the quality assessment results, electrode health information, environmental information, and operator information, and construct a multi-level retrieval system after multi-dimensional classification and organization to form a structured measurement data archive; The specific implementation process of S4 is as follows: Time series trend analysis is performed on historical data in the structured measurement data archive to identify abnormal change patterns in the data. When an abnormal trend affecting measurement accuracy or water quality safety is detected, an early warning message containing the location and cause of the abnormality is automatically generated and pushed to relevant personnel. Measurement reports with corresponding content and format are generated according to pre-configured report parameters. Multi-user hierarchical permission management and collaborative work are realized through the cloud system. Feedback information on measurement results and quality assessment results is collected, and correctly labeled data is extracted as incremental training samples. The conductivity measurement and assessment model is fine-tuned and optimized using incremental learning.

[0044] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A method for conductivity measurement and data management based on a mobile terminal, characterized in that, include: S1: The mobile terminal scans multiple communication channels and automatically selects the communication method to establish a connection with the measuring equipment based on the environmental signal strength and transmission requirements; it automatically identifies the equipment model and functional characteristics, loads the matching operation interface and measurement method; it configures the measurement parameters and verifies their rationality before synchronizing them to the measuring equipment and generating equipment parameter configuration information. S2: Based on the equipment parameter configuration information, a conductivity measurement and evaluation model is constructed; by integrating data fluctuation characteristics, temperature change patterns, and electrode response characteristics, the measuring equipment performs continuous measurement and transmits the raw data to the mobile terminal; the conductivity measurement and evaluation model is used to verify the quality of the raw data and output valid measurement data and quality evaluation results. S3: Based on the quality assessment results, combined with historical electrode usage and calibration data, calculate electrode health and remaining service life in real time; Establish a dynamic data management mechanism based on quality levels, allocate different storage periods and access permissions according to data credibility, and form a structured measurement data archive; S4: Based on the measurement data archive, perform classification statistics and trend analysis to generate abnormal early warning information and customized measurement reports; through the cloud system, perform multi-user hierarchical permission management and collaborative work, receive user feedback and continuously optimize the conductivity measurement and evaluation model.

2. The method according to claim 1, characterized in that, The specific selection process for establishing a connection between the communication method and the measuring device is as follows: The mobile terminal scans the communication channels in a preset order and collects three core indicators in real time: signal strength, transmission delay, and packet loss rate of each channel. Based on the real-time data transmission volume and transmission latency requirements of the current measurement task, each communication method is scored, and the communication method with the highest score is selected to initiate a connection request to the measurement device. If the connection fails, it automatically switches to the second-best communication method to retry. After two-way authentication, an encrypted communication connection is established.

3. The method according to claim 1, characterized in that, The specific loading process for the matching operation interface and measurement method is as follows: The mobile terminal extracts the device identifier and hardware version information returned by the measuring device, and retrieves the resource package that matches the device identifier and version from the pre-stored device resource set; It automatically parses the interface layout files and interaction logic files in the resource package, loads the operation interface that matches the device screen size and operating habits, and imports the measurement process, parameter range and operation specifications of the corresponding device.

4. The method according to claim 1, characterized in that, The specific process for generating the device parameter configuration information is as follows: Based on the identified measurement device information, the mobile terminal automatically retrieves the historical parameter configuration information of the corresponding device and pre-fills it into the operation interface; The mobile terminal performs multi-parameter correlation verification in real time, analyzes the logical matching relationship between parameters, dynamically prompts abnormal parameter combinations and provides optimization suggestions; After all parameters have been verified, all parameters are encrypted, packaged, and synchronized to the measuring device. At the same time, device parameter configuration information is generated and stored in the temporary cache area of ​​the mobile terminal.

5. The method according to claim 1, characterized in that, The conductivity measurement and evaluation model comprises a fusion layer, a mapping layer, and an output layer. The specific construction process is as follows: The total input consists of standardized data fluctuation characteristics, temperature change characteristics, and electrode response characteristics, while the total output consists of the quality score and quality level corresponding to each piece of raw data. The fusion layer performs cross-dimensional integration processing on the three types of input features, eliminating the differences in dimensionality and distribution bias between different features; The mapping layer learns the nonlinear mapping relationship between features and measurement quality, and extracts deep features that can characterize data quality. The output layer performs weighted calculations on the deep features and outputs the final quality assessment result, using historical labeled data for initial training.

6. The method according to claim 1, characterized in that, The specific process by which the measuring device performs continuous measurement and transmits the raw data to the mobile terminal is as follows: After receiving the synchronized parameter configuration information, the measuring equipment automatically performs self-test and electrode initialization. After the self-test passes, the continuous measurement process is started. The conductivity of the solution is acquired at a preset sampling interval. The analog signal is converted into a digital signal and then subjected to preliminary filtering. A timestamp and device identifier are added to each set of data. The data is then transmitted to the mobile terminal in real time through the established communication connection.

7. The method according to claim 1, characterized in that, The specific output process for the effective measurement data and quality assessment results is as follows: The mobile terminal inputs the received raw data into the conductivity measurement and evaluation model. The conductivity measurement and evaluation model extracts and analyzes multi-dimensional features of the data and outputs the quality score and quality level corresponding to each data point. Data with quality scores below a preset threshold are marked as outliers and removed. Data with quality scores that meet preset requirements are retained as valid measurement data. The quality assessment result corresponding to each valid data point is output.

8. The method according to claim 1, characterized in that, The specific calculation process for the electrode health and remaining service life is as follows: Extract multiple sets of quality assessment results from the conductivity measurement and evaluation model, and statistically analyze the variation of the deviation rate and fluctuation amplitude over time. The data were matched with the cumulative usage time, cumulative number of measurements, and performance recovery data after each calibration to establish a quantitative correlation between changes in quality assessment indicators and the degree of electrode performance degradation. Based on the quantified correspondence, the current performance degradation of the electrode is calculated, and then the electrode health value is obtained. The remaining usable time is then calculated by combining the standard service life curve of the electrode.

9. The method according to claim 1, characterized in that, The specific process for establishing the dynamic data management mechanism based on quality levels is as follows: Based on the quality assessment results, all measurement data are divided into multiple different quality levels. Taking into account the reliability of the measurement data and its business importance, corresponding differentiated management rules are formulated for each quality level. Different storage periods and access permissions are assigned to data of different quality levels. At the same time, a dynamic quality level adjustment mechanism is built to automatically update the data quality level and corresponding management strategy based on subsequent verification results.

10. The method according to claim 1, characterized in that, The specific process for forming the structured measurement data archive is as follows: Establish a one-to-one correspondence between valid measurement data and corresponding quality assessment results, electrode health information, measurement environment information and operator information to form a single complete measurement record unit; All measurement record units are categorized into layers based on four dimensions: measurement time, measurement location, measurement task type, and equipment model. A standardized data structure is adopted to organize various types of information in a unified manner, a multi-level rapid retrieval system is constructed, and each record is stored in a structured database after integrity verification, forming a traceable, searchable and analyzable structured measurement data archive.

11. The method according to claim 1, characterized in that, The specific process for generating the abnormal early warning information and the customized measurement report is as follows: Perform time series trend analysis on historical data in structured measurement data archives to identify abnormal fluctuations, continuous increases or decreases, and other abnormal change patterns in the data; When an abnormal trend affecting measurement accuracy or equipment safety is detected, an early warning message containing the abnormal location and cause is automatically generated and pushed to the mobile terminal. A measurement report with corresponding content modules and display style is generated according to the pre-configured report parameters.

12. The method according to claim 1, characterized in that, The specific optimization process of the conductivity measurement and evaluation model is as follows: The system collects confirmation and correction feedback information on measurement results and quality assessment results, extracts correctly labeled data from the feedback and organizes it into incremental training samples, retaining the original core parameters and basic feature extraction capabilities of the conductivity measurement and assessment model. The conductivity measurement and evaluation model was fine-tuned by adjusting the feature weights and quality judgment thresholds, and the optimized performance was verified using a validation set.

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