AI-based water quality online monitor intelligent diagnosis and predictive maintenance system

Abstract

The application relates to the technical field of water quality monitoring, in particular to an AI-based water quality online monitoring instrument intelligent diagnosis and predictive maintenance system, which comprises the following systems: a multidimensional database construction module used for collecting equipment operation data, water quality monitoring data and environment and operation and maintenance data, extracting cross-system correlation features and fault features, establishing a data correlation mechanism and constructing a database; a cross-system intelligent diagnosis module used for realizing early identification, accurate positioning and root tracing of cross-system collaborative faults of the dominant faults of an inlet sample system and the recessive faults of a heating system; a predictive maintenance module used for dynamically generating individualized maintenance schemes; and a modular compatible deployment module used for integrating the cross-system intelligent diagnosis module and the predictive maintenance module in an existing monitoring instrument, realizing intelligent upgrading without replacement of core components and adapting to water quality online monitoring equipment of different monitoring factors. The application helps realize accurate maintenance and flexible equipment compatibility.

Description

Technical Field

[0001] This application relates to the field of water quality monitoring technology, and in particular to an AI-based intelligent diagnostic and predictive maintenance system for online water quality monitoring instruments. Background Technology

[0002] With increasingly stringent requirements for water quality environmental supervision, online water quality monitoring instruments have been widely used in real-time monitoring of various scenarios, including surface water, domestic sewage, and industrial wastewater. However, existing online water quality monitoring instruments suffer from several technical challenges during long-term operation: aging peristaltic pump pipes and pipe blockages in the influent sampling system lack early warning mechanisms, often leading to data interruptions due to "sampling shortage" alarms; hidden faults in the heating system, such as heater scaling and thermocouple drift, can cause incomplete total nitrogen digestion or incomplete colorimetric reactions of anionic surfactants, resulting in data distortion; the operation and maintenance model relies on fixed-cycle inspections or post-incident repairs, failing to dynamically adjust maintenance strategies based on the actual health status of the equipment, leading to over-maintenance or untimely maintenance; high compatibility and upgrade costs are also significant. Different monitoring factors correspond to different monitoring principles and core parameters, and existing equipment is mostly dedicated models, making flexible adaptation difficult. Furthermore, intelligent upgrades require replacement of core components, resulting in long transformation cycles and high costs.

[0003] Therefore, in order to address the above problems, there is an urgent need for an intelligent system with precise maintenance and flexible compatibility to break through the existing technological bottlenecks. Summary of the Invention

[0004] Therefore, it is necessary to provide an AI-based intelligent diagnostic and predictive maintenance system for online water quality monitoring instruments that can achieve precise maintenance and flexible compatibility, addressing the aforementioned technical issues.

[0005] In a first aspect, this application provides an AI-based intelligent diagnosis and predictive maintenance system for online water quality monitoring instruments, the system comprising: a multi-dimensional database construction module, a cross-system intelligent diagnosis module, a predictive maintenance module, and a modular compatible deployment module; The multi-dimensional database construction module is used to collect equipment operation data, water quality monitoring data, and environmental and maintenance data, and extract cross-system correlation features and fault features, establish a data correlation mechanism, and build a database. The cross-system intelligent diagnostic module is used to train a differentiated diagnostic model based on the database, so as to realize the early identification and accurate location of obvious faults in the influent sample system and latent faults in the heating system, as well as the root cause tracing of cross-system collaborative faults. The predictive maintenance module is used to dynamically generate personalized maintenance plans based on the analysis results of the database and the cross-system intelligent diagnostic module. The modular compatible deployment module is used to integrate the cross-system intelligent diagnostic module and the predictive maintenance module into the existing monitoring instrument through lightweight hardware embedding and iterative software function upgrades, so as to achieve intelligent upgrades without replacing core components and adapt to online water quality monitoring equipment with different monitoring factors.

[0006] In one embodiment, the cross-system intelligent diagnostic module includes a visible fault diagnosis model for the influent sample system and a hidden fault diagnosis model for the heating system. The explicit fault diagnosis model of the water intake system adopts the XGBoost algorithm, which takes into account the pump operating current fluctuation coefficient, water intake flow rate attenuation rate, and pipeline pressure abnormality frequency characteristics, and outputs the fault type, risk level, and remaining safe operating time. The latent fault diagnosis model of the heating system adopts a deep neural network, which takes into account the heating rate, temperature holding deviation, and heat transfer efficiency characteristics, and outputs the latent fault type.

[0007] In one embodiment, the cross-system intelligent diagnostic module further includes a cross-system collaborative diagnostic mechanism. When the monitoring data is distorted, the operating data of the influent sample system and the heating system are analyzed synchronously through a preset correlation model to locate the root cause of the fault.

[0008] In one embodiment, the cross-system intelligent diagnostic module further includes a model self-calibration mechanism, which automatically calls standard sample verification data according to a preset time period to verify the output results of the water sample system explicit fault diagnosis model and the heating system implicit fault diagnosis model. If the standard sample verification deviation exceeds a preset threshold range, the model parameters are fine-tuned.

[0009] In one embodiment, the fault types output by the explicit fault diagnosis model of the water intake system include pump pipe aging, pipeline blockage, valve failure, and decreased water sampling efficiency. The latent fault types output by the latent fault diagnosis model of the heating system include heater scaling, thermocouple drift, and heating wire aging.

[0010] In one embodiment, the predictive maintenance module includes a component life prediction unit, a maintenance plan generation unit, and a full-scenario maintenance optimization unit; The component life prediction unit is used to predict the remaining service life of key components based on component operation data and environmental impact data in the database, using a preset prediction model. The maintenance plan generation unit is used to recommend the optimal maintenance cycle and maintenance content based on the predicted remaining service life, failure risk level and operation and maintenance cost. The full-scenario maintenance optimization unit is used to generate a pipeline cleaning plan for idle equipment within a preset time, and to generate a full-process cleaning plan for idle equipment exceeding the preset time. The full-process cleaning plan includes pure water soaking, pipeline cleaning, and drain protection.

[0011] In one embodiment, the prediction model employs a survival analysis algorithm. The input component operation data includes the cumulative running time of the peristaltic pump tube, the number of times the heating wire operates, the thermocouple usage cycle, and the valve switching frequency. The environmental impact data includes the operating environment temperature, humidity, and water quality corrosivity level.

[0012] In one embodiment, the modular compatible deployment module includes a hardware deployment unit and a software deployment unit; The hardware deployment unit is used to add a lightweight AI processing module based on ARM architecture. The lightweight AI processing module based on ARM architecture is integrated into the main control board of the detector through a standardized interface, and is compatible with the core components of the existing monitor. The software deployment unit is used to package the differentiated diagnostic model of the cross-system intelligent diagnostic module and the predictive model of the predictive maintenance module into a lightweight executable file and embed it into the existing monitoring instrument operating system.

[0013] In one embodiment, the database uses a distributed time-series database to store data, and uses timestamps to associate water sampling status, reaction status and monitoring results, and supports hierarchical management of data according to user permissions.

[0014] In one embodiment, the different monitoring factors include total nitrogen and anionic surfactants. By loading the characteristic parameter library of the corresponding monitoring factors and the preset diagnostic threshold, the measurement requirements of alkaline potassium persulfate spectrophotometry and methylene blue spectrophotometry are adapted.

[0015] In summary, this application includes the following beneficial technical effects: By constructing a multi-dimensional database module to collect data and extract cross-system correlation and fault characteristics, and establishing a data correlation mechanism, the problem of isolated data from different systems and the inability to trace the root cause of faults can be solved, laying a data foundation for cross-system collaborative diagnosis. Through the differentiated diagnostic model of the cross-system intelligent diagnosis module, the system can identify and accurately locate obvious faults in the influent sampling system and latent faults in the heating system in advance, as well as trace the root cause of cross-system collaborative faults. This solves the pain points of traditional equipment's lagging identification of obvious faults and difficulty in detecting latent faults, and can accurately locate the core causes of multi-system cascading faults. The predictive maintenance module dynamically generates personalized maintenance plans based on the analysis results of the database and the cross-system intelligent diagnosis module, which can accurately match equipment operation requirements and maintenance resources, and improve the scientificity and pertinence of maintenance plans. The modular compatible deployment module solves the problem of poor compatibility of traditional dedicated models through lightweight hardware embedding and iterative software function upgrades, which can improve equipment reuse rate and applicability. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of an AI-based online water quality monitoring instrument intelligent diagnosis and predictive maintenance system in one embodiment; Figure 2 This is a schematic diagram of the structure of an AI-based online water quality monitoring instrument intelligent diagnosis and predictive maintenance system in another embodiment. Detailed Implementation

[0017] This invention provides an AI-based intelligent diagnostic and predictive maintenance system for online water quality monitoring instruments.

[0018] The embodiments of the present invention will now be described in more detail with reference to the accompanying drawings. While some embodiments of the present invention are shown in the drawings, it should be understood that the present invention can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the accompanying drawings and embodiments are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0019] In the description of the embodiments disclosed in this invention, the term "comprising" and similar terms should be understood as open-ended inclusion, i.e., "including but not limited to". The term "based on" should be understood as "at least partially based on". The term "one embodiment" or "the embodiment" should be understood as "at least one embodiment". The terms "first", "second", etc., may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

[0020] For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1One embodiment of the AI-based intelligent diagnosis and predictive maintenance system for online water quality monitoring instruments in this invention includes: a multi-dimensional database construction module, a cross-system intelligent diagnosis module, a predictive maintenance module, and a modular compatible deployment module; The multi-dimensional database construction module is used to collect equipment operation data, water quality monitoring data, and environmental and maintenance data, and extract cross-system correlation features and fault features, establish a data correlation mechanism, and build a database. The cross-system intelligent diagnostic module is used to train differentiated diagnostic models based on the database, enabling early identification and accurate location of overt faults in the influent sample system and latent faults in the heating system, as well as root cause tracing of cross-system collaborative faults. The predictive maintenance module is used to dynamically generate personalized maintenance plans based on the analysis results of the database and the cross-system intelligent diagnostic module. Modular compatibility deployment modules are used to integrate cross-system intelligent diagnostic modules and predictive maintenance modules into existing monitors through lightweight hardware embedding and iterative software function upgrades, enabling intelligent upgrades without the need to replace core components, and adapting to online water quality monitoring equipment with different monitoring factors.

[0021] Specifically, the AI-based intelligent diagnosis and predictive maintenance system for online water quality monitoring instruments includes a dimensional database construction module, a cross-system intelligent diagnosis module, a predictive maintenance module, and a modular compatible deployment module. The modules complement each other and work together to achieve intelligent operation of the entire process from data collection to maintenance implementation. A multi-dimensional database construction module serves as the data foundation, comprehensively collecting equipment operation data, water quality monitoring data, and environmental and maintenance data. Equipment operation data includes parameters such as pump operating current, water flow rate, pipeline pressure, and valve switching frequency for the influent sampling system, as well as parameters such as heating rate, temperature hold-up deviation, heat transfer efficiency, and thermocouple voltage for the heating system. Water quality monitoring data includes core data such as concentration values, absorbance, standard sample verification values, and reagent consumption rates for monitored factors like total nitrogen and anionic surfactants. Environmental and maintenance data includes environmental parameters such as operating temperature, humidity, and water corrosivity levels, as well as maintenance data such as fault type labels, maintenance records, and component replacement times. Through feature engineering techniques, cross-system correlation features (such as the correlation between water flow rate attenuation rate and digestion efficiency, and the coupling features between abnormal pipeline pressure and temperature fluctuations) and fault features (such as pump operating current fluctuation coefficient and heating rate attenuation value) are deeply extracted to establish a data association mechanism and construct the database. The cross-system intelligent diagnostic module trains differentiated AI models based on a database to accurately identify overt faults in the influent sampling system and latent faults in the heating system. It also possesses cross-system collaborative diagnostic capabilities, enabling root cause tracing. The predictive maintenance module dynamically generates personalized maintenance plans by combining database data and diagnostic results, replacing the traditional fixed-cycle maintenance model. The modular compatible deployment module integrates intelligent functions into existing detectors through lightweight hardware embedding and iterative software upgrades. Upgrades can be completed without replacing core components, adapting to different monitoring factor requirements. This architecture ensures both the integrity and intelligence of system functions while also considering the reusability of existing equipment, significantly lowering the application threshold.

[0022] In one embodiment, the cross-system intelligent diagnostic module includes a visible fault diagnosis model for the influent sampling system and a hidden fault diagnosis model for the heating system. The visible fault diagnosis model for the influent sampling system adopts the XGBoost algorithm, inputs the pump operating current fluctuation coefficient, water sampling flow rate attenuation rate, and pipeline pressure anomaly frequency characteristics, and outputs the fault type, risk level, and remaining safe operating time. The hidden fault diagnosis model for the heating system adopts a deep neural network, inputs the heating rate, temperature holding deviation, and heat transfer efficiency characteristics, and outputs the hidden fault type.

[0023] Specifically, the cross-system intelligent diagnostic module is the core of achieving accurate fault identification. It adopts differentiated AI models to adapt to the diagnostic needs of both explicit and implicit faults, and clearly defines the input and output parameters to ensure diagnostic accuracy. Among them, the explicit fault diagnosis model of the influent sample system adopts the XGBoost algorithm, which has strong feature learning and classification capabilities and can accurately capture the quantitative characteristics of explicit faults. The input parameters are selected as pump operating current fluctuation coefficient, water intake flow rate attenuation rate, and pipeline pressure anomaly frequency. These features directly reflect the core operating status of the influent sample system. The pump operating current fluctuation coefficient can reflect the degree of pump pipe wear, the water intake flow rate attenuation rate can reflect the pipeline unobstructedness and pump performance, and the pipeline pressure anomaly frequency can quickly identify valve faults or blockages. The model outputs the fault type, risk level, and remaining safe operating time, providing a clear basis for operation and maintenance decisions. The latent fault diagnosis model for heating systems employs a deep neural network. This model excels at uncovering deep correlations in data and is well-suited to the characteristic that latent faults lack intuitive representations. Input parameters include heating rate, temperature holding deviation, and heat transfer efficiency, which correspond to the heating system's heating performance, temperature control accuracy, and energy transfer efficiency, respectively. It can effectively capture subtle changes in latent fault characteristics such as heater scaling and thermocouple drift, output specific latent fault types, and achieve early warning of latent faults.

[0024] In one embodiment, the cross-system intelligent diagnostic module also includes a cross-system collaborative diagnostic mechanism. When the monitoring data is distorted, the operating data of the influent sample system and the heating system are analyzed synchronously through a preset correlation model to locate the root cause of the fault.

[0025] Specifically, a cross-system collaborative diagnostic mechanism is key to solving the problem of locating the root cause of cascading failures. In actual monitoring, data distortion is often not caused by a single system failure, but by the collaborative effect of cross-systems. For example, a decrease in the water flow rate of the influent sampling system may lead to insufficient digestion in the heating system, resulting in excessive deviations in the standard sample verification. Based on this, this system adds a collaborative diagnostic mechanism to the cross-system intelligent diagnostic module. When monitoring data is distorted, the system retrieves the operating data of the influent sampling system and the heating system, analyzes the parameter coupling relationship between them, such as the correlation between water volume and digestion time, and pipeline pressure and temperature fluctuations, thereby accurately locating the root cause of the failure. This mechanism breaks through the limitations of single-system fault diagnosis, significantly shortens the fault investigation time, and improves operation and maintenance efficiency.

[0026] In one embodiment, the cross-system intelligent diagnostic module also includes a model self-calibration mechanism, which automatically calls standard sample verification data according to a preset time period to verify the output results of the water sample system explicit fault diagnosis model and the heating system implicit fault diagnosis model. If the standard sample verification deviation exceeds the preset threshold range, the model parameters are fine-tuned.

[0027] Specifically, to ensure the long-term accuracy and stability of the diagnostic model, a model self-calibration mechanism is added to the cross-system intelligent diagnostic module. This mechanism is automatically triggered according to a preset time period (e.g., 168 hours / week), calling the standard sample verification data stored in the database to verify the output results of the explicit fault diagnosis model of the influent sample system and the implicit fault diagnosis model of the heating system. The standard sample verification data is the core quality control data generated by the online water quality monitoring instrument after performing a complete measurement process on standard samples of known accurate concentrations. It is the core basis for verifying the measurement accuracy of the equipment and the reliability of the diagnostic model. If the standard sample verification deviation exceeds the preset threshold range (default ±5%), it indicates that the model parameters may have drifted due to factors such as equipment aging and environmental changes. The system will automatically fine-tune the model's weight coefficients, thresholds, and other key parameters based on the latest standard sample verification data and corresponding operating parameters to achieve adaptive optimization of the model. This mechanism can ensure that the diagnostic model remains in a high-accuracy state for a long time without manual intervention, avoiding misjudgments or omissions caused by model drift.

[0028] In one embodiment, the fault types output by the explicit fault diagnosis model of the influent sampling system include pump pipe aging, pipeline blockage, valve failure, and decreased water sampling efficiency; the implicit fault types output by the implicit fault diagnosis model of the heating system include heater scaling, thermocouple drift, and heating wire aging.

[0029] Specifically, the explicit fault diagnosis model for the influent sampling system outputs fault types including pump pipe aging, pipeline blockage, valve malfunction, and decreased water sampling efficiency. These are common fault types encountered during long-term operation of the influent sampling system. Pump pipe aging is caused by long-term friction, pipeline blockage is often due to sedimentation of water impurities, valve malfunctions include jamming and poor sealing, and decreased water sampling efficiency may be caused by pump performance degradation and increased pipeline resistance. The implicit fault diagnosis model for the heating system includes heater scaling, thermocouple drift, and heating wire aging. Heater scaling leads to decreased heat transfer efficiency, thermocouple drift causes temperature measurement deviations, and heating wire aging affects heating power and service life. Clear fault type classification enables maintenance personnel to quickly locate problems and take targeted measures, avoiding blind troubleshooting.

[0030] In one embodiment, the predictive maintenance module includes a component lifespan prediction unit, a maintenance plan generation unit, and a full-scenario maintenance optimization unit. The component lifespan prediction unit is used to predict the remaining service life of key components based on component operation data and environmental impact data in the database, using a preset prediction model. The maintenance plan generation unit is used to recommend the optimal maintenance cycle and maintenance content based on the predicted remaining service life, fault risk level, and operation and maintenance cost. The full-scenario maintenance optimization unit is used to generate a pipeline cleaning plan for idle equipment within a preset time, and to generate a full-process cleaning plan for idle equipment exceeding the preset time. The full-process cleaning plan includes pure water immersion, pipeline cleaning, and drain protection.

[0031] Specifically, such as Figure 2 As shown, the predictive maintenance module includes a component lifespan prediction unit, a maintenance plan generation unit, and a full-scenario maintenance optimization unit. The component lifespan prediction unit focuses on the aging status of key components, using component operation data and environmental impact data from a database to accurately predict remaining service life through a pre-set prediction model. The maintenance plan generation unit comprehensively considers the lifespan prediction results, the fault risk level (risk level output by the cross-system intelligent diagnostic module), and maintenance costs, balancing maintenance effectiveness and cost investment, and recommends the optimal maintenance cycle (e.g., after 3 days, within 1 week) and specific maintenance content (e.g., replacing pump pipes, cleaning pipelines, calibrating thermocouples, etc.). The full-scenario maintenance optimization unit develops differentiated cleaning plans based on the idle time of equipment to avoid problems such as pipeline contamination and component corrosion caused by prolonged idleness. Specifically, for idle equipment within a preset time, it generates a pipeline-only cleaning plan, which is simple and efficient, avoiding resource waste. For idle equipment exceeding the preset time, it generates a full-process cleaning plan, executing the steps of "pure water immersion → pipeline cleaning → drain protection" to comprehensively ensure equipment performance.

[0032] In one embodiment, the prediction model employs a survival analysis algorithm. The input component operation data includes the cumulative running time of the peristaltic pump tube, the number of times the heating wire operates, the thermocouple usage cycle, and the valve switching frequency. The environmental impact data includes the operating environment temperature, humidity, and water quality corrosivity level.

[0033] Specifically, the prediction model employs a survival analysis algorithm. This algorithm handles the correlation analysis between lifespan and influencing factors, effectively integrating component operating data and environmental impact data to improve the accuracy of lifespan prediction. Input component operating data includes the cumulative runtime of the peristaltic pump tubing, the number of times the heating wire operates, the thermocouple's service life, and the frequency of valve opening and closing. These parameters directly reflect the intensity of component use and are core factors affecting lifespan. Environmental impact data includes operating environment temperature, humidity, and water corrosivity levels. The aging rate of components varies significantly under different environments; for example, high temperature and high humidity environments accelerate the corrosion of metal components, while highly corrosive water shortens the service life of pipes and valves. By modeling the above multi-dimensional data using the survival analysis algorithm, the remaining service life of key components such as peristaltic pump tubing, heating wire, thermocouples, and valves can be accurately predicted, providing reliable data support for maintenance planning.

[0034] In one embodiment, the modular compatible deployment module includes a hardware deployment unit and a software deployment unit; the hardware deployment unit is used to add a lightweight AI processing module based on the ARM architecture, which is integrated into the main control board of the detector through a standardized interface and is compatible with the core components of the existing monitor; the software deployment unit is used to package the differentiated diagnostic model of the cross-system intelligent diagnostic module and the predictive model of the predictive maintenance module into a lightweight executable file and embed it into the existing monitor operating system.

[0035] Specifically, the modular and compatible deployment module adopts a lightweight hardware embedding and one-click software upgrade approach to achieve low-cost, low-barrier intelligent upgrades of existing equipment. For example... Figure 2 As shown, the modular compatible deployment module includes a hardware deployment unit and a software deployment unit. The hardware deployment unit adds a lightweight AI processing module based on the ARM architecture. This lightweight AI processing module, based on the ARM architecture, features small size, low power consumption, and sufficient computing power. It is the core hardware carrier of the modular compatible deployment module. It is integrated into the main control board of the detector through a standardized interface. It can be compatible with the core components of the existing detector, such as sensors, peristaltic pumps, solenoid valves, and thermocouples, without modifying the original circuit layout, thus expanding the AI ​​computing function. The software deployment unit is used to package the differentiated diagnostic models of the cross-system intelligent diagnostic module and the predictive models of the predictive maintenance module into lightweight executable files. It is compatible with the existing mainstream detector operating systems, supports one-click upgrade via USB flash drive, automatically backs up the original system parameters and historical data during the upgrade process, and can automatically roll back to the original version if the upgrade fails, ensuring the upgrade process is safe and reliable.

[0036] In one embodiment, the database uses a distributed time-series database to store data, uses timestamps to correlate water sampling status, reaction status, and monitoring results, and supports hierarchical data management according to user permissions.

[0037] Specifically, the database adopts a distributed time-series database, which has the advantages of high-concurrency writing, efficient storage of time-series data, and correlation querying, making it suitable for the time-series characteristics of water quality monitoring data. Through timestamps, water sampling status data (such as water flow rate and pipeline pressure), reaction status data (such as heating rate and temperature hold-up deviation), and monitoring result data (such as water quality concentration values ​​and standard sample verification values) are accurately correlated, forming a complete "water sampling-reaction-monitoring" data chain. This facilitates tracing the time point of a fault and related parameters. Simultaneously, the database supports hierarchical data management according to user permissions, ensuring the security and standardization of data management and preventing misoperation or data leakage.

[0038] In one embodiment, different monitoring factors include total nitrogen and anionic surfactants. By loading the characteristic parameter library of the corresponding monitoring factors and the preset diagnostic threshold, the measurement requirements of alkaline potassium persulfate spectrophotometry and methylene blue spectrophotometry can be adapted.

[0039] Specifically, the system supports flexible adaptation to different monitoring factors such as total nitrogen and anionic surfactants. For the alkaline potassium persulfate spectrophotometric method used for total nitrogen monitoring, the system loads a dedicated feature parameter library, including core parameters such as standard digestion time, heating rate baseline, and temperature hold deviation threshold, as well as targeted diagnostic thresholds for faults such as pump tube aging and heater scaling. For the methylene blue spectrophotometric method used for anionic surfactant monitoring, the system loads a corresponding feature parameter library, covering parameters such as colorimetric reaction temperature range, absorbance fluctuation threshold, and reagent consumption rate baseline. Simultaneously, the fault diagnosis logic is optimized, focusing on faults related to colorimetric reaction stability (such as reagent contamination). Users only need to select the target monitoring factor on the device interface, and the system will automatically load the corresponding parameter library and thresholds, enabling rapid switching of monitoring methods and significantly improving the reusability and applicability of the equipment.

[0040] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. An AI-based intelligent diagnostic and predictive maintenance system for online water quality monitoring instruments, characterized in that, It includes a multi-dimensional database construction module, a cross-system intelligent diagnosis module, a predictive maintenance module, and a modular compatible deployment module; The multi-dimensional database construction module is used to collect equipment operation data, water quality monitoring data, and environmental and maintenance data, and extract cross-system correlation features and fault features, establish a data correlation mechanism, and build a database. The cross-system intelligent diagnostic module is used to train a differentiated diagnostic model based on the database, so as to realize the early identification and accurate location of obvious faults in the influent sample system and latent faults in the heating system, as well as the root cause tracing of cross-system collaborative faults. The predictive maintenance module is used to dynamically generate personalized maintenance plans based on the analysis results of the database and the cross-system intelligent diagnostic module. The modular compatible deployment module is used to integrate the cross-system intelligent diagnostic module and the predictive maintenance module into the existing monitoring instrument through lightweight hardware embedding and iterative software function upgrades, so as to achieve intelligent upgrades without replacing core components and adapt to online water quality monitoring equipment with different monitoring factors.

2. The AI-based intelligent diagnostic and predictive maintenance system for online water quality monitoring instruments according to claim 1, characterized in that, The cross-system intelligent diagnostic module includes a visible fault diagnosis model for the influent sample system and a hidden fault diagnosis model for the heating system. The explicit fault diagnosis model of the water intake system adopts the XGBoost algorithm, which takes into account the pump operating current fluctuation coefficient, water intake flow rate attenuation rate, and pipeline pressure abnormality frequency characteristics, and outputs the fault type, risk level, and remaining safe operating time. The latent fault diagnosis model of the heating system adopts a deep neural network, which takes into account the heating rate, temperature holding deviation, and heat transfer efficiency characteristics, and outputs the latent fault type.

3. The AI-based intelligent diagnosis and predictive maintenance system for online water quality monitoring instruments according to claim 2, characterized in that, The cross-system intelligent diagnostic module also includes a cross-system collaborative diagnostic mechanism. When the monitoring data is distorted, the system uses a preset correlation model to simultaneously analyze the operating data of the influent sample system and the heating system to locate the root cause of the fault.

4. The AI-based intelligent diagnosis and predictive maintenance system for online water quality monitoring instruments according to claim 2, characterized in that, The cross-system intelligent diagnostic module also includes a model self-calibration mechanism, which automatically calls standard sample verification data according to a preset time period to verify the output results of the explicit fault diagnosis model of the water sample system and the implicit fault diagnosis model of the heating system. If the standard sample verification deviation exceeds the preset threshold range, the model parameters are fine-tuned.

5. The AI-based intelligent diagnosis and predictive maintenance system for online water quality monitoring instruments according to claim 2, characterized in that, The fault types output by the explicit fault diagnosis model of the water intake system include pump pipe aging, pipeline blockage, valve failure, and decreased water intake efficiency. The latent fault types output by the latent fault diagnosis model of the heating system include heater scaling, thermocouple drift, and heating wire aging.

6. The AI-based intelligent diagnosis and predictive maintenance system for online water quality monitoring instruments according to claim 1, characterized in that, The predictive maintenance module includes a component life prediction unit, a maintenance plan generation unit, and a full-scenario maintenance optimization unit. The component life prediction unit is used to predict the remaining service life of key components based on component operation data and environmental impact data in the database, using a preset prediction model. The maintenance plan generation unit is used to recommend the optimal maintenance cycle and maintenance content based on the predicted remaining service life, failure risk level and operation and maintenance cost. The full-scenario maintenance optimization unit is used to generate a pipeline cleaning plan for idle equipment within a preset time, and to generate a full-process cleaning plan for idle equipment exceeding the preset time. The full-process cleaning plan includes pure water soaking, pipeline cleaning, and drain protection.

7. The AI-based intelligent diagnosis and predictive maintenance system for online water quality monitoring instruments according to claim 6, characterized in that, The prediction model uses a survival analysis algorithm. The input component operation data includes the cumulative running time of the peristaltic pump tube, the number of times the heating wire operates, the thermocouple usage cycle, and the valve switching frequency. The environmental impact data includes the operating environment temperature, humidity, and water quality corrosivity level.

8. The AI-based intelligent diagnostic and predictive maintenance system for online water quality monitoring instruments according to claim 1, characterized in that, The modular compatible deployment module includes a hardware deployment unit and a software deployment unit; The hardware deployment unit is used to add a lightweight AI processing module based on ARM architecture. The lightweight AI processing module based on ARM architecture is integrated into the main control board of the detector through a standardized interface, and is compatible with the core components of the existing monitor. The software deployment unit is used to package the differentiated diagnostic model of the cross-system intelligent diagnostic module and the predictive model of the predictive maintenance module into a lightweight executable file and embed it into the existing monitoring instrument operating system.

9. The AI-based intelligent diagnosis and predictive maintenance system for online water quality monitoring instruments according to claim 1, characterized in that, The database uses a distributed time-series database to store data, and uses timestamps to associate water sampling status, reaction status and monitoring results, and supports hierarchical management of data according to user permissions.

10. The AI-based intelligent diagnosis and predictive maintenance system for online water quality monitoring instruments according to claim 1, characterized in that, The different monitoring factors include total nitrogen and anionic surfactants. By loading the characteristic parameter library of the corresponding monitoring factors and the preset diagnostic threshold, the measurement requirements of alkaline potassium persulfate spectrophotometry and methylene blue spectrophotometry are adapted.

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