Pump station intelligent scheduling and energy consumption optimization method and system

Abstract

The present application relates to the technical field of pump station operation control, in particular to a pump station intelligent scheduling and energy consumption optimization method and system, comprising the following steps: based on the sensitivity coefficient of unit efficiency to head and flow, self-adaptively and dynamically adjusting the division interval of working condition matrix, constructing a refined head-flow working condition matrix, then constructing a double-objective optimization model containing minimization of total water delivery power consumption and maximization of unit operation stability, introducing equipment health state and pipeline head loss calculation, and using a three-order solving process combining multi-dimensional pre-screening and hierarchical enumeration to solve the optimal unit combination and flow distribution. In the present application, the working condition redundancy is eliminated through the adaptive division strategy driven by sensitivity, the double-objective optimization of health state and stability fills the technical gap of life loss, the standardized total table generated by multi-source data fusion significantly improves the response speed and accuracy of scheduling decision, and the dynamic iteration mechanism solves the problem of poor long-term adaptability of static scheme.

Description

Technical Field

[0001] This invention relates to the field of pump station operation control technology, and in particular to a method and system for intelligent scheduling and energy consumption optimization of pump stations. Background Technology

[0002] Pump station operation control technology refers to a comprehensive technical field that utilizes automation control theory and information technology to monitor the status of pumping station equipment, allocate loads, and optimize scheduling. Multi-unit pumping stations, as core infrastructure for water resource allocation, account for a significant proportion of the total energy consumption of water conservancy projects. Their economic operation level directly determines the cost-effectiveness and response efficiency of water resource allocation.

[0003] Traditional pump station economic operation calculations primarily rely on the efficiency characteristic curves of individual units, using static table lookups or simple overlay calculations to formulate start-up and shutdown plans and flow allocation strategies. Essentially, these are theoretical derivations based on ideal operating conditions. However, existing technologies have significant shortcomings in practical applications. Traditional methods over-rely on static single-unit characteristics, ignoring the nonlinear characteristics of pipeline head loss changing dynamically with operating conditions during actual operation. They also fail to incorporate equipment health status into decision-making, leading to scheduling schemes that often deviate from the actual optimal operating point. This not only wastes energy but may also shorten equipment lifespan due to unit malfunctions or frequent adjustments. Furthermore, traditional calculation methods are inefficient in the face of complex dynamic operating conditions, failing to meet the timeliness requirements of real-time scheduling. Moreover, once static charts are generated, they struggle to adapt to the reality of equipment performance degradation over time, resulting in a gradual decrease in long-term operational adaptability. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method and system for intelligent scheduling and energy consumption optimization of pump stations.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: a method for intelligent scheduling and energy consumption optimization of pumping stations, comprising the following steps: Step S1: Adaptive partitioning of the operating condition matrix. Collect pump station unit operating data and computer group efficiency sensitivity coefficients to head and flow rate respectively. Dynamically adjust the head partitioning interval and flow rate partitioning ratio coefficient according to the magnitude of the sensitivity coefficients. Discretize the static head range and rated total flow range into several continuous non-overlapping intervals, and combine them in pairs to form a multi-dimensional operating condition matrix. Step S2: Joint optimization of multiple units under operating conditions. For each operating condition in the operating condition matrix, a dual-objective optimization model is constructed with the goals of minimizing total water transmission power consumption and maximizing unit operation stability. The unit health status correction coefficient and pipeline head loss calculation logic are introduced. Through a three-order solution process that includes multi-dimensional pre-screening, hierarchical enumeration of effective combinations, and energy consumption stability balance flow allocation, the optimal unit combination and flow allocation scheme under the operating condition are determined. Step S3: Construct a standardized economic operation master table, integrate the optimal parameters under all operating conditions, and establish a standardized economic operation master table with head range and flow target range as dual indexes; Step S4: The master table is dynamically iteratively updated. Actual operating data is collected periodically and the deviation of key parameters is calculated. When the deviation of the key parameters exceeds the preset threshold, the model is corrected, the optimal parameters of the working conditions to be optimized are recalculated, and the standardized economic operation master table is updated.

[0006] As a further aspect of the present invention, in step S1, the calculation of the sensitivity coefficient and the logic for dividing the head range specifically include: defining the sensitivity coefficient of unit efficiency to head. It can be calculated using the following formula: According to the above Numerical dynamic setting of head division interval When the When the input is in a preset high, medium, or low range, different levels of precision are matched accordingly. Numerical values, and static head range Divide into M head ranges; in, This represents the sensitivity coefficient of the generator unit's efficiency to its head. This represents the change in efficiency of the i-th generating unit within the current head range. This represents the change in head within that range. Represents dynamically determined head intervals. Represents the initial static head. This indicates the termination of static lift. This represents the total number of head ranges.

[0007] As a further aspect of the present invention, in step S1, the logic for dividing the target flow range specifically includes: defining the sensitivity coefficient of unit efficiency to flow. It can be calculated using the following formula: According to the above The numerical value and the unit regulation type dynamically adjust the flow division ratio coefficient The rated total flow range is divided into P flow target intervals, and the p-th flow target interval is... The calculation formula is: ; in, This represents the sensitivity coefficient of unit efficiency to flow rate. This represents the change in efficiency of the i-th generating unit within the current flow range. This represents the change in flow rate of the i-th generating unit within the current flow range. Represents the rated total flow rate of the pumping station. Represents the traffic allocation ratio coefficient. This represents the flow range of the p-th flow target interval. The index represents the sequence number of the traffic range.

[0008] As a further aspect of the present invention, in step S2, the construction process of the dual-objective optimization model specifically includes: establishing a primary objective function. With secondary objective function The calculation formulas are as follows: ; in, Represents the total electricity consumption for water transfer. This represents the actual operating power of the i-th unit. Represents the duration of water transfer. Represents the density of water. Represents gravitational acceleration. This represents the actual flow rate of the i-th generating unit. The average head range representing the current operating conditions. This represents the head loss in pressurized pipelines. This represents the correction factor for the unit's health status. This represents the actual operating efficiency of the i-th generating unit. This represents the average value of the unit's operational stability index. This represents the operational stability index of the i-th generating unit. This represents the number of operating units.

[0009] As a further aspect of the present invention, the unit health status correction coefficient and the aforementioned operational stability indicators The calculation logic specifically includes: calculating the unit health status correction coefficient using the following formula. : The operational stability index is calculated using the following formula. : ; in, The health status coefficient of the i-th unit ranges from 0 to 1. This represents the absolute value of the deviation between the actual flow rate and the optimal flow rate of the unit. Represents the threshold for traffic fluctuation; This represents the absolute value of the deviation between the unit's actual efficiency and its optimal efficiency. This represents the threshold for efficiency fluctuations.

[0010] As a further aspect of the present invention, the head loss in pressurized pipelines... The calculation is performed by segmenting and superimposing data, and the formula is as follows: ; in, Represents total head loss. Represents head loss along the route, This represents local head loss. Represents the friction coefficient along the pipeline. Represents the total length of the pipeline. Represents the pipe diameter. Represents the velocity of water flow inside the pipe. This represents the sum of the local resistance coefficients of the pipeline.

[0011] As a further solution of the present invention, in step S2, the specific execution logic of the third-order solution process is as follows: perform multi-dimensional pre-screening, eliminate invalid combinations based on flow adaptation, efficiency threshold, equipment compatibility, head adaptation, health status and redundancy combination judgment rules, and generate the remaining combinations after sorting based on the total health coefficient of the combination and the historical adaptation score. Perform hierarchical enumeration, sequentially enumerating the number of operating units and combinations of unit numbers, substituting constraints for full quantitative verification, and locking the valid set of unit combinations; Perform balanced flow allocation, construct a four-dimensional correlation model to fit the functional relationship, determine the total flow target, allocate the initial flow according to efficiency and health dual weights, and fine-tune and optimize the flow of each unit through dual objectives until the total power consumption change rate converges and all units meet the stability requirements.

[0012] As a further aspect of the present invention, in step S3, the core fields included in the standardized economic operation summary table are: operating condition number, head range, flow target range, head loss value, optimal unit combination list, optimal flow allocation value for each unit, power consumption per unit flow, total power consumption and scheduling suggestions. In step S4, the trigger condition for model correction is: when the total power consumption deviation calculated from the actually collected data... Or stability index deviation When the value is greater than or equal to the preset 5% threshold, it is marked as a condition that needs optimization and parameter calibration is initiated.

[0013] As a further aspect of the present invention, the constraints include at least: total output of the pumping station, start-up and shutdown status of the unit, water flow demand, unit flow, unit head, pipeline flow, unit efficiency, unit health status, and operational stability.

[0014] A pump station operation control system is provided, the system being used to implement the above-mentioned pump station intelligent scheduling and energy consumption optimization method, the system comprising: an adaptive partitioning module for operating condition matrix, used to collect pump station unit operating data, calculate sensitivity coefficients and dynamically adjust the partitioning parameters of head and flow rate, and construct a multi-dimensional operating condition matrix; The multi-unit joint optimization module is used to construct a dual-objective optimization model, execute a third-order solution process, and calculate the optimal unit combination and flow allocation scheme. Run the master table construction module to integrate optimization results and generate a standardized economic operation master table; A dynamic iterative update module is used to monitor actual operational deviations and trigger model correction and updates to the standardized economic operation master table when the deviation exceeds the limit, thus optimizing the scheduling strategy in a closed loop. Compared with the prior art, the advantages and positive effects of this invention are: This invention quantifies the dependence of efficiency on head and flow rate by introducing a sensitivity-driven adaptive operating condition partitioning mechanism, achieving refinement and redundancy removal of the operating condition matrix. This significantly improves the computational efficiency and targeting of the optimization model, constructs a dual-objective optimization model that integrates energy consumption minimization and stability maximization, and innovatively incorporates equipment health status and pipeline head loss into the calculation logic. This effectively solves the problem of traditional schemes neglecting equipment lifespan and pipeline dynamic characteristics, extending the service life of the unit while saving energy and reducing consumption. A standardized master table for multi-source data fusion and a dynamic iterative update mechanism are established, which not only significantly shortens the response time of scheduling decisions but also automatically calibrates model parameters based on actual operating deviations, ensuring high accuracy and strong adaptability of the scheduling scheme throughout the pump station's entire life cycle. Attached Figure Description

[0015] Figure 1 This is a flowchart of the intelligent scheduling and energy consumption optimization method for pump stations according to the present invention; Figure 2 This is a flowchart of the adaptive partitioning process of the working condition matrix in this invention; Figure 3 This is a flowchart of the multi-component joint optimization process for operating conditions of the present invention; Figure 4 This is a flowchart illustrating the construction of the standardized economic operation master table for this invention. Figure 5 This is a flowchart illustrating the dynamic iterative update process of the master table in this invention. Detailed Implementation

[0016] To make the objectives, technical solutions, and advantages of this invention clearer, the software-based technical solution is described in detail below with reference to system architecture diagrams and embodiments. It should be understood that the specific embodiments described herein are only for explaining the technical solutions of this invention and do not constitute a limitation on the scope of protection.

[0017] In the description of this invention, the system architecture relationships or data processing flows indicated by terms such as "layer," "module," "interface," "data flow," "client," and "server" are all defined based on the architecture diagram or flowchart corresponding to the embodiments. This way of describing is only used to clearly illustrate the logical relationships between the elements in the technical solution, and not to limit the physical deployment form. The term "multiple" includes two or more technical units, including but not limited to multiple data nodes, processing threads, service instances, or functional components and other scalable elements. The specific number is determined according to the actual business scenario and needs to be specifically specified.

[0018] Please see Figure 1 and Figure 2 This invention provides a technical solution: a method for intelligent scheduling and energy consumption optimization of pump stations, comprising the following steps: S1: adaptive division of operating condition matrix, collecting pump station unit operating data, calculating the sensitivity coefficients of unit efficiency to head and flow rate respectively, dynamically adjusting the head division interval and flow rate division ratio coefficient according to the magnitude of the sensitivity coefficients, discretizing the static head range and rated total flow range into several continuous non-overlapping intervals, and combining them in pairs to form a multi-dimensional operating condition matrix.

[0019] The calculation of the sensitivity coefficient and the logic for dividing the head range specifically include: defining the sensitivity coefficient of unit efficiency to head. It can be calculated using the following formula: ; according to Numerical dynamic setting of head division interval ,when When the input is in a preset high, medium, or low range, different levels of precision are matched accordingly. Numerical values, and static head range It is divided into M head ranges.

[0020] in, This represents the sensitivity coefficient of the generator unit's efficiency to its head. This represents the change in efficiency of the i-th generating unit within the current head range. This represents the change in head within that range. Represents dynamically determined head intervals. Represents the initial static head. This indicates the termination of static lift. This represents the total number of head ranges.

[0021] The logic for dividing the target flow range specifically includes: defining the sensitivity coefficient of unit efficiency to flow. It can be calculated using the following formula: ; according to The numerical value and the unit regulation type dynamically adjust the flow division ratio coefficient The rated total flow range is divided into P flow target intervals, and the p-th flow target interval is... The calculation formula is: .

[0022] in, This represents the sensitivity coefficient of unit efficiency to flow rate. This represents the change in efficiency of the i-th generating unit within the current flow range. This represents the change in flow rate of the i-th generating unit within the current flow range. Represents the rated total flow rate of the pumping station. Represents the traffic allocation ratio coefficient. This represents the flow range of the p-th flow target interval. The index represents the sequence number of the traffic range.

[0023] The industrial control computer, using the Modbus TCP communication protocol and a sampling period of 100 milliseconds, adaptively partitions the operating condition matrix to collect real-time operating parameters of four centrifugal water pump units within the pumping station. This data is obtained through pressure transmitters and ultrasonic flow meters deployed at the pumping station's inlet and outlet. The data includes the real-time speed, inlet and outlet pressures, instantaneous flow rate, and motor input power for each unit. For the collected raw data, a moving average filtering algorithm is first used to remove random noise caused by water flow turbulence. A moving average window of 50 sampling points is set, and the arithmetic mean of the values ​​within the continuous sampling window is calculated to obtain a smooth steady-state operating data sequence. Subsequently, the industrial control computer retrieves the unit characteristic curve data stored in the historical database and, combined with the currently collected real-time operating conditions, begins to calculate the sensitivity coefficient.

[0024] For the calculation of the head sensitivity coefficient, the current static head of 35 meters is selected as the benchmark point, and the unit efficiency values ​​at heads of 34.5 meters and 35.5 meters are extracted respectively. When the head change is 1 meter, the unit efficiency change is calculated to be 0.04. Substituting this value into the head sensitivity coefficient calculation formula, the head sensitivity coefficient is calculated to be 0.04. The system has built-in dynamic mapping rules for head division intervals: when the head sensitivity coefficient is greater than or equal to 0.05, it is determined to be a high-sensitivity zone, and the head division interval is set to 0.2 meters; when the head sensitivity coefficient is between 0.02 and 0.05, it is determined to be a medium-sensitivity zone, and the head division interval is set to 0.5 meters; when the head sensitivity coefficient is less than 0.02, it is determined to be a low-sensitivity zone, and the head division interval is set to 1.0 meter. In this embodiment, since the calculated head sensitivity coefficient is 0.04, which falls into the medium sensitivity zone, the static head range of 30 meters to 40 meters is discretized at intervals of 0.5 meters, dividing the static head range into 20 continuous and non-overlapping head intervals.

[0025] The calculation of the flow sensitivity coefficient is based on a rated total flow rate of 10,000 cubic meters per hour for the pumping station. Under the current flow conditions, a flow change of 500 cubic meters per hour is detected, corresponding to an efficiency change of 0.025. Substituting these parameters into the flow sensitivity coefficient formula, the relative flow change rate in the denominator is first calculated, i.e., 500 divided by 10,000, resulting in 0.05. Then, the efficiency change of 0.025 is divided by 0.05, yielding a flow sensitivity coefficient of 0.5. The system dynamically matches the flow division ratio coefficient based on the flow sensitivity coefficient value. The rules are set as follows: if the flow sensitivity coefficient is greater than or equal to 0.8, the flow division ratio coefficient is 0.05; if the flow sensitivity coefficient is between 0.4 and 0.8, the flow division ratio coefficient is 0.1; if the flow sensitivity coefficient is less than 0.4, the flow division ratio coefficient is 0.2. In this embodiment, the flow sensitivity coefficient is 0.5, therefore, the flow division ratio coefficient is determined to be 0.1. Based on this flow rate division ratio, the rated total flow rate range of 0 to 10,000 cubic meters per hour is divided into 10 flow rate target intervals. Taking the third flow rate interval as an example, the calculated lower limit of the flow rate for this third flow rate interval is 2,000 cubic meters per hour, and the upper limit is 3,000 cubic meters per hour.

[0026] By combining the generated 20 head ranges with 10 flow target ranges, a multi-dimensional operating condition matrix containing 200 independent operating condition units is constructed. Each operating condition unit is associated with the corresponding upper and lower limits of head and flow.

[0027] The aforementioned moving average filtering algorithm is a linear smoothing tool that uses the arithmetic mean of a continuously collected set of input data and takes the average value as the output at the current moment to eliminate high-frequency fluctuations in the signal.

[0028] The aforementioned unit characteristic curves refer to mathematical relationship curves describing the relationship between the pump's head, power, and efficiency and the flow rate. They serve as the physical basis for determining the optimal operating point of the unit.

[0029] Please see Figure 1 and Figure 3 S2: Joint optimization of multiple unit groups under operating conditions. For each operating condition in the operating condition matrix, a dual-objective optimization model is constructed with the goals of minimizing total water transmission power consumption and maximizing unit operation stability. The unit health status correction coefficient and pipeline head loss calculation logic are introduced. Through a three-order solution process that includes multi-dimensional pre-screening, hierarchical enumeration of effective combinations and energy consumption stability balance flow allocation, the optimal unit combination and flow allocation scheme under the operating condition are determined.

[0030] The construction process of a dual-objective optimization model specifically includes: establishing the primary objective function. With secondary objective function The calculation formulas are as follows: , , .

[0031] in, Represents the total electricity consumption for water transfer. This represents the actual operating power of the i-th unit. Represents the duration of water transfer. Represents the density of water. Represents gravitational acceleration. This represents the actual flow rate of the i-th generating unit. The average head range representing the current operating conditions. This represents the head loss in pressurized pipelines. This represents the correction factor for the unit's health status. This represents the actual operating efficiency of the i-th generating unit. This represents the average value of the unit's operational stability index. This represents the operational stability index of the i-th generating unit. This represents the number of operating units.

[0032] Unit health status correction factor and operational stability indicators The calculation logic specifically includes: using the following formula, the computer group health status correction coefficient. : ; The operational stability index is calculated using the following formula. : .

[0033] in, The health status coefficient of the i-th unit ranges from 0 to 1. This represents the absolute value of the deviation between the actual flow rate and the optimal flow rate of the unit. Represents the threshold for traffic fluctuation; This represents the absolute value of the deviation between the unit's actual efficiency and its optimal efficiency. This represents the threshold for efficiency fluctuations.

[0034] Head loss in pressurized pipelines The calculation is performed by segmenting and superimposing data, and the formula is as follows: .

[0035] in, Represents total head loss. Represents head loss along the route, This represents local head loss. Represents the friction coefficient along the pipeline. Represents the total length of the pipeline. Represents the pipe diameter. Represents the velocity of water flow inside the pipe. This represents the sum of the local resistance coefficients of the pipeline.

[0036] The specific execution logic of the third-order solution process is as follows: perform multi-dimensional pre-screening, eliminate invalid combinations based on flow adaptation, efficiency threshold, equipment compatibility, head adaptation, health status and redundancy combination judgment rules, and generate the remaining combinations after sorting based on the total health coefficient of the combination and the historical adaptation score. Perform hierarchical enumeration, sequentially enumerating the number of operating units and combinations of unit numbers, substituting constraints for full quantitative verification, and locking the valid set of unit combinations; Perform balanced flow allocation, construct a four-dimensional correlation model to fit the functional relationship, determine the total flow target, allocate the initial flow according to efficiency and health dual weights, and fine-tune and optimize the flow of each unit through dual objectives until the total power consumption change rate converges and all units meet the stability requirements.

[0037] The constraints must include at least the following: total output of the pumping station, start-up and shutdown status of the unit, water flow demand, unit flow, unit head, pipeline flow, unit efficiency, unit health status, and operational stability.

[0038] Multi-unit joint optimization for operating conditions targets a specific operating condition, index C-12, in the multi-dimensional operating condition matrix. This condition corresponds to a head range of 36 to 36.5 meters and a target flow range of 4000 to 5000 cubic meters per hour. A multi-dimensional pre-screening process is executed. Real-time status data of the four units is read, and units 1 through 4 are checked for fault alarms indicating bearing temperatures exceeding 85°C or vibration amplitudes exceeding 0.05 mm. If any fault alarm is found, the unit is excluded. The rated head of each unit is verified to be greater than the required 36.5 meters for the current operating condition. If the rated head does not meet the requirement, the unit is excluded. After screening, units 1, 2, and 3 are identified as usable units.

[0039] A hierarchical enumeration process is performed, traversing all available unit operation combinations to generate seven logical combinations, including single-unit operation, dual-unit parallel operation, and three-unit parallel operation. For each logical combination, a flow adaptation check is performed. For example, if the combined rated total flow of Unit 1 and Unit 2 is 5500 cubic meters per hour, which covers the target flow range of 4000 to 5000 cubic meters per hour, then the combination of Unit 1 and Unit 2 is marked as a valid unit combination.

[0040] The balanced flow distribution process was implemented, with a total flow target of 4500 cubic meters per hour set for the combination of Units 1 and 2. First, the head loss in the pressurized pipeline was calculated, taking the total pipeline length as 1000 meters, the pipe diameter as 0.8 meters, the friction factor as 0.02, and the sum of local resistance factors as 1.5. Based on the flow rate of 4500 cubic meters per hour, the flow velocity in the pipe was calculated to be 2.49 m / s, and the gravitational acceleration was taken as 9.81 m / s². Substituting these parameters into the friction head loss formula, the result was 7.9 meters. Substituting these parameters into the local head loss formula, the result was 0.47 meters. Adding the friction head loss and the local head loss, the total head loss was 8.37 meters. The total head required to overcome by the units was the average static head of 36.25 meters plus the total head loss of 8.37 meters, totaling 44.62 meters.

[0041] The health status coefficients for Unit 1 and Unit 2 were determined to be 0.9 and 0.7, respectively. Substituting these coefficients into the health status correction coefficient formula, the correction coefficients for Unit 1 and Unit 2 were calculated to be 0.98 and 0.94, respectively. Initial flow rates were allocated based on the weighted product of efficiency and the health correction coefficients: 2300 cubic meters per hour for Unit 1 and 2200 cubic meters per hour for Unit 2. Through dual-objective fine-tuning optimization, the flow rates for each unit were adjusted in steps of 10 cubic meters per hour until the total power consumption change rate was below 0.1%.

[0042] To calculate the operational stability index, taking Unit 1 as an example, the monitored absolute value of the deviation between the actual flow rate and the optimal flow rate was 20 cubic meters per hour, and the flow fluctuation threshold was set at 115 cubic meters per hour. The monitored absolute value of the deviation between the actual efficiency and the optimal efficiency was 0.01, and the efficiency fluctuation threshold was 0.034. Substituting these values ​​into the stability calculation logic, the flow rate calculation result was 0.174, and the efficiency calculation result was 0.294. Taking the maximum value of the flow rate and efficiency calculation results (0.294), and subtracting this maximum value from 1, we obtain the stability index of Unit 1 as 0.706.

[0043] Table 1. Pipeline Hydraulic Parameters and Loss Calculation Table The aforementioned head loss in pressurized pipelines refers to the energy consumed by the frictional resistance caused by fluid viscosity and the local resistance generated by pipeline accessories when water flows in a closed pipeline.

[0044] The aforementioned multi-dimensional pre-screening refers to the preliminary filtering of all possible unit operation arrangements based on equipment operating status, physical constraints, and performance thresholds, in order to narrow down the search space for subsequent calculations.

[0045] Please see Figure 4 S3: Construction of a standardized economic operation master table, integrating the optimal parameters under all operating conditions, and establishing a standardized economic operation master table with head range and flow target range as dual indexes.

[0046] The core fields included in the standardized economic operation summary table are: operating condition number, head range, flow target range, head loss value, optimal unit combination list, optimal flow allocation value for each unit, power consumption per unit flow, total power consumption, and dispatching recommendations.

[0047] The standardized economic operation master table is constructed using a database management system, with head range and flow target range as dual indexes. The program iterates through optimization schemes for all operating conditions and writes the optimized unit combinations, flow allocations, and energy efficiency indicators into the storage unit.

[0048] For operating condition C-12, record the head range as 36 meters to 36.5 meters and the target flow range as 4000 to 5000 cubic meters per hour in the standardized economic operation summary table. Enter the calculated head loss value of 8.37 meters into the corresponding field. Store Unit 1 and Unit 2 in the optimal unit combination field. Record Unit 1 as 2300 cubic meters per hour and Unit 2 as 2200 cubic meters per hour in the optimal flow allocation value field for each unit.

[0049] To calculate the power consumption per unit flow, divide the total operating power of 450 kW by the total flow rate of 4500 cubic meters per hour, resulting in a power consumption of 0.1 kWh / cubic meter. Generate specific frequency control instructions in the dispatch suggestion field, such as setting the frequency of Unit 1 to 48.5 Hz.

[0050] Table 2. Examples of partial data from the Standardized Economic Operation Summary Table The aforementioned standardized economic operation summary table refers to a database structure that stores the set of optimal operating parameters calculated through optimization for all typical operating conditions of a pumping station.

[0051] The power consumption per unit flow rate mentioned above refers to the electrical energy consumed in transporting a unit volume of water resources, and is a core indicator for evaluating the economic efficiency of pump station operation.

[0052] Please see Figure 5 S4: The master table is dynamically updated iteratively. Actual operating data is collected periodically and the deviation of key parameters is calculated. When the deviation of key parameters exceeds the preset threshold, the model is corrected, the optimal parameters of the working conditions to be optimized are recalculated, and the standardized economic operation master table is updated.

[0053] The trigger condition for model correction is: when the total power consumption calculated from the actual collected data deviates from the target value. Or stability index deviation When the value is greater than or equal to the preset 5% threshold, it is marked as a condition that needs optimization and parameter calibration is initiated.

[0054] The master table dynamic iterative update system monitors data every 1 minute, retrieving real-time operational data from the SCADA system. It calculates the deviation between the current actual total power consumption and the theoretical total power consumption in the standardized economic operation master table.

[0055] Set the model correction trigger condition. When the total power consumption calculated from the actual collected data is 485 kW, while the theoretical value in the standardized economic operation summary table is 450 kW, calculate the total power consumption deviation. Compare the calculated total power consumption deviation value of 7.78% with the preset threshold of 5%. Since 7.78% is greater than 5%, the system determines that the current operating condition model is invalid and marks the operating condition as a state that needs optimization.

[0056] The system initiates a parameter calibration procedure to re-measure the actual resistance coefficient of the pipeline. If it is found that the friction coefficient has increased from 0.02 to 0.022 due to pipeline siltation, the corrected physical parameters are substituted into the bi-objective optimization model for recalculation. After calculating the new optimal allocation scheme, the corresponding entries in the standardized economic operation table are updated to achieve closed-loop optimization.

[0057] The aforementioned SCADA system refers to a data acquisition and monitoring control system, used for remote monitoring, data recording, and control command issuance of on-site electromechanical equipment.

[0058] The aforementioned model correction trigger condition refers to a pre-set logical threshold used to determine whether the actual operating energy efficiency deviates significantly from the theoretical design value, thereby deciding whether to restart the optimization calculation.

[0059] A pump station intelligent scheduling and energy consumption optimization system is provided. The system is used to execute the above-mentioned pump station intelligent scheduling and energy consumption optimization method. The system includes: an adaptive partitioning module for operating condition matrix, which is used to collect pump station unit operating data, calculate sensitivity coefficients and dynamically adjust the partitioning parameters of head and flow rate to construct a multi-dimensional operating condition matrix. The multi-unit joint optimization module is used to construct a dual-objective optimization model, execute a third-order solution process, and calculate the optimal unit combination and flow allocation scheme. Run the master table construction module to integrate optimization results and generate a standardized economic operation master table; The dynamic iterative update module is used to monitor actual operational deviations and trigger model correction and standard economic operation summary table updates when deviations exceed limits, thus optimizing the scheduling strategy in a closed loop.

[0060] The adaptive partitioning module for the operating condition matrix is ​​integrated into the data processing server, acquiring the operating sequence of the pump station units via a communication interface. The data processing unit standardizes the input voltage, current, and flow data, removing outliers. The calculation unit dynamically determines the discrete step size based on the slope of efficiency variation with head. In regions with drastic efficiency changes, the calculation unit automatically reduces the partitioning interval. Finally, the logic generation unit combines the discretized head axis and flow axis to construct a multi-dimensional operating condition matrix, storing the matrix structure in a memory-mapped file.

[0061] The aforementioned adaptive partitioning module for the operating condition matrix refers to the functional component within the system responsible for transforming the continuous operating parameter space into discrete, optimizable grid cells.

[0062] The aforementioned distance range refers to the numerical difference between two adjacent sampling points or interval boundaries when digitizing a continuous physical quantity.

[0063] The multi-unit joint optimization module is responsible for performing specific numerical optimization calculations. This module includes a multi-dimensional pre-screening unit that eliminates invalid unit combinations based on health status and physical constraints. A hierarchical enumeration unit generates all feasible operating topologies. The flow allocation unit performs iterative calculations based on a hydraulic model, finding the operating point that minimizes power consumption by calculating head loss and unit correction efficiency.

[0064] The multi-unit joint optimization module uses the following formula to correct the overall system efficiency of the computer group: ;in, This represents the correction value for the overall system efficiency. This represents the flow rate of the i-th unit. This represents the actual head of the i-th generating unit. Represents the input power of the i-th unit. Represents the density of the liquid. Represents gravitational acceleration. This represents the health status correction factor for the i-th unit.

[0065] The aforementioned multi-unit joint optimization module refers to a functional component responsible for coordinating the joint operation of multiple water pumps and calculating the load distribution of each unit to achieve optimal total energy consumption.

[0066] The above-mentioned liquid density refers to the mass of a unit volume of fluid. In this embodiment, the density of water at room temperature is taken as 1000 kg / m³.

[0067] The summary table construction module persists the optimization results using Structured Query Language (SMQ). The data transformation unit converts optimized floating-point parameters such as flow rate, frequency, and power into a database-supported storage format. The index management unit establishes hash indexes based on operating condition characteristics, ensuring extremely high response speeds for the scheduling system during queries. The summary table construction module also includes a report generation unit that periodically outputs pump station energy efficiency analysis reports.

[0068] The aforementioned master table construction module refers to the functional component responsible for organizing, classifying, and storing the calculated static optimization schemes in the database.

[0069] The hash index mentioned above refers to a key-value mapping-based data structure used to accelerate the location and retrieval of specific working condition records in the database.

[0070] The dynamic iterative update module features real-time deviation detection, continuously comparing on-site feedback data with preset optimization values. The logic judgment unit monitors deviations in total power consumption and stability indicators. When any indicator exceeds a set range of 5%, the trigger unit sends a recalculation command to the multi-unit joint optimization module. The dynamic iterative update module ensures that the scheduling strategy can self-correct in response to long-term physical changes such as pump wear and pipeline aging.

[0071] The aforementioned dynamic iterative update module refers to the functional component responsible for monitoring the actual operation of the system and triggering model recalculation and strategy updates based on performance degradation.

[0072] The aforementioned stability index deviation refers to the difference between the degree of fluctuation in unit flow or efficiency during actual operation and the theoretical stable state.

[0073] Based on the execution of the above steps and modules, experimental data shows that when the pipeline resistance coefficient deviation is set to 5%, the dynamic correction of this invention improves the overall energy efficiency of the pumping station by 12.5% ​​compared to the traditional fixed-frequency scheduling mode, and reduces the power consumption per unit flow from 0.115 kWh / m³ to 0.100 kWh / m³. In a simulation experiment where the unit health status coefficient decreased from 0.9 to 0.7, the system automatically transferred the load to units with higher health status, keeping the overall operational stability index of the pumping station above 0.82, an improvement of 18% compared to the uncorrected mode.

[0074] The above embodiments illustrate preferred embodiments of the present invention. Any equivalent adjustments to the technical solution based on software engineering methods are within the scope of protection, including but not limited to: implementing algorithm logic using different programming languages, refactoring functional modules into services, adjusting data interaction protocols, and optimizing resource scheduling strategies. Any implementation scheme derived from reasonable modifications to the data processing flow, service call chain, or system architecture layer without departing from the core technology of the present invention should be considered within the protection scope defined by the technical solution of the present invention.

Claims

1. A method for intelligent scheduling and energy consumption optimization of pumping stations, characterized in that, Includes the following steps: Step S1: Adaptive partitioning of the operating condition matrix. Collect pump station unit operating data and computer group efficiency sensitivity coefficients to head and flow rate respectively. Dynamically adjust the head partitioning interval and flow rate partitioning ratio coefficient according to the magnitude of the sensitivity coefficients. Discretize the static head range and rated total flow range into several continuous non-overlapping intervals, and combine them in pairs to form a multi-dimensional operating condition matrix. Step S2: Joint optimization of multiple units under operating conditions. For each operating condition in the operating condition matrix, a dual-objective optimization model is constructed with the goals of minimizing total water transmission power consumption and maximizing unit operation stability. The unit health status correction coefficient and pipeline head loss calculation logic are introduced. Through a three-order solution process that includes multi-dimensional pre-screening, hierarchical enumeration of effective combinations, and energy consumption stability balance flow allocation, the optimal unit combination and flow allocation scheme under the operating condition are determined. Step S3: Construct a standardized economic operation master table, integrate the optimal parameters under all operating conditions, and establish a standardized economic operation master table with head range and flow target range as dual indexes; Step S4: The master table is dynamically iteratively updated. Actual operating data is collected periodically and the deviation of key parameters is calculated. When the deviation of the key parameters exceeds the preset threshold, the model is corrected, the optimal parameters of the working conditions to be optimized are recalculated, and the standardized economic operation master table is updated.

2. The method for intelligent scheduling and energy consumption optimization of pump stations according to claim 1, characterized in that, In step S1, the calculation of the sensitivity coefficient and the logic for dividing the head range specifically include: Define the sensitivity coefficient of unit efficiency to head. It can be calculated using the following formula: According to the above Numerical dynamic setting of head division interval When the When the input is in a preset high, medium, or low range, different levels of precision are matched accordingly. Numerical values, and static head range Divide into M head ranges; in, This represents the sensitivity coefficient of the generator unit's efficiency to its head. This represents the change in efficiency of the i-th generating unit within the current head range. This represents the change in head within that range. Represents dynamically determined head intervals. Represents the initial static head. This indicates the termination of static lift. This represents the total number of head ranges.

3. The method for intelligent scheduling and energy consumption optimization of pumping stations according to claim 1, characterized in that, In step S1, the logic for dividing the target traffic interval specifically includes: Define the sensitivity coefficient of unit efficiency to flow rate. It can be calculated using the following formula: According to the above The numerical value and the unit regulation type dynamically adjust the flow division ratio coefficient The rated total flow range is divided into P flow target intervals, and the p-th flow target interval is... The calculation formula is: ； in, This represents the sensitivity coefficient of unit efficiency to flow rate. This represents the change in efficiency of the i-th generating unit within the current flow range. This represents the change in flow rate of the i-th generating unit within the current flow range. Represents the rated total flow rate of the pumping station. Represents the traffic allocation ratio coefficient. This represents the flow range of the p-th flow target interval. The index represents the sequence number of the traffic range.

4. The method for intelligent scheduling and energy consumption optimization of pumping stations according to claim 1, characterized in that, In step S2, the construction process of the dual-objective optimization model specifically includes: Establish the primary objective function With secondary objective function The calculation formulas are as follows: ； in, Represents the total electricity consumption for water transfer. This represents the actual operating power of the i-th unit. Represents the duration of water transfer. Represents the density of water. Represents gravitational acceleration. This represents the actual flow rate of the i-th generating unit. The average head range representing the current operating conditions. This represents the head loss in pressurized pipelines. This represents the correction factor for the unit's health status. This represents the actual operating efficiency of the i-th generating unit. This represents the average value of the unit's operational stability index. This represents the operational stability index of the i-th generating unit. This represents the number of operating units.

5. The intelligent scheduling and energy consumption optimization method for pump stations according to claim 4, characterized in that, The unit health status correction coefficient and the aforementioned operational stability indicators The computational logic specifically includes: The unit health status correction factor is calculated using the following formula. : The operational stability index is calculated using the following formula. : ； in, The health status coefficient of the i-th unit ranges from 0 to 1. This represents the absolute value of the deviation between the unit's actual flow rate and the optimal flow rate. Represents the threshold for traffic fluctuation; This represents the absolute value of the deviation between the unit's actual efficiency and its optimal efficiency. This represents the threshold for efficiency fluctuations.

6. The intelligent scheduling and energy consumption optimization method for pumping stations according to claim 4, characterized in that, The head loss in the pressurized pipeline The calculation is performed by segmenting and superimposing data, and the formula is as follows: ； in, Represents the total head loss. Represents head loss along the route, This represents local head loss. Represents the friction coefficient along the pipeline. Represents the total length of the pipeline. Represents the pipe diameter. Represents the velocity of water flow inside the pipe. This represents the sum of the local resistance coefficients of the pipeline.

7. The method for intelligent scheduling and energy consumption optimization of pumping stations according to claim 1, characterized in that, In step S2, the specific execution logic of the third-order solution process is as follows: Perform multi-dimensional pre-screening, eliminate invalid combinations based on traffic adaptation, efficiency threshold, equipment compatibility, head adaptation, health status and redundancy combination judgment rules, and generate the remaining combinations after sorting based on the total health coefficient of the combination and the historical adaptation score. Perform hierarchical enumeration, sequentially enumerating the number of operating units and combinations of unit numbers, substituting constraints for full quantitative verification, and locking the valid set of unit combinations; Perform balanced flow allocation, construct a four-dimensional correlation model to fit the functional relationship, determine the total flow target, allocate the initial flow according to efficiency and health dual weights, and fine-tune and optimize the flow of each unit through dual objectives until the total power consumption change rate converges and all units meet the stability requirements.

8. The method for intelligent scheduling and energy consumption optimization of pumping stations according to claim 1, characterized in that, In step S3, the core fields included in the standardized economic operation master table are: Operating condition number, head range, target flow range, head loss value, optimal unit combination list, optimal flow allocation value for each unit, power consumption per unit flow, total power consumption and dispatching recommendations; In step S4, the triggering condition for model correction is: When the total power consumption calculated from the actual collected data deviates Or stability index deviation When the value is greater than or equal to the preset 5% threshold, it is marked as a condition that needs optimization and parameter calibration is initiated.

9. The method for intelligent scheduling and energy consumption optimization of pumping stations according to claim 7, characterized in that, The constraints include at least the following: Constraints include total pump station output, unit start-up and shutdown status, water flow demand, unit flow, unit head, pipeline flow, unit efficiency, unit health status, and operational stability.

10. A pump station intelligent scheduling and energy consumption optimization system, characterized in that, The system is used to implement the intelligent scheduling and energy consumption optimization method for pumping stations according to any one of claims 1-9, and the system includes: The adaptive partitioning module for the operating condition matrix is ​​used to collect operating data of pump station units, calculate sensitivity coefficients, dynamically adjust the partitioning parameters of head and flow rate, and construct a multi-dimensional operating condition matrix. The multi-unit joint optimization module is used to construct a dual-objective optimization model, execute a third-order solution process, and calculate the optimal unit combination and flow allocation scheme. Run the master table construction module to integrate optimization results and generate a standardized economic operation master table; The dynamic iterative update module is used to monitor actual operational deviations and trigger model correction and updates to the standardized economic operation master table when the deviation exceeds the limit, thereby optimizing the scheduling strategy in a closed loop.

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