Digital twin water quality automatic monitoring station intelligent operation and maintenance management method

Abstract

This invention relates to the field of operation and maintenance management technology, and in particular to a digital twin intelligent operation and maintenance management method for automatic water quality monitoring stations. The method includes the following steps: collecting sensor signal sets from equipment via a pre-deployed sensor cluster at the automatic monitoring station and performing digital signal conversion; performing sensor signal coordination based on the digital signal conversion results to obtain equipment sensing data; determining abnormal sampling points based on the magnitude of numerical changes in the equipment sensing data; performing drift correction on the abnormal sampling points and replacing the drift correction results with the corresponding sampling values ​​in the equipment sensing data to obtain corrected sensing data; constructing an equipment operation twin model by combining the corrected sensing data with a pre-set equipment mechanism model; performing equipment operation simulation using the equipment operation twin model and determining operation and maintenance management strategies based on the simulation results. This application helps improve the overall operation and maintenance efficiency, reliability, and refined management capabilities of automatic water quality monitoring stations.

Description

Technical Field

[0001] This invention relates to the field of operation and maintenance management technology, and in particular to an intelligent operation and maintenance management method for a digital twin automatic water quality monitoring station. Background Technology

[0002] The current application of digital twin technology in water quality monitoring stations mainly involves deploying multiple types of sensors to collect multi-dimensional parameters such as temperature, pressure, and vibration of the equipment in real time, and transmitting them to a digital twin model built based on multi-physics coupling modeling and finite element analysis technology to achieve high-precision simulation of the equipment's operating status.

[0003] However, existing digital twin water quality monitoring systems still face multiple technical bottlenecks in practical applications. First, regarding data quality, sensor-collected monitoring data is prone to drift, loss, and abnormal fluctuations due to electromagnetic interference, water corrosion, and environmental fluctuations. Inconsistent measurement standards between different manufacturers' equipment lead to insufficient data comparability and consistency. Furthermore, traditional transmission methods suffer from delays, making it difficult to guarantee the real-time performance and reliability of monitoring data. Second, regarding model accuracy, water quality simulation models deviate significantly from actual environmental characteristics. Existing modeling methods struggle to accurately depict the multi-factor coupling effects in complex water bodies. Model parameter calibration relies on manual adjustments and lacks adaptive optimization mechanisms. The correlation accuracy between multi-scale models is low, and the fitting effect from point data to the watershed scale is limited. Third, regarding system integration, differences in the construction periods and communication protocols of different monitoring stations limit data exchange and resource sharing between heterogeneous systems, making monitoring data integration difficult and resulting in insufficient system compatibility and scalability. Finally, in terms of operation and maintenance management, equipment status assessment and fault early warning are still based on static thresholds, resulting in a high false alarm rate. Maintenance decisions rely on human experience and lack data-driven intelligent analysis and prediction support. Spare parts management and allocation are inefficient, making it difficult to achieve refined and preventive maintenance of monitoring equipment. Summary of the Invention

[0004] Based on this, it is necessary for the present invention to provide an intelligent operation and maintenance management method for a digital twin automatic water quality monitoring station, in order to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above objectives, a digital twin water quality automatic monitoring station intelligent operation and maintenance management method includes the following steps: Step S1: Collect the sensor signal set of the device by pre-deploying the sensor cluster at the automatic monitoring station, and transmit the sensor signal set of the device to the pre-deployed edge node to perform digital signal conversion; Step S2: Perform sensor signal coordination based on the digital signal conversion result to obtain device sensing data; Step S3: Determine abnormal sampling points based on the magnitude of changes in the device sensor data; perform drift correction on the abnormal sampling points and replace the drift correction results with the corresponding sampling values ​​in the device sensor data to obtain the sensor correction data; Step S4: Combine the sensor correction data with the preset equipment mechanism model to construct a twin model of equipment operation; use the twin model of equipment operation to perform equipment operation simulation, and determine the operation and maintenance management strategy based on the simulation results.

[0006] This application achieves high-frequency, low-latency acquisition of operating parameters of automatic water quality monitoring stations through multi-sensor cluster acquisition and edge node digital signal conversion. This effectively reduces data drift and abnormal fluctuations caused by electromagnetic interference, water corrosion, or environmental fluctuations. Simultaneously, it standardizes data from equipment from different manufacturers, improving data consistency and comparability, thus ensuring the real-time performance and reliability of monitoring data. Secondly, through collaborative sensor signal processing and drift correction mechanisms, abnormal sampling points can be identified, and offset correction values ​​can be calculated based on adjacent sensor signals and historical data. This achieves cross-cancellation of data noise and integration of fluctuation components, making the equipment sensor data closer to the actual operating state and providing high-precision foundational data for subsequent simulation and maintenance analysis. Thirdly, by combining the equipment mechanism model and the operational twin model, multi-dimensional operational simulation of the equipment is performed on a digital twin platform, and the simulation results are compared and verified with real-time acquired data. Through iterative optimization of simulation results, model parameters can be adaptively calibrated to achieve accurate simulation of the operating state of the water quality monitoring equipment, significantly improving model accuracy and multi-scale correlation capabilities, making simulations from point data to watershed scales more reliable. Furthermore, by constructing a dynamic matching mechanism between the operation and maintenance management knowledge base and the spare parts library, and combining operational simulation results to identify highly similar operation and maintenance scenarios, and dynamically adjusting historical operation and maintenance management strategies based on operational status deviations, intelligent generation of operation and maintenance strategies is achieved. This method can automatically determine operation and maintenance management deviations and classify and distribute remote control tasks or high-priority maintenance tasks according to the deviation magnitude, significantly improving fault response efficiency and the scientific nature of maintenance decisions, reducing false alarm rates, and enhancing the intelligence level of preventive maintenance and spare parts management. This application forms an end-to-end closed-loop operation and maintenance management system, covering the entire process from data acquisition, data correction, digital twin simulation, operation and maintenance strategy generation to task distribution and verification optimization, realizing real-time monitoring, intelligent analysis, and proactive maintenance of equipment operating status, and improving the overall operation and maintenance efficiency, reliability, and refined management capabilities of automatic water quality monitoring stations. Attached Figure Description

[0007] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a flowchart illustrating the steps of the intelligent operation and maintenance management method for the digital twin automatic water quality monitoring station of the present invention. Figure 2 This is a cross-sectional schematic diagram of an automatic water quality monitoring station in an embodiment of the present invention; The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0008] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0009] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.

[0010] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0011] To achieve the above objectives, please refer to Figures 1 to 2 This invention provides an intelligent operation and maintenance management method for a digital twin automatic water quality monitoring station, the method comprising the following steps: Step S1: Collect the sensor signal set of the device by pre-deploying the sensor cluster at the automatic monitoring station, and transmit the sensor signal set of the device to the pre-deployed edge node to perform digital signal conversion; In this embodiment, if the automatic monitoring station is within a continuous monitoring cycle, the sensor cluster within the monitoring area initiates synchronous data acquisition. The sensor cluster includes temperature, vibration, current, and sound pressure sensing units, respectively deployed in the equipment bearing section, outer wall of the casing, power supply port, and inside the casing cavity, forming a monitoring system covering the equipment's thermal, mechanical, and electrical three-dimensional state. Each sensor acquires analog signals at a sampling frequency of 2Hz, which are then preliminarily denoised by the acquisition controller before being sent to the edge node. The edge node is equipped with a 16-bit A / D conversion chip to perform digital signal conversion and unify the sampling timestamps of each signal to the same time base. To eliminate high-frequency interference, bandpass filtering is superimposed during digital conversion, with a passband range of 1–200Hz. The final generated digital signal data packets are aggregated according to sensor number to form the equipment sensor signal set, providing raw input for subsequent collaborative analysis.

[0012] Step S2: Perform sensor signal coordination based on the digital signal conversion result to obtain device sensing data; In one embodiment, sensor signal co-processing is performed on the sensor signals that have undergone digital signal conversion within the edge node. The converted signal set is filtered for effective sensor digital signals, using the signal-to-noise ratio (SNR) and stability coefficient within each sampling period as filtering criteria. If the SNR of the sensor output signal is below 20dB or the stability coefficient exceeds 0.15, the signal is determined to have abnormal drift or random pulse interference and is discarded. For the filtered effective sensor signals, interpolation reconstruction is performed based on their sampling timestamps to ensure strict alignment of each signal sequence on the same time axis. After signal filtering, noise cross-cancellation is performed. This process uses the amplitude deviation of adjacent sensors within the same sampling period as a coupling parameter to calculate the cross-correlation function to identify synchronization noise components. If a noise component appears in both adjacent sensors and the phase difference is less than 5°, it is determined to be a noise interference signal and is canceled by inverse phase superposition. The canceled signal is then band-limited smoothed (cutoff frequency set to 180Hz) to form a time-consistent, noise-suppressed multidimensional signal matrix. Subsequently, corresponding data sub-matrices are constructed based on the physical quantity types of different sensors (such as temperature, current, vibration, and sound pressure), and then spliced ​​together according to the sampling time sequence to form device sensing data.

[0013] Step S3: Determine abnormal sampling points based on the magnitude of changes in the device sensor data; perform drift correction on the abnormal sampling points and replace the drift correction results with the corresponding sampling values ​​in the device sensor data to obtain the sensor correction data; In this embodiment, abnormal sampling points are detected based on the magnitude of changes in the device's sensor data. Specifically, every five sampling points are used as an analysis unit. If the magnitude of the change in the current sampling point exceeds 1.5 times the average magnitude of the change in the adjacent interval, it is marked as an abnormal sampling point. For each abnormal sampling point, the magnitude difference between it and its immediate and adjacent sampling points is extracted. and the change in direction and angle As an offset identification parameter, it is used to retrieve the average change amplitude of similar sensors within the same continuous sampling period in parallel. Distribution density of directional changes This serves as a reference value for drift correction. Subsequently, it is adjusted according to a preset weight ratio (amplitude correction weight). =0.6, direction correction weight =0.4), calculate the comprehensive offset correction value. .like If the value exceeds the drift threshold, a numerical replacement correction is performed, and the corrected amplitude and orientation angle are backfilled into the corresponding positions in the device's sensing data. The corrected sensing data retains the original time series structure, ensuring data continuity and trend consistency, thereby providing reliable input for subsequent twin simulations.

[0014] Step S4: Combine the sensor correction data with the preset equipment mechanism model to construct a twin model of equipment operation; use the twin model of equipment operation to perform equipment operation simulation, and determine the operation and maintenance management strategy based on the simulation results.

[0015] In this embodiment, a twin model of equipment operation is constructed by combining sensor correction data with a preset equipment mechanism model. This mechanism model is based on the equipment manufacturer's design parameters and includes a speed-load-temperature rise coupling matrix and a power-current mapping function to characterize the physical constraints of equipment operation. The twin model uses sensor correction data as the input layer and the mechanism constraint relationship as the intermediate layer, forming a dynamic mapping of the equipment's operating state through parameter fitting. During the simulation phase, an iterative calculation is performed every 30 seconds, and the output simulation results include real-time power change curves, temperature rise curves, and vibration energy spectrum distribution. Based on the deviation between the simulation output and historical normal operation samples, the system identifies abnormal operating trends of the equipment and then determines the corresponding operation and maintenance management strategy. This strategy, based on the correction data, optimizes the operating load allocation, adjusts the temperature control trigger threshold, and revises the maintenance cycle, thus forming a complete closed loop of operation and maintenance decision-making.

[0016] Optionally, after determining the operation and maintenance management strategy in step S4, the following may also be included: The operation and maintenance management strategy is sent to the automatic monitoring station management platform, and maintenance task orders are distributed to the execution units according to the deviation between the operation and maintenance management strategy and the existing operation and maintenance management strategy in the automatic monitoring station management platform. In this embodiment, the strategy is transmitted to the automatic monitoring station management platform via encrypted transmission through edge nodes. Upon receiving the strategy, the platform first matches it with the existing operation and maintenance management strategy library based on the strategy number, calculating the deviation between the two strategies in three key parameters: equipment operating intensity, temperature control threshold, and maintenance cycle setting. The deviation is calculated using normalized difference, with a normalization threshold range of [0,1]. A deviation between 0.15 and 0.35 is defined as a medium deviation. When the deviation is within the medium range, a classification and distribution process is automatically triggered. Task orders are classified according to the deviation level. If the deviation is within the remotely adjustable range, a remote control task order is generated, along with control instructions and an encrypted control link. If the deviation exceeds the remotely adjustable limit, a high-priority maintenance task order is automatically generated, containing a maintenance plan number, equipment repair instructions, and required spare parts numbers. Each task order is pushed to the corresponding execution unit through the platform's task distribution interface. After receiving and confirming receipt, the execution unit initiates subsequent maintenance operations. This deviation-based hierarchical distribution method ensures that remote and on-site maintenance resources are rationally scheduled, avoiding maintenance response delays.

[0017] By using a sensor cluster to synchronously acquire real-time device sensor signal sets and performing digital signal conversion in coordination with sensor signals, real-time device sensing data can be obtained. Compare real-time device sensor data with simulation results to verify the simulation results; In this embodiment, the real-time acquisition program of the sensor cluster is synchronously triggered. Each sensor outputs a digital signal with a sampling period of 200ms, and digital signal conversion and signal co-processing are performed via edge nodes. Digital signal conversion includes A / D conversion and timestamp synchronization to ensure that all signals have a unified sampling reference. Signal co-processing performs effective signal filtering and noise cross-cancellation operations based on the spatial distribution relationship of each sensor in the same sampling period to generate a real-time device sensor data matrix. The dimension of this matrix is ​​defined as T×N, where T is the number of time sampling points, N is the number of sensors, and each element is the sensor amplitude data after spatiotemporal synchronization processing.

[0018] In a further embodiment, the simulation results from the previous run are used for comparison and verification. First, linear interpolation alignment is performed on the real-time device sensor data and the simulation results on the same timeline to eliminate sampling period differences. Then, the average absolute error of the two on key indicators (including flow response time, pump body temperature rise rate, and power supply current fluctuation coefficient) is calculated as the verification numerical error. If the verification numerical error of any indicator exceeds 0.08, the simulation result is determined to have a deviation. At this time, an error distribution map and a corresponding deviation source location report are output, which includes the deviation direction of each parameter and the corresponding device number. This verification result is used to provide feedback on the correction direction of the simulation model, ensuring that the calculation results of the twin model are consistent with the on-site operating status. Through this comparison and verification, dynamic self-calibration of the operating twin model under real-time monitoring conditions can be achieved.

[0019] It is worth noting that the verification results can also be uploaded to the operation and maintenance management knowledge base to update the knowledge base.

[0020] The operation and maintenance management strategy is iteratively optimized based on the verification numerical error in the verification results until the verification numerical error is less than the preset error threshold.

[0021] In this embodiment, if the verification results show deviations in the simulation results, an iterative optimization process for the operation and maintenance management strategy is initiated. Correction weights are determined based on the deviation proportions of each indicator in the error distribution diagram. If the temperature rise deviation accounts for more than 40% of the total deviation, the weight of temperature control-related parameters is increased to 0.6; if the load deviation accounts for less than 20%, the weight of the operating intensity classification parameter is decreased to 0.2. Based on the weight adjustment results, the equipment operating intensity classification, cooling trigger threshold, and maintenance cycle setting are recalculated to generate a new operation and maintenance management strategy. The updated strategy is then verified again on the platform using numerical error calculation. If the error is lower than the preset error threshold of 0.05, the optimization is considered complete; otherwise, the optimization process is repeated until the convergence condition is met. The optimized strategy is synchronously updated to the operation and maintenance knowledge base, and global strategy synchronization is achieved through version number control, enabling the digital twin system to dynamically converge with the real equipment status through multiple iterations.

[0022] Figure 2 This is a cross-sectional schematic diagram of an automatic water quality monitoring station in an embodiment of the present invention, as shown below. Figure 2 As shown: 101 is a meteorological sensor at the top of the automatic water quality monitoring station. It is installed at the top of the meteorological monitoring tower of the automatic water quality monitoring station and can monitor meteorological parameters such as temperature, humidity, and air pressure. 102 is a wireless transmission device for automatic water quality monitoring stations. It is equipped with an industrial-grade wireless communication module (supporting multiple protocols such as 4G / 5G and LoRa). It uses a high-gain antenna to realize the real-time aggregation and transmission of sensor cluster signals, and is used to transmit the sensor signals collected by the sensor cluster to the edge nodes. 103 is a cluster of sensors deployed on pipelines and devices at different depths underwater by the automatic water quality monitoring station. In this area, various types of water quality sensors, such as turbidity sensors (range 0~4000NTU, accuracy ±2%FS), dissolved oxygen sensors (range 0~20mg / L, accuracy ±0.3mg / L), pH sensors (range 0~14, accuracy ±0.1pH), and conductivity sensors (range 0~50000μS / cm, accuracy ±1%FS), are deployed in an array on sampling pipelines and multi-parameter sensing probe devices at different depths underwater in the monitoring well.

[0023] Optionally, the categorized distribution of maintenance task orders includes: If the deviation of operation and maintenance management is within the preset remote adjustment range, the remote execution unit corresponding to the operation and maintenance management strategy will be determined through the automatic monitoring station management platform, and the control instructions in the operation and maintenance management strategy and the remote control links corresponding to the remote execution units will be integrated into a remote control task sheet and distributed to the corresponding remote execution units. In this embodiment, if the automatic monitoring station management platform detects that the deviation in operation and maintenance management is within a preset remote adjustment range (e.g., 5% to 15%), it queries the corresponding execution unit identifier in the task execution index table based on the operation and maintenance management strategy number, and extracts the remote control link address based on the execution unit's network topology configuration file. Subsequently, the platform extracts parameter adjustment instructions that can be implemented through remote control from the operation and maintenance management strategy, including control instructions such as pump start-up and shutdown timing adjustment, valve opening fine-tuning, and data sampling frequency correction. The control instructions are encapsulated in the form of structured instruction blocks, each including instruction type, target device number, execution delay, and security check code. The control instructions and remote control links are integrated to generate a remote control task order, which includes a task number, task priority, and feedback channel information. After generating the task order, the platform encrypts and transmits it to the control terminal of the corresponding remote execution unit through the edge gateway, and records the issuance time and execution status in the task management table. After receiving the task order, the remote execution unit verifies the legality of the instruction by checking the security code, executes the operation within the specified time window, and simultaneously feeds back the execution status to the platform's task monitoring interface in real time, realizing closed-loop management of remote control tasks.

[0024] If the deviation in operation and maintenance management exceeds the remote adjustment range, the maintenance operation guidelines, equipment repair plans, and spare parts configuration requirements in the operation and maintenance management strategy will be integrated into a high-priority maintenance task order, and the high-priority maintenance task order will be pushed to the on-site maintenance execution unit in the corresponding area of ​​the automatic monitoring station through the automatic monitoring station management platform.

[0025] In another embodiment, if the detected deviation in operation and maintenance management exceeds the upper limit of the remote adjustment range, the platform automatically switches to the on-site maintenance task generation mode. At this time, based on the differences in equipment operating intensity, abnormal temperature rise, and maintenance time delay in the deviation data, the maintenance priority level of the affected equipment is determined. The platform extracts the maintenance operation guidelines corresponding to this level from the operation and maintenance management strategy, including pump filter cleaning procedures, power line contact detection steps, and sensor calibration procedures. Simultaneously, it calls the maintenance templates and equipment structural parameter files in the operation and maintenance management knowledge base to automatically match suitable equipment maintenance plans and determines the required spare parts configuration list based on the current equipment model and operating time. After integrating all the content, a high-priority maintenance task order is generated. The task order structure includes: ① maintenance object number; ② maintenance operation guidelines; ③ maintenance step sequence and estimated time; ④ spare parts configuration requirement list; ⑤ task priority identifier and responsible unit code. After the task order is generated, it is transmitted to the on-site maintenance execution unit terminal in the corresponding area via the on-site task push interface of the automatic monitoring station management platform. A task receipt mechanism is attached to the push, requiring the execution unit to confirm the task receipt status within 10 minutes of receiving it. After receiving a task order, on-site maintenance personnel carry out repairs according to the operation instructions in the task order, and upload the execution record and equipment operation recovery data after the task is completed. The platform compares the equipment sensor data before and after maintenance to verify the task effect and update the operation and maintenance management strategy version, realizing the reverse supplementation of the operation and maintenance strategy library by on-site maintenance data.

[0026] Optionally, the sensor signal coordination in step S2 includes: Based on the transmission timestamp sequence of each sensor signal transmitted to the edge node in the digital signal conversion result, the execution time of each sensor digital signal is synchronized and paired. In this embodiment, each sensor's digital signal is accompanied by a transmission timestamp during transmission to the edge node. These timestamp sequences are sorted and aligned to establish a time synchronization lookup table based on the sampling period. In this embodiment, the sampling period can be set to 500 milliseconds, allowing timestamp deviations within ±5 milliseconds to ensure that minor transmission delays do not affect the synchronization results. By aligning the timestamps of each sensor's digital signal, a sensor time synchronization matrix can be generated, where rows represent sensor numbers, columns represent sampling time points, and matrix elements are the digital signal values ​​at the corresponding time points. This matrix provides the basic data structure for subsequent signal collaborative analysis.

[0027] Acquire the spatial distribution information of each sensor in the sensor cluster, and detect the signal change trend of adjacent sensors in the same sampling period based on the spatial distribution information of each sensor in the time synchronization pairing results; In this embodiment, the spatial distribution information of the sensor cluster can be obtained through the automatic monitoring station management platform, including the specific deployment coordinates of the sensors in the monitoring station (such as the three-dimensional positions of x, y, and z) and a list of neighboring sensors. Based on this spatial information and the sensor time synchronization matrix, the signal change trend of adjacent sensors within each sampling period is detected. The change trend can be calculated by the numerical difference between adjacent sampling points and represented by the change in direction vector and amplitude. In this embodiment, the threshold for the direction vector angle is set to 30°, and the threshold for the amplitude change rate is ±10%, to determine whether the signals of adjacent sensors show a consistent trend. The detection result will form a signal trend consistency matrix, where the matrix element value is 0 or 1, respectively indicating whether the signal trends of adjacent sensors are consistent within the sampling period.

[0028] Based on the detection results of signal change trends, effective sensor digital signals are selected, and noise cross-cancellation is performed on the effective sensor digital signals to obtain the results of cooperative sensor digital signals.

[0029] In a further embodiment, valid sensor digital signals are screened based on a signal trend consistency matrix. For each sensor, if its signal change trend is consistent with that of more than one-third of its neighboring sensors in the same sampling period and the amplitude change is within a set threshold range, then the sensor's digital signal is determined to be a valid sensor digital signal. In noise cross-cancellation, the fluctuation components of the digital signals of the valid sensors and their three nearest neighboring sensors are extracted, the residual mean sequence is calculated, and its moving average is used as the environmental background noise benchmark. By using the fluctuation components of adjacent sensors as weights to perform inverse superposition on the fluctuations of the valid sensor digital signals, and integrating the results of each sampling period, a collaborative sensor digital signal result matrix is ​​obtained, which is used as the input for device operation simulation.

[0030] Optionally, filtering valid sensor digital signals includes: Calculate the consistency rate of the direction of change and the similarity of the magnitude of change of the digital signals of the sensor and its adjacent sensors within the same sampling period; In this embodiment, the signal change direction and amplitude changes of each sensor within a continuous sampling period are extracted. The change direction can be calculated from the difference in values ​​between adjacent sampling points and represented by an angle; the change amplitude is the absolute value of the difference in values ​​between adjacent sampling points. Subsequently, for each sensor, the consistency rate of its change direction and the similarity of its change amplitude with those of adjacent sensors within the same sampling period are calculated. The consistency rate of change direction is obtained by statistically analyzing the proportion of sampling points with a change direction difference of less than 30° within the sampling period; the similarity of change amplitude is obtained by statistically analyzing the proportion of adjacent sampling points with an amplitude change difference of less than 10%.

[0031] If the direction of change of any sensor is consistent with that of more than three adjacent sensors, and the similarity of the change amplitude is less than the similarity of the change amplitude, then the sensor is determined to be a valid sensor, and the sensor digital signal corresponding to the valid sensor is designated as the valid sensor digital signal.

[0032] In a further embodiment, if the direction of change of any sensor is consistent with that of more than three neighboring sensors by more than 80%, and the similarity of the magnitude of change is less than 10%, then the sensor is determined to be a valid sensor. The digital signal of the valid sensor will be extracted to form a valid sensor digital signal.

[0033] Optionally, performing noise cross-cancellation includes: Extract the signal strength sequence of the effective sensor and the three nearest adjacent sensors within the same sampling period from the digital signal conversion results; Calculate the residual mean sequence between the effective sensor digital signal and the adjacent sensor digital signals based on the signal strength sequence; In this embodiment, the digital signal intensity sequence of each effective sensor and its three closest adjacent sensors is extracted from the digital signal conversion result. The spatial distance can be calculated based on the preset three-dimensional coordinates of the sensor in the automatic monitoring station, ensuring that the selected adjacent sensors are the three closest devices with a distance of 0.5 to 1.5 meters. The sampling period is set to 500 milliseconds to form the adjacent sensor digital signal, which contains the signal intensity sequence of the sensor within the sampling period. Subsequently, the signal intensity residuals of each effective sensor and its adjacent sensors are calculated within the same sampling period to obtain the residual sequence. The mean of the residual sequence is further calculated to form a residual mean sequence matrix, where each element represents the average residual between a certain effective sensor and the signals of its neighboring sensors within a certain sampling period.

[0034] The moving average value in the residual mean sequence is used as the environmental background noise benchmark, and the fluctuation component of each sensor's digital signal under the environmental background noise benchmark is extracted. In a further embodiment, the residual mean sequence is averaged using a sliding window, with the window length set to 5 sampling periods, to obtain an environmental background noise reference sequence, which is used to extract the fluctuation components of each sensor's digital signal. The fluctuation components are calculated by subtracting the background noise reference obtained from the moving average from each sensor's digital signal, resulting in a high-frequency fluctuation component matrix, which represents the fluctuation components of each sensor's digital signal under the environmental background noise reference.

[0035] The fluctuation components of adjacent sensor digital signals are used as weighting terms to perform weighted inverse superposition on the fluctuation components of the effective sensor digital signals, and the weighted inverse superposition results of the effective sensor digital signals are integrated to obtain the device sensing data.

[0036] In a further embodiment, the fluctuation components of the digital signals from adjacent sensors are used as weighting terms to perform weighted inverse superposition on the fluctuation components of the effective sensor digital signals. The weighting coefficient can be set to be proportional to the reciprocal of the spatial distance between adjacent sensors, with higher weights for closer sensors, and the coefficient range is 0.25~0.4. After weighted inverse superposition, the superposition result is integrated with the original fluctuation components of the effective sensors to form the device sensing data.

[0037] It is worth noting that the integration of the superposition result with the original fluctuation components of the effective sensor can be achieved through element-by-element fusion. The weighted inverse superposition result is weighted and averaged with the fluctuation components of the effective sensor using a preset coefficient (e.g., 0.6) and a supplementary coefficient (e.g., 0.4) to obtain the corrected signal fluctuation matrix. Subsequently, the corrected fluctuation matrix is ​​added point-by-point to the baseline value of the original digital signal from the effective sensor (i.e., the reference value sequence after digital signal conversion) to obtain the device sensing data.

[0038] Optionally, determining abnormal sampling points in step S3 includes: Extract the numerical sequence of each sensor within a continuous sampling period from the device's sensing data, and calculate the magnitude and direction of change of each sensor between adjacent sampling points based on the numerical sequence; In this embodiment, the numerical sequence of each sensor within a continuous sampling period is extracted from the generated device sensing data. The sampling period is set to T = 1 second, and the total number of sampling points is N = 120, forming a time series vector of length 120. For each time series, the numerical difference between adjacent sampling points is calculated to obtain the amplitude change value of each sampling point and the direction of change between adjacent points. The direction of change can be represented by the sign of the vector difference, indicating an upward or downward trend.

[0039] Based on the variation amplitude and direction of each sensor between adjacent sampling points, calculate the average variation amplitude and direction distribution density of similar sensors within the same continuous sampling period; In a further embodiment, the amplitude change values ​​and change directions of all similar sensors are statistically analyzed within the same continuous sampling period. The average amplitude change value and the change direction distribution density are calculated respectively. The change direction distribution density can be divided into several angle segments in the range of 0° to 180° and the occurrence frequency of each segment is counted to obtain the probability distribution of the change direction of each sampling point in the group of sensors, which is convenient for identifying abnormal direction deviations.

[0040] If the change amplitude between any sampling point and its adjacent sampling points exceeds 1 to 3 times the average change amplitude, and the angle difference between the change direction and the average change direction angle of similar sensors in the same continuous sampling period in the change direction distribution density is higher than the direction difference angle difference threshold, then the sampling point is determined to be an abnormal sampling point.

[0041] In a further embodiment, for each sampling point, it is determined whether its amplitude change value exceeds the range of 1.5 to 2.5 times the average amplitude. If it does, the amplitude is considered abnormal. Simultaneously, the angle difference between the direction of change of this sampling point and the average direction of change of similar sensors is calculated. If the angle difference is greater than 30° (as a directional difference angle threshold), the sampling point is determined to be an abnormal sampling point. This method ensures that only when both amplitude and directional abnormalities are met can a point be marked as abnormal, avoiding false positives.

[0042] Optionally, performing drift correction in step S3 includes: Calculate the difference in amplitude and the change in direction angle between abnormal sampling points and adjacent sampling points within the continuous sampling period based on the numerical sequence of each sensor. In this embodiment, the numerical sequence of each abnormal sampling point and its adjacent sampling points before and after it is extracted from the device sensing data before sensing correction. Assuming the continuous sampling period is T = 1 second, the value of each sampling point is denoted as... The values ​​of the sampling points before and after are respectively denoted as and Calculate the amplitude difference between the abnormal sampling point and its adjacent sampling points. And calculate the change in direction angle. The direction angle can be represented by the angle between the slope vector of adjacent points and the positive direction, which makes it easy to judge the deviation of the upward or downward trend of the value.

[0043] Based on the amplitude difference and directional angle change between abnormal sampling points and their adjacent sampling points, and taking the average amplitude and directional distribution density of similar sensors within the same continuous sampling period as reference values, offset correction values ​​are calculated according to preset weight ratios; the offset correction values ​​include amplitude correction values ​​and directional angle correction values. In a further embodiment, the amplitude difference of each abnormal sampling point is... and change in direction and angle Average amplitude change compared to similar sensors within the same continuous sampling period Distribution density in the direction of change A comparative analysis was conducted, using preset weighting ratios. , For amplitude correction value and direction angle correction value Perform weighted calculations separately: amplitude correction value Direction angle correction value ;in For density based on angle distribution The determined average change direction of similar sensors As a correction reference, ensure that the offset correction takes into account both the changes of the abnormal sampling points themselves and the trends of the group of sensors, and avoid errors introduced by extreme values ​​at a single point.

[0044] If the offset correction value of the abnormal sampling point is within the preset correction tolerance range, then the offset correction value is taken as the drift correction result.

[0045] Of particular importance, the drift correction process also includes: If the offset correction value of an abnormal sampling point is not within the correction tolerance range, the weight ratio is iteratively corrected until the difference between the offset correction value and the amplitude of the adjacent sampling point is within the correction tolerance range.

[0046] In a further embodiment, the calculated amplitude correction value is... and direction angle correction value Tolerance judgment is performed, assuming the amplitude tolerance range is ±0.05 units. If the error falls within this tolerance range, the correction is considered reasonable; otherwise, the weight ratio can be adjusted iteratively. and This continues until the correction value falls within the tolerance range. Finally, the magnitude correction value is... With direction angle correction value It is applied to abnormal sampling points, replaces the original values, generates drift-corrected sampling point data, and updates the device sensing data for subsequent sensing correction data generation and simulation.

[0047] Optionally, determining the operation and maintenance management strategy in step S4 includes: The operation and maintenance management scenario with high similarity to the simulation results is matched by a pre-set operation and maintenance management knowledge base and spare parts library. In this embodiment, feature extraction is performed on the simulation results, including the operating load, temperature rise curve, and maintenance timeliness data sequence of each device. It is assumed that the continuous sampling period is T=1 second, the unit of operating load is kW, the unit of temperature rise is °C, and the unit of maintenance timeliness is days. These feature vectors are then compared with the feature vectors of each historical operation and maintenance management scenario in a pre-built operation and maintenance management knowledge base. Cosine similarity or Euclidean distance is used as the matching standard. When the similarity is higher than 0.85, it is determined to be a high-similarity operation and maintenance management scenario. Simultaneously, available spare parts information for each scenario is extracted from the spare parts library and associated with historical policies in the operation and maintenance management knowledge base to form a complete scenario-policy-spare parts mapping table for subsequent policy correction.

[0048] Based on the differences between highly similar operation and maintenance management scenarios and operation simulation results, operational status deviations are determined; these operational status deviations include operational load deviations, temperature rise deviations, and maintenance timeliness deviations. In a further embodiment, a difference analysis is performed between the matched high-similarity operation and maintenance management scenarios and the simulation results. The operational state deviation is calculated, including: operational load deviation. Temperature rise deviation Maintenance timeliness deviation Among them, the simulated operating load Simulated temperature rise Simulation maintenance timeliness Historical operating load is used as a basis for simulation results. Historical temperature rise Historical maintenance period This is the historical average. If the operating load deviation exceeds ±5kW, the temperature rise deviation exceeds ±2°C, or the maintenance timeliness deviation exceeds ±3 days, then a strategy correction is required.

[0049] Historical operation and maintenance management strategies corresponding to highly similar operation and maintenance management scenarios are extracted from the operation and maintenance management knowledge base, and the historical operation and maintenance management strategies are corrected according to the deviation of the operation status to obtain the operation and maintenance management strategy.

[0050] In this embodiment, historical operation and maintenance management strategies corresponding to highly similar scenarios are extracted from the operation and maintenance management knowledge base, including equipment operation intensity grading schemes, cooling trigger threshold settings, and maintenance cycle settings. These historical strategies are then dynamically adjusted based on operational status deviations: if... If the threshold is exceeded, the equipment operation intensity classification will be linearly adjusted by increasing or decreasing the threshold; if If the threshold is exceeded, adjust the equipment cooling trigger threshold to ensure temperature control remains within a safe range; if If the deviation is too large, adjust the maintenance cycle setting to ensure that the equipment maintenance timeliness matches the current operating status. Finally, combine the adjustment results with weighted coefficients. , , (For example, 0.5, 0.3, and 0.2 respectively) are weighted and merged to generate the final operation and maintenance management strategy, including the revised operation intensity classification table, cooling trigger threshold, and maintenance cycle.

[0051] Optionally, correcting historical operation and maintenance management strategies based on operational status deviations includes: Based on the deviation in operating status, the corresponding historical operation and maintenance management strategy is dynamically adjusted. If the deviation in operating load exceeds the normal load threshold, the equipment operation intensity classification in the historical operation and maintenance management strategy is corrected. If the temperature rise deviation exceeds the temperature control threshold, the equipment cooling trigger threshold in the historical operation and maintenance management strategy is adjusted. If the maintenance timeliness deviation is lower than the preset maintenance timeliness threshold, the maintenance cycle setting value in the historical operation and maintenance management strategy is corrected. In this embodiment, the historical operation and maintenance management strategy is dynamically adjusted based on the operational status deviation. Taking the equipment operation intensity classification as an example, it is assumed that the historical strategy divides the load into five levels: 0–20kW, 20–40kW, 40–60kW, 60–80kW, and 80–100kW. The simulation results calculate the operational load deviation... When the load exceeds ±5kW, the load ranges are adjusted linearly. For example, if... If the power is increased by 8kW, the original 40–60kW range will be moved up by 8kW, making the range 48–68kW, thereby correcting the operating intensity classification table to adapt to actual load changes.

[0052] In another embodiment, the device cooling trigger threshold in the historical strategy is adjusted based on the temperature rise deviation. Assuming the historical cooling trigger threshold is 60°C, the simulation results show a temperature rise deviation... When the temperature exceeds ±2°C, the threshold is adjusted linearly according to the deviation magnitude, for example... If the temperature is +3°C, the trigger threshold will be adjusted to 63°C to ensure that the device can start the cooling mechanism in time under high temperature conditions, while avoiding frequent starts that would increase energy consumption.

[0053] In another embodiment, the maintenance cycle setting in the historical strategy is corrected to address the maintenance timeliness deviation. Assuming the historical maintenance cycle is set to 30 days, simulation results calculate the maintenance timeliness deviation. If the maintenance cycle is -5 days, it will be shortened to 25 days to ensure that the equipment is maintained in a timely manner even under low time sensitivity.

[0054] Based on highly similar operation and maintenance management scenarios, determine the equipment performance impact of operating load deviation, temperature rise deviation, and maintenance timeliness deviation respectively; In this embodiment, based on the correlation between load variation and equipment failure rate or performance degradation in highly similar operation and maintenance management scenarios, the performance impact of the equipment under the current deviation is calculated. For example, the average lifespan reduction percentage of similar equipment in historical data when the load exceeds the rated value by ±5% is used as a reference, and linear interpolation or curve fitting is used to obtain the result. Corresponding performance impact The unit is the equipment performance impact ratio (0–1). This is for temperature rise deviation. Based on the equipment temperature control response, energy consumption, and losses when the temperature rise exceeds the cooling trigger threshold in highly similar operation and maintenance management scenarios, a mapping table is established between temperature rise deviation and equipment stability or lifespan. According to the current... By comparing with the mapping table, the impact of temperature rise deviation on equipment performance can be obtained. This reflects the relative impact of temperature control deviation on equipment operating efficiency and safety. It also addresses maintenance timeliness deviations. This study statistically analyzes the impact of earlier or later maintenance cycles on equipment failure rates in highly similar operation and maintenance management scenarios, and converts the failure probabilities corresponding to each cycle deviation into performance impact quantities. This is used to quantitatively represent the impact of delayed or advanced maintenance on the overall performance and availability of the equipment.

[0055] Using the impact of equipment performance as a weighting coefficient, a weighted fusion calculation is performed on the correction results of equipment operation intensity classification, equipment cooling trigger threshold adjustment results, and maintenance cycle setting value correction results to obtain the operation and maintenance management strategy.

[0056] In this embodiment, the adjustment results are weighted and fused according to their respective weighting coefficients to obtain the final operation and maintenance management strategy. Specifically, the adjustment result for equipment operating intensity classification is multiplied by the weighting coefficient. +Cooling trigger threshold adjustment result× +Maintenance cycle setting correction result× This process generates a comprehensive strategy table applicable to the current operating status. The strategy table includes revised load grading, cooling thresholds, and maintenance cycles, and can be directly used for remote control task assignment or on-site maintenance execution, ensuring safe and efficient operation of the equipment under different operating conditions.

[0057] Most importantly, the methods for obtaining the operations and maintenance management knowledge base include: Acquire historical equipment operation data of automatic monitoring stations, and extract historical equipment operation numerical data, historical maintenance records and manufacturer design parameters from the historical equipment operation data of automatic monitoring stations; In this embodiment, historical operating data of the equipment is obtained from the historical database of the automatic monitoring station. This data includes various numerical signal sequences collected by sensors, equipment status logs, and maintenance records. Historical equipment operating data is stored in the form of a time-series matrix, where rows represent different sampling time points, columns represent various equipment indicators (such as water flow rate, pump load, motor speed, temperature, etc.), and matrix elements are floating-point values. Historical maintenance records are stored in an event list structure, with each record containing maintenance start time, maintenance end time, maintenance type, and operator information. Manufacturer design parameters are stored in the form of a parameter dictionary, including equipment rated load, temperature control threshold, recommended operating cycles, and safe operating limits.

[0058] Align the timestamps between historical equipment operation data and historical maintenance records, and determine the correlation between equipment maintenance strategies and numerical changes based on the magnitude of changes in historical equipment operation data and the maintenance timestamps in historical maintenance records. In a further embodiment, historical equipment operation data and historical maintenance records are aligned according to timestamps. During the alignment process, using second-level timestamps as a benchmark, the operation data interval corresponding to each maintenance event is extracted to form an operation-maintenance comparison dataset. In this comparison dataset, the change in each equipment indicator before and after maintenance (e.g., load change) is calculated. Temperature rise change ) and maintenance operation interval The correlation between the parameters was analyzed to determine the response characteristics of each maintenance strategy to equipment operating parameters. The correlation was calculated using the Pearson correlation coefficient or the weighted mean square error method, with a reference threshold set between 0.6 and 0.8 to ensure a significant correlation between the selected maintenance strategies and the numerical changes.

[0059] Effective maintenance strategies are screened based on the correlation between equipment maintenance strategies and numerical changes, and a correspondence is established between effective maintenance strategies and abnormal equipment operation modes in historical equipment operation data. In a further embodiment, effective maintenance strategies are screened based on the aforementioned correlation analysis. An effective maintenance strategy refers to a historical maintenance plan that highly matches the abnormal operating mode of the equipment. The criteria for determining an effective maintenance strategy include: the key parameters of the equipment returning to normal range after the strategy is implemented; the frequency of strategy re-execution being lower than a preset threshold (e.g., no more than 4 times per year); and the strategy conforming to the manufacturer's design parameter constraints. These effective maintenance strategies are then correlated with the abnormal operating modes in the historical equipment operating data. Each abnormal operating mode is represented by a state mode vector, such as [high load, abnormal temperature rise, excessive vibration]. The corresponding maintenance strategy is represented by a list of operation steps, such as [adjust pump flow, start cooling device, replace filter element].

[0060] It is worth noting that the methods for identifying abnormal operating modes of equipment include: Key indicator columns, such as pump load, motor speed, temperature rise, and vibration value, are extracted from the historical equipment operation data matrix. These indicators are then standardized to allow for unified analysis of data with different dimensions. The standardized indicator columns are divided into continuous sampling windows, for example, each window containing sampling points within a 60-second period, forming a window feature vector. Within each window, the mean, variance, maximum, and minimum values ​​of each indicator are calculated as features to describe the equipment's operating status during that time period. Example operating parameters: window length 60 seconds; feature calculations include mean, variance, and extreme values. Then, based on the statistical characteristics of historical operating data and the manufacturer's design parameters, abnormal thresholds are set for each indicator. For example, pump load exceeding ±15% of rated load, temperature rise exceeding the design threshold by ±2°C, and motor vibration exceeding the safety value by ±5%. If any indicator in the window's feature vector exceeds the threshold, the window is considered to be in an abnormal operating state. Subsequently, continuous abnormal windows are clustered chronologically to form abnormal operating patterns. Clustering methods can employ hierarchical clustering based on Euclidean distance or density clustering (DBSCAN) to group similar feature vectors into the same abnormal operating pattern. Example of operating parameters: In DBSCAN, epsilon is set to 0.5, and the minimum number of sample points is set to 3. Finally, each abnormal operating mode is represented as a state mode vector, including the type and magnitude of each abnormal indicator, such as [high load, abnormal temperature rise, vibration exceeding limits], and the time distribution and duration of the mode are recorded.

[0061] The knowledge base relationship framework is constructed by using the correspondence between effective maintenance strategies and abnormal operating modes of equipment in historical equipment operation data as the basis, and the manufacturer's design parameters as constraints.

[0062] In a further embodiment, the correspondence between effective maintenance strategies and abnormal equipment operation modes is used as the core framework of the knowledge base, with manufacturer design parameters added as constraints to form a complete operation and maintenance management knowledge base. The knowledge base structure adopts a key-value mapping format: the key is an abnormal operation mode vector, and the value is the corresponding maintenance strategy and parameter constraint set; it also includes metadata, including strategy execution effects, historical failure rates, and applicable equipment models. Example of operating parameters: time alignment accuracy of 1 second, correlation threshold of 0.7, abnormal operation mode feature dimension of 5~10, and maintenance strategy constraints including load threshold ±10%, temperature control threshold ±2°C, and maintenance cycle ±7 days.

[0063] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0064] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A method for intelligent operation and maintenance management of a digital twin automatic water quality monitoring station, characterized in that, Includes the following steps: Step S1: Collect the sensor signal set of the device by pre-deploying the sensor cluster at the automatic monitoring station, and transmit the sensor signal set of the device to the pre-deployed edge node to perform digital signal conversion; Step S2: Perform sensor signal coordination based on the digital signal conversion result to obtain device sensing data; the sensor signal coordination in step S2 includes: Based on the transmission timestamp sequence of each sensor signal transmitted to the edge node in the digital signal conversion result, the execution time of each sensor digital signal is synchronized and paired. Acquire the spatial distribution information of each sensor in the sensor cluster, and detect the signal change trend of adjacent sensors in the same sampling period based on the spatial distribution information of each sensor in the time synchronization pairing results; Based on the signal change trend detection results, effective sensor digital signals are selected, and noise cross-cancellation is performed on the effective sensor digital signals to obtain the cooperative sensor digital signal results; the selection of effective sensor digital signals includes: Calculate the consistency rate of the direction of change and the similarity of the magnitude of change of the digital signals of the sensor and its adjacent sensors within the same sampling period; If the direction of change of any sensor and more than three adjacent sensors is more consistent with the direction of change than the preset direction consistency threshold, and the similarity of change amplitude is less than the similarity of change amplitude threshold, then the sensor is determined to be a valid sensor, and the sensor digital signal corresponding to the valid sensor is designated as the valid sensor digital signal. Performing noise cross-cancellation includes: Extract the signal strength sequence of the effective sensor and the three nearest adjacent sensors within the same sampling period from the digital signal conversion results; Calculate the residual mean sequence between the effective sensor digital signal and the adjacent sensor digital signals based on the signal strength sequence; The moving average value in the residual mean sequence is used as the environmental background noise benchmark, and the fluctuation component of each sensor's digital signal under the environmental background noise benchmark is extracted. The fluctuation components of adjacent sensor digital signals are used as weighting terms to perform weighted inverse superposition on the fluctuation components of the effective sensor digital signals, and the weighted inverse superposition results of the effective sensor digital signals are integrated to obtain the device sensing data. Step S3: Determine abnormal sampling points based on the magnitude of changes in the device sensor data; perform drift correction on the abnormal sampling points, and replace the corresponding sampling values ​​in the device sensor data with the drift correction results to obtain the corrected sensor data; the drift correction in step S3 includes: Calculate the difference in amplitude and the change in direction angle between abnormal sampling points and adjacent sampling points within the continuous sampling period based on the numerical sequence of each sensor. Based on the amplitude difference and directional angle change between abnormal sampling points and their adjacent sampling points, and taking the average amplitude and directional distribution density of similar sensors within the same continuous sampling period as reference values, offset correction values ​​are calculated according to preset weight ratios; the offset correction values ​​include amplitude correction values ​​and directional angle correction values. If the offset correction value of the abnormal sampling point is within the preset correction tolerance range, then the offset correction value is taken as the drift correction result; Step S4: Combine the sensor correction data with the preset equipment mechanism model to construct a twin model of equipment operation; use the twin model of equipment operation to perform equipment operation simulation, and determine the operation and maintenance management strategy based on the simulation results.

2. The intelligent operation and maintenance management method for a digital twin water quality automatic monitoring station according to claim 1, characterized in that, After determining the operation and maintenance management strategy in step S4, the following steps are also included: The operation and maintenance management strategy is sent to the automatic monitoring station management platform, and maintenance task orders are distributed to the execution units according to the deviation between the operation and maintenance management strategy and the existing operation and maintenance management strategy in the automatic monitoring station management platform. By using a sensor cluster to synchronously acquire real-time device sensor signal sets and performing digital signal conversion in coordination with sensor signals, real-time device sensing data can be obtained. Compare real-time device sensor data with simulation results to verify the simulation results; The operation and maintenance management strategy is iteratively optimized based on the verification numerical error in the verification results until the verification numerical error is less than the preset error threshold.

3. The intelligent operation and maintenance management method for a digital twin water quality automatic monitoring station according to claim 2, characterized in that, The categorized distribution of maintenance task orders includes: If the deviation of operation and maintenance management is within the preset remote adjustment range, the remote execution unit corresponding to the operation and maintenance management strategy will be determined through the automatic monitoring station management platform, and the control instructions in the operation and maintenance management strategy and the remote control links corresponding to the remote execution units will be integrated into a remote control task sheet and distributed to the corresponding remote execution units. If the deviation in operation and maintenance management exceeds the remote adjustment range, the maintenance operation guidelines, equipment repair plans, and spare parts configuration requirements in the operation and maintenance management strategy will be integrated into a high-priority maintenance task order, and the high-priority maintenance task order will be pushed to the on-site maintenance execution unit in the corresponding area of ​​the automatic monitoring station through the automatic monitoring station management platform.

4. The intelligent operation and maintenance management method for a digital twin water quality automatic monitoring station according to claim 1, characterized in that, Step S3 involves identifying anomalous sampling points, including: Extract the numerical sequence of each sensor within a continuous sampling period from the device's sensing data, and calculate the magnitude and direction of change of each sensor between adjacent sampling points based on the numerical sequence; Based on the variation amplitude and direction of each sensor between adjacent sampling points, calculate the average variation amplitude and direction distribution density of similar sensors within the same continuous sampling period; If the change amplitude between any sampling point and its adjacent sampling points exceeds 1 to 3 times the average change amplitude, and the angle difference between the change direction and the average change direction angle of similar sensors in the same continuous sampling period in the change direction distribution density is higher than the direction difference angle difference threshold, then the sampling point is determined to be an abnormal sampling point.

5. The intelligent operation and maintenance management method for a digital twin water quality automatic monitoring station according to claim 1, characterized in that, Step S4, which determines the operation and maintenance management strategy, includes: The operation and maintenance management scenario with high similarity to the simulation results is matched by a pre-set operation and maintenance management knowledge base and spare parts library. Based on the differences between highly similar operation and maintenance management scenarios and operation simulation results, operational status deviations are determined; these operational status deviations include operational load deviations, temperature rise deviations, and maintenance timeliness deviations. Historical operation and maintenance management strategies corresponding to highly similar operation and maintenance management scenarios are extracted from the operation and maintenance management knowledge base, and the historical operation and maintenance management strategies are corrected according to the deviation of the operation status to obtain the operation and maintenance management strategy.

6. The intelligent operation and maintenance management method for a digital twin water quality automatic monitoring station according to claim 5, characterized in that, Correcting historical operation and maintenance management strategies based on operational status deviations includes: Based on the deviation in operating status, the corresponding historical operation and maintenance management strategy is dynamically adjusted. If the deviation in operating load exceeds the normal load threshold, the equipment operation intensity classification in the historical operation and maintenance management strategy is corrected. If the temperature rise deviation exceeds the temperature control threshold, the equipment cooling trigger threshold in the historical operation and maintenance management strategy is adjusted. If the maintenance timeliness deviation is lower than the preset maintenance timeliness threshold, the maintenance cycle setting value in the historical operation and maintenance management strategy is corrected. Based on highly similar operation and maintenance management scenarios, determine the equipment performance impact of operating load deviation, temperature rise deviation, and maintenance timeliness deviation respectively; Using the impact of equipment performance as a weighting coefficient, a weighted fusion calculation is performed on the correction results of equipment operation intensity classification, equipment cooling trigger threshold adjustment results, and maintenance cycle setting value correction results to obtain the operation and maintenance management strategy.

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