Pump station group collaborative energy-saving operation optimization system based on accurate flow regulation and control

Abstract

The invention relates to a pump station group collaborative energy-saving operation optimization system based on accurate flow regulation and control. The system comprises a standardization module used for obtaining multi-source real-time data; performing standardization processing on the multi-source real-time data to obtain a standard data set; the operation module is used for recognizing the operation working condition of the water pump based on the standard data set according to a preset submerging rule to obtain a working condition state identification list; the flow module is used for estimating the flow of each water pump based on a working condition state identification list to obtain an actual flow list; the solving module is used for constructing an optimization proposition by taking the lowest total energy consumption as a core target based on the actual flow list and the drainage demand target; solving the optimization proposition to obtain a solution set; and the instruction module is used for mapping the solution set into an operation parameter set value of each water pump to obtain a recommendation instruction. By means of the system, working condition changes are recognized in real time, the group strategy is adjusted, and the water pump station group can be in smooth transition between the normal working condition and the submerged working condition.

Description

Technical Field

[0001] This invention belongs to the field of intelligent control, and in particular relates to a collaborative energy-saving operation optimization system for pump station groups based on precise flow control. Background Technology

[0002] With the development of smart water management, data-driven collaborative control technology for pump station groups has emerged. This technology enables centralized monitoring and linkage of distributed pump stations, featuring refined perception, intelligent decision-making, and systematic collaboration. This has spurred a collaborative operation mode for pump station groups centered on global optimization. Traditional pump operation control technology typically employs a fixed threshold-based approach, adjusting the start-up, shutdown, and frequency of individual pumps based on outlet pressure or water level setpoints. There is a lack of effective information exchange and collaboration mechanisms between pump stations, relying primarily on manual experience for macro-level scheduling. Current traditional control methods present significant problems in extreme conditions such as torrential rains and floods. Control logic relying on single flowmeter data completely fails when the outlet is flooded, leading to measurement inaccuracies. It cannot identify changes in operating conditions in real time and adjust strategies accordingly, easily causing system instability, drastic fluctuations in pipeline pressure, and even the risk of localized flooding. Furthermore, it lacks a mechanism for a smooth transition between normal and flooded conditions, resulting in uneven flow distribution during condition switching and low overall system energy efficiency, failing to meet the higher requirements of modern urban drainage systems for safety, stability, and energy conservation. Summary of the Invention

[0003] Therefore, it is necessary to provide a collaborative energy-saving operation optimization system for pump station groups based on precise flow control, which can identify changes in operating conditions in real time and adjust the group strategy to smoothly transition between normal and flooded operating conditions, in order to address the above-mentioned technical problems.

[0004] Firstly, this application provides a collaborative energy-saving operation optimization system for pump station groups based on precise flow control, comprising:

[0005] The standardization module is used to acquire multi-source real-time data and perform standardization processing on the multi-source real-time data to obtain a standard dataset. The multi-source real-time data includes pump outlet pressure, pump inlet pressure, outlet submersion depth, and pump speed.

[0006] The operation module is used to identify the operating conditions of each water pump based on a standard dataset for each pump and according to preset flooding rules, and to obtain a list of operating condition status identifiers.

[0007] The flow module is used to estimate the flow rate of each water pump based on the list of operating conditions, and obtain the actual flow rate list;

[0008] The solution module is used to construct optimization problems based on the actual flow inventory and drainage demand targets, with the core objective of minimizing total energy consumption; and to solve the optimization problems to obtain the solution set.

[0009] The instruction module is used to map the solution set to the operating parameter settings of each water pump to obtain recommended instructions; the recommended instructions are used to instruct each water pump to operate according to the operating parameter settings.

[0010] Furthermore, the running module is also used for:

[0011] Based on the pump outlet pressure and pump inlet pressure in the standard dataset, the actual pump head is calculated using the following formula:

[0012]

[0013] in, This represents the actual head of the water pump. For pump outlet pressure, The pump inlet pressure, The density of water, It is the acceleration due to gravity. The difference in elevation between the pump inlet and outlet;

[0014] Based on the pump speed and pump performance curve, calculate the theoretical pump head at the current pump speed.

[0015] Based on the actual head value, theoretical head value, and outlet submersion depth of the water pump, the operating status of the water pump is determined according to preset rules, and a preliminary abnormal operating condition indicator is obtained; the preliminary abnormal operating condition indicator is used to indicate whether the water pump is in a submerged state.

[0016] Based on the preliminary identification of abnormal operating conditions and the submersion depth of the outlet, the operating conditions of each water pump are classified to obtain a list of operating condition status indicators.

[0017] Furthermore, the running module is also used for:

[0018] The water pumps that were initially marked as not being in abnormal operating conditions are now marked as being in normal operating conditions, thus obtaining the water pumps in normal operating conditions.

[0019] Pumps that are initially identified as having abnormal operating conditions are classified as having abnormal operating conditions, and thus, pumps with abnormal operating conditions are obtained.

[0020] If the outlet submersion depth of the abnormal working condition water pump is less than the preset height threshold, the abnormal working condition water pump is marked as a transitional working condition water pump.

[0021] If the outlet submersion depth of the pump under abnormal operating conditions is not less than the height threshold, the pump under abnormal operating conditions is marked as fully submerged, and the submerged operating condition pump is obtained.

[0022] Integrate pumps in normal operating condition and / or pumps in transitional operating condition and / or pumps in submerged operating condition to obtain a list of operating condition status identifiers.

[0023] Furthermore, the traffic module is also used for:

[0024] Iterate through the list of pumps in the operating condition status identifier list, assign the pumps to the corresponding calculation strategies for the operating conditions, and obtain the calculation strategy assignment list; the calculation strategies include normal flow acquisition and flood flow estimation.

[0025] Based on the calculation strategy allocation list, the theoretical flow value of the water pumps allocated to normal flow acquisition is directly calculated to obtain the normal operating condition water pump flow list.

[0026] Based on the calculation strategy allocation list, for the pumps assigned to submerged flow estimation, the estimated flow value is calculated using the following formula to obtain the submerged pump flow rate list:

[0027]

[0028] in, To estimate the flow rate, This refers to the input active power of the motor. For the operating efficiency of the motor, For the operating efficiency of the water pump, For the density of the fluid, It is the acceleration due to gravity. This refers to the actual head of the water pump;

[0029] By integrating the normal operating condition pump flow rate list and the submerged operating condition pump flow rate list, an actual flow rate list is obtained.

[0030] Furthermore, the solver module is also used for:

[0031] Based on the actual flow inventory and drainage demand targets, an optimization time window is constructed, resulting in a multi-objective optimization problem framework.

[0032] The multi-objective optimization problem framework is transformed into an objective function using the following formula:

[0033]

[0034] Where t is the time index, k is the current time, T is the time window, i is the water pump index, and N is the total number of water pumps. Let be the power consumption of the i-th water pump at time t. For time step, This is the weighting coefficient for traffic balance. Let be the flow rate of the i-th pump at time t. Let be the flow rate of the i-th pump at time t-1. Here, j represents the pressure stability weighting coefficient, j is the monitoring point index, and M is the total number of key monitoring points. Let be the calculated pressure at time t for each monitoring point j. The target pressure for each monitoring point j;

[0035] By integrating the objective function and constraints, an optimization problem is obtained; the constraints include at least one of the following: flow balance constraint, equipment capacity constraint, safe operation constraint, and operating condition weight constraint.

[0036] Furthermore, the system also includes an execution module for:

[0037] The recommended instructions are optimized for security, resulting in an optimized instruction set.

[0038] Based on the pump speed and the optimized instruction set, calculate the difference between the pump's target speed and actual speed.

[0039] The ramp rate is set based on the difference; and based on the ramp rate, the control curve for transitioning from the current speed to the target speed is calculated to obtain the pump speed control curve; the pump speed control curve is used to indicate that the pump operates according to the pump speed control curve.

[0040] Acquire real-time data from multiple sources; and evaluate all water pumps based on the real-time data from multiple sources to obtain a quantitative evaluation report.

[0041] Furthermore, the execution module is also used for:

[0042] Based on the system whitelist, the recommended instructions are authenticated from the source to obtain a trusted instruction set;

[0043] Based on device capability data, instructions in the trusted instruction set that exceed the physical boundary are corrected to within the boundary value to obtain a safe instruction set.

[0044] Based on preset operational logic rules, state conflict checks are performed on the instructions in the security instruction set to obtain an optimized instruction set.

[0045] Secondly, this application also provides a method for optimizing the coordinated energy-saving operation of pump station groups based on precise flow control, including:

[0046] Acquire multi-source real-time data; and standardize the multi-source real-time data to obtain a standard dataset; the multi-source real-time data includes pump outlet pressure, pump inlet pressure, outlet submersion depth, and pump speed;

[0047] Based on the standard dataset for each water pump, and according to the preset flooding rules, the operating conditions of the water pump are identified, and a list of operating condition status identifiers is obtained.

[0048] Based on the list of operating conditions, the flow rate of each water pump is estimated to obtain the actual flow rate list;

[0049] Based on the actual flow inventory and drainage demand, with the minimum total energy consumption as the core objective, an optimization problem is constructed; and the optimization problem is solved to obtain the solution set.

[0050] The solution set is mapped to the operating parameter settings of each water pump to obtain the recommended instructions; the recommended instructions are used to instruct each water pump to operate according to the operating parameter settings.

[0051] Thirdly, this application also provides a computer device including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to perform any of the steps performed by the system provided in the first aspect of this application.

[0052] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, performs any of the steps performed by the system provided in the first aspect of this application.

[0053] The aforementioned pump station group collaborative energy-saving operation optimization system based on precise flow control includes the following modules: a standardization module for acquiring multi-source real-time data and standardizing it to obtain a standard dataset; the multi-source real-time data includes pump outlet pressure, pump inlet pressure, outlet submersion depth, and pump speed; an operation module for identifying the operating conditions of each pump based on the standard dataset and preset submersion rules, resulting in a list of operating condition status identifiers; a flow module for estimating the flow rate of each pump based on the operating condition status identifier list, resulting in an actual flow rate list; a solution module for constructing an optimization problem based on the actual flow rate list and drainage demand target, with the minimum total energy consumption as the core objective, and solving the optimization problem to obtain a solution set; and an instruction module for mapping the solution set to the operating parameter set values ​​of each pump, resulting in recommended instructions; these recommended instructions instruct each pump to operate according to the operating parameter set values. It can identify changes in operating conditions in real time and adjust the overall strategy of the pump station group after the outlet is flooded, causing conventional measurement methods to become inaccurate. This allows the pumps to smoothly transition between normal and flooded operating conditions, with reasonable flow distribution during operating condition switching. The system as a whole achieves high efficiency and meets the requirements of modern urban drainage systems for safety, stability and energy saving. Attached Figure Description

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

[0055] Figure 1 A schematic diagram of the structure of a pump station group collaborative energy-saving operation optimization system based on precise flow control according to an embodiment of the present invention;

[0056] Figure 2This is a schematic diagram of the process of a method for optimizing the coordinated energy-saving operation of pump station groups based on precise flow control, provided in an embodiment of the present invention. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0058] In one embodiment, such as Figure 1 As shown, a pump station group collaborative energy-saving operation optimization system 100 based on precise flow control is provided. This embodiment illustrates the system's application to a terminal, but it is understood that the system can also be applied to a server, and can be implemented through interaction between the terminal and the server. In this embodiment, the system includes the following structure:

[0059] The standardization module 101 is used to acquire multi-source real-time data and to standardize the multi-source real-time data to obtain a standard dataset. The multi-source real-time data includes pump outlet pressure, pump inlet pressure, outlet submersion depth, and pump speed.

[0060] Multi-source real-time data consists of raw, unprocessed data directly collected from various sensors and equipment within the pumping station group. This data includes key physical quantities reflecting the pump's operating status, such as: pump outlet pressure (the pressure of the fluid at the pump's outlet during operation, used to determine the pump's delivery capacity); pump inlet pressure (the pressure of the fluid at the pump's inlet during operation, used to determine suction conditions and the presence of cavitation risk); outlet submersion depth (the depth to which the pump outlet is submerged, a key indicator of whether the pump is under abnormal submersion conditions); and pump speed (the speed at which the pump impeller rotates per unit time, a core operating parameter controlling pump flow and head). The standard dataset is a standardized collection of data, eliminating the heterogeneity of the raw data and ensuring consistency, accuracy, and comparability of data processed by all subsequent modules. It forms the basis for precise analysis and optimization. The terminal continuously collects multi-source real-time data from various pressure sensors, level gauges, and tachometers on site, unifies all pressure units to kilopascals, removes outliers or invalid data caused by sensor malfunctions or signal interference, encapsulates the data into a unified data structure defined within the system, and generates a clean and standardized dataset for use by subsequent modules.

[0061] The operation module 102 is used to identify the operating conditions of each water pump based on a standard dataset for each water pump and according to preset flooding rules, and obtain a list of operating condition status identifiers.

[0062] Specifically, the preset flooding rules are predefined logical judgment criteria used to diagnose the working status of water pumps based on their actual operating data, particularly determining whether a pump is in an abnormal flooding state. Operating condition refers to the current working state type of the water pump, mainly divided into normal and abnormal operating conditions. The operating condition status identifier list contains the diagnostic results of the operating conditions of each water pump in the pumping station group. Each item in the list clearly identifies whether a water pump is in a normal, transitional, or complete flooding state. The terminal calculates the actual head for each water pump and compares it with the theoretical head. Combined with the flooding depth data of the outlet, the preset flooding rules are applied to make a comprehensive judgment and generate a clear operating condition status identifier list for all water pumps. For example, if the actual head of a water pump is much lower than the theoretical head, and its outlet is flooded, it may be initially judged as abnormal, and further subdivided into transitional or complete flooding conditions depending on whether the flooding depth exceeds a certain threshold.

[0063] The flow module 103 is used to estimate the flow rate of each water pump based on the operating condition identification list and obtain the actual flow rate list.

[0064] Specifically, the actual flow rate list is a list of estimated actual flow rates for each pump in the pumping station group, representing a quantitative reflection of the pumps' true drainage capacity at the current moment. The terminal uses different flow rate estimation methods for different pumps based on the operating condition label list. For pumps marked as operating under normal conditions, since their operation is stable, a relatively accurate theoretical flow rate value can be obtained by directly consulting the pump's performance curve based on its rotational speed. For pumps marked as operating under abnormal conditions such as transition or complete submersion, because their performance curves are distorted, an energy consumption method formula based on motor input power, efficiency, and the actual pump head is used for estimation. The estimated flow rates of all pumps are then summarized to form the actual flow rate list.

[0065] The solution module 104 is used to construct optimization problems based on the actual flow inventory and drainage demand targets, with the minimum total energy consumption as the core objective; and to solve the optimization problems to obtain the solution set.

[0066] The drainage demand objective is the total drainage task that the pumping station group needs to complete, typically a flow rate or the total volume of water to be discharged within a certain time period. The optimization problem is a formalized mathematical problem whose core objective is to minimize the total energy consumption of the pumping station group while meeting the drainage demand. It also includes other sub-objectives and a series of constraints. The solution set is the set of results obtained after solving the optimization problem, typically containing the optimal sequence of operating parameter settings for each pump over a future period. Using the actual terminal flow rate and the drainage demand objective as input, a multi-objective optimization problem is constructed. The core objective is to minimize the total power consumption of all pumps. To avoid frequent pump start-ups and shutdowns or drastic speed fluctuations, a flow stability objective is added. To ensure pipeline safety, a pressure stability objective at key nodes is added. These objectives, along with constraints such as equipment physical limits and safe operating ranges, constitute the optimization problem. The solution module calls an optimization algorithm to solve this complex mathematical problem, obtaining the solution set.

[0067] The instruction module 105 is used to map the solution set to the operating parameter settings of each water pump to obtain the recommended instruction; the recommended instruction is used to instruct each water pump to operate according to the operating parameter settings.

[0068] Among them, the operating parameter setpoints are the physical quantity setpoints that directly control the operation of the water pump, the most typical being the target value of the water pump speed. Recommended instructions are a set of control commands to be issued to each water pump, explicitly including the operating parameter setpoints. The terminal translates the numerical optimization calculation results into specific executable instructions. For example, it maps the optimized speed value of a certain water pump in the solution set to the target speed setpoint that the frequency converter can receive, generating a clear and explicit set of recommended instructions.

[0069] The pump station group collaborative energy-saving operation optimization system based on precise flow control provided in this embodiment has a standardization module for acquiring multi-source real-time data and standardizing the multi-source real-time data to obtain a standard dataset. The multi-source real-time data includes pump outlet pressure, pump inlet pressure, outlet submersion depth, and pump speed. The operation module is used to identify the operating conditions of each pump based on the standard dataset of each pump and according to preset submersion rules to obtain a list of operating condition status identifiers. The flow module is used to estimate the flow of each pump based on the operating condition status identifier list to obtain an actual flow list. The solution module is used to construct an optimization proposition based on the actual flow list and drainage demand target, with the minimum total energy consumption as the core objective, and solve the optimization proposition to obtain a solution set. The instruction module is used to map the solution set to the operating parameter set values ​​of each pump to obtain recommended instructions. The recommended instructions are used to instruct each pump to operate according to the operating parameter set values. With the above structure, even when the pump outlet is submerged and conventional measurement methods become inaccurate, the changes in the pump's operating condition can still be identified and the overall strategy of the pump station group can be adjusted. This allows the pump to smoothly transition between normal and submerged operating conditions, with reasonable flow distribution during operating condition switching. The system as a whole achieves high efficiency and meets the requirements of modern urban drainage systems for safety, stability, and energy conservation.

[0070] In one embodiment, the running module 102 is further configured to:

[0071] Step 301: Based on the pump outlet pressure and pump inlet pressure in the standard dataset, calculate the actual pump head using the following formula:

[0072]

[0073] in, This represents the actual head of the water pump. For pump outlet pressure, The pump inlet pressure, The density of water, It is the acceleration due to gravity. This refers to the elevation difference between the pump inlet and outlet.

[0074] Specifically, the actual head of a water pump is the actual energy increase gained per unit weight of liquid flowing through the pump. It reflects the total height the pump truly lifts the fluid by overcoming pipe resistance and the elevation difference between the inlet and outlet in its actual operating environment. The unit is meters, and it is a key performance indicator used to evaluate the pump's true working efficiency. The terminal extracts the pump outlet pressure and pump inlet pressure from a standard dataset. The difference between the two pressure values ​​is divided by the density of water and the acceleration due to gravity, converting the pressure difference into an equivalent liquid column height. This, combined with the inlet and outlet elevation difference obtained beforehand from pump installation drawings, transforms the pressure sensor readings into a more intuitive and engineering-meaning physical quantity.

[0075] Step 302: Based on the pump speed and pump performance curve, calculate the theoretical pump head at the current pump speed.

[0076] The theoretical head of a water pump is the head that the pump should achieve under ideal conditions and at a specific speed, according to its inherent performance curve. It represents the theoretical performance of the pump under lossless, standard operating conditions. A pump performance curve is a graph or data set representing pump performance, determined by the manufacturer through experiments, depicting the relationship between parameters such as flow rate, head, efficiency, and shaft power at a fixed speed. The terminal obtains the current actual speed of the pump and retrieves the pump's performance curve pre-stored in the system. Since the performance curve is based on the rated speed, if the current speed differs from the rated speed, the system needs to perform conversions according to the pump similarity law to deduce the theoretical head value that the pump should achieve at the current actual speed.

[0077] Step 303: Based on the actual head value of the water pump, the theoretical head value of the water pump, and the submersion depth of the outlet, the operating status of the water pump is determined according to the preset rules to obtain a preliminary abnormal operating condition indicator; the preliminary abnormal operating condition indicator is used to characterize whether the water pump is in a submerged state.

[0078] The preset rules are a set of predefined logical judgment conditions. The core is to use the deviation between the actual head and the theoretical head, combined with the outlet submersion status, to determine whether the water pump is malfunctioning. The preliminary abnormal condition indicator is a simple, binary judgment result used to initially characterize whether a water pump is in an abnormal state, especially a submerged state, without distinguishing the specific degree of abnormality; it only performs preliminary screening. The terminal compares the actual head value with the theoretical head value. Under normal operating conditions, the actual head should be close to the theoretical head. If the actual head is significantly lower than the theoretical head, it indicates that the water pump's performance has abnormally degraded. The submersion depth at the outlet is checked. If the submersion depth is greater than zero, according to the preset rules, the water pump is determined to have entered an abnormal operating condition, and the preliminary abnormal condition indicator is set to "yes"; otherwise, it is marked as "no".

[0079] Step 304: Based on the preliminary identification of abnormal operating conditions and the submersion depth of the outlet, classify the operating conditions of each water pump to obtain a list of operating condition status identifiers.

[0080] Specifically, the operating condition status label list is a detailed list containing the final operating condition diagnosis results of each pump in the pumping station group. The labels in the list are more specific categories, including normal operating condition, transitional operating condition, and complete submersion operating condition. The terminal traverses all pumps and classifies them according to their preliminary abnormal operating condition label and outlet submersion depth. For pumps with a preliminary label of "no", their operating condition is directly marked as normal. For pumps with a preliminary label of "yes", the outlet submersion depth is compared with a preset height threshold: if the submersion depth is less than the threshold, it is marked as transitional operating condition, indicating that the pump's outlet has just been submerged and is in the early stage of an abnormal state; if the submersion depth is not less than the threshold, it is marked as complete submersion operating condition, indicating that the outlet has been severely submerged and the pump is in a severely abnormal operating state. The operating condition labels of all pumps are summarized to generate a complete operating condition status label list.

[0081] This embodiment achieves precise control by providing a refined and tiered diagnosis of the pump's operating status, enabling the system to employ completely different flow estimation methods and optimized control strategies for pumps operating under different conditions.

[0082] In one embodiment, the running module 102 is further configured to:

[0083] Step 401: Mark the water pumps that were initially identified as having no abnormal operating conditions as having normal operating conditions, and obtain the water pumps with normal operating conditions.

[0084] The initial abnormal operating condition flag is an input condition indicating whether the system determined the pump was not in an abnormal flooding state during the preliminary diagnosis. Normal operating condition refers to the pump operating in its ideal, stable state, within its design limits. In this state, the pump's actual head, flow rate, and other parameters closely match its theoretical performance curves. Normal operating condition pumps are a set containing all pumps categorized as normal operating conditions and their related information. The terminal iterates through the initial abnormal operating condition flags of all pumps. For each pump flagged as "no," a simple assignment operation is performed to formally mark or classify its operating condition attribute as normal. All pumps marked in this way are grouped together to obtain the normal operating condition pumps.

[0085] Step 402: The water pumps initially identified as having abnormal operating conditions are classified as having abnormal operating conditions, thus obtaining the abnormal operating condition water pumps.

[0086] The initial identification of abnormal operating conditions is also an input condition, indicating that the system determines in the preliminary diagnosis that the water pump may be in an abnormal flooding state. Abnormal operating conditions is a broad term referring to all abnormal working states, specifically those whose performance degrades due to water flooding at the outlet. The abnormal operating condition pumps are a set containing all water pumps initially identified as abnormal and their related information; this is an intermediate result, preparing for subsequent fine-grained classification. The terminal iterates through the initial identifications of all water pumps, separating each pump identified as "abnormal" from the normal pump group and grouping it into a temporary group called "abnormal operating condition pumps."

[0087] Step 403: If the outlet submersion depth of the abnormal working condition pump is less than the preset height threshold, the abnormal working condition pump is marked as a transitional working condition pump, thus obtaining the transitional working condition pump.

[0088] Specifically, the height threshold is a pre-defined critical depth value based on engineering experience or experiments, used to distinguish the severity of flooding and serving as the boundary between mild and severe flooding. The transitional operating condition refers to the initial stage of abnormal pump operation. At this point, the pump outlet has begun to be submerged, but the flooding level has not yet reached a very severe stage. The pump's performance begins to deviate from the normal curve, but it has not yet completely failed. The terminal iterates through each pump in the abnormal operating condition pump set, reads the real-time monitored outlet submersion depth data for each pump, and compares it with the preset height threshold. If the submersion depth is less than the threshold, the system performs an assignment operation, refining the pump's operating condition from a generalized abnormal state to a transitional operating condition and placing it into the corresponding group.

[0089] Step 404: If the outlet submersion depth of the abnormal working condition pump is not less than the height threshold, then the abnormal working condition pump is marked as a fully submerged working condition, and the submerged working condition pump is obtained.

[0090] Specifically, the complete submersion condition refers to the severe stage of abnormal pump operation. At this point, the outlet is completely submerged, the pump's actual head and efficiency drop sharply, its performance deviates significantly from the theoretical curve, and backflow may even occur. The submersion condition pump set is a collection of all pumps categorized as being in the complete submersion condition. The terminal continues processing the remaining pumps in the abnormal condition pump set, performing an assignment operation to mark their operating status as complete submersion condition and assigning them to the corresponding group.

[0091] Step 405: Integrate the pumps in normal operating condition and / or the pumps in transitional operating condition and / or the pumps in submerged operating condition to obtain a list of operating condition status identifiers.

[0092] Specifically, the operating condition status identifier list is an ordered list that clearly lists each pump in the pumping station group and its corresponding finely categorized operating condition status, representing the final conclusion of the entire operation module. The terminal merges the sets of pumps in normal operating condition, transitional operating condition, and submerged operating condition into a new, global list data structure, where each record uniquely corresponds to a pump and its final determined operating condition status.

[0093] This embodiment generates a comprehensive, accurate, and structured list of the operational health status of the pump station group. Subsequently, based on this list, appropriate calculation methods and control commands are assigned to pumps under different operating conditions, thereby achieving precise and differentiated management and improving the accuracy and reliability of coordinated energy-saving operation.

[0094] In one embodiment, the flow module 103 is further configured to:

[0095] Step 501: Traverse the list of pumps in the operating condition status identifier list and assign the pumps to the corresponding calculation strategies for the operating conditions to obtain the calculation strategy allocation list; the calculation strategies include normal flow acquisition and flooding flow estimation.

[0096] The calculation strategy refers to the specific flow calculation method adopted for water pumps under different operating conditions. Two main strategies are defined: Normal Flow Acquisition: This strategy is applicable to water pumps operating under normal conditions. It directly calculates the flow rate based on the pump performance curve or theoretical formula, relying on the pump's own design parameters and operating speed. Submerged Flow Estimation: This strategy is applicable to water pumps operating under submerged conditions. When the pump performance curve fails due to abnormal operating conditions, it indirectly estimates the flow rate by monitoring external parameters such as motor power and efficiency. The calculation strategy allocation list maps water pumps to their corresponding calculation strategies, clearly indicating which method should be used for flow calculation for each pump. The terminal reads the operating condition status identifier list, processes each water pump sequentially, and selects and assigns the corresponding calculation strategy based on the operating condition status attributes of each pump in the list. For example, if the pump status is normal, the normal flow acquisition strategy is assigned; if the status is transitional or fully submerged, the submerged flow estimation strategy is assigned, generating an allocation list containing all water pumps and their corresponding strategies.

[0097] Step 502: Based on the calculation strategy allocation list, directly calculate the theoretical flow value of the water pumps allocated for normal flow acquisition to obtain the normal operating condition water pump flow list.

[0098] The theoretical flow rate refers to the flow rate that a water pump should output at a specific speed, based on its inherent performance curve, reflecting the pump's capability under ideal operating conditions. The normal operating condition pump flow rate list is a list of all pumps marked as operating normally and their theoretically calculated flow rate values. The terminal allocates the list according to the calculation strategy, filtering out all pumps assigned the normal flow rate acquisition strategy. For these pumps, the system obtains the current real-time speed, then queries the pre-stored performance curve of that pump, and directly obtains the theoretical flow rate value corresponding to the current speed and head through interpolation or conversion. The flow rate values ​​of all normal pumps are then summarized into a list.

[0099] Step 503: Based on the calculation strategy allocation list, for the pumps assigned to flood flow estimation, calculate the estimated flow value using the following formula to obtain the flood condition pump flow list:

[0100]

[0101] in, To estimate the flow rate, This refers to the input active power of the motor. For the operating efficiency of the motor, For the operating efficiency of the water pump, For the density of the fluid, It is the acceleration due to gravity. This represents the actual head of the water pump.

[0102] Specifically, the estimated flow rate refers to the flow rate indirectly calculated from the energy conversion relationship of a submerged pump with malfunctioning performance. It is an estimate, but closer to the actual value than a distorted theoretical value. The motor's input active power refers to the actual power consumed by the motor driving the pump from the power grid, measured in kilowatts, and is a key parameter that can be directly measured. The motor's operating efficiency refers to the percentage of efficiency by which the motor converts input electrical energy into mechanical energy; this is the rated value. The pump's operating efficiency refers to the percentage of efficiency by which the pump converts received mechanical energy into fluid potential and kinetic energy. Under abnormal operating conditions, this efficiency will be significantly lower than the rated value and needs to be estimated based on historical data. The submerged pump flow rate list is a list of all pumps marked as being in submerged condition and their estimated flow rates using the power method. The terminal selects all pumps assigned to flood flow estimation strategies based on the calculation strategy allocation list. For each such pump, the necessary parameters are collected: the input active power of the motor is obtained from the power monitoring system; the current operating efficiency of the motor and the pump is obtained from the database or model; the actual head is obtained; and the known density and gravitational acceleration are used to substitute these values ​​into the formula for calculation. The physical essence of the formula is that the effective power ultimately delivered by the pump to the fluid is equal to the remaining part of the motor input power after the efficiency loss of the motor and pump stages. The estimated flow values ​​of all pumps under flood conditions are summarized and listed.

[0103] Step 504: Integrate the normal operating condition pump flow rate list and the submerged operating condition pump flow rate list to obtain the actual flow rate list.

[0104] Specifically, the actual flow inventory is a complete list containing the flow values ​​of all pumps in the pumping station group, reflecting the actual drainage capacity of each pump at the current moment. The terminal merges the normal operating condition pump flow list and the submerged condition pump flow list to ensure the uniqueness of each pump and the accuracy of the flow value, generating a globally unified flow inventory.

[0105] This embodiment utilizes the principle of energy conservation to find a method that can still reliably estimate flow rate even under performance distortion conditions. It overcomes the problem of not being able to accurately calculate flow rate using performance curves under abnormal operating conditions, fills a key data gap, and thus makes the subsequent collaborative optimization of pump station groups more reliable.

[0106] In one embodiment, the solving module 104 is further configured to:

[0107] Step 601: Based on the actual flow inventory and drainage demand target, construct the optimization time window to obtain the multi-objective optimization problem framework.

[0108] The drainage demand target refers to the total drainage task that the pumping station group needs to complete within a specific time period. This can be a fixed total water volume requirement or a flow process line that varies over time. The optimization time window is a future time range considered during optimization calculations. Optimization requires developing an optimal operating plan for the entire time window. The multi-objective optimization problem framework is a structured prototype of an optimization problem, clearly defining the optimization time range, multiple objectives that need to be coordinated, and related decision variables, but without specific mathematical formulas. The terminal uses the actual flow inventory and drainage demand target as input, sets the optimization time window, and plans uniformly for a series of consecutive future moments. Based on the time window and known optimization objectives, a multi-objective optimization problem framework is built. The framework defines the problem's boundaries and structure, that is, what needs to be optimized and within what time range, transforming the static, current-moment optimization problem into a dynamic, forward-looking, rolling optimization problem.

[0109] Step 602, transform the multi-objective optimization problem framework into an objective function using the following formula:

[0110]

[0111] Where t is the time index, k is the current time, T is the time window, i is the water pump index, and N is the total number of water pumps. Let be the power consumption of the i-th water pump at time t. For time step, This is the weighting coefficient for traffic balance. Let be the flow rate of the i-th pump at time t. Let be the flow rate of the i-th pump at time t-1. Here, j represents the pressure stability weighting coefficient, j is the monitoring point index, and M is the total number of key monitoring points. Let be the calculated pressure at time t for each monitoring point j. Let be the target pressure at each monitoring point j.

[0112] Specifically, the objective function is a mathematical expression that needs to be minimized, quantifying the overall goal of the optimization problem. In this system, the objective function is the weighted sum of three sub-objectives, with the core objective being to minimize this sum. The first term of the objective function is the total energy consumption term, which calculates the sum of the power consumption of all pumps within the entire time window, representing the total power consumption. Minimizing this term directly achieves the core goal of energy saving. The second term is the flow balance term, which calculates the sum of the squares of the changes in flow rate of each pump at adjacent times. Minimizing this term penalizes large fluctuations in flow rate, encourages stable pump operation, reduces frequent start-stops or drastic speed changes, and helps extend equipment lifespan. The third term is the pressure stability term, which calculates the sum of the squares of the deviations between the actual pressure and the target pressure at key monitoring points in the pipeline network. Minimizing this term ensures the safe operation of the pipeline network, preventing pipe bursts due to excessive pressure or service interruptions due to excessively low pressure. Weighting coefficients are used to adjust the importance of the three sub-objectives in the overall goal. The terminal integrates the multiple objectives in the framework into a single, comprehensive objective function through weighted summation. The decision variable is the flow rate of each water pump at each future moment. By minimizing this objective function, the terminal seeks an operating scheme that best balances energy consumption, flow rate changes, and pressure stability.

[0113] Step 603: Integrate the objective function and constraints to obtain the optimization proposition; the constraints include at least one of the following: flow balance constraint, equipment capacity constraint, safe operation constraint, and operating condition weight constraint.

[0114] Specifically, constraints are the restrictions that decision variables must satisfy during the optimization process. They define the feasible region of the solution, ensuring that the optimization result is physically and logically achievable. For example, constraints might include: flow balance constraints requiring the sum of the flow rates of all pumps to meet the drainage demand at any given time—the most basic functional constraint; equipment capacity constraints requiring the operating parameters of each pump to be within their physical limits; safe operation constraints requiring the system's operating state to meet safety regulations; and operating condition weight constraints imposing different restrictions on pumps under different operating conditions based on a list of operating condition identifiers. An optimization proposition is a complete mathematical definition of an optimization problem, containing the objective function to be minimized and all the constraints that must be followed. Finally, the objective function is combined with a series of predefined constraints to obtain the optimization proposition.

[0115] This embodiment transforms a complex, multi-directional engineering optimization problem into a clear numerical computation problem solvable by mathematical optimization algorithms by constructing an optimization proposition. This enables automated intelligent decision-making and accurately describes, in the computational field, the process of finding the most energy-efficient, stable, and safest pump station group operation scheme under all practical constraints, thereby improving the pertinence of collaborative optimization.

[0116] In one embodiment, the system further includes an execution module for:

[0117] Step 701: Optimize the security of the recommended instructions to obtain an optimized instruction set.

[0118] The recommended instructions are a theoretically optimal set of operating parameter settings for each water pump, calculated based on a mathematical model, and may not fully consider actual physical limitations and immediate safety risks. The optimized instruction set is the final set of instructions after safety optimization, eliminating potential risks, and is then sent to field equipment for execution after further processing. The terminal inputs the recommended instructions into a safety engine for processing, verifying the reliability of the instruction source, preventing illegal or erroneous control commands, comparing the parameters in the instructions with the physical limits of the water pumps, motors, and other equipment, automatically correcting any instructions exceeding the hardware's tolerance to within safety boundaries, checking for logical contradictions in the instructions based on preset operating logic rules, and generating a safe and reliable optimized instruction set after all verifications and corrections are completed.

[0119] Step 702: Based on the pump speed and the optimized instruction set, calculate the difference between the target speed and the actual speed of the pump.

[0120] Specifically, the current pump speed is the actual rotational speed of the pump impeller, collected in real time by sensors. The target speed is a new speed setting value that the pump is expected to achieve, obtained from the optimization instruction set. The difference is the arithmetic difference between the target speed and the current speed, quantifying the magnitude and direction of the speed adjustment required. The terminal reads the current speed of each pump, retrieves the corresponding target speed from the optimization instruction set, calculates the difference, and generates a specific speed change for each pump that needs adjustment.

[0121] Step 703: Set the ramp rate based on the difference; and calculate the control curve for the current speed transition to the target speed based on the ramp rate to obtain the pump speed control curve; the pump speed control curve is used to indicate that the pump operates according to the pump speed control curve.

[0122] Specifically, the ramp rate refers to the maximum allowable change in pump speed per unit time. It is dynamically set based on the calculated difference and equipment characteristics; a larger difference corresponds to a slower ramp rate to achieve a smooth transition. The control curve is a time function curve describing how the speed smoothly transitions from the current value to the target value, specifying the exact speed value to be reached at each moment during the transition. The pump speed control curve is used to directly guide the sequence of control commands to the actuators. Based on the magnitude of the difference and the equipment's safety requirements, the terminal sets a suitable ramp rate for each pump. Based on this rate, it calculates the transition path from the current speed to the target speed, ensuring that the speed begins to change with constant acceleration and then approaches the target value with constant deceleration, thereby avoiding mechanical shock to the motor and pump body, and generating a specific pump speed control curve.

[0123] Step 704: Obtain multi-source real-time data; and evaluate all water pumps based on the multi-source real-time data to obtain a quantitative evaluation report.

[0124] The multi-source real-time data consists of field data continuously collected during system operation, such as pump outlet pressure, pump inlet pressure, outlet submersion depth, pump speed, and motor power. The quantitative evaluation report is a summary document of the system's operational status, containing specific numerical indicators, objectively reflecting the effectiveness of the optimized control strategy. During and after the system executes optimization commands, the terminal continuously acquires multi-source real-time data, runs the evaluation algorithm, calculates a series of key performance indicators, and organizes the calculation results into a structured quantitative evaluation report. For example, performance indicators may include: total energy consumption and actual energy saving rate, representing the difference in energy consumption before and after optimization; flow target achievement rate, representing the degree of agreement between the actual total flow and the drainage demand target; equipment operational stability indicators, representing equipment operational fluctuations; and abnormal operating condition statistics, representing the number and duration of pumps experiencing abnormalities.

[0125] This embodiment transforms instantaneous speed switching commands into a control sequence that changes smoothly over time, effectively preventing water hammer, reducing mechanical stress, protecting equipment, and making changes in flow and pressure more stable, thereby improving the operational stability of the entire pipeline system. An evaluation report is generated to verify the effectiveness of this optimized control, providing decision support for operators. Historical data can also be used for the self-learning and improvement of the optimization algorithm, enabling the system to continuously evolve, improve long-term performance, and thus enhance the targeting of energy-saving optimization.

[0126] In one embodiment, the execution module is further configured to:

[0127] Step 801: Based on the system whitelist, perform source authentication on the recommended instructions to obtain a trusted instruction set.

[0128] The system whitelist is a pre-defined list of command source identifiers recognized by the system as legitimate and trustworthy. Source authentication is a security operation that verifies the identity and permissions of the command issuer, ensuring that commands originate from legitimate control sources, rather than unauthorized intrusions or erroneous operations. The trusted command set is a subset of recommended commands checked through source authentication; its source is trustworthy, but its physical and logical security is not verified. The execution module performs source authentication, checking the identity tags of recommended commands. Commands that pass authentication are aggregated into the trusted command set. For example, this involves verifying the digital signature of recommended commands, checking the identifier of the sending process or the source network address, comparing the identity information with the system whitelist, and allowing the command to pass if its source is on the whitelist; otherwise, the command is marked as untrustworthy and blocked or flagged with an alert.

[0129] Step 802: Based on the device capability data, correct the instructions in the trusted instruction set that exceed the physical boundary to within the boundary value to obtain the security instruction set.

[0130] Specifically, equipment capability data is a set of data describing the inherent performance limits and permissible operating range of physical equipment such as pumps, motors, and frequency converters. Physical boundaries are the upper and lower limits of parameters defined by the equipment capability data, ensuring the safe and stable operation of the equipment; the set values ​​in the instructions cannot exceed these boundaries. The safety instruction set is a set of instructions corrected for physical boundaries, ensuring that the parameter values ​​of all instructions are within the physical tolerance range of the equipment. The terminal reads the equipment capability data stored in the database and compares the parameter values ​​in the instructions with the corresponding physical boundaries of the equipment one by one. If the instruction value is within the boundary, it is retained; if the instruction value exceeds the boundary, the system automatically corrects the instruction value to the nearest boundary value. All instructions undergo this processing to generate the safety instruction set.

[0131] Step 803: Based on preset operation logic rules, perform state conflict checks on the instructions in the security instruction set to obtain an optimized instruction set.

[0132] Specifically, the preset operating logic rules are a rule base that encodes equipment operating procedures, process interlock logic, and safety procedures, defining which operating instructions are logically mutually exclusive or have sequential requirements. The optimized instruction set is the final, safely issued set of instructions obtained after state conflict checking and parsing. It is reliable in origin, physically feasible, and logically self-consistent and secure. The terminal applies the preset operating logic rules to check the logical consistency of the instructions in the safe instruction set. If a conflict is found, it is automatically parsed according to the priority of the rules and preset strategies. After eliminating all conflicts, the final optimized instruction set is obtained. For example, state conflicts may include checking whether contradictory start and stop commands have been issued to the same water pump simultaneously; checking whether the inlet valve of a pump has been opened by command before starting it; and checking whether the joint operation mode of multiple water pumps is allowed.

[0133] This embodiment employs three lines of security defense, filtering from three dimensions: instruction source, device physical limits, and system operating logic, to ensure that every instruction ultimately issued is reliable, feasible, and secure, thereby improving the reliability of collaborative optimization.

[0134] It should be understood that although the steps in the flowcharts of the embodiments described above are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the embodiments described above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages of other steps.

[0135] Based on the same inventive concept, this application also provides a method for implementing the above-mentioned pump station group collaborative energy-saving operation optimization system based on precise flow control, specifically a method for optimizing the collaborative energy-saving operation of pump station groups based on precise flow control. The solution provided by this method is similar to the implementation scheme described in the above system. Therefore, the specific limitations in one or more embodiments of the method for optimizing the collaborative energy-saving operation of pump station groups based on precise flow control provided below can be found in the limitations of the pump station group collaborative energy-saving operation optimization system based on precise flow control described above, and will not be repeated here.

[0136] In one exemplary embodiment, such as Figure 2 As shown, a method for optimizing the coordinated energy-saving operation of pump station groups based on precise flow control is provided, including:

[0137] Step 201: Acquire multi-source real-time data; and standardize the multi-source real-time data to obtain a standard dataset; the multi-source real-time data includes pump outlet pressure, pump inlet pressure, outlet submersion depth, and pump speed;

[0138] Step 202: Based on the standard dataset of each water pump, identify the operating conditions of the water pump according to the preset flooding rules, and obtain a list of operating condition status identifiers.

[0139] Step 203: Based on the operating condition identification list, estimate the flow rate of each water pump to obtain the actual flow rate list;

[0140] Step 204: Based on the actual flow inventory and drainage demand target, with the minimum total energy consumption as the core objective, construct an optimization problem; and solve the optimization problem to obtain the solution set;

[0141] Step 205: Map the solution set to the operating parameter settings of each water pump to obtain the recommended instructions; the recommended instructions are used to instruct each water pump to operate according to the operating parameter settings.

[0142] Furthermore, based on the standard dataset for each water pump and according to preset flooding rules, the operating conditions of the water pumps are identified, resulting in a list of operating condition identifiers, including:

[0143] Based on the pump outlet pressure and pump inlet pressure in the standard dataset, the actual pump head is calculated using the following formula:

[0144]

[0145] in, This represents the actual head of the water pump. For pump outlet pressure, The pump inlet pressure, The density of water, It is the acceleration due to gravity. The difference in elevation between the pump inlet and outlet;

[0146] Based on the pump speed and pump performance curve, calculate the theoretical pump head at the current pump speed.

[0147] Based on the actual head value, theoretical head value, and outlet submersion depth of the water pump, the operating status of the water pump is determined according to preset rules, and a preliminary abnormal operating condition indicator is obtained; the preliminary abnormal operating condition indicator is used to indicate whether the water pump is in a submerged state.

[0148] Based on the preliminary identification of abnormal operating conditions and the submersion depth of the outlet, the operating conditions of each water pump are classified to obtain a list of operating condition status indicators.

[0149] Furthermore, based on the preliminary identification of abnormal operating conditions and the submersion depth of the outlet, the operating conditions of each water pump are classified to obtain a list of operating condition status indicators, including:

[0150] The water pumps that were initially marked as not being in abnormal operating conditions are now marked as being in normal operating conditions, thus obtaining the water pumps in normal operating conditions.

[0151] Pumps that are initially identified as having abnormal operating conditions are classified as having abnormal operating conditions, and thus, pumps with abnormal operating conditions are obtained.

[0152] If the outlet submersion depth of the abnormal working condition water pump is less than the preset height threshold, the abnormal working condition water pump is marked as a transitional working condition water pump.

[0153] If the outlet submersion depth of the pump under abnormal operating conditions is not less than the height threshold, the pump under abnormal operating conditions is marked as fully submerged, and the submerged operating condition pump is obtained.

[0154] Integrate pumps in normal operating condition and / or pumps in transitional operating condition and / or pumps in submerged operating condition to obtain a list of operating condition status identifiers.

[0155] Furthermore, based on the operating condition identification list, the flow rate of each water pump is estimated to obtain an actual flow rate list, including:

[0156] Iterate through the list of pumps in the operating condition status identifier list, assign the pumps to the corresponding calculation strategies for the operating conditions, and obtain the calculation strategy assignment list; the calculation strategies include normal flow acquisition and flood flow estimation.

[0157] Based on the calculation strategy allocation list, the theoretical flow value of the water pumps allocated to normal flow acquisition is directly calculated to obtain the normal operating condition water pump flow list.

[0158] Based on the calculation strategy allocation list, for the pumps assigned to submerged flow estimation, the estimated flow value is calculated using the following formula to obtain the submerged pump flow rate list:

[0159]

[0160] in, To estimate the flow rate, This refers to the input active power of the motor. For the operating efficiency of the motor, For the operating efficiency of the water pump, For the density of the fluid, It is the acceleration due to gravity. This refers to the actual head of the water pump;

[0161] By integrating the normal operating condition pump flow rate list and the submerged operating condition pump flow rate list, an actual flow rate list is obtained.

[0162] Furthermore, based on the actual flow inventory and drainage demand targets, and with the minimum total energy consumption as the core objective, optimization problems are constructed, including:

[0163] Based on the actual flow inventory and drainage demand targets, an optimization time window is constructed, resulting in a multi-objective optimization problem framework.

[0164] The multi-objective optimization problem framework is transformed into an objective function using the following formula:

[0165]

[0166] Where t is the time index, k is the current time, T is the time window, i is the water pump index, and N is the total number of water pumps. Let be the power consumption of the i-th water pump at time t. For time step, This is the weighting coefficient for traffic balance. Let be the flow rate of the i-th pump at time t. Let be the flow rate of the i-th pump at time t-1. Here, j represents the pressure stability weighting coefficient, j is the monitoring point index, and M is the total number of key monitoring points. Let be the calculated pressure at time t for each monitoring point j. The target pressure for each monitoring point j;

[0167] By integrating the objective function and constraints, an optimization problem is obtained; the constraints include at least one of the following: flow balance constraint, equipment capacity constraint, safe operation constraint, and operating condition weight constraint.

[0168] Furthermore, after mapping the solution set to the operating parameter settings of each water pump to obtain the recommended instructions, it also includes:

[0169] The recommended instructions are optimized for security, resulting in an optimized instruction set.

[0170] Based on the pump speed and the optimized instruction set, calculate the difference between the pump's target speed and actual speed.

[0171] The ramp rate is set based on the difference; and based on the ramp rate, the control curve for transitioning from the current speed to the target speed is calculated to obtain the pump speed control curve; the pump speed control curve is used to indicate that the pump operates according to the pump speed control curve.

[0172] Acquire real-time data from multiple sources; and evaluate all water pumps based on the real-time data from multiple sources to obtain a quantitative evaluation report.

[0173] Furthermore, the recommended instructions are optimized for security, resulting in an optimized instruction set, including:

[0174] Based on the system whitelist, the recommended instructions are authenticated from the source to obtain a trusted instruction set;

[0175] Based on device capability data, instructions in the trusted instruction set that exceed the physical boundary are corrected to within the boundary value to obtain a safe instruction set.

[0176] Based on preset operational logic rules, state conflict checks are performed on the instructions in the security instruction set to obtain an optimized instruction set.

[0177] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of a pump station group collaborative energy-saving operation optimization system based on precise flow control as described above.

[0178] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the steps in the above system embodiments.

[0179] For the device embodiments, since they basically correspond to the system embodiments, the relevant parts can be referred to in the description of the system embodiments. The device embodiments described above are merely illustrative. The components described as separate parts may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this disclosure according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0180] The above-described embodiments are merely illustrative of several implementation methods of the embodiments of this application, and their descriptions are relatively specific and detailed. However, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the embodiments of this application, and these modifications and improvements all fall within the protection scope of the embodiments of this application.

Claims

1. A collaborative energy-saving operation optimization system for pump station groups based on precise flow control, characterized in that, The system includes: A standardization module is used to acquire multi-source real-time data and perform standardization processing on the multi-source real-time data to obtain a standard dataset; the multi-source real-time data includes pump outlet pressure, pump inlet pressure, outlet submersion depth, and pump speed. The operation module is used to identify the operating conditions of each water pump based on the standard dataset of each water pump and according to the preset flooding rules, and obtain a list of operating condition status identifiers. The flow module is used to estimate the flow rate of each water pump based on the operating condition identification list, and obtain the actual flow rate list; The solution module is used to construct an optimization problem based on the actual flow rate list and drainage demand target, with the minimum total energy consumption as the core objective; and to solve the optimization problem to obtain a solution set. The instruction module is used to map the solution set to the operating parameter setting values ​​of each of the water pumps to obtain recommended instructions; the recommended instructions are used to instruct each of the water pumps to operate according to the operating parameter setting values.

2. The system according to claim 1, characterized in that, The operating module is also used for: Based on the pump outlet pressure and pump inlet pressure in the standard dataset, the actual pump head is calculated using the following formula: in, This represents the actual head of the water pump. For pump outlet pressure, The pump inlet pressure, The density of water, It is the acceleration due to gravity. The difference in elevation between the pump inlet and outlet; Based on the pump speed and pump performance curve, calculate the theoretical pump head at the current pump speed. Based on the actual head value of the water pump, the theoretical head value of the water pump, and the submersion depth of the outlet, the operating status of the water pump is determined according to preset rules to obtain a preliminary abnormal operating condition indicator; the preliminary abnormal operating condition indicator is used to characterize whether the water pump is in a submerged state. Based on the preliminary identification of the abnormal operating conditions and the submersion depth of the outlet, the operating conditions of each water pump are classified to obtain the list of operating condition status identifiers.

3. The system according to claim 2, characterized in that, The operating module is also used for: The water pump that was initially identified as not to be in abnormal operating condition is marked as to be in normal operating condition, thus obtaining the water pump in normal operating condition; The water pumps that are initially identified as having abnormal operating conditions are classified as having abnormal operating conditions, thus obtaining the abnormal operating condition water pumps; If the outlet submersion depth of the abnormal working condition water pump is less than a preset height threshold, the abnormal working condition water pump is marked as a transitional working condition, and a transitional working condition water pump is obtained. If the outlet submersion depth of the abnormal working condition water pump is not less than the height threshold, then the abnormal working condition water pump is marked as a fully submerged working condition, and a submerged working condition water pump is obtained. By integrating the normal operating condition water pumps and / or the transitional operating condition water pumps and / or the submerged operating condition water pumps, a list of operating condition status identifiers is obtained.

4. The system according to claim 3, characterized in that, The flow module is also used for: Iterate through the water pumps in the list of operating condition status identifiers, and assign the water pumps to the corresponding operating condition calculation strategies to obtain a calculation strategy allocation list; The calculation strategy includes normal flow acquisition and flood flow estimation; Based on the calculation strategy allocation list, the theoretical flow value of the water pumps allocated to normal flow acquisition is directly calculated to obtain the normal operating condition water pump flow list. Based on the calculation strategy allocation list, for the pumps assigned to flood flow estimation, the estimated flow value is calculated using the following formula to obtain the flood condition pump flow list: in, To estimate the flow rate, This refers to the input active power of the motor. For the operating efficiency of the motor, For the operating efficiency of the water pump, For the density of the fluid, It is the acceleration due to gravity. This refers to the actual head of the water pump; By integrating the normal operating condition pump flow rate list and the submerged operating condition pump flow rate list, the actual flow rate list is obtained.

5. The system according to claim 1, characterized in that, The solution module is also used for: Based on the actual flow rate list and drainage demand target, an optimization time window is constructed, resulting in a multi-objective optimization problem framework. The aforementioned multi-objective optimization problem framework is transformed into an objective function using the following formula: Where t is the time index, k is the current time, T is the time window, i is the water pump index, and N is the total number of water pumps. Let be the power consumption of the i-th water pump at time t. For time step, This is the traffic balance weighting coefficient. Let be the flow rate of the i-th pump at time t. Let be the flow rate of the i-th pump at time t-1. Here, j represents the pressure stability weighting coefficient, j is the monitoring point index, and M is the total number of key monitoring points. Let be the calculated pressure at the j-th monitoring point at time t. The target pressure for each monitoring point j; By integrating the objective function and constraints, the optimization proposition is obtained; the constraints include at least one of the following: flow balance constraint, equipment capacity constraint, safe operation constraint, and operating condition weight constraint.

6. The system according to claim 1, characterized in that, The system also includes an execution module for: The recommended instructions are then optimized for security reasons to obtain an optimized instruction set. Based on the pump speed and the optimized instruction set, calculate the difference between the target speed and the actual speed of the pump. The ramp rate is set based on the difference; and the control curve for transitioning the current speed to the target speed is calculated based on the ramp rate to obtain the pump speed control curve; the pump speed control curve is used to indicate that the pump operates according to the pump speed control curve. The multi-source real-time data is acquired; and all the water pumps are evaluated based on the multi-source real-time data to obtain a quantitative evaluation report.

7. The system according to claim 6, characterized in that, The execution module is further configured to: Based on the system whitelist, the recommended instructions are authenticated to obtain a trusted instruction set; Based on device capability data, instructions that exceed the physical boundary in the trusted instruction set are corrected to within the boundary value to obtain a secure instruction set. Based on preset operational logic rules, state conflict checks are performed on the instructions in the security instruction set to obtain the optimized instruction set.

8. A method for optimizing the coordinated energy-saving operation of pump station groups based on precise flow control, characterized in that, The method includes: Acquire multi-source real-time data; and perform standardization processing on the multi-source real-time data to obtain a standard dataset; the multi-source real-time data includes pump outlet pressure, pump inlet pressure, outlet submersion depth, and pump speed; Based on the standard dataset for each water pump, and according to the preset flooding rules, the operating conditions of the water pump are identified, and a list of operating condition status identifiers is obtained. Based on the operating condition identification list, the flow rate of each pump is estimated to obtain the actual flow rate list; Based on the actual flow rate list and drainage demand target, with the minimum total energy consumption as the core objective, an optimization problem is constructed; and the optimization problem is solved to obtain the solution set. The solution set is mapped to the operating parameter settings of each of the water pumps to obtain recommended instructions; the recommended instructions are used to instruct each of the water pumps to operate according to the operating parameter settings.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it performs the steps of the system as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the system execution according to any one of claims 1 to 7.

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