Digital intelligent water affair global data operation decision analysis comprehensive service system

Abstract

The invention relates to the technical field of data processing, and discloses a digital intelligent water affair global data operation decision analysis comprehensive service system. The system comprises an acquisition module for acquiring standardized processing data; the construction module generates a water plant three-dimensional model and virtual representation; the storage module executes hierarchical storage to obtain quality enhancement data; the identification module constructs an index system to diagnose the operation state; the evaluation module uses a Bayesian network to check asset cost; and the generation module provides decision support and an early warning scheme. According to the invention, by constructing the global data operation decision analysis comprehensive service method, the data barrier is broken, the whole-process data integration, analysis and intelligent decision support of the water affair system from the source to the terminal are realized, and the operation efficiency and management level of the water affair system are improved.

Description

Technical Field

[0001] The present invention relates to the field of data processing technology, and in particular to a digital water services global data operation decision-making analysis comprehensive service system. Background Art

[0002] Traditional water management systems typically employ a decentralized data collection and monitoring architecture, such as SCADA systems for basic data collection and monitoring, enabling basic monitoring of water plant production processes. These systems can collect data on the status of key equipment, process parameters, and water quality, and provide basic visualization interfaces and alarm functions. With the development of information technology, some water companies have begun to introduce GIS systems to manage pipeline assets, apply basic automated control technologies to optimize production processes, and establish simple hydraulic models to assist in pipeline network analysis. In terms of operations and maintenance management, traditional water systems primarily rely on fixed-period inspections and passive-response fault repair models, making decisions on pipeline repair or replacement based on the age of the equipment and the number of faults, and conducting basic cost accounting and asset management.

[0003] However, this traditional water management system has many technical deficiencies. First, data between multiple water plant systems and equipment is scattered in isolated systems with inconsistent data formats and standards, making data sharing and integration difficult, resulting in a serious information island phenomenon. Second, traditional SCADA systems are mostly built using industrial configuration software, which has poor compatibility, simple water plant process scene graphics, and cannot fully reflect the three-dimensional process production scene of the water plant, lacking an immersive monitoring experience. Third, equipment control and maintenance mainly rely on manual experience and judgment, lacking data-driven scientific decision-making support, resulting in high labor costs and low efficiency. Fourth, traditional systems are difficult to connect with the group's water platform data, with low data utilization, and unable to achieve intelligent analysis and optimization of data such as water supply, drug consumption, and energy consumption. Finally, existing systems generally lack comprehensive network security protection and data backup mechanisms, facing the risk of data leakage and loss. Summary of the Invention

[0004] The present invention provides a digital water system for global data operation decision analysis, which is used to break down data barriers by constructing a global data operation decision analysis comprehensive service method, realize the full-process data integration, analysis and intelligent decision support of the water system from source to terminal, and improve the operation efficiency and management level of the water system.

[0005] The present invention provides a digital water service global data operation decision analysis integrated service system, the digital water service global data operation decision analysis integrated service system comprising: The acquisition module is used to collect multi-level data of water system operating parameters and obtain standardized processed data; A construction module is used to construct a three-dimensional model of the water plant and a process function model based on the standardized processing data, establish a synchronization mechanism between the physical entity and the virtual model, and obtain virtual representation data of the water system; A storage module, configured to perform hierarchical storage design on the standardized processing data and the water system virtual representation data to obtain quality-enhanced data; an identification module, configured to construct a hierarchical evaluation index system and identify an operation mode based on the quality enhancement data and the water system virtual representation data, thereby obtaining an operation status diagnosis result; An evaluation module, configured to perform lifecycle cost accounting for pipeline assets based on the operating status diagnosis results and the quality enhancement data, and to evaluate the impact of maintenance and update strategies and operating parameter adjustments on long-term costs through a Bayesian neural network to obtain an asset management optimization plan; The generation module is used to generate a decision support view based on the asset management optimization plan and the operating status diagnosis results, establish a knowledge push mechanism and an early warning threshold adjustment system, and obtain an execution plan.

[0006] The technical solution provided by the present invention achieves significant technical effects through the organic combination of technical features such as multi-level data acquisition, three-dimensional model construction, hierarchical storage design, multi-dimensional evaluation system and Bayesian neural network. The multi-level data acquisition mechanism realizes the comprehensive acquisition of water system operating parameters, standardizes the heterogeneous data scattered in different systems, effectively solves the problem of data islands in traditional water systems, and lays a solid data foundation for global analysis. The construction of the three-dimensional model of the water plant and the process function model, combined with the synchronization mechanism of physical entities and virtual models, realizes the visual representation of the water system, transforms abstract data relationships into intuitive spatial relationships, and significantly improves the system's situational awareness and decision-making intuitiveness. The hierarchical storage design adopts specialized storage strategies for different types of data characteristics, which not only ensures the efficient processing of real-time data, but also realizes high-compression storage of historical data, balancing system performance and storage overhead. The hierarchical evaluation index system constructs comprehensive evaluation standards from the equipment to the system level, and combines operation mode recognition technology to achieve accurate diagnosis of the operating status of the water system, transforming traditional empirical judgment into data-driven scientific evaluation. Of particular note is the application of a Bayesian neural network for pipeline asset lifecycle costing. This algorithm not only leverages the robust fitting capabilities of traditional neural networks but also incorporates a probabilistic reasoning mechanism to address the uncertainty inherent in pipeline maintenance decisions, providing quantified and reliable predictions. This significantly enhances the scientific nature and reliability of asset management decisions. By implementing probability weighting and posterior probability updates, the Bayesian neural network overcomes the limitations of traditional deterministic models in pipeline failure prediction, enabling asset management optimization solutions that balance economic costs with service levels and risk control. A hierarchical display of decision-support views and a knowledge push mechanism ensure precise access to decision-making information, while a warning threshold adjustment system dynamically optimizes the sensitivity of anomaly detection. The combined implementation loop ensures comprehensive control from problem discovery to resolution. Overall, this solution, through its comprehensive technical chain encompassing data collection, modeling and analysis, evaluation and prediction, and decision support, significantly enhances the refined operational management of water systems, reduces energy and chemical consumption, extends the lifespan of equipment and pipeline networks, improves water quality assurance, and shifts water management from reactive response to proactive prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0007] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0008] Figure 1This is a schematic diagram of an embodiment of the digital water management global data operation decision analysis comprehensive service system in an embodiment of the present invention. DETAILED DESCRIPTION

[0009] An embodiment of the present invention provides a digital water service global data operation decision analysis integrated service system. The terms "first", "second", "third", "fourth", etc. (if any) in the description and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or inherent to these processes, methods, products or apparatus.

[0010] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 In one embodiment of the present invention, a comprehensive service system for digital water services global data operation decision analysis includes: The acquisition module is used to collect multi-level data on the operating parameters of the water system and obtain standardized processed data; The construction module is used to build a three-dimensional model of the water plant and a process function model based on standardized processing data, establish a synchronization mechanism between the physical entity and the virtual model, and obtain virtual representation data of the water system; A storage module is used to perform hierarchical storage design on standardized processing data and water system virtual representation data to obtain quality-enhanced data; The identification module is used to construct a hierarchical evaluation indicator system and identify the operation mode based on the quality enhancement data and the virtual representation data of the water system to obtain the operation status diagnosis results; An evaluation module is used to perform pipeline asset lifecycle cost accounting based on operational status diagnosis results and quality enhancement data. It uses a Bayesian neural network to evaluate the impact of maintenance and update strategies and operational parameter adjustments on long-term costs, and obtain asset management optimization solutions; The generation module is used to generate decision support views based on the asset management optimization plan and operation status diagnosis results, establish a knowledge push mechanism and early warning threshold adjustment system, and obtain an implementation plan.

[0011] It is understood that the execution subject of the present invention can be the digital water global data operation decision analysis integrated service system, or it can be a terminal or server, and the specific implementation is not limited here. The embodiment of the present invention is described by taking the server as the execution subject as an example.

[0012] Specifically, the acquisition module performs multi-level data collection on the operating parameters of the water system. Flow monitoring devices, pressure sensors, water quality parameter analyzers, and equipment status detectors are deployed within the water plant to conduct real-time monitoring of water intakes, sedimentation tanks, filtration systems, disinfection systems, and water distribution systems. After these monitoring devices collect the raw data, they convert the format using industrial communication protocols to achieve data unification between devices from different manufacturers. The sampling frequency is dynamically adjusted based on the process status, increasing the sampling density during critical operating conditions and reducing the frequency during stable operation. The edge computing gateway filters outliers, compresses data, and calibrates timestamps on the collected data to form pre-processed data. Quality assessment constructs a scoring matrix by calculating the signal quality index, data integrity score, and time continuity coefficient. After secure encryption processing and transmission priority sorting, standardized processed data is obtained.

[0013] The plant's external framework is constructed based on engineering design drawings. Detailed structures for key equipment such as pumps, valves, and flowmeters are then constructed based on equipment parameter information. The internal structures of process units, such as flocculation tanks, sedimentation tanks, and filtration tanks, are then constructed to form physical models of these units. Hydraulic calculations associate flow, pressure, and water level data with the three-dimensional structure to construct a hydraulic functional model. The process functional model maps process unit parameters and performance. A message queue mechanism enables real-time synchronization between the physical and virtual models, completing a virtual representation of the water system. The storage module implements a hierarchical storage design for standardized processing data and the virtual representation of the water system. Water quality parameters, flow, pressure, and equipment operating status data are stored in a time-series database, with dedicated storage partitions established. Three-dimensional structure data and functional parameter data are stored in a spatial database, with spatial indexing and attribute associations established. Historical data is transferred to a cold storage area and compressed, and statistical values ​​are calculated to form a multi-granularity historical dataset. A process flow diagram and equipment dependency relationships are established between process units to form a water system knowledge network. Water quality data is processed through physical correlation verification, and energy consumption and drug consumption data are attributed to process units and missing values ​​are filled to form quality-enhanced data.

[0014] The identification module constructs a four-tiered evaluation indicator system based on quality-enhanced data and a virtual representation of the water system, covering the equipment, process unit, system, and plant levels. Time-series clustering analysis identifies normal and emergency operating modes, forming a basic operating mode library. Correlation analysis is performed on pump station load rates, chemical dosages, and backwash frequencies to distinguish between low-load and high-load modes. Filter blockage patterns are identified by changes in backwash cycles, and water quality changes are identified by changes in disinfectant dosage. Source tracing analysis determines the propagation path and type of anomalies. Finally, a matching degree calculation confirms the current operating mode and generates an operating status diagnosis. The evaluation module performs lifecycle costing of pipeline assets based on the operating status diagnosis results and quality-enhanced data. Pipeline operating data, fault records, and repair cost records are extracted from the quality-enhanced data to construct a pipeline asset evaluation dataset. A Bayesian neural network analyzes indicators such as pipeline age and pressure fluctuations. The input layer receives these features, the hidden layer calculates conditional probability values ​​using probability weights, and the output layer generates a fault probability distribution. Based on the risk assessment results, the lifecycle cost of the maintenance and update strategy is calculated, including direct maintenance costs, update investment costs, and operating parameter adjustment costs. Multi-objective optimization analysis generates an asset management optimization plan. The generation module generates a decision support view based on the asset management optimization plan and the operating status diagnosis results. The management level view displays the pipeline update plan and investment costs, and the operation level view displays abnormal operating conditions and equipment status. Permissions are assigned according to user roles to form a multi-level decision support view. The knowledge push mechanism automatically pushes processing suggestions and relevant experience when critical events occur. The warning threshold adjustment system optimizes the warning conditions for water quality indicators, pressure parameters, and equipment status based on historical data. The intelligent assistance content and warning system are integrated into the response and disposal process to form an implementation plan.

[0015] In the embodiments of the present invention, significant technical effects are achieved through the organic combination of technical features such as multi-level data acquisition, three-dimensional model construction, hierarchical storage design, a multidimensional evaluation system, and a Bayesian neural network. The multi-level data acquisition mechanism enables comprehensive acquisition of water system operating parameters, standardizes heterogeneous data scattered across different systems, effectively solves the data silo problem in traditional water systems, and lays a solid data foundation for global analysis. The construction of a three-dimensional model of the water plant and a process function model, combined with a synchronization mechanism between physical entities and virtual models, enables a visual representation of the water system, transforming abstract data relationships into intuitive spatial relationships, significantly improving the system's situational awareness and intuitive decision-making capabilities. The hierarchical storage design adopts specialized storage strategies based on the characteristics of different types of data, ensuring both efficient processing of real-time data and high-compression storage of historical data, balancing system performance and storage overhead. The hierarchical evaluation indicator system establishes comprehensive evaluation criteria from the device to the system level. Combined with operational pattern recognition technology, it enables accurate diagnosis of the operating status of the water system, transforming traditional empirical judgments into data-driven scientific assessments. Of particular note is the application of a Bayesian neural network for pipeline asset lifecycle costing. This algorithm not only leverages the robust fitting capabilities of traditional neural networks but also incorporates a probabilistic reasoning mechanism to address the uncertainty inherent in pipeline maintenance decisions, providing quantified and reliable predictions. This significantly enhances the scientific nature and reliability of asset management decisions. By implementing probability weighting and posterior probability updates, the Bayesian neural network overcomes the limitations of traditional deterministic models in pipeline failure prediction, enabling asset management optimization solutions that balance economic costs with service levels and risk control. A hierarchical display of decision-support views and a knowledge push mechanism ensure precise access to decision-making information, while a warning threshold adjustment system dynamically optimizes the sensitivity of anomaly detection. The combined implementation loop ensures comprehensive control from problem discovery to resolution. Overall, this solution, through its comprehensive technical chain encompassing data collection, modeling and analysis, evaluation and prediction, and decision support, significantly enhances the refined operational management of water systems, reduces energy and chemical consumption, extends the lifespan of equipment and pipeline networks, improves water quality assurance, and shifts water management from reactive response to proactive prediction.

[0016] In a specific embodiment, the acquisition module is used to: Deploy flow monitoring devices, pressure sensors, water quality parameter analyzers, and equipment status detectors within the water plant to collect real-time parameters of the water intake, sedimentation tank, filtration system, disinfection system, and water distribution system to obtain raw monitoring data. Build a standardized data acquisition protocol based on the original monitoring data, and convert the data formats of devices from different manufacturers through industrial communication protocols to obtain data in a unified format; Dynamically adjust the data sampling frequency for unified format data, increase the sampling density under critical working conditions according to the process status, and reduce the sampling frequency during stable operation to obtain optimized sampling data; The optimized sampling data is input into the edge computing gateway and preprocessed through outlier filtering, data compression, and timestamp calibration to obtain preprocessed data; Perform data quality assessment based on preprocessed data, construct a quality scoring matrix by calculating the signal quality index, data integrity score, and time continuity coefficient, and obtain the quality assessment results; The data is securely encrypted based on the quality assessment results, a hierarchical transmission channel is established, and key process parameters and alarm information are prioritized and transmitted to obtain standardized processing data.

[0017] Specifically, multiple types of monitoring devices, including electromagnetic flowmeters, pressure transmitters, online water quality analyzers, and equipment status detectors, are deployed at key nodes in the water supply system. These devices are located in process links such as water intake areas, coagulation and sedimentation tanks, filtration systems, disinfection contact tanks, and water distribution systems, forming a comprehensive data acquisition network. Electromagnetic flowmeters collect inlet and outlet flow data, pressure transmitters monitor pressure changes at various points in the pipeline network, water quality analyzers provide real-time monitoring of water quality indicators such as turbidity, pH, and residual chlorine, and equipment status detectors monitor pump operating status, valve opening, and dosing equipment operating parameters. These devices collect data at a preset frequency to form a raw monitoring data set. Raw monitoring data sets have different formats depending on the equipment manufacturer, so the digital water system requires the establishment of a standardized acquisition protocol to unify the format. The system analyzes the data output formats of various devices and develops data conversion rules for different industrial communication protocols such as Modbus RTU, Modbus TCP, and OPC UA. For Modbus protocol data, the system extracts function codes and data area contents, mapping register addresses to standard variable names. For OPC UA protocol data, the system reads node IDs and values ​​and converts them according to predefined templates. This conversion process not only unifies the data format but also standardizes data units, such as converting m³ / h and L / s from different manufacturers' flow meters to m³ / h. After processing, all data is converted to a unified JSON format, including fields for timestamp, measurement point ID, measurement value, and quality tag, creating a unified data format.

[0018] The sampling frequency of unified format data must be dynamically adjusted based on process status to balance data accuracy and system load. The system determines current operating conditions by analyzing process parameter fluctuations in real time. When process parameter fluctuations exceed a preset threshold, such as the rate of change in influent turbidity exceeding twice the standard deviation, the system identifies a critical operating condition and automatically increases the sampling frequency, shortening the standard 5-minute interval to 1 minute or even 30 seconds. When process parameters remain stable for longer than a preset period, such as when turbidity or pH fluctuates within 50% of the standard deviation for an hour, the system identifies stable conditions and automatically reduces the sampling frequency to 10 minutes, reducing communication and storage burdens. This dynamic adjustment mechanism generates optimized sampling data, ensuring data accuracy at critical moments while avoiding data redundancy. The optimized sampling data is transmitted to the edge computing gateway for preprocessing and outlier filtering. The system uses a sliding window median method to identify outliers. Using a window of 10 data points, the system calculates the median and median of absolute deviation (MAD). If a point differs from the median by more than three times the MAD, it is identified as an outlier and flagged. For continuous parameters such as water level, the system also applies a first-order difference test to identify physically impossible mutations. Data compression is then performed. For stable data, a dead-band compression algorithm is used, recording new values ​​only when the numerical change exceeds a preset dead-band. For fluctuating data, piecewise linear compression is applied, using straight-line segments to approximate trends. Simultaneously, the system calibrates the timestamps of each measurement point's data, compensating for device clock drift and network latency, ensuring temporal alignment of data from different sources. These three steps result in higher-quality preprocessed data.

[0019] The preprocessed data is then subjected to a quality assessment. The system calculates three core indicators to construct a quality scoring matrix. The signal quality index measures signal reliability by analyzing signal-to-noise ratio, volatility, and compliance with physical constraints. The data integrity score measures data completeness by calculating the proportion of valid data points to the total number of required sampling points. The temporal continuity coefficient assesses the uniformity of data point spacing by calculating the standard deviation of the time interval. The system combines these three indicators into a two-dimensional matrix based on weights. Rows represent different measurement points, and columns represent different quality dimensions. Each matrix element ranges from 0 to 100, visually reflecting the performance of each measurement point in each quality dimension. Using threshold rules, the system color-codes matrix elements: red indicates severe quality issues, yellow indicates minor issues, and green indicates normal, providing a visual quality assessment. Based on the quality assessment results, the data is securely encrypted and a hierarchical transmission mechanism is established. The system categorizes data into three levels based on sensitivity and importance: critical, including alarm information and abnormal operating condition data; important, including routine process parameters and equipment operating status; and general, including auxiliary information and statistical data. The system uses AES-256 encryption for critical data, AES-128 encryption for important data, and basic hash checksums for general data. At the transport layer, the system establishes independent transmission channels for different data levels. Critical data receives the highest bandwidth priority, ensuring immediate delivery; important data is given a lower priority, and general data is transmitted when network resources are sufficient. The system also implements a transmission retry mechanism, automatically retransmitting critical data that fails multiple times until receipt is confirmed. After this series of processing, the data is ultimately delivered securely and efficiently to the storage layer in a standardized format, completing the full functionality of the acquisition module.

[0020] In a specific embodiment, a building block is provided for: Extract spatial parameters from the standardized data, build the plant's external structure based on the engineering design drawings, and obtain the external framework of the water plant's three-dimensional model. Based on the external framework of the water plant 3D model and the equipment parameter information in the standardized processing data, the structure of water pumps, valves, flow meters, level gauges, and pressure transmitters is constructed to obtain the 3D model of the water plant; Based on the three-dimensional model of the water plant and the process parameters in the standardized treatment data, the internal structures of the flocculation tank, sedimentation tank, filtration tank, disinfection contact tank, and clear water tank were constructed to obtain the physical model of the process unit; Perform hydraulic calculations on the physical model of the process unit, and associate the flow, pressure, and water level data in the standardized processing data with the three-dimensional structure to obtain a hydraulic function model; Based on the hydraulic function model and the water quality parameters in the standardized treatment data, the parameter mapping relationship of the process treatment unit is constructed to obtain the process function model; A real-time synchronization mechanism is established between the three-dimensional model and process function model of the water plant and the actual physical entity, and incremental data updates and time series synchronization are performed through the message queue to obtain virtual representation data of the water system.

[0021] Specifically, spatial parameters are extracted from the standardized data to extract spatial information such as building coordinates, structure dimensions, and pipeline directions. The spatial parameters in the standardized data include equipment GPS positioning information, structure boundary coordinates, and elevation data. The extracted spatial data is integrated with the CAD data in the engineering design drawings. The engineering drawings provide building structure dimensions, elevations, and layout information. By superimposing and comparing these two types of data, the coordinate deviation is corrected to form a spatial reference system. Subsequently, three-dimensional geometric modeling is performed to convert the two-dimensional plan view into a three-dimensional structure. The external structures of the water plant, such as the main building, roads, and walls, are constructed through stretching, rotation, and Boolean operations to form the external framework of the three-dimensional model of the water plant. Based on the external framework of the water plant's 3D model, the internal equipment model of the water plant is constructed by combining equipment parameter information from the standardized processing data. Equipment parameter information includes pump model, power parameters, head curve, valve caliber, flowmeter range, level gauge measurement range, and pressure transmitter pressure range. 3D model templates for the corresponding equipment are extracted from the equipment parameter library and parameterized to match the actual equipment dimensions and characteristics. For example, for pumps, the pump casing shape, impeller diameter, and inlet and outlet calibers are determined based on the model; for valves, the valve body geometry is customized based on the type and caliber. The equipment model not only includes the external geometry but also encodes the equipment's operating principle and technical parameters. The equipment models are arranged in the corresponding positions within the external framework according to their actual installation locations, forming a 3D water plant model, a comprehensive digital representation of the building structure and equipment layout. Process parameters from the standardized processing data are combined to construct detailed internal models of the process units. Process parameters include key process indicators such as the mixing time of the flocculation tank, the surface loading rate of the sedimentation tank, the filtration rate of the filtration tank, the residence time of the disinfection contact tank, and the effective volume of the clear water tank. Based on these parameters, the internal structural dimensions, partition wall layout, water flow channel design, and functional zoning of each process unit are determined. For example, for sedimentation tanks, the tank dimensions are calculated based on the surface load rate and hydraulic retention time, and the inclined plate or tube bundle structure is designed. For filtration tanks, the filter depth and cross-sectional dimensions are determined based on the designed filtration rate and filter layer configuration. By adding internal components, pipe connections, and liquid flow paths, the internal structure of each process unit is refined to form a process unit physical model, which is a precise digital representation of the physical entity of each water treatment functional unit, including geometric shape and process construction information.

[0022] Hydraulic calculations are performed on the physical model of the process unit, and real-time data such as flow, pressure, and water level in the standardized processing data are associated with the three-dimensional structure. Hydraulic calculations are based on the principles of fluid mechanics and calculate the flow velocity distribution, pressure distribution, and water level changes in each process unit. The calculation process takes into account factors such as pipe friction, the impact of structure shape on the flow field, and the operating point of the pump. For example, when the water inlet flow data at a certain moment is input, the system calculates the flow velocity and pressure loss of each pipe section under the flow conditions, the water level elevation within each structure, and the operating head and efficiency point of the pump. The calculation results are superimposed on the three-dimensional model in the form of color gradients, streamlines, or numerical labels to intuitively display the water flow status. This dynamic hydraulic model can reflect the status of the water plant hydraulic system in real time and constitutes a hydraulic functional model. It is a functional extension of the physical model that adds fluid mechanics behavior information.

[0023] Based on the hydraulic function model and water quality parameters from standardized treatment data, parameter mapping relationships for process treatment units are constructed. Water quality parameters include indicators such as turbidity, pH, residual chlorine, and organic matter content. By analyzing historical operating data, the changing patterns of water quality parameters before and after treatment for each process unit are established, forming parameter mapping relationships. These mapping relationships are trained using statistical regression or machine learning algorithms and can predict treatment outcomes under different operating conditions. For example, by analyzing effluent turbidity data from sedimentation tanks under different surface loading rates and coagulant dosages, a relationship model between input parameters and effluent turbidity is established. Similarly, by analyzing residual chlorine concentration data from disinfection units under different chlorine dosages and contact times, a disinfection effectiveness prediction model is developed. These mapping relationships are integrated to form a process function model, a mathematical representation of the functional characteristics of each process unit, containing the quantitative relationship between process parameters and treatment outcomes. A real-time synchronization mechanism is established between the water plant's three-dimensional model and the process function model and the actual physical entity. Message queue technology is used for incremental data updates and time-series synchronization. Message queues are an asynchronous communication technology that efficiently handles large data streams, ensuring that data is transmitted and processed in chronological order. When the sensor data in the physical entity changes, the changed data is sent to the message queue. The system reads data from the queue at a fixed frequency and only processes the incremental data that has changed since the last synchronization, rather than the full amount of data, to reduce the computational burden. Different update strategies are used for different types of data: equipment status data has the highest priority and is updated in real time; water quality parameters are second and updated regularly; and three-dimensional structural changes are updated at a low frequency. Timing synchronization ensures that data from different sources and frequencies are aligned on the timeline to avoid logical errors caused by inconsistent data timestamps. After this series of processing, virtual representation data of the water system is formed. It is a complete twin expression of the physical entity of the water plant in the digital space, including geometric, physical and functional characteristics.

[0024] In a specific embodiment, the storage module is used to: The water quality parameter data, flow data, pressure data, and equipment operation status data in the standardized processing data are stored in a dedicated time series database. Different storage partitions are set according to the acquisition frequency to obtain a real-time operation data set. The three-dimensional structure data and functional parameter data in the virtual representation data of the water system are stored in a spatial database, and spatial indexes and attribute associations are established to obtain a structured spatial data set; Transfer historical data older than 30 days from the real-time data set to the cold storage area, compress the data, and calculate statistics by day, week, and month to obtain a multi-granularity historical data set. Based on multi-granularity historical data sets and structured spatial data sets, we establish process association diagrams and equipment dependency relationships between water plant process units, and obtain a water affairs knowledge relationship network. Perform physical correlation verification on the water quality data in the real-time operation data set, use the adjacent measurement point data and historical data trends to correct outliers, and obtain the water quality data verification set; Based on the water quality data verification set and the water affairs knowledge relationship network, the energy consumption data and drug consumption data are attributed to process units and missing values ​​are filled to obtain quality enhanced data.

[0025] Specifically, standardized data is classified and stored in a dedicated time series database. A time series database is a database type optimized for time series data, offering high write performance and efficient time range query capabilities. Water quality parameter data, flow data, pressure data, and equipment operating status data are stored in separate data tables according to data type. Different storage partitioning strategies are set based on data collection frequency: hourly partitioning for high-frequency data, daily partitioning for medium-frequency data, and monthly partitioning for low-frequency data. Partitioned storage reduces the amount of data in a single table and optimizes query performance. The original timestamp, measurement point ID, value, and quality label are retained when data is stored, forming a complete, real-time operational dataset. A specialized spatial database is used to store the 3D structural data and functional parameter data in the virtual representation of the water system. Spatial databases are capable of handling geometric objects and spatial relationships, supporting spatial indexing and spatial queries. 3D structural data includes the geometric shapes of plant buildings, the 3D structures of process structures, and equipment models. These data are stored as polygonal meshes or parametric descriptions. Functional parameter data, including equipment technical parameters, process unit capacity parameters, and hydraulic characteristic parameters, are stored as associated attributes. The spatial database establishes an R-tree-based spatial index, enabling rapid retrieval of 3D objects by spatial location. It also maps object IDs to attribute data, enabling efficient querying of spatial and attribute data. This storage structure creates a structured spatial dataset, ensuring efficient access to 3D geometric data and tightly binding functional parameters to spatial objects.

[0026] The data in real-time datasets accumulates rapidly over time, impacting query performance and consuming significant storage resources. To address this issue, a data lifecycle management strategy is implemented, transferring historical data older than 30 days to cold storage. This cold storage area uses a columnar storage engine optimized for batch reads and compression rates. During the transfer process, the raw data is compressed using time series-specific compression algorithms, such as run-length encoding (RLE) for stable segments and delta encoding combined with dictionary compression for fluctuating data. Data is also downsampled, and statistics are calculated at daily, weekly, and monthly granularities, including maximum, minimum, average, standard deviation, and median. For example, raw minute-by-minute water quality data is calculated at the daily granularity for the 24-hour average and fluctuation range, at the weekly granularity for seven-day trends, and at the monthly granularity for monthly patterns. This multi-granular historical dataset preserves critical information for long-term trend analysis while significantly reducing storage space and query burden.

[0027] Based on multi-granular historical datasets and structured spatial datasets, a process association diagram and equipment dependency relationships are constructed between water plant process units. The process association diagram describes the flow paths between process units. By analyzing the pipeline connections in the spatial data and the correlation between historical flow data, upstream and downstream relationships are automatically identified. For example, by analyzing the flow direction and pipeline connections, the coagulation tank is determined to be the upstream unit of the sedimentation tank, which is in turn the upstream unit of the filtration tank. The equipment dependency relationship describes the functional relationship between devices. By analyzing the temporal characteristics and correlations of device start and stop records, logical relationships between devices are identified. For example, analysis shows that the start and stop of the dosing pump is closely related to the influent flow rate, and the operation of the backwash pump depends on the status of a specific filter tank. These relationships are stored in a graph structure, with nodes representing process units or equipment and edges representing process relationships or dependency relationships. Attribute information such as the strength of the relationship is attached to the edges, forming a water management knowledge network that provides knowledge support for subsequent data verification and association analysis. Water quality data in the real-time operational dataset is physically verified for correlation, cross-validating the data using known physicochemical laws between water quality parameters and the relationships between adjacent measurement points. For example, there is a positive correlation between pH value and alkalinity, a linear relationship between turbidity and suspended solids content (SS), and residual chlorine concentration is related to water temperature and pH value. When the value of a water quality parameter is abnormal, the rationality of the abnormality is judged by querying the value of the relevant parameter. At the same time, the data of adjacent measuring points are used to verify spatial correlation. For example, the effluent quality of a process unit should be basically consistent with the influent quality of the next unit (taking into account time delay). In addition, through the analysis of historical data trends, the normal variation range and pattern of each parameter are established, and when the data deviates from the historical trend, it is marked and corrected. Correction methods include linear interpolation, moving average replacement, or estimated value replacement based on a regression model. After this series of verification and correction, a water quality data verification set is formed, which improves the credibility and integrity of the water quality data.

[0028] Based on the water quality data validation set and the water supply knowledge network, energy consumption data and chemical consumption data are assigned to process units and missing values ​​are filled. This process unit assignment process breaks down overall energy and chemical consumption data into specific process units. Based on the process structure and equipment relationships within the knowledge network, and incorporating historical operating patterns, the allocation ratio for each unit is calculated. For example, the total energy consumption of a pumping station is distributed among each pump based on their rated power and actual operating time. The total chemical dosage is allocated based on the designed dosage rate and actual water volume at each dosing point. For missing data, a knowledge-based inference filling method is used, drawing inferences based on association rules within the water supply knowledge network. For example, if chemical consumption data for a specific chemical dosing device is missing, the consumption is estimated based on the relationship between treated water volume and historical unit consumption. If energy consumption data for a specific device is missing, the energy consumption is inferred based on its operating status and load relationship. This series of processes creates quality-enhanced data, which not only corrects anomalies and missing data but also enhances logical consistency and integrity across the data.

[0029] For example, the operational data storage and processing process at a water treatment plant demonstrates the effectiveness of this technology. The plant processes 50,000 tons of water daily, with up to 2,000 collection points. Raw data is stored in real time in a time-series database, with high-frequency pressure data (5-second intervals) stored in hourly partitions and water quality data (10-minute intervals) in daily partitions. Three-dimensional model data is stored in a spatial database, containing geometric information for 10 major process structures and 250 pieces of equipment. When 30 days of historical data are transferred to cold storage, the raw minute-level data is compressed and a daily average is calculated, reducing the original 500GB of data to 50GB while retaining key information for trend analysis. By analyzing pipeline connections and flow data, a knowledge network reflecting the "coagulation-sedimentation-filtration-disinfection" process flow is automatically constructed. During a water quality monitoring session, a pH sensor displayed an abnormal reading of 8.9 (far above the normal range of 7.2-7.5). Based on the normal reading of 7.3 at adjacent measurement points and the unchanged alkalinity, the system immediately identified the sensor as abnormal and automatically replaced it with the historical average value, marking it for maintenance. At the same time, based on the equipment association relationship in the knowledge network, the total power consumption data is accurately distributed to each process unit, and the filtration system with abnormally high energy consumption is identified, providing accurate data support for subsequent energy-saving optimization.

[0030] In a specific embodiment, the identification module is configured to: A four-tier evaluation index system was constructed based on quality enhancement data, which graded and calculated the pump operating efficiency, sedimentation tank turbidity removal rate, system hydraulic balance, and factory water quality compliance rate to obtain multi-level evaluation results. Conduct time series cluster analysis on historical data of multi-level assessment results, identify regular operation modes through seasonal variation characteristics, and identify emergency operation modes through mutation characteristics, thus obtaining a basic operation mode library; According to the basic operation mode library, the correlation analysis of the pump station load rate, reagent dosage, and backwash frequency in the quality enhancement data is carried out. The low load mode and high load mode are distinguished by the load rate threshold to obtain the load characteristic mode set. The load characteristic pattern set is matched with the process parameters in the virtual representation of the water system. The filter blockage pattern is identified by the change of the backwash cycle, and the water quality change pattern is identified by the change of the disinfectant dosage, thus obtaining the process abnormality pattern set. Extract key characteristic parameters from process anomaly patterns, conduct traceability analysis of anomaly propagation paths based on virtual representation of the water system, determine anomaly types through correlation between process unit states, and obtain anomaly location results. Based on the abnormality location results, a comprehensive comparison and analysis is performed on the current operating status with the basic operating mode library, load characteristic mode set and process abnormality mode set. The current operating mode is confirmed through matching calculation, and the alarm level and processing suggestions are generated to obtain the operating status diagnosis results.

[0031] Specifically, a four-tiered evaluation indicator system is constructed based on quality enhancement data. These indicators are at the equipment, process unit, system, and plant levels, forming a comprehensive evaluation system from the micro to the macro level. Equipment-level indicators assess key equipment performance, such as pump efficiency. This is calculated by the ratio of actual head to power consumption and compared with the equipment's rated efficiency curve to determine the equipment's operating status. Process-unit-level indicators assess the performance of individual treatment units, such as sedimentation tank turbidity removal efficiency, by calculating the ratio of the difference between inlet and outlet turbidity to the inlet turbidity. System-level indicators assess the synergistic performance of multiple process units, such as system hydraulic balance, by calculating the standard deviation of flow rate deviations between process units to reflect the uniformity of water flow distribution. Plant-level indicators assess the overall water plant's operating performance, such as the rate of water quality compliance at the factory, by calculating the ratio of the number of water quality parameters that meet standards to the total number of parameters tested. These indicators are assigned a normal value range based on historical operating data and are categorized into four levels: excellent, normal, fair, and poor. This generates a multi-tiered evaluation result, creating a comprehensive picture of the water plant's operating status. Time series cluster analysis was performed on historical data from multi-level evaluation results to identify different operating modes. Time series cluster analysis is a technique for grouping similar time series data. First, time series features were extracted from historical evaluation indicator data, including trend, seasonal, and random components. Fourier transforms were used to extract the cyclical variation characteristics of the evaluation indicators, identifying seasonal patterns that vary across the year. For example, seasonal variations such as decreased turbidity removal efficiency during high temperatures in summer and reduced efficacy of reagents during low temperatures in winter were observed. These patterns with clear seasonal variations and gentle fluctuations in indicator values ​​were classified as normal operating modes. Furthermore, by calculating the rate of change of evaluation indicators and employing a mutation detection algorithm, drastic short-term changes in indicator values ​​were identified, such as sudden increases in influent turbidity or ammonia nitrogen content. These mutation patterns were classified as emergency operating modes. This analysis clustered the historical operating data into several typical patterns, forming a basic operating mode library that provides a reference benchmark for identifying the current operating status.

[0032] Based on the basic operating mode library, correlation analysis is performed on parameters such as pump station load rate, reagent dosage, and backwash frequency in the quality-enhanced data. The pump station load rate is the ratio of actual water flow to designed water flow, reflecting the water plant load level. Correlations between the pump station load rate and other operating parameters in historical data are analyzed to determine the threshold range for the load rate. When the pump station load rate falls below the set threshold (typically 40% of the design value), it is considered to be in low-load mode, which is typically accompanied by increased unit energy consumption and decreased pump efficiency. When the pump station load rate exceeds the set threshold (typically 85% of the design value), it is considered to be in high-load mode, which may be accompanied by increased pressure in the treatment unit and decreased residence time. Cluster analysis of various operating parameters under different load rate conditions extracts characteristic parameter combinations for each load mode, forming a load characteristic pattern set. This sets typical operating parameter configurations and performance indicators under different load conditions, providing a basis for operating condition identification and parameter optimization. The load characteristic pattern set is then matched with process parameters in the virtual representation of the water system to identify potential process anomalies. First, analyze the changes in the backwash cycle. The backwash cycle refers to the time interval between two backwashes of the filter and reflects the filter status. By comparing the current backwash cycle with the historical normal value, when the backwash cycle is significantly shortened (below the standard deviation range of the historical average), it is judged to be in filter clogging mode. Possible causes include deterioration of influent water quality, poor coagulation effect, or abnormal filter media status. Similarly, by analyzing the changes in disinfectant dosage, when the disinfectant dosage increases significantly (exceeding the water volume growth ratio), it is judged to be a water quality change mode, indicating that the organic matter or ammonia nitrogen content in the water may increase. In this way, multiple abnormal patterns are identified from the changes in process parameters, including but not limited to the coagulant excess mode, the sedimentation tank overload mode, the unstable residual chlorine mode, etc., forming a process abnormality pattern set, recording the characteristic parameter patterns and possible causes of various abnormal states.

[0033] Key characteristic parameters are extracted from the process anomaly patterns to conduct a traceability analysis of the anomaly propagation path. First, the process unit and measurement point to which the anomaly indicator belongs are determined. The process flow chart in the virtual representation of the water system is then queried to identify the upstream and downstream process units associated with the anomaly unit. The operating status data of the upstream units is then examined to determine whether an anomaly propagation chain exists. For example, if the turbidity of the filter tank effluent is abnormal, the turbidity and coagulation performance of the sedimentation tank effluent are traced upstream to determine whether the anomaly originated upstream or in the unit itself. By analyzing the time series correlation of the status of each process unit, the initial location of the anomaly and the direction of its propagation are determined. When an anomaly occurs only in a single process unit and does not propagate downstream, it is considered a single-point anomaly; when an anomaly propagates sequentially along the process flow after a certain point in time, it is considered a chain anomaly; and when unrelated anomalies occur simultaneously in multiple process units, it is considered a systemic anomaly. This traceability analysis generates anomaly localization results that clearly identify the source, impact range, and nature of the anomaly, providing precise positioning for subsequent processing.

[0034] Based on the anomaly location results, the current operating state is comprehensively compared and analyzed against various pattern libraries to confirm the current operating mode. First, the current evaluation indicator values ​​at each layer are compared with the patterns in the basic operating pattern library to calculate the similarity with various normal and emergency modes. This similarity is calculated using weighted Euclidean or Mahalanobis distance, taking into account the correlation between the indicators. Second, the current load rate and other parameters are compared with the load characteristic pattern set to determine the load state. Third, the process parameter variation characteristics are compared with the process anomaly pattern set to check for known anomaly patterns. Finally, the results of these three comparisons are combined. When the matching degree for a pattern exceeds a preset threshold, the current operating state is confirmed to belong to that pattern. Based on the confirmed operating mode and anomaly location results, the corresponding alarm level and handling recommendations are generated. Alarm levels are divided into four levels: prompt, warning, caution, and emergency, and are determined based on the severity, impact scope, and duration of the anomaly. The handling recommendations are based on historical success cases and expert knowledge bases, providing specific parameter adjustment plans or operational guidance for specific anomaly types. This results in an operating state diagnosis and guides operators in making informed decisions and handling.

[0035] In a specific embodiment, the evaluation module is configured to: Extract pipeline operation data, historical fault records, and maintenance cost records from the quality-enhanced data, and combine them with the abnormal location results in the operation status diagnosis results to obtain a pipeline asset assessment dataset. The pipeline asset assessment dataset is input into the input layer of the Bayesian neural network. The input features include pipeline age, pressure fluctuation value, hydraulic load rate, and historical failure frequency, and the network input vector is obtained. The network input vector is processed through the hidden layer, each neuron in the hidden layer is connected with a probability weight, and the conditional probability value is calculated using the prior probability distribution to obtain a probability feature map; The probability feature map is passed to the output layer of the Bayesian neural network, and the pipeline failure probability distribution is calculated through posterior probability update to obtain the failure risk assessment result; Based on the failure risk assessment results, the life cycle costs under different maintenance and update strategies are calculated, including direct maintenance costs, update investment costs, and operating parameter adjustment costs, to obtain a cost assessment matrix. Perform multi-objective optimization analysis on the cost assessment matrix to balance maintenance costs and service level objectives, generate pipeline hierarchical update plans and operation strategy adjustment suggestions, and obtain asset management optimization solutions.

[0036] Specifically, the assessment module extracts pipeline asset-related data from the quality-enhanced data to establish a pipeline risk assessment and cost optimization model. Three key data types are extracted from the quality-enhanced data: pipeline operation data, including real-time dynamic monitoring information such as pressure, flow, and water quality; historical fault records, including information such as fault type, occurrence time, cause, and repair method; and repair cost records, including cost data such as labor, material costs, and indirect costs. This data is then correlated and integrated with the anomaly location results from the operational status diagnosis. These anomaly location results provide information on the location and nature of anomalies within the pipeline network, helping to determine whether the anomaly is caused by inherent pipeline failure or external factors. Through this data integration process, a pipeline asset assessment dataset is constructed, encompassing basic pipeline attributes (material, diameter, installation year), operating parameters (pressure, flow, hydraulic characteristics), fault history (fault type, frequency, severity), and cost information (repair cost, replacement cost). This pipeline asset assessment dataset is then processed using a Bayesian neural network. A Bayesian neural network is a machine learning model that combines Bayesian probability theory with neural network architecture to handle uncertainty and provide probabilistic predictions. The input layer receives four key features: pipeline age reflects the pipeline's degradation and service life; pressure fluctuation indicates the magnitude and frequency of pressure fluctuations experienced by the pipeline; hydraulic load rate refers to the ratio of the pipeline's actual flow rate to its designed flow rate; and historical failure frequency records the number of pipeline failures per unit time. Each feature undergoes normalization to bring data of varying dimensions into the same range. For example, the pipeline age is divided by its expected maximum service life, the standard deviation of pressure fluctuations is divided by its average pressure value, and the historical failure frequency is converted to a failure rate per unit time (typically per year). After normalization, these features are combined to form the network input vector, which serves as input data for the Bayesian neural network.

[0037] The network input vector is passed to the hidden layer of a Bayesian neural network for processing. The hidden layer is the core computational unit of the neural network and consists of multiple neurons, each connected to all neurons in the input layer. Unlike traditional neural networks, the connection weights in a Bayesian neural network are not fixed values ​​but random variables that follow a specific probability distribution. Weights are typically assumed to follow a normal distribution, described by two parameters: mean and variance. This probabilistic weighting design enables the network to express prediction uncertainty. The hidden layer processing first calculates the weighted input for each hidden neuron. This is then converted to a neuron output value using an activation function (typically a Reluctant Unit (ReLU) or tanh function). Because the weights are probability distributions, the same input produces different outputs in different samples. Multiple sampling calculations are performed to determine the probability distribution of each hidden neuron's output. The probability outputs of these hidden neurons collectively form a probabilistic feature map, which represents the distribution of the input features in the high-dimensional feature space.

[0038] The probability feature map is passed to the output layer of the Bayesian neural network to calculate the pipeline failure probability distribution. The output layer receives the probability feature map from the hidden layer, connects it with probability weights, and performs a weighted summation. The result is then converted to a probability value between 0 and 1 using a sigmoid activation function, representing the probability of pipeline failure. A key feature of the Bayesian neural network is the use of posterior probability updates. Using Bayes' theorem, the prior probability (the initial distribution of model parameters) and the likelihood function (the conditional probability of the observed data) are combined to calculate the posterior probability (the distribution of model parameters given the observed data). This process is typically implemented using variational inference or Markov Chain Monte Carlo methods. The output layer not only provides the mean of the pipeline failure probability but also the variance of the probability distribution, reflecting the uncertainty of the prediction. The output results are divided by pipeline segment to form a pipeline network failure risk distribution map, identify high-risk segments, and obtain a failure risk assessment result.

[0039] Based on the failure risk assessment results, the lifecycle costs of different maintenance and replacement strategies are calculated. Lifecycle cost analysis considers all costs throughout the pipeline's lifecycle, from installation to retirement. For each pipeline segment, the costs under three strategies are calculated based on the failure risk assessment results: direct maintenance costs represent the emergency repair costs incurred when a pipeline failure occurs, including direct material costs, labor costs, and the cost of service interruption caused by the failure; replacement investment costs represent the fixed investment required to replace the pipeline in advance, including material procurement, construction and installation, and related management costs; and operating parameter adjustment costs represent the costs of extending the pipeline life by adjusting operating parameters (such as reducing pressure and optimizing flow distribution). These costs include equipment adjustment costs and potential service level reductions. For each strategy, a Monte Carlo simulation method is used to generate a large number of random scenarios based on the failure probability distribution. The total cost under each scenario is calculated, resulting in a cost probability distribution for each strategy. These analysis results are integrated into a cost evaluation matrix, with rows representing different pipeline segments and columns representing different maintenance strategies. The matrix elements contain the average cost, cost variance, and risk level. The cost evaluation matrix then enters the multi-objective optimization analysis phase, balancing multiple conflicting objectives such as minimizing maintenance costs, maximizing service reliability, and optimizing capital efficiency. Multi-objective optimization uses the Pareto optimality principle to find a set of solutions such that no single objective can be improved without compromising the others. The optimization process begins by defining the objective function and constraints. The objective function includes minimizing total cost, minimizing the risk of service disruption, and balancing resource utilization; the constraints include budget constraints, technical feasibility, and service level requirements. Genetic algorithms or particle swarm algorithms are then used to search for the optimal solution set and evaluate the overall performance of different pipeline renewal combinations. Finally, based on the decision maker's preference weights, the most appropriate solution is selected from the Pareto optimal set. This generates a prioritized, hierarchical pipeline renewal plan, along with operational strategy adjustment recommendations such as pressure management, hydraulic balance, and monitoring strategies, forming a complete asset management optimization solution.

[0040] In a specific embodiment, the generating module is configured to: Generate pipeline renewal plan charts based on asset management optimization solutions, visualize pipeline network priorities and investment costs, and obtain a management view of decision support views; Generate an operation monitoring display interface based on the operation status diagnosis results, display abnormal operating conditions and equipment status on the process flow chart, and obtain an operation layer view of the decision support view; Configure permissions for decision support views, set information access scope and operation permissions based on different user roles, and obtain multi-level decision support views; A knowledge push mechanism is built based on multi-level decision support views. When key events occur, handling suggestions and relevant experience are automatically pushed to relevant personnel to obtain intelligent assistance content. Based on the historical data of the operating status diagnosis results, the early warning conditions of water quality indicators, pressure parameters and equipment status are optimized and adjusted to obtain the early warning threshold adjustment system; Integrate intelligent assistance content and warning threshold adjustment systems into a response and disposal process, trigger corresponding notifications and processing steps based on the severity of the incident, and obtain an execution plan.

[0041] Specifically, a pipeline renewal plan chart is generated based on the asset management optimization plan. The pipeline renewal plan chart processing process includes three steps: data stratification, priority visualization, and investment cost presentation. In the data stratification step, the pipeline network renewal plan in the asset management optimization plan is categorized by renewal timeframe: near-term (within one year), medium-term (1-3 years), and long-term (3-5 years). Pipeline importance is also categorized into three categories: critical sections (affecting mainline water supply), important sections (affecting regional water supply), and general sections (affecting only a small number of users). In the priority visualization step, a color-coding system is used to convert pipeline section renewal priorities into a color gradient, with red indicating the highest priority, yellow indicating medium priority, and green indicating low priority. The location and priority of each pipeline section are intuitively displayed on a GIS map. In the investment cost presentation step, the renewal cost, expected benefits, and return on investment data for each pipeline section are displayed in various chart formats, including bar charts, pie charts, and radar charts. A summary view of key indicators such as total investment, annual investment, and investment return ratio is also provided. These charts are organized in the form of dashboards to form decision-support views for management, allowing senior managers to quickly grasp the overall update plan and resource allocation.

[0042] Based on the operational status diagnosis results, an operational monitoring display interface is generated, displaying abnormal operating conditions and equipment status on the process flow diagram. The generation process of the operational monitoring display interface includes three steps: process flow diagram construction, real-time data overlay, and abnormal status marking. The process flow diagram construction phase utilizes 3D model data from the virtual representation of the water system to extract 2D schematics and connectivity diagrams of core process units and equipment, arranging them according to the process flow to form a complete process flow diagram skeleton. The real-time data overlay phase binds key parameter values ​​from the operational status diagnosis results to corresponding locations on the process flow diagram in real time. These values ​​include flow, pressure, water level, water quality indicators, and equipment operational status, and are displayed using digital labels and trend charts. The abnormal status marking phase focuses on abnormal information in the diagnostic results, clearly marking detected abnormal operating conditions and equipment status. For example, equipment failures are indicated by a flashing red icon, parameter abnormalities are highlighted by a yellow border, and process abnormalities are indicated by a specific overlay pattern. Furthermore, the severity of abnormalities is differentiated by color shading and icon size, making it easier for operators to quickly identify key issues. This intuitive visual interface forms an operational-level view for decision support, providing frontline operators with a clear overview of operational status and guidance for handling abnormalities. Decision support views are configured with permissions, setting information access scopes and operational permissions based on different user roles. The specific process involves three steps: user role definition, permission matrix creation, and access control implementation. The user role definition phase categorizes system users into typical roles, such as senior administrator, department administrator, technical expert, operations operator, and maintenance personnel. Each role corresponds to specific job responsibilities and information requirements. The permission matrix creation phase creates a three-dimensional "role-function-permission" matrix, with user roles on the horizontal axis and system functional modules on the vertical axis. The matrix elements represent specific permission levels (such as read-only, edit, manage, and no permissions). For example, a senior administrator has full permissions on the asset management view, while an operator has only read-only permissions. Conversely, an operator has edit permissions on the operation monitoring view, while an administrator only requires read-only permissions. The access control implementation phase utilizes a role-based access control (RBAC) model. When a user logs into the system, the corresponding permission configuration is automatically loaded based on their authenticated role identity, dynamically generating a personalized interface that displays only the content and functions that their role has access to. This multi-level decision support view ensures that each user type has access to information and operational capabilities appropriate to their responsibilities, ensuring both information security and improving work efficiency.

[0043] A knowledge push mechanism, built on a multi-level decision support view, automatically pushes handling suggestions and relevant experience to relevant personnel when critical events occur. The knowledge push mechanism's processing flow includes three steps: event identification, knowledge matching, and targeted push. The event identification step continuously monitors operational data and status changes in the decision support view. Detection of predefined critical events (such as equipment alarms, process parameter violations, and system anomalies) triggers the knowledge push process. The knowledge matching step retrieves relevant handling experience and recommendations from the knowledge base based on the identified event characteristics. The knowledge base consists of historical cases, expert rules, and best practices. Using semantic search technology, it calculates the similarity between event descriptions and knowledge items and selects the knowledge items with the highest similarity as candidate content. The targeted push step identifies the responsible person for responding to the incident based on permissions and user roles. Matched knowledge content is ranked by importance and accurately pushed to relevant personnel through multiple channels, including internal system messages, SMS, and email. The push content includes the event description, impact area, handling suggestions, and similar historical cases, forming structured intelligent assistance content to help responsible personnel quickly understand the issue and take effective measures. Based on historical data from operational status diagnostics, the warning conditions for water quality indicators, pressure parameters, and equipment status are optimized and adjusted, forming a warning threshold adjustment system. The warning threshold adjustment process includes three steps: historical data analysis, warning performance evaluation, and threshold optimization. The historical data analysis step collects long-term historical data for each monitoring indicator and the corresponding warning records, analyzing the indicator's statistical distribution characteristics, including mean, standard deviation, and seasonal variation patterns. The warning performance evaluation step retrospectively analyzes historical warning records and calculates performance indicators such as warning accuracy (the proportion of true anomalies that receive a successful warning), false alarm rate (the proportion of warnings that are actually normal), and missed alarm rate (the proportion of true anomalies that receive no warning) under the current threshold. The threshold optimization step, based on the performance evaluation results, automatically searches for the optimal threshold using machine learning algorithms (such as decision trees and receiver operating characteristic (ROC) curve analysis), balancing the dual goals of accurate warnings and reducing false alarms. Differentiated thresholds are also considered for different operating conditions, such as using different pressure warning thresholds during high-load and low-load periods, and different water quality warning thresholds for rainy and dry seasons. The optimized threshold configuration forms a dynamic warning rule base, which enables adaptive adjustment of warning thresholds and improves the accuracy and practicality of warnings.

[0044] The intelligent assistance content and the warning threshold adjustment system are integrated into a response and handling process. This process triggers appropriate notifications and handling steps based on the severity of the incident, generating a complete implementation plan. This integrated response and handling process includes three steps: event classification, process definition, and response coordination. The event classification step categorizes abnormal events in the water system into four levels based on the warning threshold and the scope of the incident: information level (minor fluctuation, requiring only logging), attention level (parameter limit violations, requiring manual confirmation), warning level (clear abnormality, requiring timely handling), and emergency level (serious failure, requiring immediate response). The process definition step designs standardized handling procedures for each level of incident, clearly defining response timelines, handling steps, responsible individuals, and escalation paths. For example, the process for a warning-level incident includes: automatic notification of the on-duty operator (within 5 minutes) → operator confirmation and preliminary handling (within 30 minutes) → notification of technical experts for evaluation (if necessary) → recording of handling results (within 24 hours). The response coordination step integrates the intelligent assistance content with the handling process. When an event is triggered, handling suggestions and process instructions are simultaneously delivered, forming a complete task card. This supports real-time recording and information sharing of the handling process, ensuring efficient and collaborative handling of abnormal situations by multiple roles. The final implementation plan is a dynamic and executable workflow that guides relevant personnel to respond to various water affairs abnormal events in a scientific and standardized manner.

[0045] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A digital water management global data operation decision analysis integrated service system, characterized by: The system comprises: The acquisition module is used to collect multi-level data of water system operating parameters and obtain standardized processed data; A construction module is used to construct a three-dimensional model of the water plant and a process function model based on the standardized processing data, establish a synchronization mechanism between the physical entity and the virtual model, and obtain virtual representation data of the water system; A storage module, configured to perform hierarchical storage design on the standardized processing data and the water system virtual representation data to obtain quality-enhanced data; an identification module, configured to construct a hierarchical evaluation index system and identify an operation mode based on the quality enhancement data and the water system virtual representation data, thereby obtaining an operation status diagnosis result; An evaluation module, configured to perform lifecycle cost accounting for pipeline assets based on the operating status diagnosis results and the quality enhancement data, and to evaluate the impact of maintenance and update strategies and operating parameter adjustments on long-term costs through a Bayesian neural network to obtain an asset management optimization plan; The generation module is used to generate a decision support view based on the asset management optimization plan and the operating status diagnosis results, establish a knowledge push mechanism and an early warning threshold adjustment system, and obtain an execution plan.

2. The digital water affairs global data operation decision analysis comprehensive service system according to claim 1 is characterized by: The acquisition module is used to: Deploy flow monitoring devices, pressure sensors, water quality parameter analyzers, and equipment status detectors within the water plant to collect real-time parameters of the water intake, sedimentation tank, filtration system, disinfection system, and water distribution system to obtain raw monitoring data. Building a standardized data acquisition protocol based on the raw monitoring data, and converting the data formats of devices from different manufacturers through industrial communication protocols to obtain data in a unified format; Dynamically adjust the data sampling frequency of the unified format data, increase the sampling density under critical working conditions according to the process status, and reduce the sampling frequency during stable operation to obtain optimized sampling data; The optimized sampling data is input into the edge computing gateway, and preprocessed by outlier filtering, data compression, and timestamp calibration to obtain preprocessed data; Performing data quality assessment based on the preprocessed data, constructing a quality scoring matrix by calculating a signal quality index, a data integrity score, and a time continuity coefficient, and obtaining a quality assessment result; The data is securely encrypted according to the quality assessment results, a hierarchical transmission channel is established, and key process parameters and alarm information are prioritized and transmitted to obtain the standardized processing data.

3. The digital water affairs global data operation decision analysis comprehensive service system according to claim 1 is characterized by: The building blocks are used to: Extracting spatial parameters from the standardized processed data, building the plant's external structure based on engineering design drawings, and obtaining an external framework of the water plant's three-dimensional model; Based on the external framework of the water plant three-dimensional model and the equipment parameter information in the standardized processing data, the structure of the water pump, valve, flow meter, liquid level gauge, and pressure transmitter is constructed to obtain the water plant three-dimensional model; According to the three-dimensional model of the water plant and the process parameters in the standardized processing data, the internal structures of the flocculation tank, the sedimentation tank, the filtration tank, the disinfection contact tank, and the clear water tank are constructed to obtain a physical model of the process unit; Performing hydraulic calculations on the physical model of the process unit, correlating the flow, pressure, and water level data in the standardized processing data with the three-dimensional structure to obtain a hydraulic function model; Based on the hydraulic function model and the water quality parameters in the standardized processing data, a parameter mapping relationship of the process treatment unit is constructed to obtain a process function model; A real-time synchronization mechanism is established between the three-dimensional model of the water plant and the process function model and the actual physical entity, and incremental data update and time sequence synchronization are performed through a message queue to obtain virtual representation data of the water system.

4. The digital water affairs global data operation decision analysis comprehensive service system according to claim 1 is characterized by: The storage module is used for: The water quality parameter data, flow data, pressure data, and equipment operation status data in the standardized processing data are stored in a dedicated time series database, and different storage partitions are set according to the acquisition frequency to obtain a real-time operation data set; Storing the three-dimensional structure data and functional parameter data in the virtual representation data of the water system in a spatial database, establishing spatial indexes and attribute associations, and obtaining a structured spatial data set; Transferring historical data older than 30 days from the real-time running data set to a cold storage area, compressing the data and calculating statistical values ​​by day, week, and month to obtain a multi-granularity historical data set; Based on the multi-granularity historical data set and the structured spatial data set, a process association diagram and equipment dependency relationship between process units of the water plant are established to obtain a water affairs knowledge relationship network; Performing physical correlation verification on the water quality data in the real-time operation data set, correcting outliers using adjacent measurement point data and historical data trends, and obtaining a water quality data verification set; According to the water quality data verification set and the water affairs knowledge relationship network, the energy consumption data and the drug consumption data are subjected to process unit attribution processing and missing value filling to obtain the quality enhancement data.

5. The digital water affairs global data operation decision analysis comprehensive service system according to claim 1 is characterized by: The identification module is used to: A four-tier evaluation index system is constructed based on the quality enhancement data, and the water pump operating efficiency, sedimentation tank turbidity removal rate, system hydraulic balance and factory water quality compliance rate are graded and calculated to obtain multi-level evaluation results; Performing time series cluster analysis on the historical data of the multi-level evaluation results, identifying the normal operation mode through seasonal variation characteristics, and identifying the emergency operation mode through mutation characteristics, to obtain a basic operation mode library; performing a correlation analysis on the pump station load rate, reagent dosage, and backwash frequency in the quality enhancement data according to the basic operation mode library, distinguishing low load mode from high load mode by a load rate threshold, and obtaining a load characteristic mode set; Matching the load characteristic pattern set with the process parameters in the virtual representation of the water system, identifying the filter pool blockage pattern through the backwash cycle change, and identifying the water quality change pattern through the disinfectant dosage change, to obtain a process abnormality pattern set; Extract key characteristic parameters from the process anomaly pattern set, perform source tracing analysis on the anomaly propagation path based on the virtual representation of the water system, determine the anomaly type through the correlation of process unit states, and obtain an anomaly location result; Based on the abnormality location result, a comprehensive comparison and analysis is performed on the current operating status and the basic operating mode library, the load characteristic mode set and the process abnormality mode set. The current operating mode is confirmed through matching calculation, and an alarm level and processing suggestions are generated to obtain the operating status diagnosis result.

6. The digital water affairs global data operation decision analysis comprehensive service system according to claim 1 is characterized by: The evaluation module is used to: Extracting pipeline operation data, historical fault records, and maintenance cost records from the quality enhancement data, and combining them with abnormality location results in the operation status diagnosis results to obtain a pipeline asset assessment data set; Inputting the pipeline asset assessment dataset into the input layer of the Bayesian neural network, wherein the input features include pipeline age, pressure fluctuation value, hydraulic load rate, and historical failure frequency, to obtain a network input vector; The network input vector is processed through a hidden layer, each neuron in the hidden layer is connected using a probability weight, and a conditional probability value is calculated using a priori probability distribution to obtain a probability feature map; The probability feature map is passed to the output layer of the Bayesian neural network, and the pipeline failure probability distribution is calculated by posterior probability update to obtain a failure risk assessment result; Based on the failure risk assessment results, the life cycle costs under different maintenance and update strategies are calculated, including direct maintenance costs, update investment costs, and operating parameter adjustment costs, to obtain a cost assessment matrix; A multi-objective optimization analysis is performed on the cost assessment matrix to balance maintenance costs and service level objectives, generate pipeline hierarchical update plans and operation strategy adjustment suggestions, and obtain the asset management optimization plan.

7. The digital water management global data operation decision analysis comprehensive service system according to claim 1 is characterized by: The generating module is used to: Generate a pipeline renewal plan chart based on the asset management optimization plan, present the pipeline network priority and investment cost in a visual manner, and obtain a management view of the decision support view; Generate an operation monitoring display interface based on the operation status diagnosis result, display abnormal operating conditions and equipment status on the process flow chart, and obtain an operation layer view of the decision support view; Performing permission configuration on the decision support view, setting information access scope and operation permissions according to different user roles, and obtaining a multi-level decision support view; The knowledge push mechanism is constructed based on the multi-level decision support view, and when a key event occurs, processing suggestions and relevant experience are automatically pushed to relevant personnel to obtain intelligent assistance content; Optimizing and adjusting the warning conditions of water quality indicators, pressure parameters, and equipment status based on the historical data of the operating status diagnosis results to obtain the warning threshold adjustment system; The intelligent assistance content and the warning threshold adjustment system are integrated into a response and disposal process, and corresponding notifications and processing steps are triggered according to the severity of the event to obtain the execution plan.

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