Water quality detection equipment calibration system based on artificial intelligence assistance

Abstract

The invention relates to the technical field of hydrological detection, and discloses a water quality detection equipment calibration system based on artificial intelligence assistance, which comprises a data preprocessing module, a drift identification module, a model comparison module, a self-adaptive calibration module and a feedback updating module. According to the water quality detection equipment calibration system based on artificial intelligence assistance provided by the invention, the aspects of data processing precision, drifting identification capability, calibration response efficiency and model adaptability are remarkably improved. Through cooperative work of multiple modules, the system can realize full-process intelligent operation from original data acquisition to calibration execution to model self-updating, the human intervention frequency is reduced, and the stability and accuracy of long-term operation of equipment are improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of hydrological detection, in particular to a water quality detection equipment calibration system based on artificial intelligence assistance. BACKGROUND

[0002] In water environment monitoring, water quality sensors, as key equipment, can obtain important parameters such as pH, dissolved oxygen, conductivity and turbidity in water in real time, and are basic tools for realizing automatic and intelligent water quality management. In recent years, with the development of artificial intelligence technology, AI assisted systems have begun to be introduced into water quality detection equipment, and have shown significant potential in equipment state judgment and data calibration. With the help of machine learning algorithms, these systems can help analyze the stability of sensor data and identify potential error sources, thereby improving the accuracy and reliability of detection.

[0003] However, there are still key problems in current practical applications: water quality sensors may drift during long-term operation, and the drift phenomenon may be manifested as systematic deviation or local anomaly. Traditional manual calibration methods often have long cycles, high dependence on manual work and delayed response, and it is difficult to timely distinguish and respond to these two types of drift. If the AI model cannot effectively distinguish between systematic deviation and local anomaly, it may lead to misjudgment or misadjustment, thereby affecting the credibility of the monitoring results.

[0004] Therefore, we propose a water quality detection equipment calibration system based on artificial intelligence assistance to solve the above problems. SUMMARY

[0005] The purpose of the present application is to provide a water quality detection equipment calibration system based on artificial intelligence assistance to solve the problems of hydrological detection in the background art.

[0006] To achieve the above purpose, the present application provides the following technical scheme: a water quality detection equipment calibration system based on artificial intelligence assistance, comprising a data preprocessing module, a drift identification module, a model comparison module, an adaptive calibration module and a feedback updating module. The data preprocessing module is used for real-time acquisition of original data of the water quality sensor, and performs denoising, outlier processing and the like. The drift identification module is used for identifying drift signals in sensor output by using statistical analysis and AI algorithms; and distinguishing between systematic deviation and local anomaly according to mode characteristics. The model comparison module is used for constructing a physical model based on historical stable data as an ideal output reference, and dynamically comparing with the current sensor readings. The adaptive calibration module is used for implementing systematic or local calibration adjustment to optimize the output result. The feedback updating module is used for feedback learning and model updating.

[0007] Preferably, the data preprocessing module comprises a data acquisition unit, a data cleaning unit and a data smoothing unit. The data acquisition unit is used for acquiring original detection data of the water quality sensor in real time, including water quality sensor original parameters, sensor device parameters and environmental variable parameters. The data cleaning unit is used for performing preliminary processing on the collected original data, including noise filtering, missing value repair and abnormal value elimination. The data smoothing unit is used for performing smoothing processing on the cleaned time series data.

[0008] Preferably, the data smoothing unit processes the data to be arranged into a water quality sensor original data group, a sensor device data group and an environmental variable data group. The water quality sensor original data group comprises pH value A, dissolved oxygen value B, conductivity C, turbidity D and temperature E. The sensor device data group comprises working voltage F, working current G, signal-to-noise ratio H and device temperature I. The environmental variable data group comprises water flow rate J, water flow volume K, humidity L and rainfall M.

[0009] Preferably, the drift identification module comprises a feature extraction unit, a drift detection unit and a type classification unit. The feature extraction unit is used for extracting key feature information reflecting data change trend from the preprocessed water quality sensor time series data, including pH value A, dissolved oxygen value B, conductivity C, turbidity D, temperature E, working voltage F, working current G, signal-to-noise ratio H, device temperature I, water flow rate J, water flow volume K, humidity L, rainfall M, water flow rate J, water flow volume K, humidity L and rainfall M. The drift detection unit is used for performing drift discriminant analysis based on the extracted feature data by using a statistical method.

[0010] Preferably, the drift detection unit performs drift discriminant analysis by the following steps: S1, input the extracted feature sequence, including pH value A, dissolved oxygen value B, conductivity C, turbidity D, temperature E, working voltage F, working current G, signal-to-noise ratio H, device temperature I, water flow rate J, water flow volume K, humidity L, rainfall M, water flow rate J, water flow volume K, humidity L and rainfall M, and set the time sliding window length to form a time window slice for each feature parameter; S2, in each sliding window, for each parameter, the mean, variance, standard deviation, skewness, kurtosis, first-order difference and sliding mean difference are calculated respectively. S3, calculate the statistical distance between the current window of each parameter and the baseline reference window to generate KL divergence and PSI value, and set a threshold, when both KL divergence and PSI value are greater than the threshold, mark as drift candidate; S4, apply cumulative sum control chart, exponential weighted moving average and change point detection algorithm to the statistical feature sequence of the sliding window to detect whether there is a trend or mutation drift; S5, if the change point detection or statistical index is continuously over the threshold, record the drift starting point, duration and drift amplitude, combine the environmental variable data set, and distinguish systematic drift, environment-induced drift and local abnormal drift through correlation analysis.

[0011] Preferably, the model comparison module comprises a reference modeling unit, a real-time prediction unit and a deviation evaluation unit; The reference modeling unit is used to construct a reference model based on stable water quality data in the historical stage to simulate ideal output values under normal working conditions. The reference modeling unit selects data in the period when there is no drift, the sensor works normally and the environment is stable from the historical stage, constructs a training data set, takes sensor device parameters and environmental variable parameters as model input features, takes water quality sensor original data as model ideal output target, performs secondary preprocessing on input data, uses a water body physical-chemical rule model, uses historical data, fits the mapping relationship between input and ideal output based on a supervised learning method, and prevents overfitting by using cross-validation; The real-time prediction unit is used to generate ideal prediction values at the corresponding moment in real time according to the input data of the current water quality sensor and related environmental variables, as a comparison benchmark; The deviation evaluation unit is used to compare and analyze the real-time observation data of the water quality sensor with the prediction values of the reference model to dynamically evaluate whether the current sensor data has abnormal deviation.

[0012] Preferably, the adaptive calibration module comprises a dynamic calibration strategy unit and an efficiency evaluation feedback unit; The dynamic calibration strategy unit is used to continuously monitor input data flow or model output to identify potential data distribution drift or concept drift; The efficiency evaluation feedback unit is used to dynamically select and apply appropriate calibration strategies according to the detected drift type and degree.

[0013] Compared with the prior art, the present application has the following beneficial effects: 1. Compared with the prior art of relying on artificial periodic calibration or fixed parameter model, the water quality detection device calibration system based on artificial intelligence assistance provided by the present application has achieved significant improvement in data processing accuracy, drift identification capability, calibration response efficiency and model adaptability. Through the cooperative work of multiple modules, the system can realize intelligent operation of the whole process from raw data acquisition to calibration execution to model self-update, reducing the frequency of human intervention and improving the stability and accuracy of long-term operation of the equipment.

[0014] 2. The system divides the preprocessed data into three data groups according to the source and function, so that different modules can call the corresponding dimension data for targeted analysis, modeling and calibration. This structured organization form improves the data processing efficiency, enhances the pertinence and interpretability of model input features, and provides higher quality data support for model comparison module 3 and adaptive calibration module 4. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 The system flowchart of the present application.

[0016] In the figure: 1, data preprocessing module; 11, data acquisition unit; 12, data cleaning unit; 13, data smoothing unit; 2, drift identification module; 21, feature extraction unit; 22, drift detection unit; 3, model comparison module; 31, reference modeling unit; 32, real-time prediction unit; 33, bias evaluation unit; 4, adaptive calibration module; 41, dynamic calibration strategy unit; 42, performance evaluation feedback unit; 5, feedback update module. DETAILED DESCRIPTION

[0017] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0018] Embodiment one: please refer to Figure 1 A water quality detection device calibration system based on artificial intelligence assistance, comprising a data preprocessing module 1, a drift identification module 2, a model comparison module 3, an adaptive calibration module 4 and a feedback update module 5. The data preprocessing module 1 is used for real-time acquisition of raw data of water quality sensors, and performs denoising, outlier processing and The drift identification module 2 is used for identifying drift signals in sensor output by statistical analysis and AI algorithm; according to the pattern characteristics, it is divided into systematic bias and local anomaly; Model comparison module 3 is used to build a physical model based on historical stable data as an ideal output reference for dynamic comparison with current sensor readings; Adaptive calibration module 4 is used to implement systematic or local calibration adjustment to optimize output results; Feedback update module 5 is used for feedback learning and model update module.

[0019] In this embodiment: Data preprocessing module 1 is used to preliminarily process the original data collected by the water quality sensor in real time, specifically including signal denoising, outlier identification and filtering, etc. By applying filtering algorithms, statistical models and anomaly detection techniques, this module can significantly reduce the influence of environmental noise and accidental errors of equipment on data accuracy, and improve the stability and reliability of data. This module effectively completes the quality assurance task of the original data, provides a reliable data basis for subsequent drift identification and model comparison, thereby helping the accurate execution of the overall system judgment and calibration strategy.

[0020] Drift identification module 2 is used to detect and classify potential drift signals in sensor output by combining statistical methods and artificial intelligence algorithms. The system identifies the fluctuation of sensor performance over time through trend analysis of time series data, and further distinguishes drift types into systematic bias and local anomaly based on data patterns. This module can identify measurement offset problems caused by sensor aging, pollution or external environmental disturbance in advance, provide a basis for timely and effective calibration adjustment, and significantly improve the stability and early warning capability of the system.

[0021] Model comparison module 3 is used to build a reference model based on historical stable operation data. The model can be a physical mechanism model or a data-driven model, which is used as a comparison standard for ideal output. This module dynamically compares the current sensor real-time readings with the reference model output to identify the deviation amplitude and trend of the current data. Through the introduction of this module, the system can evaluate the credibility of sensor output in real time, enhance the scientificity and basis of calibration decision, and effectively reduce the dependence on human intervention for results.

[0022] Adaptive calibration module 4 automatically selects and adjusts the matching calibration strategy according to the output results of drift identification module 2 and model comparison module 3. Systematic bias can be corrected by model re-adjustment, global coefficient compensation, etc., and local anomaly can be fine-tuned by local interpolation or data segment replacement, etc. This module realizes dynamic calibration operation according to different scenarios without human intervention, thereby maintaining the continuous accuracy of water quality detection results and improving the self-operation ability and reliability of the equipment.

[0023] The feedback updating module 5 is used for evaluating the calibration effect of the system and forming a learning closed loop. By comparing the adjusted output with subsequent actual water quality inspection data or artificial reference values, the system can optimize and train the original model and parameters, continuously improving the self-adaptive ability of the calibration algorithm. At the same time, this module also supports long-term trend analysis and model retraining mechanism to cope with changes in sensor performance and environmental conditions. This module gives the system the ability to continuously evolve, effectively maintaining the accuracy and robustness of the model in a long-term running environment.

[0024] Compared with the existing traditional technology which relies on artificial periodic calibration or fixed parameter model, the water quality detection equipment calibration system based on artificial intelligence assistance provided by the present application has achieved significant improvement in data processing accuracy, drift identification ability, calibration response efficiency and model adaptability. Through the collaborative work of multiple modules, the system can realize intelligent operation of the whole process from raw data collection to calibration execution to model self-update, reducing the frequency of human intervention and improving the stability and accuracy of long-term operation of the equipment.

[0025] Embodiment two: please refer to Figure 1 The data preprocessing module 1 includes a data acquisition unit 11, a data cleaning unit 12 and a data smoothing unit 13. The data acquisition unit 11 is used to acquire real-time raw detection data of the water quality sensor, including water quality sensor raw parameters, sensor device parameters and environmental variable parameters. The data cleaning unit 12 is used to preliminarily process the collected raw data, including noise filtering, missing value repair and outlier removal. The data smoothing unit 13 is used to perform smoothing processing on the cleaned time series data.

[0026] The data processed by the data smoothing unit 13 is arranged into a water quality sensor raw data group, a sensor device data group and an environmental variable data group. The water quality sensor raw data group includes pH value A, dissolved oxygen value B, conductivity C, turbidity D and temperature E. The sensor device data group includes working voltage F, working current G, signal-to-noise ratio H and device temperature I. The environmental variable data group includes water flow rate J, water flow volume K, humidity L and rainfall M.

[0027] In this embodiment: the water quality sensor raw parameters, sensor device running parameters and environmental variable parameters are synchronously collected by the data acquisition unit 11, effectively realizing multi-source data fusion. This mechanism not only improves the comprehensive perception ability of the sensor running state and external interference factors, but also provides a more rich and context-related data basis for the subsequent calibration model, enhancing the recognition robustness and background adaptability of the system to water quality abnormalities.

[0028] By filtering and correcting noise, missing values and outliers through the data cleaning unit 12, the problems of drift misjudgment, model deviation and calibration failure caused by abnormal original data are effectively avoided. Compared with the traditional method of directly using unfiltered original data, this processing strategy significantly improves the overall data quality of the system, enhances the accuracy and stability of subsequent AI model training and comparison analysis.

[0029] The data smoothing unit 13 can suppress short-term fluctuations and measurement jitter in the data by performing smoothing processing on the cleaned time series data, strengthening the continuity of data changes and trend expression. This step is particularly important for pattern analysis and classification in the drift identification module 2, and helps to accurately distinguish the differences between long-term offset signals and short-term abnormal fluctuations, improving the system's ability to distinguish and respond to change patterns.

[0030] The system divides the preprocessed data into three data groups according to the source and function, making it easy for different modules to call corresponding dimensional data when performing analysis, modeling and calibration. This structured organization improves data processing efficiency, enhances the relevance and interpretability of model input features, and provides higher quality data support for the model comparison module 3 and the adaptive calibration module 4.

[0031] Example three: please refer to Figure 1 The drift identification module 2 includes a feature extraction unit 21, a drift detection unit 22, and a type classification unit 23. The feature extraction unit 21 is used to extract key feature information reflecting data change trends from preprocessed water quality sensor time series data, including pH value A, dissolved oxygen value B, conductivity C, turbidity D, temperature E, working voltage F, working current G, signal-to-noise ratio H, device temperature I, water flow rate J, water flow rate K, humidity L, rainfall M, water flow rate J, water flow rate K, humidity L, and rainfall M. The drift detection unit 22 is used to perform drift discrimination analysis based on the extracted feature data using statistical methods.

[0032] In this embodiment: the feature extraction unit 21 extracts core variables from the multi-dimensional time series output by the data preprocessing module 1, including water quality parameters, device state parameters and environmental variables. This unit not only realizes global capture of data change trends, but also enhances the system's comprehensive identification ability of abnormal evolution trends in complex scenarios, providing rich and highly correlated input basis for drift detection.

[0033] The drift detection unit 22 combines feature data and uses statistical methods including moving average analysis, Z-score test, control chart method, CUSUM algorithm, etc. to model and identify the drift trend in time series data. Compared with traditional threshold setting or manual observation, this unit has stronger objectivity, reproducibility and characteristics suitable for automatic judgment, significantly reducing the false positive rate and false negative rate, and enhancing the stability of drift detection in long-term operation.

[0034] The type classification unit 23 classifies the drift phenomenon into two types of systematic deviation and local anomaly according to the drift feature index output by the detection unit, combined with the variation amplitude, duration, feature variable correlation degree and other parameters. This classification structure helps the adaptive calibration module 4 to select the most appropriate correction strategy according to the specific drift type, thereby improving the pertinence and efficiency of the overall calibration.

[0035] By integrating water quality, equipment and environmental multi-dimensional features into the drift discrimination logic, this module can effectively avoid the misidentification of "non-drift fluctuations" as "drift", reduce false alarms or unnecessary calibration operations caused by environmental disturbances, and improve the practicality and economy of the system, especially suitable for complex application scenarios with dynamic changes in water quality.

[0036] Embodiment four: please refer to Figure 1 The drift detection unit 22 performs drift discrimination analysis by the following steps: S1, input the extracted feature sequence, including pH value A, dissolved oxygen value B, conductivity C, turbidity D, temperature E, working voltage F, working current G, signal-to-noise ratio H, equipment temperature I, water flow rate J, water flow K, humidity L, rainfall M, water flow rate J, water flow K, humidity L and rainfall M, and set the time sliding window length to form a time window slice for each feature parameter; S2, in each sliding window, calculate the mean, variance, standard deviation, skewness, kurtosis, first-order difference and moving average difference for each parameter; S3, calculate the statistical distance between the current window and the baseline reference window for each parameter to generate KL divergence and PSI value, and set a threshold value. When both KL divergence and PSI value are greater than the threshold value, it is marked as a drift candidate; S4, apply cumulative sum control chart, exponential weighted moving average and change point detection algorithm to the statistical feature sequence of the sliding window to detect whether there is a trend or sudden drift; S5, if the change point detection or statistical index continues to exceed the threshold, record the drift starting point, duration and drift amplitude, combine with the environmental variable data set, and distinguish between systematic drift, environmental induced drift and local anomaly drift through correlation analysis.

[0037] In this embodiment: by setting the sliding time window length, and constructing slice sequence of key parameters in each window, the system realizes the segmented processing of dynamic changes of data. This method can effectively identify the trend variation and structural mutation in the short term, especially suitable for detecting complex scenarios where gradual drift and sudden abnormality coexist, and improves the timeliness and local response ability of drift identification.

[0038] In each sliding window, the system calculates the mean, variance, standard deviation, skewness, kurtosis, first-order difference and sliding mean difference of each parameter, forming a comprehensive time series behavior feature vector. This method can comprehensively capture data fluctuations, drift trends and variation characteristics, effectively support subsequent drift measurement and trend identification, and realize in-depth modeling of non-stationary data.

[0039] The system calculates the Kullback-Leibler divergence and Population Stability Index value between the current window and the historical baseline window, and sets double-index threshold to realize quantitative discrimination of drift candidate state. This design significantly improves the sensitivity of the system to distribution changes and the objectivity of the judgment basis, especially suitable for dealing with long-term trend drift or performance degradation caused by sensor aging.

[0040] Cumulative sum control chart, exponential weighted moving average and multiple algorithms such as change point detection are used to comprehensively identify drift trends, effectively enhancing the parallel identification ability of gradual and sudden drifts. This design realizes the structural decomposition of drift behavior, which helps to finely divide the characteristics of different drift types and provides data support for subsequent classification and response strategy.

[0041] By correlating the detected drift interval with the environmental variable data set, the system can further classify the drift type into systematic drift, environment-induced drift and local abnormal drift. This method effectively reduces the risk of misidentification and avoids misjudging transient fluctuations caused by external disturbances as sensor failure, enhancing the system's ability to adapt to changing environmental conditions.

[0042] The drift detection unit 22 constructs a multi-feature statistical analysis process based on sliding window, integrates distribution measurement and trend detection algorithms, and combines environmental factors to realize cause classification, greatly improving the accuracy, real-time and intelligent level of drift identification. This technical solution effectively solves the problems of response lag, high misjudgment rate and unexplainability in existing methods, provides a reliable premise and decision basis for subsequent adaptive calibration, and enhances the stability and adaptive ability of the entire system in long-term running environment.

[0043] Embodiment five: please refer to Figure 1 , the model comparison module 3 includes a reference modeling unit 31, a real-time prediction unit 32, and a bias evaluation unit 33; The reference modeling unit 31 is used to construct a reference model based on stable water quality data in the historical stage to simulate ideal output values in normal working conditions. The reference modeling unit 31 screens data in periods when there is no drift, the sensor is working normally, and the environment is stable from the historical stage, constructs a training data set, takes sensor device parameters and environmental variable parameters as model input features, takes water quality sensor raw data as the ideal output target of the model, performs secondary preprocessing on the input data, uses a water body physics-chemistry rule model, uses historical data, and fits the mapping relationship between the input and the ideal output based on a supervised learning method, and adopts cross-validation to prevent overfitting. The real-time prediction unit 32 is used to generate ideal prediction values at the corresponding time according to the input data of the current water quality sensor and related environmental variables, as a comparison benchmark. The deviation evaluation unit 33 is used to compare and analyze the real-time observation data of the water quality sensor with the prediction values of the reference model, and dynamically evaluate whether the current sensor data has abnormal deviation.

[0044] In the embodiment, the reference modeling unit 31 screens time period data when the sensor is running stably, no drift occurs, and the environmental conditions fluctuate less from the historical data, and constructs a reference model for simulating ideal output in normal working conditions. The modeling process takes sensor device parameters and environmental variables as input features, and takes raw observation values of the water quality sensor as target output of the model. Compared with the traditional method of relying on experience to set a threshold, this method provides a more objective, data-driven deviation judgment standard, which can significantly improve the identification accuracy of the system for abnormal drift.

[0045] In the reference modeling unit 31, the water body physics-chemistry rule is introduced as a model structure constraint, and a supervised learning algorithm is combined to fit and train the relationship between the input and the output. To avoid overfitting of the model on specific historical data, a cross-validation mechanism is further introduced to improve its generalization performance. This method not only retains the logical rationality of the domain knowledge, but also has strong modeling flexibility and scalability, and can adapt to different water quality scenes and sensor combinations, improving the actual deployment effect.

[0046] The real-time prediction unit 32 is used to receive water quality sensor input data and related environmental variables at the current time, and call the reference model established by the reference modeling unit 31 to generate ideal prediction values at the time. The prediction result is used as a comparison benchmark for difference analysis with the actual observation value, which can realize continuous online monitoring of the sensor running state, significantly improve the system response speed, and reduce the dependence on manual calibration.

[0047] The bias evaluation unit 33 is configured to compare the real-time observation data of the water quality sensor with the reference prediction value output by the real-time prediction unit 32, identify whether there is an abnormal bias in the sensor by calculating the degree of difference and combining a threshold judgment strategy. This module can not only identify long-term and stable systematic drift, but also identify short-term and sudden local abnormalities, thereby effectively supporting the differentiation and classification processing of abnormal types and improving the fault tolerance and stability of the system.

[0048] The model comparison module 3 provides an automatic and continuous sensor performance monitoring mechanism, which can realize continuous evaluation of the quality of sensor output without relying on frequent manual intervention. This module can be used as a key link in the data quality control chain to provide reliable and high-quality data support for downstream water quality trend prediction, pollution warning and intelligent decision-making systems, and help to build a stable, efficient and intelligent water environment monitoring platform.

[0049] Embodiment six: please refer to Figure 1 The adaptive calibration module 4 includes a dynamic calibration strategy unit 41 and a performance evaluation feedback unit 42. The dynamic calibration strategy unit 41 is configured to continuously monitor the input data stream or model output to identify potential data distribution drift or concept drift. The performance evaluation feedback unit 42 is configured to dynamically select and apply appropriate calibration strategies according to the detected drift type and degree.

[0050] In this embodiment: the dynamic calibration strategy unit 41 is configured to monitor the input data stream or output result of the model in real time to detect potential data distribution drift or concept drift. Specifically, the unit continuously analyzes the input data feature distribution or output confidence distribution through a set of data distribution statistical methods, drift detection algorithms or distribution fitting techniques, and identifies the drift type and its intensity when detecting abnormal changes.

[0051] The performance evaluation feedback unit 42 is configured to automatically select and call appropriate strategies from the preset calibration strategy set according to the drift type and drift degree identified by the dynamic calibration strategy unit, and apply them to the post-processing process of the model output; at the same time, the unit evaluates the performance of the calibration result based on indicators such as expected calibration error, negative log likelihood, Brier score. According to the evaluation result, the unit can feed back and adjust the strategy selection logic to realize the iterative optimization and adaptive update of the calibration strategy.

[0052] The dynamic calibration strategy unit 41 can continuously monitor the input data and output results of the target model, and determine whether there is data distribution drift or concept drift through statistical analysis. When drift occurs, the unit adaptively selects a matching calibration method from a pre-set calibration strategy library according to the type and degree of drift, and adjusts the model output probability without modifying the original model structure. After the calibration is completed, the performance evaluation feedback unit 42 evaluates the calibration effect according to multiple performance indicators, and feeds back the evaluation results to the strategy unit 41, forming a closed-loop adjustment mechanism to realize dynamic optimization and evolution of the strategy. The module supports archiving and learning of strategy execution history, and can quickly match the optimal strategy in subsequent similar drift scenarios, improving system response speed and calibration accuracy. The present application has good robustness and scalability, and is suitable for model deployment requirements in complex dynamic environments such as medical diagnosis, financial risk control, industrial intelligence, etc.

[0053] The contents not described in detail in the specification belong to the prior art known to those skilled in the art.

[0054] Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions recorded in the foregoing embodiments, or make equivalent replacements for part of the technical features, as long as they are within the spirit and principles of the present application. Any modification, equivalent replacement, improvement, etc. made shall be included in the protection scope of the present application.

Claims

1. A calibration system for water quality testing equipment based on artificial intelligence, characterized in that: It includes a data preprocessing module (1), a drift identification module (2), a model comparison module (3), an adaptive calibration module (4), and a feedback update module (5); The data preprocessing module (1) is used to collect raw data from the water quality sensor in real time and perform noise reduction, outlier processing, and other functions. The drift identification module (2) is used to identify drift signals in the sensor output using statistical analysis and AI algorithms; and to distinguish between systematic deviations and local anomalies based on pattern characteristics. The model comparison module (3) is used to construct a physical model based on historical stable data as an ideal output reference and to dynamically compare it with the current sensor readings. The adaptive calibration module (4) is used to implement systematic or local calibration adjustments to optimize the output results; The feedback update module (5) is used for feedback learning and model update modules.

2. The water quality testing equipment calibration system based on artificial intelligence assisted according to claim 1, characterized in that: The data preprocessing module (1) includes a data acquisition unit (11), a data cleaning unit (12), and a data smoothing unit (13). The data acquisition unit (11) is used to acquire the raw detection data of the water quality sensor in real time, including the raw parameters of the water quality sensor, the sensor equipment parameters and the environmental variable parameters. The data cleaning unit (12) is used to perform preliminary processing on the collected raw data, including noise filtering, missing value repair and outlier removal; The data smoothing unit (13) is used to perform smoothing processing on the cleaned time series data.

3. The water quality testing equipment calibration system based on artificial intelligence assistance according to claim 2, characterized in that: The data preprocessed by the data smoothing unit (13) is organized into a water quality sensor raw data group, a sensor device data group, and an environmental variable data group. The raw data set of the water quality sensor includes pH value (A), dissolved oxygen value (B), conductivity (C), turbidity (D), and temperature (E). The sensor device data set includes operating voltage F, operating current G, signal-to-noise ratio H, and device temperature I; The environmental variable data set includes water flow velocity J, water flow rate K, humidity L, and rainfall M.

4. The water quality testing equipment calibration system based on artificial intelligence assistance according to claim 3, characterized in that: The drift recognition module (2) includes a feature extraction unit (21), a drift detection unit (22), and a type classification unit (23). The feature extraction unit (21) is used to extract key feature information reflecting the trend of data change from the pre-processed water quality sensor time series data, including pH value A, dissolved oxygen value B, conductivity C, turbidity D, temperature E, working voltage F, working current G, signal-to-noise ratio H, equipment temperature I, water flow rate J, water flow rate K, humidity L, rainfall M, water flow rate J, water flow rate K, humidity L, and rainfall M; The drift detection unit (22) is used to perform drift discrimination analysis based on the extracted feature data using statistical methods.

5. The water quality testing equipment calibration system based on artificial intelligence assistance according to claim 4, characterized in that: The drift detection unit (22) performs drift discrimination analysis through the following steps: S1. Input the extracted feature sequences, including pH value A, dissolved oxygen value B, conductivity C, turbidity D, temperature E, working voltage F, working current G, signal-to-noise ratio H, equipment temperature I, water flow rate J, water flow rate K, humidity L, rainfall M, and set the time sliding window length to form a time window slice for each feature parameter; S2. Within each sliding window, calculate the mean, variance, standard deviation, skewness, kurtosis, first difference, and moving mean difference for each parameter. S3. Calculate the statistical distance between the current window and the baseline reference window for each parameter to generate KL divergence and PSI values, and set a threshold. When both KL divergence and PSI values ​​are greater than the threshold, they are marked as drift candidates. S4. Apply cumulative sum control charts, exponentially weighted moving averages, and change point detection algorithms to the statistical characteristic sequence of the sliding window to detect whether there is a trend or abrupt drift. S5. If the change point detection or statistical indicators continue to exceed the threshold, record the drift start point, duration, and drift amplitude. Combine with the environmental variable data set and use correlation analysis to distinguish between systematic drift, environmentally induced drift, and local abnormal drift.

6. The water quality testing equipment calibration system based on artificial intelligence assistance according to claim 5, characterized in that: The model comparison module (3) includes a reference modeling unit (31), a real-time prediction unit (32), and a deviation evaluation unit (33). The reference modeling unit (31) is used to build a reference model based on stable water quality data from historical periods, and to simulate the ideal output value under normal working conditions. The reference modeling unit (31) selects data from historical periods when there is no drift, the sensor is working normally, and the environment is stable, and builds a training dataset. The sensor equipment parameters and environmental variable parameters are used as the model input features, and the original water quality sensor data is used as the ideal output target of the model. The input data is preprocessed twice, and the model of water body physical-chemical laws is used. The historical data is used to fit the mapping relationship between the input and the ideal output based on the supervised learning method, and cross-validation is used to prevent overfitting. The real-time prediction unit (32) is used to call the reference model to generate the ideal prediction value at the corresponding time in real time based on the input data of the current water quality sensor and related environmental variables, as a comparison benchmark; The deviation assessment unit (33) is used to compare and analyze the real-time observation data of the water quality sensor with the predicted value of the reference model, and dynamically assess whether there is an abnormal deviation in the current sensor data.

7. The water quality testing equipment calibration system based on artificial intelligence assistance according to claim 6, characterized in that: The adaptive calibration module (4) includes a dynamic calibration strategy unit (41) and a performance evaluation feedback unit (42). The dynamic calibration strategy unit (41) is used to continuously monitor the input data stream or model output to identify potential data distribution drift or concept drift. The performance evaluation feedback unit (42) is used to dynamically select and apply an appropriate calibration strategy based on the detected drift type and degree.

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