Intelligent water affair dynamic supervision method and platform based on digital twinning

Abstract

The invention specifically relates to an intelligent water affair dynamic supervision method and platform based on digital twinning, and relates to the technical field of intelligent water affair supervision. Risk early warning and situation deduction; emergency decision making and dispatching command; and post-treatment evaluation and knowledge precipitation. According to the method, a comprehensive and scientific risk quantification system is constructed through a combined weighting mechanism of multi-source data fusion, an analytic hierarchy process and an entropy weight method, and dynamic and accurate description of risks of each section of pipeline and each node of a pipe network is achieved in combination with a real-time working condition correction coefficient and a second-level updating mechanism; and grading accurate early warning triggered by a dynamic threshold value is matched with risk situation deduction of a simulation model, so that the influence range and the development trend of risks such as pipe explosion and water quality pollution can be pre-judged in advance.

Description

Technical Field

[0001] This invention relates to the field of smart water management technology, and in particular to a smart water dynamic management method and platform based on digital twins. Background Technology

[0002] As a core component of urban infrastructure, water systems undertake crucial functions such as water supply, water transmission, and water quality assurance. Their operational safety directly impacts residents' lives, industrial production, and the ecological environment. Traditional water management models, relying primarily on manual inspections, periodic maintenance, and post-event reviews, have significant limitations. First, risk identification is static, relying on paper files or fixed thresholds to judge risks, which cannot respond in real time to dynamic factors such as pipeline operating conditions and environmental changes, resulting in frequent missed or misjudged risks. Secondly, the early warning mechanism is too simplistic and lacks a precise early warning system with different levels and categories. The early warning information is vague and not delivered in a timely manner, making it difficult to detect and deal with risks early. Third, emergency decision-making relies on past experience. When faced with sudden accidents such as pipe bursts and water pollution, the operation and maintenance personnel rely on their past experience to formulate disposal plans, which lacks scientific simulation and deduction support, and is prone to improper handling and expansion of losses.

[0003] Digital twin technology enables real-time interaction and simulation between the physical and virtual worlds by constructing virtual mappings of physical entities, providing a new technical approach for dynamic monitoring of water systems.

[0004] However, existing water management technologies based on digital twins mostly focus on optimizing a single aspect, and there is still room for improvement in terms of the accuracy of risk quantification, the fit of simulation and inference, and the scientific nature of decision-making.

[0005] Therefore, a smart water management dynamic monitoring method and platform based on digital twins is proposed to address the aforementioned problems. Summary of the Invention

[0006] The purpose of this invention is to provide a smart water management dynamic monitoring method and platform based on digital twins in order to solve the above-mentioned problems.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A smart water management dynamic monitoring method based on digital twins includes: Risk data fusion; through multi-source data fusion, risk factor extraction and weighting, and dynamic quantitative calculation, a visualized risk heat map is generated to achieve comprehensive coverage and accurate characterization of risks in the water system; Risk warning and situation simulation: Based on dynamic threshold triggering, graded warnings are pushed out in a targeted manner, and the impact of risk scenarios is simulated with the help of coupled simulation models; Emergency Decision-Making and Dispatch Command: Locate the event and assess its impact, determine the optimal emergency response plan through simulation optimization, issue standardized instructions and track their execution; Post-disposal assessment and knowledge accumulation: Compare actual disposal data with predicted data, analyze the reasons for deviations, calibrate the digital twin model and update the emergency response plan knowledge base to build a self-iterative closed loop for risk management.

[0008] Preferably, the risk data fusion specifically includes: Collect multi-dimensional risk-related data, including: real-time operating condition data; static asset data; environmental correlation data; and historical event data. Identify the key factors affecting the risks of water systems and scientifically allocate their weights. Typical categories include physical pipe burst risk factors; water pollution risk factors; and equipment failure risk factors. The weighting method combines the analytic hierarchy process (AHP) and the entropy weighting method: first, water industry experts compare the importance of risk factors pairwise to construct a judgment matrix and calculate subjective weights; then, based on the information entropy of historical data, objective weights are calculated; finally, the subjective and objective weights are combined in a 7:3 ratio.

[0009] Preferably, the method further includes: The single-factor risk value is calculated using a weighted summation model, and then the comprehensive risk score is calculated using the comprehensive risk index formula: Comprehensive risk score = Σ (standardized value of single factor × corresponding weight), with a score range of 0-10, and is divided into levels; Introducing a real-time operating condition correction factor: When real-time data shows abnormal fluctuations, the correction factor is automatically triggered, and the weight ratio of the corresponding risk factor is increased. Risk heatmap generation and presentation: Spatial matching: The comprehensive risk score is precisely bound to the pipeline network nodes and pipeline segments in the digital twin model. The grid is divided with a spatial accuracy of 10 meters × 10 meters, and the average risk score within the grid is calculated. Visual presentation: In the GIS map and 3D digital twin model, four-color gradient rendering of green, light yellow, orange and dark red is used to mark the core risk factors of each risk area.

[0010] Preferably, the risk warning and situation simulation specifically include: A dynamic threshold mechanism is adopted: based on the 95th percentile of historical data over the past 3 months, the real-time operating condition fluctuation range, and industry safety standards, the warning thresholds for each region and each device are automatically calculated; the thresholds are updated once a month in combination with operating data to ensure that the thresholds adapt to changes in system status. The warning information includes the risk type, location of occurrence, current risk score, core triggers, similar historical cases, and preliminary handling suggestions.

[0011] Preferably, the method further includes: Based on the digital twin model of the pipeline network topology, hydraulic characteristic parameters, water quality diffusion model, and equipment operating parameters, a coupled simulation model of hydraulics, water quality, and equipment is constructed. Parameter input: Import the current risk area's operating conditions, environmental data, and asset data in real time as the basis for the simulation.

[0012] Preferably, the emergency decision-making and dispatch command specifically includes: Event location method: Multi-source data cross-validation: By combining sensor alarm data, on-site personnel reports, user complaints, and video surveillance, the precise location of the event is located in the digital twin model, and the event type and severity are labeled. Location result confirmation: Automatically generate an event location report, including location basis, on-site photos and surrounding pipeline topology map, and push it to on-site verification personnel. The confirmation result will be fed back within 10 minutes after verification. And conduct an analysis of the scope and extent of the impact: Based on event type, scope of impact, and existing resources, similar cases are matched from the emergency response plan knowledge base, and combined with the network constraints of the digital twin model, a preset number of feasible disposal plans are automatically generated. Perform a full-process simulation of each solution in the digital twin model to simulate the effect after the solution is executed: Each solution is scored, and a solution comparison and scoring table is generated; For the highest-scoring solution, potential problems are automatically identified, details are adjusted and optimized, and the optimal solution is formed. The comparative scoring table of the proposed solutions, the optimal solution, and the impact analysis report are compiled and sent to the emergency command leadership group, which then makes a decision based on the actual situation.

[0013] Preferably, the post-treatment evaluation and knowledge accumulation specifically include: Collect actual data throughout the entire process, including: execution data; effect data; and feedback data. The actual data and the previous simulation prediction data are classified according to the same dimension to form a data table comparing prediction and reality; The deviation rate for the core indicator is calculated as: |actual value - predicted value| / predicted value × 100%; The causes of deviations are classified, and a deviation analysis report is generated.

[0014] Preferably, the method further includes: Digital twin model calibration: The calibration targets include hydraulic models, water quality models, and risk calculation models. And perform calibration and verification; The details of this incident, the handling plan, the analysis of the deviation between simulation and reality, the successful experiences, and the improvement suggestions will be entered into the knowledge base in a unified format and tagged with the incident. Establish a case matching algorithm so that when a similar event occurs later, the algorithm automatically retrieves the handling plan for this event from the knowledge base and recommends it as the priority solution.

[0015] A smart water management dynamic monitoring platform based on digital twins includes: Data fusion and risk quantification module: Collects multi-source data, extracts risk factors and assigns them weights through a combination of analytic hierarchy process and entropy weighting method, and generates a risk score of 0-10 through dynamic weighted calculation; Risk warning and situation simulation module: Based on the dynamic threshold mechanism, it triggers multi-level accurate warnings and pushes them in a targeted manner. With the help of a coupled simulation model, it simulates the impact range and development trend of risk scenarios. Emergency Decision-Making and Dispatch Execution Module: Locates events through cross-validation of multi-source data and quickly analyzes their impact, automatically generates multiple emergency response plans, and simulates and optimizes to select the optimal plan; Model calibration and knowledge accumulation module: compare actual emergency response data with predicted data, analyze the causes of deviations, calibrate core model parameters, structure and store event handling experience, and update the emergency plan knowledge base; Visualization and Interactive Management Module: Presents risk distribution with a risk heat map with a spatial accuracy of 10 meters × 10 meters, and supports full-process visualization and interaction.

[0016] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention constructs a comprehensive and scientific risk quantification system through a combination of multi-source data fusion and weighting mechanisms using the analytic hierarchy process (AHP) and entropy weighting. Combined with real-time operating condition correction coefficients and a second-level update mechanism, it achieves dynamic and accurate characterization of the risks of each section of pipeline and each node in the pipeline network. The hierarchical and accurate early warning triggered by dynamic thresholds, coupled with the risk situation extrapolation of simulation models, can predict the impact range and development trend of risks such as pipe bursts and water pollution in advance.

[0017] 2. This invention, through full-process simulation and multi-dimensional quantitative comparison using a digital twin model, can quickly select the optimal solution with the least loss and highest efficiency, overcoming the limitations of traditional experience-based decision-making. Standardized command issuance and automated linkage with IoT devices significantly shorten response time and ensure water supply safety for special users. Simultaneously, by analyzing the deviation between actual and predicted data after the fact, the parameters of the digital twin model are calibrated, and the handling experience is structured and stored in a knowledge base, enabling the regulatory system to have self-iterative capabilities and continuously improve the intelligence level and long-term operational guarantee capabilities of water affairs supervision. Attached Figure Description

[0018] Further details, features, and advantages of this application are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which: Figure 1 This is a structural diagram of the method of the present invention; Figure 2 This is a platform module diagram of the present invention. Detailed Implementation

[0019] Several embodiments of this application will now be described in more detail with reference to the accompanying drawings to enable those skilled in the art to implement this application. This application may be embodied in many different forms and for various purposes and should not be limited to the embodiments set forth herein. These embodiments are provided to make this application thorough and complete, and to fully convey the scope of this application to those skilled in the art. The embodiments described do not limit this application.

[0020] Unless otherwise defined, all terms used herein (including technical and scientific terms) shall have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains. It will be further understood that terms such as those defined in commonly used dictionaries shall be interpreted as having a meaning consistent with their meaning in the relevant field and / or the context of this specification, and shall not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0021] Example 1 Its specific implementation method is combined with the appendix Figure 1 and attached Figure 2 Please provide a detailed explanation.

[0022] Appendix Figure 1 The flowchart of a smart water management dynamic monitoring method based on digital twins provided in this embodiment of the invention illustrates the complete steps from multi-source data fusion to building a self-iterative closed loop for risk management.

[0023] Appendix Figure 2 This invention provides a structural block diagram of a smart water affairs dynamic monitoring platform based on digital twins, which shows the connection relationship between the data fusion and risk quantification module and the visualization and interactive control module, and marks the main functional interaction process of each module.

[0024] In this embodiment, it includes: Risk data fusion; through multi-source data fusion, risk factor extraction and weighting, and dynamic quantitative calculation, a visualized risk heat map is generated to achieve comprehensive coverage and accurate characterization of risks in the water system; Specifically, it includes: Collect multi-dimensional risk-related data, specifically including: Real-time operating data: pipeline pressure, water flow rate, water quality indicators (turbidity, residual chlorine content, etc.), pump and valve operating status; Static asset data: Pipe material, laying year, corrosion and wear status, historical maintenance records (synchronized from the asset management system). Environmental data: soil corrosivity levels, earthquake monitoring data, and real-time information on third-party construction activities; Historical event data: records of past pipe bursts and archives of water pollution accidents.

[0025] By combining industry expertise with big data analysis results, key factors influencing water system risks are identified and their weights are scientifically allocated. Typical classifications are as follows: Physical pipe burst risk factors: Pipe type (assignment: cast iron 1.0, cement 0.9, PE pipe 0.3, ductile iron 0.2), pipe age (assignment: >30 years 1.0, 20-30 years 0.7, 10-20 years 0.4, <10 years 0.1), operating pressure (current pressure / rated pressure, ratio >1.2 1.0, 1.0-1.2 0.7, 0.8-1.0 0.3, <0.8 0.1), corrosion rate. Degree (ultrasonic wall thickness loss rate: >30% is 1.0, 20%-30% is 0.7, 10%-20% is 0.4, <10% is 0.1), soil corrosivity (pH <5.5 or >8.5 is 1.0, 5.5-6.5 or 7.5-8.5 is 0.6, 6.5-7.5 is 0.2), third-party construction distance (construction within 5 meters of the pipeline is 1.0, 5-10 meters is 0.5, >10 meters is 0.1); Water pollution risk factors: residual chlorine concentration (<0.3 mg / L = 1.0, 0.3-0.5 mg / L = 0.6, >0.5 mg / L = 0.1), water flow stagnation time (>24 hours = 1.0, 12-24 hours = 0.7, 6-12 hours = 0.3, <6 hours = 0.1), type of cross-connection point (crossing with sewage pipe = 1.0, crossing with rainwater pipe = 0.5, no cross-connection = 0.1), water source water quality compliance rate (<95% = 1.0, 95%-98% = 0.4, >98% = 0.1), pipeline leakage rate (>5% = 1.0, 3%-5% = 0.6, <3% = 0.2); Equipment failure risk factors: cumulative pump unit operating time (>8000 hours = 1.0, 5000-8000 hours = 0.7, <5000 hours = 0.2), vibration amplitude (>0.1mm = 1.0, 0.05-0.1mm = 0.6, <0.05mm = 0.1), historical maintenance frequency (annual average maintenance >3 times = 1.0, 1-3 times = 0.5, <1 time = 0.1), equipment depreciation rate (>80% = 1.0, 50%-80% = 0.6, <50% = 0.2); The combined weighting method of Analytic Hierarchy Process (AHP) and entropy weighting is used: First, water industry experts (in the fields of pipeline operation and maintenance, water quality monitoring, and equipment management) compare the importance of risk factors pairwise to construct a judgment matrix and calculate subjective weights; then, based on the information entropy of historical data, objective weights are calculated; finally, the subjective and objective weights are integrated in a 7:3 ratio to ensure that the weights are both in line with industry experience and in line with the actual characteristics of the data.

[0026] The single-factor risk value is calculated using a weighted summation model, and then the comprehensive risk score is calculated using the comprehensive risk index formula: Comprehensive risk score = Σ (standardized value of single factor × corresponding weight), with a score range of 0-10, and is divided into levels according to 0-3 (low risk), 3-6 (medium risk), 6-8 (high risk), and 8-10 (extremely high risk); Introducing a real-time operating condition correction coefficient: When real-time data shows abnormal fluctuations (such as a sudden increase in pressure of 20% or a sudden decrease in residual chlorine of 30%), the correction coefficient (1.2-1.5 times) is automatically triggered, increasing the weight of the corresponding risk factor to ensure that the risk score can quickly respond to sudden operating conditions.

[0027] Update frequency: Recalculated every 15 minutes under normal operating conditions; triggered second-level update when real-time data is abnormal, to ensure that the risk score is synchronized with the system status; Risk heatmap generation and presentation: Spatial matching: The comprehensive risk score is precisely bound to the pipeline nodes and pipeline segments in the digital twin model. The grid is divided with a spatial accuracy of 10 meters × 10 meters, and the average risk score within the grid is calculated.

[0028] Visual presentation: In the GIS map and 3D digital twin model, a four-color gradient rendering of green (low risk), light yellow (medium risk), orange (high risk), and dark red (extremely high risk) is used to mark the core risk factors of each risk area (e.g., dark red area: management age 35 years+, pressure exceeds the standard); it supports zooming, panning, and click query, and clicking on any area can display a detailed risk report (factor score, weight, calculation basis).

[0029] Risk warning and situation simulation: Based on dynamic threshold triggering, graded warnings are pushed out in a targeted manner. Coupled simulation models are used to simulate the impact of risk scenarios, so as to achieve early detection and early prediction of risks. Specifically, it includes: A dynamic threshold mechanism is adopted instead of fixed values: based on the 95th percentile of historical data from the past 3 months, the real-time operating condition fluctuation range, and industry safety standards, the warning thresholds for each region and each piece of equipment are automatically calculated (e.g., the risk threshold for pipe bursting in old pipelines is set to 6 points, and the threshold for newly built pipelines is set to 7 points); the thresholds are updated once a month based on the operating data to ensure that the thresholds adapt to changes in system status. Tiered early warning standards: General warning (3-6 points): Pop-up notification in the system, synchronized to the APP of the operation and maintenance team leader; Severe warning (6-8 points): PC pop-up window, APP push, SMS notification, synchronized to the head of the operation and maintenance department and regional management personnel; Severe warning (8-10 points): Real-time push notifications through multiple channels (pop-up windows, APP, SMS, telephone reminders), simultaneously sent to the company's responsible leaders and emergency response team, and 24-hour on-call system activated; The early warning information includes six core fields: risk type (pipe burst / water pollution / equipment failure), location of occurrence (accurate to road address and pipeline pile number), current risk score, core cause, similar historical cases, and preliminary handling suggestions (such as "increase the frequency of water quality sampling in this area"), to avoid vague descriptions; Push logic: Push notifications are targeted to responsible areas and job permissions to ensure that warning information reaches the relevant responsible persons directly; a read confirmation function is supported, and if not confirmed within 15 minutes, the notification will be automatically escalated to the superior leader to avoid information omission.

[0030] Based on the digital twin model of the pipeline network topology, hydraulic characteristic parameters (such as pipe diameter, roughness, and resistance coefficient), water quality diffusion model (such as ADCP diffusion equation), and equipment operating parameters, a hydraulic-water quality-equipment coupled simulation model is constructed to ensure that the simulation results are consistent with reality. Parameter input: Import real-time operating data (pressure, flow rate, water quality), environmental data (temperature, wind speed), and asset data (pipeline status, equipment location) of the current risk area as the basic input for the simulation; Specific steps for scenario simulation: Pipe rupture risk simulation: Set up a simulation scenario: Assume that a high-risk pipeline (such as a 35-year-old cast iron pipe on XX Road in Area A) has a 50mm diameter leak. Input parameters such as the compressive strength of the pipe material, the current pressure, and the connection relationship of the surrounding pipeline network. Simulation content: Using a 1-minute time step, simulate the pipeline pressure changes (the time when the pressure at critical nodes drops to the lowest value), water loss (cumulative loss in cubic meters), affected user range (statistical count of users categorized by residential users, commercial users, and special users (schools / hospitals)) and the risk of secondary pipe bursts in surrounding pipelines (the risk point where a sudden drop in pressure causes abnormal stress on other old pipelines). Output results: Generate pressure change curves, user distribution maps, water loss statistics tables, and mark high-priority protection areas (such as hospitals and water sources).

[0031] Water pollution risk simulation: Set up the simulation scenario: Suppose that pollutants (such as excessive heavy metals) are detected in a water source, and input parameters such as pollutant concentration, diffusion coefficient, and water flow velocity in the pipeline network; Simulation content: Simulate the diffusion path of pollutants in the pipe network (the propagation trajectory along the water flow direction), the time to reach each node (accurate to the minute), the duration of water quality exceeding the standard in each area, and the degradation pattern of pollutants (such as the disinfection effect of residual chlorine on pollutants). Output results: Generate pollutant diffusion time series diagram, early warning list of users exceeding standards (marking the arrival time of pollutants), and water quality recovery prediction curve.

[0032] Emergency Decision-Making and Dispatch Command: Locate events and assess their impact, determine the optimal emergency response plan through simulation optimization, issue standardized instructions and track their execution to achieve rapid response and scientific handling; Specifically, it includes: Rapid event location and impact analysis: Event location method: Multi-source data cross-validation: Combining sensor alarm data (such as sudden pressure drop, water quality sensor exceeding standards), on-site personnel reports (APP text and image reports), user complaints (hotline calls, online feedback), and video surveillance (cameras around the pipeline network), the precise location of the event is locked in the digital twin model (error ≤ 5 meters), and the event type (pipe burst / water pollution / equipment failure) and severity (such as pipe burst leakage amount, pollution concentration) are marked. Location result confirmation: Automatically generate an event location report, including location basis, on-site photos (if any), and surrounding pipeline topology map, and push it to on-site verification personnel. The confirmation result will be fed back within 10 minutes after verification. And conduct an analysis of the scope and extent of the impact: Core analytical metrics: Water supply impact: Area of ​​water outage (square kilometers), total number of affected users (classified by type), and estimated duration of water outage (based on fault complexity). Water quality impact: Exceeding water quality standards, affected water supply area, and potential risks to human health (such as whether key drinking water indicators exceed standards); Equipment impact: type of faulty equipment, whether it affects the coordinated operation of other equipment (e.g., pump station failure leading to insufficient water supply pressure in the area), and difficulty of equipment repair (whether special tools are required, and the availability of spare parts). Analysis tools: By calling up hydraulic / water quality simulation models and combining them with real-time data, automated analysis can be completed within 5 minutes, generating an impact analysis report that clarifies the core impact areas and priority response targets (such as prioritizing the restoration of hospital water supply). Emergency response plan simulation and optimization: Based on the event type, scope of impact, and existing resources (location of emergency repair teams, inventory of emergency equipment, quantity of spare parts, and distribution of temporary water supply points), similar cases are matched from the emergency plan knowledge base. Combined with the network constraints of the digital twin model (such as valve switching logic and pipeline connection relationships), a preset number of feasible disposal solutions are automatically generated. Core content of the solution (taking pipe burst as an example): Valve closure plan: Clearly define the valve number, location, and operation sequence that need to be closed (to avoid accidental closure that could cause secondary impacts); Emergency repair plan: dispatch of emergency repair team (from the nearest emergency repair point), emergency repair equipment (excavator, sealing tools), spare parts models (matching the specifications of the faulty pipeline), and estimated repair time; Water supply guarantee plan: location of temporary water supply points (covering the affected core areas), emergency water supply vehicle dispatch routes, and point-to-point water supply plan for special users (hospitals / schools); User notification plan: Notification scope, notification method (SMS, APP, community announcement), notification content (water outage duration, location of temporary water supply point, contact number); Scheme simulation and optimization: Simulation content: Perform a full-process simulation of each solution in the digital twin model to simulate the effect after the solution is executed. Option A: Simulation results of shutting off valves V1 and V2: 300 affected users are isolated, and the repair time is 8 hours, but it causes a water outage for a nearby hospital, requiring the deployment of 2 additional emergency water supply trucks; Option B: Simulation results of shutting off valves V3, V4, and V5: 500 affected users are isolated, repair time is 6 hours, hospital water supply is normal, temporary water supply points cover 80% of affected users, and the arrival time of the repair team is shortened by 30 minutes; Scheme C: Simulation results of closing valves V2 and V3 and activating the backup pipeline: 250 users were isolated, the repair time was 10 hours, the backup pipeline pressure was insufficient, and the water supply flow in some areas was low.

[0033] Quantitative comparison dimensions: Each solution is scored (out of 100 points) based on five dimensions: number of affected users (the fewer the better), repair time (the shorter the better), handling cost (manpower, equipment, and material costs, the lower the better), special user protection (whether it covers hospitals / schools, if so, bonus points), and operational complexity (the fewer steps, the better), generating a solution comparison score table; Solution optimization: For the highest-scoring solution, potential problems are automatically identified (such as valve operation conflicts and emergency repair route congestion), and details are adjusted and optimized (such as optimizing the valve closing sequence and changing the emergency repair team route) to form the optimal handling solution; Optimal decision-making and instruction issuance: Decision confirmation process: The system compiles the scheme comparison and scoring table, the optimal disposal scheme, and the impact analysis report, and pushes them to the emergency command leadership group. The leadership group, based on the actual situation (such as whether there are major events or special weather), completes the decision confirmation within 30 minutes. If adjustments are needed, the scheme parameters can be modified in the digital twin model and re-simulated for verification.

[0034] Decision-making results record: Automatically generate emergency decision-making minutes, including the basis for the decision, the content of the plan, and the approval opinions, and store them in the system archive for easy traceability later.

[0035] Command issuance and execution: Instruction Generation: Based on the optimal solution, a standardized list of operation instructions (operation tickets) is automatically generated, categorized by the executing entity: Emergency repair team: Includes the repair location, task content (such as closing the V3 valve, replacing the damaged pipe), required equipment / spare parts, and time requirements (such as arriving at the site within 1 hour). Dispatch Center: Includes pump station operating parameter adjustments (such as increasing the water supply pressure of the standby pump station to 0.4MPa) and temporary water supply equipment start-up instructions; Customer service department: Includes user notification SMS templates and inquiry response scripts; Command issuance: After authorization review (signature confirmation by the department head), the command is issued to each executing entity with one click through channels such as APP, SMS, and IoT platform; the IoT platform can directly connect to on-site smart valves, pump stations and other equipment to realize automated linkage from command to execution (such as remote control of valve closure).

[0036] Execution tracking: Receive feedback information from each executing entity in real time (such as the emergency repair team has set off, the valve has been closed, and the temporary water supply point has been activated), and dynamically update the execution progress in the digital twin model; for tasks that are not completed on time, automatically trigger reminders (such as SMS or phone calls) to ensure execution closure.

[0037] Post-disposal assessment and knowledge accumulation: Compare actual disposal data with predicted data, analyze the reasons for deviations, calibrate the digital twin model and update the emergency response plan knowledge base, and build a self-iterative closed loop for risk management; Specifically, it includes: Review of the treatment results: Data collection and organization: Collect actual data throughout the entire process, including: Execution data: actual valve shut-off time, emergency repair team arrival time, total repair time, actual number of equipment / spare parts used, and details of disposal costs; Results data: actual number of affected users, actual water loss, time for water quality to return to standard, number of user complaints, and special user protection measures; Feedback data: Operational feedback from on-site personnel (e.g., whether the plan is unreasonable), user satisfaction survey (online questionnaire, telephone follow-up); Data standardization and organization: Classify the actual data and the previous simulation prediction data according to the same dimension (such as repair time, number of affected users) to form a data table comparing prediction and actual data; Deviation analysis and root cause identification: Deviation calculation: For core indicators, the deviation rate is calculated as |actual value - predicted value| / predicted value × 100%. For example, if the predicted repair time is 6 hours and the actual time is 8 hours, the deviation rate is 33.3%. Classify the causes of deviations: Model bias: The parameters of the digital twin model do not match reality (e.g., the pipeline resistance coefficient is set too low, resulting in a shorter predicted repair time). Execution deviations: Improper on-site operation (such as valves not being closed in a timely manner), and delays in resource allocation (such as emergency repair equipment not arriving on time). Unexpected factors: New situations arise during the handling process (such as sudden rainfall affecting emergency repairs, or new user complaints leading to an expansion of the handling scope); Generate a deviation analysis report: clearly define the deviation rate of each indicator, the cause of the core deviation, and the responsible party (e.g., model deviation is the responsibility of the technical department, and execution deviation is the responsibility of the operations and maintenance department).

[0038] Digital twin model calibration: The calibration targets include: adjusting core parameters to address model issues identified in the deviation analysis. Hydraulic model: Corrects parameters such as pipe friction coefficient, valve flow coefficient, and pump station head curve to ensure more accurate pressure and flow prediction; Water quality model: Adjust parameters such as pollutant diffusion coefficient and degradation rate to improve the accuracy of water quality change prediction; Risk calculation model: Optimize the weight of risk factors (e.g., if the impact of third-party construction is found to be greater than expected during actual handling, increase its weight) and correct the dynamic threshold calculation logic; Then perform calibration and verification: re-input the calibrated model with the actual data from this event to verify whether the deviation rate between the prediction results and the actual data has dropped to within 10%; if it does not meet the standard, repeat the calibration process until the accuracy requirements are met. Emergency response plan knowledge base update: Structured storage experience: The event details (type, cause, severity), handling plans (optimal plan, other alternative plans), simulation and actual deviation analysis, successful experiences (such as setting up temporary water supply points in the community center is more efficient), improvement suggestions (such as replenishing the inventory of spare parts for a certain type of pipe) of this event are entered into the knowledge base in a unified format and tagged with event tags (such as pipe burst - cast iron pipe - old city area) to facilitate subsequent retrieval; Knowledge base application optimization: Establish a case matching algorithm so that when similar events occur later (such as the same pipeline type or similar area), the system automatically retrieves the handling plan for this event from the knowledge base and recommends it as the priority solution; at the same time, the knowledge base is reviewed every quarter to remove outdated cases (such as solutions that are no longer applicable after equipment updates) and integrate similar cases to form standardized handling templates.

[0039] Example 2 Please see Figure 2 A smart water management dynamic monitoring platform based on digital twins includes the following components: Data fusion and risk quantification module: Collects multi-source data such as real-time operating conditions, static assets, environmental correlations, and historical events, extracts risk factors and assigns them weights through a combination of the analytic hierarchy process and the entropy weight method, and generates a risk score of 0-10 through dynamic weighted calculation, providing a quantitative basis for subsequent supervision; Risk warning and situation simulation module: Based on the dynamic threshold mechanism, it triggers multi-level accurate early warnings and pushes them in a targeted manner. With the help of the hydraulic-water quality-equipment coupled simulation model, it simulates the impact range and development trend of risk scenarios such as pipe bursts and water pollution. Emergency Decision-Making and Dispatch Execution Module: Locates events and quickly analyzes their impact through cross-validation of multi-source data, automatically generates multiple emergency response plans, simulates and optimizes them to select the optimal plan, issues standardized operating instructions and tracks the execution progress to ensure rapid response and scientific handling; Model calibration and knowledge accumulation module: compare actual emergency response data with predicted data, analyze the causes of deviations, calibrate core model parameters such as hydraulics, water quality, and risk calculation, and store event handling experience in a structured manner and update the emergency plan knowledge base; Visualization and Interactive Management Module: Presents risk distribution using a risk heat map with a spatial accuracy of 10m×10m (rendered in four colors: green-light yellow-orange-dark red), supports full-process visualization and interaction such as event location, scheme simulation, and execution progress, and provides operation functions such as click query, zoom and pan.

[0040] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0041] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

[0042] It should be noted that, in this document, the use of relational terms such as "first" and "second" is merely for distinguishing one entity or operation from another, and does not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes the element.

[0043] It should be understood that in the various embodiments of this application, the order of the above-mentioned processes does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0044] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0045] Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0046] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0047] In addition, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0048] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0049] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for dynamic monitoring of smart water affairs based on digital twins, characterized in that, include: Risk data fusion; By integrating multi-source data, extracting and weighting risk factors, and performing dynamic quantitative calculations, a visualized risk heat map is generated, enabling comprehensive coverage and accurate characterization of risks in the water system. Risk warning and situation simulation: Based on dynamic threshold triggering, graded warnings are pushed out in a targeted manner, and the impact of risk scenarios is simulated with the help of coupled simulation models; Emergency Decision-Making and Dispatch Command: Locate the event and assess its impact, determine the optimal emergency response plan through simulation optimization, issue standardized instructions and track their execution; Post-disposal assessment and knowledge accumulation: Compare actual disposal data with predicted data, analyze the reasons for deviations, calibrate the digital twin model and update the emergency response plan knowledge base to build a self-iterative closed loop for risk management.

2. The method for dynamic monitoring of smart water affairs based on digital twins according to claim 1, characterized in that, Risk data fusion specifically includes: Collect multi-dimensional risk-related data, including: real-time operating condition data; static asset data; environmental correlation data; and historical event data. Identify the key factors affecting the risks of water systems and scientifically allocate their weights. Typical categories include physical pipe burst risk factors; water pollution risk factors; and equipment failure risk factors. The weighting method combines the analytic hierarchy process (AHP) and the entropy weighting method: first, water industry experts compare the importance of risk factors pairwise to construct a judgment matrix and calculate subjective weights; then, based on the information entropy of historical data, objective weights are calculated; finally, the subjective and objective weights are combined in a 7:3 ratio.

3. The method for dynamic monitoring of smart water affairs based on digital twins according to claim 2, characterized in that, Also includes: The single-factor risk value is calculated using a weighted summation model, and then the comprehensive risk score is calculated using the comprehensive risk index formula: Comprehensive risk score = Σ (standardized value of single factor × corresponding weight), with a score range of 0-10, and is divided into levels; Introducing a real-time operating condition correction factor: When real-time data shows abnormal fluctuations, the correction factor is automatically triggered, and the weight ratio of the corresponding risk factor is increased. Risk heatmap generation and presentation: Spatial matching: The comprehensive risk score is precisely bound to the pipeline network nodes and pipeline segments in the digital twin model. The grid is divided with a spatial accuracy of 10 meters × 10 meters, and the average risk score within the grid is calculated. Visual presentation: In the GIS map and 3D digital twin model, four-color gradient rendering of green, light yellow, orange and dark red is used to mark the core risk factors of each risk area.

4. The method for dynamic monitoring of smart water affairs based on digital twins according to claim 1, characterized in that, Risk warning and situation simulation, specifically including: A dynamic threshold mechanism is adopted: based on the 95th percentile of historical data over the past 3 months, the real-time operating condition fluctuation range, and industry safety standards, the warning thresholds for each region and each device are automatically calculated; the thresholds are updated once a month in combination with operating data to ensure that the thresholds adapt to changes in system status. The warning information includes the risk type, location of occurrence, current risk score, core triggers, similar historical cases, and preliminary handling suggestions.

5. The method for dynamic monitoring of smart water affairs based on digital twins according to claim 4, characterized in that, Also includes: Based on the digital twin model of the pipeline network topology, hydraulic characteristic parameters, water quality diffusion model, and equipment operating parameters, a coupled simulation model of hydraulics, water quality, and equipment is constructed. Parameter input: Import the current risk area's operating conditions, environmental data, and asset data in real time as the basis for the simulation.

6. The method for dynamic monitoring of smart water affairs based on digital twins according to claim 1, characterized in that, Emergency decision-making and dispatching command specifically include: Event location method: Multi-source data cross-validation: By combining sensor alarm data, on-site personnel reports, user complaints, and video surveillance, the precise location of the event is located in the digital twin model, and the event type and severity are labeled. Location result confirmation: Automatically generate an event location report, including location basis, on-site photos and surrounding pipeline topology map, and push it to on-site verification personnel. The confirmation result will be fed back within 10 minutes after verification. And conduct an analysis of the scope and extent of the impact: Based on event type, scope of impact, and existing resources, similar cases are matched from the emergency response plan knowledge base, and combined with the network constraints of the digital twin model, a preset number of feasible disposal plans are automatically generated. Perform a full-process simulation of each solution in the digital twin model to simulate the effect after the solution is executed: Each solution is scored, and a solution comparison and scoring table is generated; For the highest-scoring solution, potential problems are automatically identified, details are adjusted and optimized, and the optimal solution is formed. The comparative scoring table of the proposed solutions, the optimal solution, and the impact analysis report are compiled and sent to the emergency command leadership group, which then makes a decision based on the actual situation.

7. The method for intelligent water management dynamic monitoring based on digital twins according to claim 1, characterized in that, Post-treatment assessment and knowledge accumulation specifically include: Collect actual data throughout the entire process, including: execution data; effect data; and feedback data. The actual data and the previous simulation prediction data are classified according to the same dimension to form a data table comparing prediction and reality; The deviation rate for the core indicator is calculated as: |actual value - predicted value| / predicted value × 100%; The causes of deviations are classified, and a deviation analysis report is generated.

8. The method for dynamic monitoring of smart water affairs based on digital twins according to claim 7, characterized in that, Also includes: Digital twin model calibration: The calibration targets include hydraulic models and water quality models. Risk calculation model; And perform calibration and verification; The details of this incident, the handling plan, the analysis of the deviation between simulation and reality, the successful experiences, and the improvement suggestions will be entered into the knowledge base in a unified format and tagged with the incident. Establish a case matching algorithm so that when a similar event occurs later, the algorithm automatically retrieves the handling plan for this event from the knowledge base and recommends it as the priority solution.

9. A smart water management dynamic monitoring platform based on digital twins, and a smart water management dynamic monitoring method based on digital twins according to any one of claims 1-8, characterized in that, include: Data fusion and risk quantification module: Collects multi-source data, extracts risk factors and assigns them weights through a combination of analytic hierarchy process and entropy weighting method, and generates a risk score of 0-10 through dynamic weighted calculation; Risk warning and situation simulation module: Based on the dynamic threshold mechanism, it triggers multi-level accurate warnings and pushes them in a targeted manner. With the help of coupled simulation models, it simulates the impact range and development trend of risk scenarios. Emergency Decision-Making and Dispatch Execution Module: Locates events through cross-validation of multi-source data and quickly analyzes their impact, automatically generates multiple emergency response plans, and simulates and optimizes to select the optimal plan; Model calibration and knowledge accumulation module: compare actual emergency response data with predicted data, analyze the causes of deviations, calibrate core model parameters, structure and store event handling experience, and update the emergency plan knowledge base; Visualization and Interactive Management Module: Presents risk distribution with a risk heat map with a spatial accuracy of 10 meters × 10 meters, and supports full-process visualization and interaction.

Images