Comprehensive assessment methods, devices, equipment, and media for pH electrode health

Abstract

This invention discloses a comprehensive assessment method for the health of a pH electrode, comprising: acquiring health assessment data of the pH electrode to be assessed, the assessment data including time series of multiple electrode performance index values; the multiple electrode performance index values ​​including: response slope, zero-point potential, and internal resistance; the internal resistance is obtained by online real-time measurement and temperature normalization; obtaining the historical drift trend based on the changing trend of one or more of the multiple electrode performance index values ​​in the time series; and obtaining the pH electrode health score by weighted sum of the time series of multiple electrode performance index values ​​and the historical drift trend, which can effectively reduce the misjudgment rate of electrode status.

Description

Technical Field

[0001] The embodiments of the present invention relate to the field of chemical sensor health assessment technology, and in particular to a method, apparatus, device and medium for comprehensive assessment of pH electrode health. Background Technology

[0002] pH electrodes are key sensors for online pH monitoring in industrial production, and their performance gradually degrades over time. Currently, methods for determining the health status of pH electrodes mainly rely on threshold judgments for individual performance indicators. For example:

[0003] 1. One known technique is to measure the slope of the electrode's response in a standard buffer solution, and determine that the electrode needs to be replaced when the slope is less than 90% of its nominal value.

[0004] 2. The second known technology focuses on monitoring the zero-point potential (equipotential point) of the electrode, and issues an alarm when the zero-point potential drifts by more than ±30mV.

[0005] 3. Some advanced instruments have an electrode internal resistance measurement function. When the internal resistance exceeds a certain fixed threshold (such as 1000 MΩ), it indicates electrode aging.

[0006] However, existing technologies rely on a single dimension for assessment, which is insufficient to reflect the true health status of the electrode itself, resulting in a high rate of misjudgment. Therefore, how to effectively evaluate the health of electrodes and thus reflect their true health status has become a pressing technical problem that needs to be solved in this field. Summary of the Invention

[0007] This invention provides a method, apparatus, device, and medium for comprehensive evaluation of pH electrode health. By integrating multiple indicators, the health of the electrode is comprehensively evaluated, effectively reducing the electrode misjudgment rate.

[0008] In a first aspect, embodiments of the present invention provide a comprehensive assessment method for the health of a pH electrode, comprising:

[0009] Health assessment data of the pH electrode to be evaluated is obtained, and the assessment data includes time series of multiple electrode performance index values; the multiple electrode performance index values ​​include: response slope, zero-point potential, and internal resistance; wherein, the internal resistance is obtained by online real-time measurement and temperature normalization.

[0010] The historical drift trend is obtained by analyzing the changing trend of one or more of the time series values ​​of the multiple electrode performance indicators.

[0011] The health score of the pH electrode is obtained by weighting the time series of the multiple electrode performance index values ​​and the historical drift trend.

[0012] As one embodiment, the method further includes:

[0013] The category of the pH electrode is determined according to a preset classification algorithm;

[0014] The weights of each index of the pH electrode are obtained based on the category of the pH electrode and the corresponding category weight relationship.

[0015] As one embodiment, determining the category of the pH electrode according to a preset classification algorithm includes:

[0016] Obtain the characteristic data of the pH electrode to be classified;

[0017] The feature data is preprocessed;

[0018] The affinity clustering algorithm is used to obtain the category of each pH electrode to be classified based on the preprocessed feature data.

[0019] As an example, the characteristic data of the pH electrode to be classified includes: index-type characteristic data and operating condition characteristic data;

[0020] The indicator-type feature data includes one or more of the following indicators:

[0021] The temperature drift coefficients of the response slope, zero potential, internal resistance, liquid junction potential, zero potential and / or response slope, glass film ratio of the measuring electrode, response slope decay rate, zero potential decay rate, and internal resistance aging rate are included; the temperature drift coefficient is the amount of drift of the zero potential and / or response slope with temperature.

[0022] The operating condition characteristic data includes one or more of the following: temperature, pressure, flow rate, conductivity coefficient, electrode contamination coefficient, and electrode wear coefficient.

[0023] Accordingly, the preprocessing of the feature data includes:

[0024] The feature vectors of the corresponding electrodes are obtained by preprocessing the index-type feature data and the operating condition feature data respectively and then concatenating them.

[0025] The process of obtaining the category of each pH electrode to be classified using an affinity clustering algorithm based on the preprocessed feature data includes:

[0026] Calculate the similarity matrix based on the feature vectors of the electrodes;

[0027] Initialize responsibility values ​​and attribution levels;

[0028] The responsibility value and affiliation degree are updated according to the responsibility value and affiliation degree update formula to update the cluster centers iteratively;

[0029] Determine whether the cluster centers are stable or the maximum number of iterations has been reached. If so, determine the cluster centers; otherwise, continue to update and iterate the cluster centers.

[0030] The cluster neighbors of each cluster center are calculated based on the obtained cluster centers; the classification results of the pH electrodes are obtained based on the cluster centers and cluster neighbors.

[0031] As one embodiment, the method further includes:

[0032] The aging rate of the pH electrode is obtained based on the historical data of the response slope of the pH electrode;

[0033] The remaining lifespan of the pH electrode is predicted based on the aging rate and the current health score of the pH electrode.

[0034] As one embodiment, the method for obtaining health assessment data of the pH electrode to be evaluated further includes:

[0035] The response slope alarm threshold of the pH electrode is dynamically adjusted based on the response slope time series of the pH electrode.

[0036] As one embodiment, the internal resistance is obtained by online real-time measurement and temperature normalization, including:

[0037] The internal resistance of the pH electrode is measured by applying a preset AC signal to the pH electrode.

[0038] The temperature information of the pH electrode when measuring the internal resistance is obtained by a temperature sensor;

[0039] The normalized internal resistance is obtained by normalizing the temperature based on the temperature information, the measured internal resistance, and the Arrhenius equation.

[0040] Secondly, embodiments of the present invention provide a comprehensive pH electrode health assessment device, comprising:

[0041] The acquisition module is used to acquire health assessment data of the pH electrode to be evaluated. The assessment data includes time series of multiple electrode performance index values. The multiple electrode performance index values ​​include: response slope, zero-point potential, and internal resistance. The internal resistance is obtained by online real-time measurement and temperature normalization.

[0042] The trend calculation module is used to obtain the historical drift trend based on the changing trend of one or more of the time series values ​​of the multiple electrode performance indicators.

[0043] The scoring module is used to obtain a health score for the pH electrode based on the time series of the multiple electrode performance index values ​​and the weighted sum of historical drift trends.

[0044] Thirdly, embodiments of the present invention provide a positioning device, including a memory and a processor;

[0045] A memory is used to store a computer program; the processor is used to read the computer program in the memory and, when executing the program, implement the comprehensive pH electrode health assessment method as described above.

[0046] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the comprehensive assessment method for pH electrode health as described in the first aspect.

[0047] Compared with the prior art, the technical solution provided by the embodiments of the present invention has at least the following positive effects:

[0048] In the technical solution of this invention, health assessment data of the pH electrode to be evaluated is obtained. The assessment data includes time series of multiple electrode performance index values. The multiple electrode performance index values ​​include: response slope, zero-point potential, and internal resistance. The internal resistance is obtained by online real-time measurement and temperature normalization. The historical drift trend is obtained based on the changing trend of one or more of the multiple electrode performance index values ​​in the time series. The health score of the pH electrode is obtained by weighting the time series of multiple electrode performance index values ​​and the historical drift trend. By fusing multi-dimensional indicators, a comprehensive electrode health score is obtained, avoiding high misjudgment due to a single dimension. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart illustrating the comprehensive assessment method for pH electrode health provided in Embodiment 1 of the present invention.

[0051] Figure 2 This is a schematic diagram of the pH electrode health assessment device provided in Embodiment 2 of the present invention.

[0052] Figure 3 This is a schematic diagram of the pH electrode health comprehensive assessment device provided in Embodiment 3 of the present invention. Detailed Implementation

[0053] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.

[0054] The inventors discovered the following problems with current pH electrode health assessment methods:

[0055] The judgment is based on a single dimension, resulting in a high misjudgment rate: relying solely on a single indicator such as response slope, zero-point potential, or internal resistance makes it highly susceptible to instantaneous fluctuations in the measurement conditions. For example, in the low-conductivity water environment of a power plant, the measured slope of a new electrode may remain below 90% for an extended period, which would be misjudged as failure according to this standard; conversely, instantaneous zero-point drift caused by drastic temperature changes may also be misjudged as electrode damage.

[0056] The influence of environmental operating conditions was not considered: key environmental parameters affecting electrode response, such as temperature, pressure, and fluid flow rate, were not normalized, resulting in the evaluation results not accurately reflecting the health status of the electrode itself. For example, temperature changes significantly affect the internal resistance of the electrode, and pressure or flow rate changes affect the stability of the liquid junction potential.

[0057] Lack of forward-looking lifetime prediction: Existing methods can only make a binary judgment of "good / bad" for the current state of the electrode, and cannot predict the remaining lifetime based on historical performance degradation trends, so users cannot implement predictive maintenance.

[0058] Poor adaptability: Using a fixed, one-size-fits-all judgment threshold cannot adapt to the performance differences of electrodes at different aging stages (such as new electrodes and old electrodes), which may lead to premature replacement or use beyond the expiration date.

[0059] Based on this, the embodiments of the present invention use a multi-dimensional index fusion, operating condition normalization, and threshold adaptive joint mechanism to judge the health status of the electrode, thereby improving the accuracy of the judgment. At the same time, it can also accurately predict the remaining life of the electrode based on the historical performance degradation trend of the electrode, thereby improving the electrode maintenance and management capabilities.

[0060] Figure 1This is a flowchart illustrating the comprehensive pH electrode health assessment method provided in this embodiment of the invention, used to accurately assess the pH electrode health status. This method can be executed by a comprehensive pH electrode health assessment device provided in this embodiment of the invention. This device can be implemented using software and / or hardware and configured in an industrial control system's comprehensive pH electrode health evaluation equipment. The comprehensive pH electrode health evaluation system includes a signal acquisition module. The pH electrode and temperature, pressure, and flow rate sensors are respectively connected to the signal acquisition module. The signal acquisition module transmits data to the comprehensive pH electrode health evaluation equipment, which is responsible for executing the steps of the comprehensive pH electrode health assessment method of this invention. The calculation results are displayed locally through a display module and can be uploaded to a remote monitoring system via a communication interface (such as 4-20mA, HART, Modbus). Figure 1 As shown, the comprehensive assessment method for pH electrode health in this application includes the following steps:

[0061] Step 101: Obtain the health assessment data of the pH electrode to be evaluated. The assessment data includes time series of multiple electrode performance index values.

[0062] Several electrode performance parameters include: response slope, zero-point potential, and internal resistance. The internal resistance is obtained through online real-time measurement and temperature normalization.

[0063] Multiple electrode performance indicators include: response slope, zero-point potential, internal resistance, and temperature drift coefficient. The response slope, zero-point potential, and temperature drift coefficient are periodically collected to obtain corresponding indicator value sequences. Specifically, the zero-point potential can be the potential value measured by the pH electrode in a pH 7.00 standard buffer solution. The response slope can be calculated as the percentage of the actual slope to the theoretical Nernst slope by measuring the potential of the electrode in at least two standard buffer solutions with different pH values ​​(e.g., pH 4.01 and pH 9.18). The temperature drift coefficient can be the amount of drift of either the zero-point potential or the response slope with temperature. Taking the temperature drift coefficient of the zero-point potential as an example, when collecting the zero-point potential, the standard buffer solution is controlled at different temperatures, and the zero-point potential corresponding to different temperatures is collected, thus obtaining the temperature drift coefficient of the zero-point potential. Similarly, the temperature drift coefficient of the response slope can be obtained.

[0064] Obtaining the internal resistance through online real-time measurement and temperature normalization may include: measuring the internal resistance of the pH electrode by applying a preset AC signal to it; acquiring the temperature information of the pH electrode during the internal resistance measurement using a temperature sensor; and performing temperature normalization based on the temperature information, the measured internal resistance, and the Arrhenius equation to obtain the normalized internal resistance. Temperature compensation for the internal resistance is performed using the Arrhenius equation, and the correction formula is as follows:

[0065] Rcorr = Rmeas * exp[β * (Tmeas^(-1) - Tref^(-1))]; where Rcorr is the internal resistance corrected to the reference temperature Tref (e.g., 25℃), Rmeas is the internal resistance measured at the measurement temperature Tmeas, and β is the characteristic constant of the electrode material, which needs to be converted to Kelvin temperature during calculation. By normalizing the online acquired internal resistance to the internal resistance at the reference temperature, the influence of ambient temperature fluctuations on the internal resistance value can be eliminated, thus truly reflecting the aging and contamination degree of the electrode.

[0066] Step 102: Obtain the historical drift trend based on the changing trend of one or more of the time series values ​​of multiple electrode performance indicators.

[0067] Specifically, the historical drift trend of the electrode can be obtained based on the historical drift trends of one or more indicators. The drift trend of the response slope is determined as follows: An ideal drift velocity threshold and a failure drift velocity threshold are set, for example, 0.1% / month and 1% / month, respectively; the actual monthly drift velocity is calculated based on the response slope time series obtained in step 101, and a response slope drift trend score is calculated accordingly. The scoring uses a percentage system: when the monthly drift velocity is less than or equal to the ideal drift velocity threshold (0.1% / month), the score is 100 points; when the monthly drift velocity is greater than or equal to the failure drift velocity threshold (1% / month), the score is 0 points; for values ​​between these two, a score is calculated using linear interpolation, thus obtaining the current historical drift score of the response slope. Similarly, drift trend scores for zero-point potential and internal resistance can be obtained. The historical drift trend of the pH electrode can be obtained by weighted summing the drift trends of the response slope, zero-point potential, and internal resistance. Using historical drift trends can increase the confidence of new electrodes and avoid misjudgments based on a single indicator.

[0068] Step 103: Obtain the health score of the pH electrode based on the time series of multiple electrode performance index values ​​and the weighted sum of historical drift trends.

[0069] Specifically, the scores and weights for each dimension of the index, including response slope, zero-point potential, internal resistance, and historical drift trend, are determined. The health score HS = (S_slope × W1) + (S_zero-point × W2) + (S_internal resistance × W3) + (S_trend × W4). W1 to W4 represent the weights of the corresponding indices. For example, W1 to W4 might take values ​​of 40%, 30%, 20%, and 10%, respectively. Due to different electrode conditions, the weights can be adjusted accordingly; for instance, W1 to W4 could also be 35%, 35%, 20%, and 10%. For example, the response slope score can be obtained as follows: for a new electrode, a score of 100 points can be set when the response slope is greater than or equal to 54 mV / pH, and a score of 0 points when the response slope is less than 50 mV / pH. Then, scores for other response slope values ​​are obtained through interpolation or piecewise linear mapping. Similarly, scores for zero-point potential, internal resistance, and historical drift trend can be obtained, which will not be elaborated here. It should be noted that the scoring weight of each indicator can be set based on experience or expert rules, which will not be elaborated here.

[0070] The normalized indicators of each dimension are weighted and fused to obtain a comprehensive health score, thereby achieving a quantitative assessment of the electrode status.

[0071] In one example, the category of pH electrode can be determined by a preset classification algorithm. The weights of each index of pH electrode can be obtained based on the category of pH electrode and the correspondence between the category weights. By classifying pH electrodes, electrodes with similar index characteristics can reuse the scoring weights, thereby improving the accuracy of the scoring and reducing the workload of accurately setting the scoring weights for different electrodes.

[0072] Specifically, determining the category of pH electrodes according to a preset classification algorithm may include: acquiring feature data of pH electrodes to be classified, preprocessing the feature data, and using an affinity clustering algorithm based on the preprocessed feature data to obtain the category of each pH electrode to be classified.

[0073] The characteristic data of the pH electrodes to be classified includes: index-type characteristic data and operating condition characteristic data. Index-type characteristic data may include one or more of the following indicators: response slope, zero-point potential, internal resistance, liquid junction potential, temperature drift coefficient of zero-point potential and / or response slope, glass film ratio of the measuring electrode, response slope decay rate, zero-point potential decay rate, and internal resistance aging rate; the temperature drift coefficient is the amount of drift of zero-point potential and / or response slope with temperature change. The response slope and zero-point potential can be measured in a standard buffer solution, but are not limited to this; they can also be values ​​obtained under other conditions that characterize the electrode's linear response capability and zero-point drift. Internal resistance can be measured online. Liquid junction potential can also be measured online or in a standard buffer solution. The glass film ratio of the measuring electrode can be found through product model and specifications. The response slope decay rate, zero-point potential decay rate, and internal resistance aging rate can be calculated based on the collected index sequences, such as the daily decay rate and monthly decay rate of the response slope, which can be determined according to the required data accuracy.

[0074] Operating condition characteristic data includes one or more of the following: temperature, pressure, flow rate, conductivity coefficient, electrode contamination coefficient, and electrode wear coefficient. It is understood that any index or parameter that can characterize electrode health and is obtainable can be used in this application, without undue limitation. For example, the aging rate of electrodes differs in different temperature ranges, and aging accelerates in some temperature ranges; furthermore, different indicators decay at different rates during aging. Electrode wear varies under different pressures and flow rates. Electrode health deterioration also varies in environments with varying degrees of physical wear and chemical corrosion.

[0075] Feature data preprocessing may include: preprocessing index-type feature data and operating condition feature data separately, and then concatenating them to obtain the corresponding electrode feature vector. Specifically, normalization is performed on indicators such as response slope, zero-point potential, and internal resistance. Normalization is also performed on temperature, pressure, and flow rate. Thresholding is applied to the electrode ID information. The electrode contamination coefficient characterizes the ability of the measuring liquid to contaminate the electrode, and the electrode wear coefficient characterizes the wear performance of the liquid on the electrode. Other operating condition data can be segmented and mapped according to coefficients, converting them into corresponding level feature vectors. The index-type feature vector and the operating condition feature vector are then concatenated to obtain the electrode feature vector.

[0076] The process of using affinity clustering algorithm to classify each pH electrode based on the preprocessed feature data may include the following sub-steps:

[0077] Sub-step 201: Calculate the similarity matrix based on the eigenvectors of the electrodes.

[0078] Specifically, the distance matrix of the electrodes to be classified can be obtained by calculating the Euclidean distance, and then the distance elements in the distance matrix can be negatively evaluated to obtain the corresponding similarity matrix. Alternatively, the distance can be converted into similarity values ​​using the Gaussian kernel function to obtain the similarity matrix.

[0079] Sub-step 202: Initialize responsibility value and attribution degree.

[0080] Sub-step 203: Update the responsibility value and belonging degree according to the responsibility value and belonging degree update formula to update the iterative cluster centers.

[0081] Sub-step 204: Determine whether the cluster center is stable or the maximum number of iterations has been reached. If yes, determine the cluster center; otherwise, continue to update and iterate the cluster center.

[0082] Sub-step 205: Calculate the cluster neighbors of each cluster center based on the obtained cluster centers.

[0083] Then, the pH electrode classification results are obtained based on the cluster centers and their neighbors. The number of cluster centers represents the number of electrode categories. Electrodes belonging to the same cluster center are of the same category and can reuse a set of scoring weights. It should be noted that electrodes can be classified periodically, thereby dynamically adjusting the electrode weights.

[0084] It is worth mentioning that the aging rate of the pH electrode can also be obtained from historical data on the response slope of the pH electrode, and the remaining lifespan of the pH electrode can be predicted based on the aging rate and the current health score of the pH electrode. Alternatively, based on the historical curve of the health score decreasing over time, prediction algorithms such as linear regression and exponential smoothing can be used to calculate the time required for the health score to decrease to a preset failure threshold (e.g., 60 points), which is the estimated remaining lifespan. No specific limitations are imposed here.

[0085] It should be noted that the pH electrode's response slope alarm threshold can also be dynamically adjusted based on the time series of the pH electrode's response slope. For example, the alarm threshold for the electrode slope can be determined based on the interval in which the electrode's slope falls over a recent period, thus avoiding frequent false alarms online.

[0086] The following describes two application examples of the pH electrode health comprehensive assessment method according to embodiments of the present invention.

[0087] Application Example 1: Application in the boiler feedwater system of thermal power plants

[0088] The boiler feedwater in a thermal power plant is low conductivity water, with a temperature fluctuating between 25°C and 50°C, and a pipeline pressure stable at around 1.0 MPa. An online monitoring device supporting the pH electrode health comprehensive assessment method of this invention is connected to the pH electrode in the boiler feedwater system.

[0089] The online monitoring device automatically performs an evaluation periodically (e.g., every 8 hours): First, the automatic calibration unit is controlled to introduce standard buffer solutions of pH 6.86 and pH 4.01 into the electrode, and the potential values ​​are collected to calculate the zero-point potential and response slope. At the same time, the water temperature and pipeline pressure are collected.

[0090] The measured internal resistance values ​​are uniformly corrected to the standard value at 25℃. At the same time, based on real-time flow data, the judgment requirements for short-term fluctuations in zero-point potential are automatically and temporarily relaxed during low-flow operation of the system.

[0091] The system calculated the current overall health score (HS) of the pH electrode to be 82. Historical data indicates that the electrode's slope has been slowly declining at an average rate of 0.2% per month over the past year. The system predicts its remaining lifespan to be approximately 15 months. Due to the slow rate of decline, the system classifies the electrode as "sub-healthy and still usable" and recommends a reassessment in 12 months.

[0092] The comprehensive pH electrode health assessment method of this invention avoids the risk of misjudging electrode failure based on a single slope standard, avoids premature replacement of a single electrode, and significantly saves costs.

[0093] Application Example 2: Management of aging electrodes in chemical reaction processes

[0094] At a chemical plant's reactor pH monitoring point, an electrode had been in continuous use for 18 months. The system recognized this prolonged use and automatically adjusted its slope alarm threshold from 95% (for new electrodes) to 80%. In a recent assessment, its temperature-corrected slope was 82%, with a comprehensive health score (HS) of 70. Although the slope was above the adaptive threshold (80%), the status was "Caution." Further analysis of historical data revealed that the slope degradation rate had accelerated to 1.0% per month over the past two months, far exceeding the preset warning line of 0.5% per month. The system immediately triggered a "Performance Accelerated Degradation" warning and, based on the accelerated degradation model, re-estimated its remaining lifespan to be only about one month. This provided the maintenance team with clear decision-making information and ample preparation time, allowing them to complete electrode replacement during planned downtime and effectively avoiding the risk of production interruptions and product defects that could result from sudden electrode failure during operation.

[0095] Compared with the prior art, the embodiments of the present invention have the following significant advantages:

[0096] 1. Significantly improved accuracy and reliability: By integrating multi-dimensional information such as zero point, slope, internal resistance, temperature drift, and historical trends, the limitations of relying on single indicators are effectively avoided, significantly reducing the misjudgment rate. Experiments show that under complex operating conditions, the misjudgment rate can be reduced from over 30% with traditional methods to less than 5%.

[0097] 2. Strong resistance to operating condition interference: Through operating condition normalization correction, the interference of environmental factors such as temperature, pressure, and flow rate on the evaluation indicators is eliminated, so that the evaluation results can more accurately reflect the degradation of the electrode body performance and improve the comparability and accuracy of the evaluation results.

[0098] 3. Predictive maintenance is achieved: By analyzing historical drift trends and predicting remaining service life, this invention enables maintenance personnel to plan electrode replacement in advance, transforming passive maintenance into predictive maintenance and effectively reducing the risk of unplanned downtime.

[0099] 4. Strong adaptability and significant economic benefits: The threshold adaptive mechanism conforms to the objective law of electrode aging, avoids premature replacement of old electrodes with still acceptable performance, extends the effective service life of electrodes, and reduces spare parts costs and maintenance expenses.

[0100] Embodiment 2 of the present invention provides a comprehensive pH electrode health assessment device, which is configured in a comprehensive pH electrode health assessment equipment. For example... Figure 2 As shown, the evaluation device 200 includes: an acquisition module 201, a trend calculation module 202, and a scoring module 203.

[0101] The acquisition module 201 is used to acquire the health assessment data of the pH electrode to be evaluated. The assessment data includes a time series of multiple electrode performance index values. The multiple electrode performance index values ​​include: response slope, zero-point potential, and internal resistance. The internal resistance is obtained by online real-time measurement and temperature normalization.

[0102] The trend calculation module 202 is used to obtain the historical drift trend based on the changing trend of one or more of the time series values ​​of the multiple electrode performance indicators.

[0103] The scoring module 203 is used to obtain the health score of the pH electrode based on the time series of the multiple electrode performance index values ​​and the weighted sum of historical drift trends.

[0104] Optionally, the evaluation device 200 may further include a classification module for determining the category of the pH electrode according to a preset classification algorithm; and a weight setting module for obtaining the weights of each index of the pH electrode according to the category of the pH electrode and the correspondence between category weights.

[0105] Optionally, the classification module may include: a feature acquisition module, a preprocessing module, and an affinity clustering module.

[0106] The feature acquisition module is used to acquire feature data of the pH electrodes to be classified. The preprocessing module is used to preprocess the feature data. The affinity clustering module is used to obtain the category of each pH electrode to be classified based on the preprocessed feature data using an affinity clustering algorithm.

[0107] The characteristic data of the pH electrodes to be classified include: index-type characteristic data and operating condition characteristic data;

[0108] The index-type feature data includes one or more of the following indicators: response slope, zero-point potential, internal resistance, liquid junction potential, temperature drift coefficient of zero-point potential and / or response slope, glass film ratio of measuring electrode, response slope decay rate, zero-point potential decay rate, and internal resistance aging rate; the temperature drift coefficient is the amount of drift of zero-point potential and / or response slope with temperature change.

[0109] The operating condition characteristic data includes one or more of the following: temperature, pressure, flow rate, conductivity coefficient, electrode contamination coefficient, and electrode wear coefficient.

[0110] Accordingly, the preprocessing module is specifically used to preprocess the index-type feature data and the operating condition feature data respectively, and then concatenate them to obtain the feature vector of the corresponding electrode.

[0111] The affinity clustering module is specifically used for: calculating a similarity matrix based on the feature vectors of the electrodes; initializing responsibility values ​​and affiliation degrees; updating responsibility values ​​and affiliation degrees according to the responsibility value and affiliation degree update formula to update the iterative cluster centers; determining whether the cluster centers are stable or have reached the maximum number of iterations; if so, determining the cluster centers; otherwise, continuing to update and iterate the cluster centers; calculating the cluster neighbors of each cluster center based on the obtained cluster centers; and obtaining the classification results of the pH electrodes based on the cluster centers and their cluster neighbors.

[0112] Optionally, the evaluation apparatus may further include:

[0113] An aging data calculation module is used to obtain the aging rate of the pH electrode based on historical data of the pH electrode's response slope; a lifespan prediction module is used to predict the remaining lifespan of the pH electrode based on the aging rate and the current health score of the pH electrode.

[0114] The evaluation device also includes:

[0115] The threshold adaptive adjustment module is used to dynamically adjust the pH electrode response slope alarm threshold based on the pH electrode response slope time series.

[0116] The acquisition module 201 is also used to measure the internal resistance of the pH electrode by applying a preset AC signal to the pH electrode; acquire the temperature information of the pH electrode when measuring the internal resistance by a temperature sensor; and perform temperature normalization based on the temperature information, the measured internal resistance and the Arrhenius equation to obtain the normalized internal resistance.

[0117] Figure 3This is a schematic diagram of the evaluation device provided in Embodiment 3 of the present invention. The control device 30 includes a memory 31 and a processor 32;

[0118] The memory 31 is used to store computer programs; the processor 32 is used to read the computer programs stored in the memory 31 and, when executing the programs, implement the comprehensive pH electrode health assessment method as described in the foregoing embodiments.

[0119] Embodiment 4 of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a computer processor, is used to perform the technical solution of any method embodiment.

[0120] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or grid device, etc.) to execute the methods described in the various embodiments of the present invention.

[0121] It is worth noting that in the embodiments of the above-mentioned device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of the present invention.

[0122] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.

Claims

1. A comprehensive assessment method for the health of a pH electrode, characterized in that, include: Obtain health assessment data for the pH electrode to be evaluated, the assessment data including time series of multiple electrode performance index values; The multiple electrode performance indicators include: response slope, zero-point potential, and internal resistance; wherein, the internal resistance is obtained by online real-time measurement and temperature normalization. The historical drift trend is obtained by analyzing the changing trend of one or more of the time series values ​​of the multiple electrode performance indicators. The health score of the pH electrode is obtained by weighting the time series of the multiple electrode performance index values ​​and the historical drift trend.

2. The method according to claim 1, characterized in that, The method further includes: The category of the pH electrode is determined according to a preset classification algorithm; The weights of each index of the pH electrode are obtained based on the category of the pH electrode and the corresponding category weight relationship.

3. The method according to claim 2, characterized in that, The step of determining the category of the pH electrode according to a preset classification algorithm includes: Obtain the characteristic data of the pH electrode to be classified; The feature data is preprocessed; The affinity clustering algorithm is used to obtain the category of each pH electrode to be classified based on the preprocessed feature data.

4. The method according to claim 3, characterized in that, The characteristic data of the pH electrode to be classified includes: index-type characteristic data and operating condition characteristic data; The indicator-type feature data includes one or more of the following indicators: The temperature drift coefficients of the response slope, zero potential, internal resistance, liquid junction potential, zero potential and / or response slope, glass film ratio of the measuring electrode, response slope decay rate, zero potential decay rate, and internal resistance aging rate are included; the temperature drift coefficient is the amount of drift of the zero potential and / or response slope with temperature. The operating condition characteristic data includes one or more of the following: temperature, pressure, flow rate, conductivity coefficient, electrode contamination coefficient, and electrode wear coefficient. Accordingly, the preprocessing of the feature data includes: The feature vectors of the corresponding electrodes are obtained by preprocessing the index-type feature data and the operating condition feature data respectively and then concatenating them. The process of obtaining the category of each pH electrode to be classified using an affinity clustering algorithm based on the preprocessed feature data includes: Calculate the similarity matrix based on the feature vectors of the electrodes; Initialize responsibility values ​​and attribution levels; The responsibility value and affiliation degree are updated according to the responsibility value and affiliation degree update formula to update the cluster centers iteratively; Determine whether the cluster centers are stable or the maximum number of iterations has been reached. If so, determine the cluster centers; otherwise, continue to update and iterate the cluster centers. The cluster neighbors of each cluster center are calculated based on the obtained cluster centers; the classification results of the pH electrodes are obtained based on the cluster centers and cluster neighbors.

5. The method according to claim 1, characterized in that, The method further includes: The aging rate of the pH electrode is obtained based on the historical data of the response slope of the pH electrode; The remaining lifespan of the pH electrode is predicted based on the aging rate and the current health score of the pH electrode.

6. The method according to claim 1, characterized in that, The method further includes: The response slope alarm threshold of the pH electrode is dynamically adjusted based on the response slope time series of the pH electrode.

7. The method according to claim 1, characterized in that, The internal resistance is obtained through online real-time measurement and temperature normalization, including: The internal resistance of the pH electrode is measured by applying a preset AC signal to the pH electrode. The temperature information of the pH electrode when measuring the internal resistance is obtained by a temperature sensor; The normalized internal resistance is obtained by normalizing the temperature based on the temperature information, the measured internal resistance, and the Arrhenius equation.

8. A comprehensive pH electrode health assessment device, characterized in that, include: The acquisition module is used to acquire health assessment data of the pH electrode to be evaluated, the assessment data including time series of multiple electrode performance index values; The multiple electrode performance indicators include: response slope, zero-point potential, and internal resistance; wherein, the internal resistance is obtained by online real-time measurement and temperature normalization. The trend calculation module is used to obtain the historical drift trend based on the changing trend of one or more of the time series values ​​of the multiple electrode performance indicators. The scoring module is used to obtain a health score for the pH electrode based on the time series of the multiple electrode performance index values ​​and the weighted sum of historical drift trends.

9. A comprehensive pH electrode health assessment device, characterized in that, Including memory and processor; A memory for storing a computer program; the processor for reading the computer program in the memory and, when executing the program, implementing the pH electrode health comprehensive assessment method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the comprehensive assessment method for pH electrode health as described in any one of claims 1-7.

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