A real-time water regime driven water supply scheduling method

Abstract

The present application relates to the technical field of urban water supply operation scheduling, and particularly relates to a real-time water regime driven water supply scheduling method, comprising the following steps: S1: synchronously acquiring water quality parameters of a water source, pressure data of a main pipe network and instantaneous flow of a user end; S2: generating a dynamic water quality safety margin based on the water quality parameters; S3: calculating a regional pressure-flow imbalance factor according to the pressure data of the main pipe network and the instantaneous flow of the user end; S4: fusing the dynamic water quality safety margin and the pressure-flow imbalance factor to generate a real-time water supply resilience index; and S5: mapping the water supply resilience index into a pressure regulation instruction. According to the present application, the water supply resilience index is constructed by fusing water quality and hydraulic state, and a hierarchical pressure regulation strategy is executed according to the water supply resilience index, so that dynamic guarantee of stability and safety of a water supply system under complex water regime is realized.

Description

Technical Field

[0001] This invention relates to the field of urban water supply operation and scheduling technology, and in particular to a real-time water situation-driven water supply scheduling method. Background Technology

[0002] With the continuous expansion of urban water supply systems and the development of zonal control technology, the traditional water supply scheduling method based on static water pressure setting and timed water quality sampling is no longer able to meet the complex and ever-changing water demand and the response requirements for sudden water quality events.

[0003] Most current water supply scheduling schemes still focus on single-parameter regulation, such as maintaining constant pressure by controlling the main pump frequency or adjusting regional pressure through terminal pressurization equipment. However, they often lack collaborative analysis of multi-source data such as water quality, water pressure, and flow rate, resulting in lagging scheduling behavior or rigid strategies, making it difficult to balance water supply security and scheduling flexibility. Therefore, there is an urgent need for a real-time water situation-driven water supply scheduling method to solve the above problems. Summary of the Invention

[0004] To achieve the above objectives, the present invention provides a real-time water condition-driven water supply scheduling method.

[0005] A real-time water situation-driven water supply scheduling method includes the following steps:

[0006] S1: Simultaneously acquire water quality parameters of water sources, pressure data of main pipelines, and instantaneous flow at user terminals;

[0007] S2: Generate dynamic water quality safety margins based on water quality parameters;

[0008] S3: Calculate the regional pressure-flow imbalance factor based on the main pipeline pressure data and the instantaneous flow rate at the user end;

[0009] S4: Integrates dynamic water quality safety margin and pressure-flow imbalance factors to generate a real-time water supply resilience index;

[0010] S5: Maps the water supply resilience index to pressure control commands, including maintaining pressure, tiered pressure reduction, or initiating an emergency low-pressure mode.

[0011] Optionally, S1 specifically includes:

[0012] S11: Real-time water quality parameters, including turbidity and residual chlorine concentration, are obtained by an online water quality sensor installed at the outlet of the water source;

[0013] S12: Install piezoresistive pressure transmitters at the nodes of the hydraulic zones of the pipeline network to record pressure data at a sampling frequency of 100Hz;

[0014] S13: Collect instantaneous flow data through a remote flow meter installed at the user end;

[0015] S14: Align the data obtained from S11 to S13 according to a unified timestamp to form a standardized time series data set.

[0016] Optionally, S2 specifically includes:

[0017] S21: Extract the turbidity and residual chlorine concentration data obtained in S1, and compare the two water quality parameters with the corresponding water quality safety thresholds to determine whether the current water quality meets the safety standards.

[0018] S22: Based on the degree of turbidity exceeding the standard or the degree of insufficient residual chlorine concentration, corresponding risk levels are assigned respectively, and the risk levels are divided into five levels: extremely low, relatively low, medium, relatively high and extremely high.

[0019] S23: Based on the risk level determined in S22, match the corresponding water quality safety margin coefficient.

[0020] Optionally, S22 specifically includes:

[0021] S221: Compare the turbidity value obtained in S1 with the preset maximum safety limit and calculate the turbidity deviation rate. ,when When, it is judged as extremely low risk; when At that time, the deviation was classified as low, medium, high, or very high according to its degree;

[0022] S222: Compare the residual chlorine concentration value obtained in S1 with the preset minimum safety limit to calculate the residual chlorine deficiency rate. ,when When it is determined to be extremely low risk, At that time, the risk was classified into five levels according to the degree of inadequacy: low, medium, high, or very high.

[0023] S223: Record the two risk levels obtained from S221 and S222 respectively, and take the higher level as the final risk level of the current water quality parameter.

[0024] Optionally, S23 specifically includes:

[0025] S231: Receive the water quality risk level output from S22, denoted as... The values ​​range from 1 to 5, corresponding to extremely low, relatively low, moderate, relatively high, and extremely high water quality risks, respectively.

[0026] S232: Based on the stated risk level Match the corresponding water quality safety margin coefficient, wherein the safety margin coefficient is in Values ​​are assigned within the interval according to a fixed function, the expression of which is:

[0027] ,in, This refers to the water quality safety margin coefficient. The current water quality risk level.

[0028] Optionally, S3 specifically includes:

[0029] S31: Based on the main pipeline pressure data obtained in S1, the pressure of each node is aggregated in time according to the hydraulic zoning structure to form a pressure change sequence, and the average pressure change rate and maximum node pressure difference of each zone are extracted.

[0030] S32: Based on the instantaneous flow data of the user terminal obtained in S1, the total flow of users in each hydraulic zone is weighted and summarized, and the current instantaneous flow deviation rate is calculated by combining the conventional water load model in the zone.

[0031] S33: Correlate the pressure fluctuation index and flow deviation rate in each hydraulic zone, set multi-dimensional state combination rules, and determine whether there is pressure drop caused by a sudden increase in flow or backflow caused by pressure imbalance in the zone.

[0032] S34: Based on the judgment results of S33, assign a regional pressure-flow imbalance factor to each hydraulic zone.

[0033] Optionally, the setting of multi-dimensional state combination rules includes:

[0034] Rule 1: When the average pressure change rate within a zone is negative and the flow deviation rate is positive, and the pressure difference exceeds the set threshold, it is determined to be a pressure drop caused by a sudden increase in flow.

[0035] Rule 2: When the average pressure change rate within a zone is positive, the flow deviation rate is negative, and the pressure at the upstream node is higher than that at the downstream node, it is determined to be backflow caused by pressure imbalance.

[0036] Rule 3: When the frequency of pressure fluctuations within a zone increases abnormally beyond a preset threshold, and there is no change in flow rate, it is determined to be pipeline oscillation.

[0037] Rule 4: When both the flow deviation rate and the pressure change rate are negative, it is determined that the stability has decreased due to a low water supply load.

[0038] Optionally, S34 specifically includes:

[0039] S341: For each identified hydraulic zone in S33, extract its corresponding state type label, including pressure drop caused by sudden flow surge, backflow caused by pressure imbalance, network oscillation and stability decline, and set a corresponding basic imbalance score value for each state type. ;

[0040] S342: Based on the basic imbalance score, a real-time pressure change magnitude factor for each zone is added. Factor of Flow Deviation ;

[0041] S343: Based on the influence of each factor on the degree of imbalance, calculate the final regional pressure-flow imbalance factor, the expression of which is: ,in, The factor representing the degree of imbalance in the current hydraulic zoning is limited to a range of values ​​within a certain range. .

[0042] Optionally, S4 specifically includes:

[0043] S41: Obtain the water quality safety margin coefficient calculated in S23 and the regional pressure-flow imbalance factor calculated in S34 The two indicators were normalized and verified to ensure they were both within the specified range. Inside;

[0044] S42: The two parameters are weighted and fused to obtain the real-time water supply resilience index, as shown in the formula: ,in, For real-time water supply resilience index, and These represent the weighting coefficients for the water quality dimension and the hydraulic dimension, respectively.

[0045] Optionally, S5 specifically includes:

[0046] S51: Obtain real-time water supply resilience index It is then compared with a preset three-segment resilience threshold range to determine the current control status;

[0047] The threshold range includes:

[0048] The first threshold range [0.7, 1] indicates a stable water supply, corresponding to the pressure maintenance mode;

[0049] The second threshold range [0.4, 0.7) indicates a water shortage, corresponding to a tiered pressure reduction mode;

[0050] The third threshold range [0, 0.4) is considered a high risk for water supply, corresponding to the emergency low-pressure mode.

[0051] The beneficial effects of this invention are:

[0052] This invention collects real-time data from multiple sources, including water quality at water sources, pipeline pressure, and user flow, to construct a dynamic water quality safety margin and regional pressure-flow imbalance factor. These factors are then integrated to form a water supply resilience index, enabling a comprehensive quantitative assessment of the stability and safety of the water supply system and providing a basis for the precise generation of dispatch instructions.

[0053] This invention, through a graded response mechanism based on the resilience index, enables the dispatching system to dynamically match three types of control strategies: maintaining pressure, graded pressure reduction, and emergency low pressure. This improves the system's ability to identify and respond to water quality risks and hydraulic disturbances, and enhances the water supply system's adaptability and fault tolerance under complex water conditions. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] Figure 1 This is a schematic diagram of the water supply scheduling method according to an embodiment of the present invention;

[0056] Figure 2 This is a schematic diagram of the process for generating the regional pressure-flow imbalance factor according to an embodiment of the present invention. Detailed Implementation

[0057] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. It should also be noted that, to make the embodiments more comprehensive, the following embodiments are the best and preferred embodiments, and those skilled in the art can use other alternative methods to implement some well-known technologies; moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0058] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0059] Generally, terms can be understood at least partly from their use in context. For example, depending at least partly on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but rather, alternatively, depending at least partly on the context, to allow for the presence of other factors that are not necessarily explicitly described.

[0060] like Figures 1-2 As shown, a real-time water situation-driven water supply scheduling method includes the following steps:

[0061] S1: Simultaneously acquire water quality parameters of water sources, pressure data of main pipelines, and instantaneous flow at user terminals;

[0062] S2: Generate dynamic water quality safety margins based on water quality parameters;

[0063] S3: Calculate the regional pressure-flow imbalance factor based on the main pipeline pressure data and the instantaneous flow rate at the user end;

[0064] S4: Integrates dynamic water quality safety margin and pressure-flow imbalance factors to generate a real-time water supply resilience index;

[0065] S5: Maps the water supply resilience index to pressure control commands, including maintaining pressure, tiered pressure reduction, or initiating an emergency low-pressure mode.

[0066] S1 specifically includes:

[0067] S11: Real-time water quality parameters are obtained by an online water quality sensor installed at the water source outlet, including turbidity and residual chlorine concentration. Turbidity is collected by a light scattering turbidimeter, and residual chlorine concentration is collected by an electrochemical residual chlorine electrode. The collected data is then uploaded to the dispatch platform in real time.

[0068] S12: Install piezoresistive pressure transmitters at the nodes of the hydraulic zones of the pipeline network, record pressure data at a sampling frequency of 100Hz, and transmit the data back to the edge computing gateway in real time via industrial Ethernet.

[0069] S13: Instantaneous flow data is collected through a remote flow meter installed at the user end. The remote flow meter includes an ultrasonic flow sensor and a GPRS communication module, which can upload the instantaneous flow value of each terminal to the central server in a 1-second cycle.

[0070] S14: Align the data acquired from S11 to S13 according to a unified timestamp to form a standardized time-series data set, which serves as the input basis for subsequent water quality margin assessment and water supply resilience calculation. The above steps, by synchronously collecting and time-aligning the water quality parameters of the water source, pipeline pressure, and terminal flow data within a second-level time scale, can significantly improve the response timeliness and overall perception accuracy of the data-driven water supply scheduling system, providing accurate and real-time data support for subsequent water quality margin analysis and resilience decision-making.

[0071] S2 specifically includes:

[0072] S21: Extract the turbidity and residual chlorine concentration data obtained in S1, and compare the two water quality parameters with the corresponding water quality safety thresholds to determine whether the current water quality meets the safety standards.

[0073] S22: Based on the degree of turbidity exceeding the standard or the degree of residual chlorine concentration being insufficient, corresponding risk levels are assigned. The risk levels are divided into five levels: extremely low, relatively low, medium, relatively high, and extremely high, which are used to indicate the current water quality risk level.

[0074] S23: Based on the risk level determined in S22, match the corresponding water quality safety margin coefficient. This coefficient is a quantitative indicator that reflects the current water quality safety margin. The higher the value, the lower the risk. By establishing a water quality parameter mapping mechanism with risk level as the medium, the level of real-time water quality status is quantified. This helps the subsequent scheduling process to dynamically adjust the water supply strategy under the premise of ensuring water supply safety, and improve the overall system's water quality adaptability and decision-making accuracy.

[0075] S22 specifically includes:

[0076] S221: The turbidity value obtained in S1 With the preset maximum safety limit Compare and calculate turbidity deviation rate Its expression is: , among which, when When, it is judged as extremely low risk; when When the deviation is high, it is classified into low, medium, high, or very high, with the specific grading criteria as follows: when... At that time, the risk is relatively low; when At that time, it was considered a medium risk; when At that time, it is considered a higher risk; when At that time, it was considered extremely high risk;

[0077] The aforementioned turbidity is an important parameter reflecting the amount of suspended particles in water, and it usually has a legally mandated maximum allowable value. When the real-time turbidity exceeds this threshold, it means that the water quality has begun to deteriorate. The deviation rate is essentially a quantification of the severity of exceeding the standard by calculating the ratio of the difference between the actual turbidity value and the safe threshold. The logic is: if the turbidity is below or equal to the threshold, the deviation rate is zero, indicating no risk; as the turbidity value gradually exceeds the standard, the deviation rate increases, indicating that the degree of risk gradually increases. This method converts the extent of exceeding the standard into a dimensionless value, allowing it to be treated uniformly with other indicators.

[0078] S222: The residual chlorine concentration value obtained in S1 Compared with the preset minimum safety limit Compare and calculate the residual chlorine deficiency rate. Its expression is: , among which, when When it is determined to be extremely low risk, At that time, risks are classified into five levels—low, medium, high, or very high—based on the degree of inadequacy. The specific classification criteria are as follows: when... At that time, the risk is relatively low; when At that time, it was considered a medium risk; when At that time, it is considered a higher risk; when At that time, it was considered extremely high risk;

[0079] The aforementioned residual chlorine is a key factor in the sterilization and maintenance of water quality safety in water supply systems, and is usually required to be no less than a certain concentration threshold. When the actual residual chlorine concentration is lower than the threshold, the sterilization and protection capabilities of the water supply are weakened, and the risk increases. The principle of the deficiency rate is to reflect the degree of water quality risk by calculating the ratio of the residual chlorine concentration gap to the threshold: when the residual chlorine is higher than the threshold, the deficiency rate is zero, indicating safety; when the residual chlorine is lower than the threshold, the deficiency rate increases as the gap increases, intuitively reflecting the degree to which the safety margin of the water supply is weakened. Through this dimensionless description, a symmetrical risk quantification framework can be formed with the turbidity index.

[0080] S223: Record the two risk levels obtained from S221 and S222 respectively, and take the higher level as the final risk level of the current water quality parameter, which will serve as the basis for subsequent steps to generate water quality safety margin. The above steps achieve clear classification of risk levels by quantifying deviation rate and deficiency rate and setting segmented thresholds, so that the system can accurately identify the degree of water quality anomaly, thereby ensuring that the subsequent scheduling process has a clear basis for judging the degree of risk.

[0081] S23 specifically includes:

[0082] S231: Receive the water quality risk level output from S22, denoted as... The values ​​range from 1 to 5, corresponding to extremely low, relatively low, moderate, relatively high, and extremely high water quality risks, respectively.

[0083] S232: Based on risk level Match the corresponding water quality safety margin coefficient, the safety margin coefficient is in Values ​​are assigned within the interval according to a fixed function, the expression of which is:

[0084] ,in, This refers to the water quality safety margin coefficient. Given the current water quality risk level, according to this functional relationship, the margin coefficient is negatively correlated with the risk level, that is, the higher the risk level, the lower the safety margin. The above steps realize the structured transformation of water quality risk information into the input parameters of the scheduling model by constructing a mathematical functional relationship between the water quality risk level and the safety margin coefficient. This enables the system to generate numerical margin evaluation indicators in real time when water quality deviates, providing high-timeliness and high-stability basic parameter support for subsequent resilient scheduling.

[0085] S3 specifically includes:

[0086] S31: Based on the main pipeline pressure data obtained in S1, the pressure of each node is aggregated in time according to the hydraulic zoning structure to form a pressure change sequence, and the average pressure change rate and maximum node pressure difference of each zone are extracted to characterize the local water supply stability.

[0087] S32: Based on the instantaneous flow data of the user terminal obtained in S1, the total flow of users in each hydraulic zone is weighted and summarized, and combined with the conventional water load model in the zone, the current instantaneous flow deviation rate is calculated to identify whether the flow of the user terminal is fluctuating abnormally.

[0088] S33: Correlate the pressure fluctuation index and flow deviation rate in each hydraulic zone, set multi-dimensional state combination rules, and determine whether there is pressure drop caused by a sudden increase in flow or backflow caused by pressure imbalance in the zone.

[0089] S34: Based on the judgment result of S33, a regional pressure-flow imbalance factor is assigned to each hydraulic zone. This factor comprehensively considers pressure stability, flow deviation degree and its temporal coupling characteristics, reflecting the degree of abnormal operation of the current regional hydraulic system. The value ranges from 0 to 1, and the larger the value, the higher the degree of imbalance. The above steps, by integrating the spatial fluctuation characteristics of pipeline pressure and the instantaneous deviation characteristics of user flow, and establishing a multi-dimensional state judgment mechanism, achieve highly sensitive identification of non-steady-state hydraulic behavior in the region, effectively capture potential anomalies in system operation, and improve the dispatching system's proactive response capability and identification accuracy to local imbalance states.

[0090] The rules for setting multidimensional state combinations include:

[0091] Rule 1: When the average pressure change rate within a zone is negative and the flow deviation rate is positive, and the pressure difference exceeds the set threshold, it is determined to be a pressure drop caused by a sudden increase in flow.

[0092] Rule 2: When the average pressure change rate within a zone is positive, the flow deviation rate is negative, and the pressure at the upstream node is higher than that at the downstream node, it is determined to be backflow caused by pressure imbalance.

[0093] Rule 3: When the frequency of pressure fluctuations within a zone increases abnormally beyond a preset threshold, and there is no change in flow rate, it is determined to be pipeline oscillation.

[0094] Rule 4: When both the flow deviation rate and the pressure change rate are negative, it is determined that the stability is reduced due to the low water supply load. By constructing a three-dimensional state judgment rule that covers the pressure change trend, the extreme value of the pressure difference and the degree of flow deviation, and defining typical combination scenarios, the structured identification of complex imbalance behaviors within the hydraulic zone is realized, which effectively improves the scheduling system's ability to classify and judge abnormal patterns and the accuracy of hydraulic diagnosis.

[0095] S34 specifically includes:

[0096] S341: For each identified hydraulic zone in S33, extract its corresponding state type label, including pressure drop caused by sudden flow surge, backflow caused by pressure imbalance, network oscillation and stability decline, and set a corresponding basic imbalance score value for each state type. The values ​​were set to 0.6, 0.7, 0.5, and 0.4 respectively.

[0097] S342: Based on the basic imbalance score, a real-time pressure change magnitude factor for each zone is added. Factor of Flow Deviation ;

[0098] The two factors are calculated as follows:

[0099] Pressure variation amplitude factor: ;in, The average pressure change rate of the zone. This is a reference pressure value;

[0100] Flow deviation factor: ,in, For the partition flow deviation rate, This is the baseline deviation value under the standard load model;

[0101] S343: Based on the influence of each factor on the degree of imbalance, calculate the final regional pressure-flow imbalance factor, the expression of which is: ,in, The factor representing the degree of imbalance in the current hydraulic zoning is limited to a range of values ​​within a certain range. Furthermore, when the boundary is exceeded, upper and lower limit truncation is performed. The above steps effectively realize the quantitative characterization of regional hydraulic anomalies by numerically fusing state identification labels with multi-source deviation factors and constructing a standardized scoring calculation model. This enhances the system's perceptibility to local imbalance risks and its parameterized expression capabilities, providing a stable and comparable input basis for subsequent scheduling decisions.

[0102] S4 specifically includes:

[0103] S41: Obtain the water quality safety margin coefficient calculated in S23 and the regional pressure-flow imbalance factor calculated in S34 The two indicators were normalized and verified to ensure they were both within the specified range. Internally, these variables serve as input variables for subsequent fusion models;

[0104] S42: The two parameters are weighted and fused to obtain the real-time water supply resilience index, as shown in the formula: ,in, For real-time water supply resilience index, and These represent the weighting coefficients for the water quality dimension and the hydraulic dimension, respectively, satisfying... The weights can be preset in the scheduling model according to the regional characteristics. The above steps integrate water quality and hydraulic state indicators with fixed weights to calculate the water supply resilience index, and establish a unified quantitative evaluation index system based on multidimensional water sentiment perception. This provides the scheduling system with a real-time basis for judging the current stability and carrying capacity of the water supply system.

[0105] The aforementioned basic imbalance score is a preset initial value for the risk level of different imbalance types (such as pressure drop, backflow, pipeline oscillation, etc.); its design logic is:

[0106] Different types of imbalances pose varying degrees of threat to system stability. Therefore, different initial scores are set for them based on expert experience or historical big data. This score serves as a baseline risk, which is subsequently adjusted by adding pressure change factors and flow deviation factors. The principle of this method is to first establish basic distinctions using qualitative knowledge, and then achieve quantitative adjustments through dynamic parameter calculations, thereby maintaining clear classification while also possessing flexible adaptability.

[0107] S5 specifically includes:

[0108] S51: Obtain real-time water supply resilience index It is then compared with a preset three-segment resilience threshold range to determine the current control status;

[0109] The threshold range includes:

[0110] The first threshold range [0.7, 1] indicates a stable water supply, corresponding to the pressure maintenance mode;

[0111] The second threshold range [0.4, 0.7) indicates a water shortage, corresponding to a tiered pressure reduction mode;

[0112] The third threshold range [0, 0.4) is considered a high risk for water supply, corresponding to the emergency low-pressure mode.

[0113] The aforementioned resilience threshold range is used to map the water supply resilience index to control commands, forming a tiered response mechanism. Its design principles include: when the resilience index is close to 1, it indicates that the water quality is safe, the pressure is balanced, the system is in a stable state, and the pressure can remain constant; when the resilience index is in the middle range, it indicates that some risks exist, requiring proactive mitigation measures, thus setting a tiered pressure reduction strategy; when the resilience index is very low, it indicates a serious deficiency in water supply security, necessitating the entry into emergency mode to reduce overall pressure to ensure water supply security in key areas. By dividing the resilience index into different ranges, continuous quantitative indicators can be converted into discrete decision-making actions, thereby improving the operability and controllability of scheduling execution.

[0114] If the system determines that the pressure maintenance mode is to be maintained, the existing pressure regulating valve opening and water supply pump frequency will be kept unchanged through the scheduling system, and only the boundary nodes will be monitored for slight pressure fluctuations to avoid excessive intervention.

[0115] If the system is determined to be in a graded pressure reduction mode, the pressure regulation priority is allocated in descending order according to the magnitude of the pressure-flow imbalance factor in each hydraulic zone. The dispatching system gradually reduces the pressure setpoint in the high imbalance area according to the set ratio, with a typical pressure reduction range of 5% to 15%.

[0116] If the emergency low-pressure mode is determined, the emergency water supply strategy for the corresponding area will be activated immediately, including reducing the operating frequency of the main pump, shutting down some terminal branch lines, locking the water supply protection range of key areas, and simultaneously sending a dispatch alarm signal to the monitoring platform. By dividing the water supply resilience index into three threshold ranges and corresponding to different control strategies, the system achieves a fine-grained hierarchical response to the pressure dispatch logic, enabling the system to automatically execute appropriate control actions based on the comprehensive water supply status, effectively balancing water supply security and system stability, and improving the intelligence level of dispatch decision-making.

[0117] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0118] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A real-time water situation-driven water supply scheduling method, characterized in that, Includes the following steps: S1: Simultaneously acquire water quality parameters of water sources, pressure data of main pipelines, and instantaneous flow at user terminals; S2: Generate dynamic water quality safety margins based on water quality parameters; S3: Calculate the regional pressure-flow imbalance factor based on the main pipeline pressure data and the instantaneous flow rate at the user end; S3 specifically includes: S31: Based on the main pipeline pressure data obtained in S1, the pressure of each node is aggregated in time according to the hydraulic zoning structure to form a pressure change sequence, and the average pressure change rate and maximum node pressure difference of each zone are extracted. S32: Based on the instantaneous flow data of the user terminal obtained in S1, the total flow of users in each hydraulic zone is weighted and summarized, and the current instantaneous flow deviation rate is calculated by combining the conventional water load model in the zone. S33: Correlate the pressure fluctuation index and flow deviation rate in each hydraulic zone, set multi-dimensional state combination rules, and determine whether there is pressure drop caused by a sudden increase in flow or backflow caused by pressure imbalance in the zone. S34: Based on the judgment results of S33, assign a regional pressure-flow imbalance factor to each hydraulic zone; S4: Integrates dynamic water quality safety margin and pressure-flow imbalance factors to generate a real-time water supply resilience index; S5: Maps the water supply resilience index to pressure control commands, including maintaining pressure, tiered pressure reduction, or initiating an emergency low-pressure mode.

2. The real-time water situation-driven water supply scheduling method according to claim 1, characterized in that, S1 specifically includes: S11: Real-time water quality parameters, including turbidity and residual chlorine concentration, are obtained by an online water quality sensor installed at the outlet of the water source; S12: Install piezoresistive pressure transmitters at the nodes of the hydraulic zones of the pipeline network to record pressure data at a sampling frequency of 100Hz; S13: Collect instantaneous flow data through a remote flow meter installed at the user end; S14: Align the data obtained from S11 to S13 according to a unified timestamp to form a standardized time series data set.

3. The real-time water situation-driven water supply scheduling method according to claim 2, characterized in that, S2 specifically includes: S21: Extract the turbidity and residual chlorine concentration data obtained in S1, and compare the two water quality parameters with the corresponding water quality safety thresholds to determine whether the current water quality meets the safety standards. S22: Based on the degree of turbidity exceeding the standard or the degree of insufficient residual chlorine concentration, corresponding risk levels are assigned respectively, and the risk levels are divided into five levels: extremely low, relatively low, medium, relatively high and extremely high. S23: Based on the risk level determined in S22, match the corresponding water quality safety margin coefficient.

4. The real-time water situation-driven water supply scheduling method according to claim 3, characterized in that, S22 specifically includes: S221: Compare the turbidity value obtained in S1 with the preset maximum safety limit and calculate the turbidity deviation rate. ,when When, it is judged as extremely low risk; when At that time, the deviation was classified as low, medium, high, or very high according to its degree; S222: Compare the residual chlorine concentration value obtained in S1 with the preset minimum safety limit to calculate the residual chlorine deficiency rate. ,when When it is determined to be extremely low risk, At that time, the risk was classified into five levels according to the degree of inadequacy: low, medium, high, or very high. S223: Record the two risk levels obtained from S221 and S222 respectively, and take the higher level as the final risk level of the current water quality parameter.

5. The real-time water situation-driven water supply scheduling method according to claim 4, characterized in that, S23 specifically includes: S231: Receive the water quality risk level output from S22, denoted as... The values ​​range from 1 to 5, corresponding to extremely low, relatively low, moderate, relatively high, and extremely high water quality risks, respectively. S232: Based on the stated risk level Match the corresponding water quality safety margin coefficient, wherein the safety margin coefficient is in Values ​​are assigned within the interval according to a fixed function, the expression of which is: ,in, This refers to the water quality safety margin coefficient. The current water quality risk level.

6. The real-time water situation-driven water supply scheduling method according to claim 5, characterized in that, The rules for setting multi-dimensional state combinations include: Rule 1: When the average pressure change rate within a zone is negative and the flow deviation rate is positive, and the pressure difference exceeds the set threshold, it is determined to be a pressure drop caused by a sudden increase in flow. Rule 2: When the average pressure change rate within a zone is positive, the flow deviation rate is negative, and the pressure at the upstream node is higher than that at the downstream node, it is determined to be backflow caused by pressure imbalance. Rule 3: When the frequency of pressure fluctuations within a zone increases abnormally beyond a preset threshold, and there is no change in flow rate, it is determined to be pipeline oscillation. Rule 4: When both the flow deviation rate and the pressure change rate are negative, it is determined that the stability has decreased due to a low water supply load.

7. The real-time water situation-driven water supply scheduling method according to claim 6, characterized in that, S34 specifically includes: S341: For each identified hydraulic zone in S33, extract its corresponding state type label, including pressure drop caused by sudden flow surge, backflow caused by pressure imbalance, network oscillation and stability decline, and set a corresponding basic imbalance score value for each state type. ; S342: Based on the basic imbalance score, a real-time pressure change magnitude factor for each zone is added. Factor of Flow Deviation ; S343: Based on the influence of each factor on the degree of imbalance, calculate the final regional pressure-flow imbalance factor, the expression of which is: ,in, The factor representing the degree of imbalance in the current hydraulic zoning is limited to a range of values ​​within a certain range. .

8. The real-time water situation-driven water supply scheduling method according to claim 7, characterized in that, S4 specifically includes: S41: Obtain the water quality safety margin coefficient calculated in S23 and the regional pressure-flow imbalance factor calculated in S34 The two indicators were normalized and verified to ensure they were both within the specified range. Inside; S42: The two parameters are weighted and fused to obtain the real-time water supply resilience index, as shown in the formula: ,in, For real-time water supply resilience index, and These represent the weighting coefficients for the water quality dimension and the hydraulic dimension, respectively.

9. A real-time water situation-driven water supply scheduling method according to claim 8, characterized in that, S5 specifically includes: S51: Obtain real-time water supply resilience index It is then compared with a preset three-segment resilience threshold range to determine the current control status; The threshold range includes: The first threshold range [0.7, 1] indicates a stable water supply, corresponding to the pressure maintenance mode; The second threshold range [0.4, 0.7) indicates a water shortage, corresponding to a tiered pressure reduction mode; The third threshold range [0, 0.4) is considered a high risk for water supply, corresponding to the emergency low-pressure mode.

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