Parallel water pump intelligent algorithm optimization combination and control method

Abstract

The invention provides a parallel water pump intelligent algorithm optimization combination and control method, which comprises the following steps of: configuring a sensor and a calculation module on an edge node of each water pump, acquiring and preprocessing operation data of each water pump in real time, and generating a unified real-time feature vector and a time sequence feature; dynamically calculating the service life state of the water pump according to the real-time feature vector, and quantifying the residual service life; designing a dynamic start-stop scheme based on a fuzzy logic algorithm according to the service life state, and balancing load requirements and water pump service life distribution; and based on federal learning, the residual life of the water pump is optimized in a global perspective, and an operation strategy based on a start-stop scheme is dynamically optimized and adjusted. A complete solution is formed, and a brand new technical path is provided for efficient and reliable operation of the parallel water pumps.

Description

Technical Field

[0001] The present invention belongs to the field of parallel water pumps, and in particular relates to an intelligent algorithm optimization combination and control method for parallel water pumps. Background Art

[0002] In modern water supply networks, industrial circulating water systems, agricultural irrigation and other application scenarios, parallel pump systems are widely used because they can flexibly adjust flow and adapt to diverse needs. The parallel pump system uses multiple pumps to work simultaneously and dynamically adjust the water supply capacity to cope with load changes and user needs. However, with the increasing complexity of demand and the improvement of energy efficiency, life and stability requirements, the existing control technology has exposed many problems in actual operation and is difficult to meet the needs of modern application scenarios.

[0003] First, from the perspective of energy efficiency optimization, most of the current parallel pump control methods are based on simple operating curve control or fixed rule start-stop methods. Such methods usually operate according to the design operating conditions of the pump or the start-stop sequence of the equipment, and it is difficult to optimize the pump combination in real time according to actual demand changes. Traditional control often aims to meet flow requirements and ignores the efficiency characteristics and operating conditions of different pumps. This causes some pumps to operate under non-optimal conditions for a long time, which not only increases overall energy consumption, but may also cause local pumps to overload. In addition, when the load fluctuates greatly, the traditional method has a delayed response and frequent start-stop operations, which is not only inefficient, but may also cause failures due to frequent operations.

[0004] Secondly, from the perspective of equipment life management, the existing technology for managing the life of water pumps mostly remains at the static maintenance stage. For example, most systems only determine whether maintenance is needed based on the cumulative operating time or start-stop times of the equipment, and lack the dynamic adjustment capabilities of real-time life management. In actual operation, the life of a water pump is affected by a combination of factors, including the start-stop frequency, the fluctuation range of the operating pressure, the operating time, and the distribution of flow loads. The interaction of these factors will significantly accelerate the aging of the equipment. However, the traditional method does not take these dynamic factors into consideration, nor does it reasonably allocate the load and working time of the water pump according to the actual operating status, which can easily cause some equipment to fail prematurely, increasing the maintenance cost of the system and the risk of unplanned downtime.

[0005] Third, from the perspective of system real-time and intelligence, most of the existing parallel pump control systems are still based on a centralized architecture. The centralized architecture relies on the central controller to collect and calculate the operating parameters of all pumps to unify the scheduling strategy. However, in large-scale parallel pump systems, this centralized mode is prone to data transmission delays and computing bottlenecks. Especially when the load fluctuates violently or emergency conditions occur, it is difficult for the central controller to issue optimization instructions in time, which in turn affects the system response speed. In addition, the centralized system has a high risk of single-point failure. Once a problem occurs in the central controller, the entire pump system may be paralyzed. At the same time, traditional control methods are still at a low level in data analysis and model optimization. It is difficult to use real-time data for dynamic adaptation, and the overall intelligence of the system is not high.

[0006] In summary, the existing parallel pump control methods have significant deficiencies in energy efficiency optimization, life management, and real-time response capabilities. These technical defects not only limit the improvement of the overall efficiency of the system, but also lead to increased equipment maintenance costs and reduced operational stability, seriously affecting the long-term reliability and economic benefits of the system. Summary of the invention

[0007] The purpose of the present invention is to propose an intelligent algorithm optimization combination and control method for parallel water pumps, which can comprehensively optimize from system architecture to control strategy, taking into account energy efficiency, life and real-time performance, and solving the bottleneck of the existing technology.

[0008] In order to achieve the above object, the present invention provides a parallel water pump intelligent algorithm optimization combination and control method, the method comprising the following steps:

[0009] S1. Configure sensors and computing modules on the edge nodes of each water pump to collect and preprocess the operating data of each water pump in real time to generate a unified real-time feature vector and time series feature; the operating data includes the flow rate Q of the water pump during operation. i 、Pressure P i , Power consumption E i 、N number of starts and stops i and the current load demand D;

[0010] The time series feature extraction is as follows:

[0011] Generate a feature sequence window in real time every minute Extract the historical trend of the water pump operation, the length of which is the data of the past 5 minutes, as shown below:

[0012]

[0013] Among them, X i [t-4] represents the feature vector of the window at time t-4, Xi [t-3] represents the feature vector of the window at time t-3, X i [t-2] represents the feature vector of the window at time t-2, X i [t-1] represents the feature vector of the window at time t-1, X i [t] represents the feature vector of the window at time t;

[0014] Each edge node sends the latest feature vector window every minute Upload to the central node for model training and global optimization;

[0015] S2, dynamically calculate the life score of the water pump based on the real-time feature vector and quantify the remaining life;

[0016] S3. Designing a dynamic start-stop scheme based on a fuzzy logic algorithm according to the remaining life to balance load demand and pump life distribution;

[0017] S4. Based on federated learning, optimize the remaining life of the water pump from a global perspective, and dynamically optimize and adjust the operation strategy based on the start-stop scheme;

[0018] The design of a dynamic start-stop scheme based on a fuzzy logic algorithm according to the remaining life to balance load demand and pump life distribution specifically includes:

[0019] Design the input variables and output variables of the fuzzy logic algorithm; wherein the input variables are life score, pressure fluctuation and start-stop frequency, and the output variable is priority;

[0020] In view of the special needs of water pump start-stop optimization, combined with fuzzy logic algorithm, pressure sensitive items and start-stop wear compensation items are designed; wherein, the pressure sensitive item is used to reduce the priority when the load fluctuates violently to avoid excessive participation of the water pump in the adjustment of unstable load; the start-stop wear compensation item is used to dynamically adjust the priority to reduce the operation opportunity of the water pump with too high start-stop frequency; the pressure sensitive item is expressed as follows:

[0021] ψ i =λ1·|ΔP i | 2

[0022] Where λ1 is the pressure fluctuation sensitivity factor, |ΔP i | 2 Used to amplify the suppressive effect of high pressure fluctuations on priority;

[0023] The start-stop wear compensation term is expressed as follows:

[0024] ξ i =λ2·max(0,ΔN i )

[0025] Among them, λ2 is the start-stop wear adjustment coefficient;

[0026] The input and output variables are combined with the pressure sensitive term and the start-stop wear compensation term to express as follows:

[0027] Priority i =μ L ·L i -ψ i -ξ i

[0028] Among them, L i is the lifespan score, μ L is the weight of the lifespan score, which directly reflects the main impact of lifespan on priority; ψ i It is a pressure sensitive item and suppresses excessive burden of fluctuation on the water pump. i This is a start-stop wear compensation item that limits the continued operation of pumps with high start-stop frequencies;

[0029] Calculate Priority i After that, all water pumps are sorted by priority, and the load is distributed one by one from high to low until the current load demand D of the system is met; if the load demand is reduced to the minimum, the water pumps are stopped one by one from the lowest to the highest priority until the load meets the minimum requirement;

[0030] Finally, the optimization plan Schedule is generated.

[0031] Furthermore, the preprocessing includes:

[0032] For flow rate Q i and flow rate P i A continuous value signal is used, and a sliding window smoothing algorithm is used to calculate the mean value every 5 sampling points to suppress random noise; the sliding window smoothing algorithm is expressed as follows:

[0033]

[0034] in, is the smoothed value at time t, w is the window size, X i [k] is the original sampling data;

[0035] The flow rate Q i , flow rate P i and energy consumption E i Normalized to [0,1] according to their respective historical extreme value ranges, expressed as follows:

[0036]

[0037] in, and The variables X are i The historical minimum and maximum values.

[0038] Furthermore, the real-time feature vector X i It is expressed as follows:

[0039]

[0040] in, Indicates the normalized value of flow; represents the normalized value of pressure; Represents the normalized value of power consumption; N i It indicates the number of starts and stops per unit time; D indicates the load demand.

[0041] Furthermore, the S2 specifically includes:

[0042] Constructing a pump life state model Predict the life score L of the water pump based on the feature sequence window i , where the lifespan score L i The range is [0,1], 1 means the life is complete, 0 means the life is exhausted; the water pump life state model It is expressed as follows:

[0043]

[0044] Among them, β0 represents the initialization bias term, which represents the initial life of the device; β j It represents the weight of time series characteristics, reflecting the contribution of different characteristics to life prediction; γ is the weight coefficient of cumulative fatigue factor; j represents time j; φ i It represents the cumulative fatigue factor, which describes the cumulative loss effect of high load and frequent start and stop;

[0045] When the pump is actually running, the edge node of each pump inputs its characteristic window in real time. To life model And calculate the current life score L i , the life state is generated according to the calculation results, which is expressed as follows:

[0046]

[0047] in, Represents the time series feature window, providing short-term pump operation trend information; L i Represents the life score, which is used to quantify the remaining life of the current water pump;

[0048] A dynamic anomaly detection mechanism is designed to improve the robustness of the water pump life status model to abnormal data.

[0049] Furthermore, the cumulative fatigue factor is expressed as follows:

[0050]

[0051] Among them, ΔP i [k] is the pressure fluctuation amplitude at the kth moment in the window, N i [k] is the number of starts and stops, α k is the time decay weight.

[0052] Furthermore, the dynamic anomaly detection mechanism specifically includes:

[0053] If the difference between the lifespan scores calculated twice in a row exceeds the threshold δ, an abnormal flag is recorded and an automatic verification mechanism is triggered; wherein the automatic verification mechanism specifically includes:

[0054] Complete Feature Window Upload to the central node.

[0055] The central node is retrained using the latest collected pump operation status data The parameters β0,β j ,γ, avoids the error expansion caused by long-term drift of the model.

[0056] Furthermore, if the load demand is reduced to the minimum, the water pumps are stopped one by one according to the priority from the lowest to the highest until the load meets the minimum requirement. The generated optimization plan is: Schedule = {Pump1: on, Pump2: on, Pump3: off}, and the plan update cycle is 1 minute.

[0057] Furthermore, the S4 specifically includes:

[0058] Design an optimization objective function J that comprehensively considers energy consumption and life loss; wherein the optimization objective function J satisfies the following constraints:

[0059] A. The total flow distribution meets the system load requirement D, which is expressed as follows:

[0060]

[0061] B. Flow distribution Q of each water pump i Not exceeding its maximum capacity, expressed as follows:

[0062]

[0063] in, Indicates the maximum flow distribution of each pump;

[0064] C. The deactivated pump does not distribute flow: If the Pump i:off, then Q i =0;

[0065] The objective function J is optimized using the distributed gradient descent method;

[0066] After the optimization is completed, the optimal flow distribution plan FlowPlan = {Q1, Q2, ..., Q n}Satisfy the constraints and minimize the objective function J, and update the start-stop optimization plan Schedule ′ By checking the flow rate Q i Determine: If Q i =0, then Pump i :off;

[0067] Collect optimized traffic distribution Q i And energy consumption data E i , as the calculation of the sample updated lifetime score to adapt it to the latest operating conditions.

[0068] Furthermore, the optimization objective function J is defined as follows:

[0069]

[0070] Where n represents the total number of pumps, η i Indicates the flow rate ratio of pump i, E i represents energy consumption, σ i represents the life weighting term, L i represents the lifespan score;

[0071] The distributed gradient descent method is used to optimize the objective function J, specifically:

[0072] By priority i Sort the pumps, prioritize the flow to the high-priority pumps, and ensure that the initial allocation meets the constraints. Calculate the gradient of each pump for the objective function J. The calculation is as follows:

[0073]

[0074] in, is the gradient of the effect of flow rate on the life score;

[0075] At the same time, the gradient descent method is used to adjust the flow distribution of each water pump, as shown below:

[0076]

[0077] Among them, α is the step size factor, which ensures the stability of the optimization process;

[0078] When the flow adjustment of all water pumps meets the constraint conditions and the objective function change ΔJ is less than the set threshold, the iteration is stopped.

[0079] Furthermore, the flow rate ratio η of the water pump i is i , calculated as follows:

[0080]

[0081] Among them, Q i is the flow rate allocated to pump i, D is the total load demand of the system, is the maximum flow capacity of water pump i; the energy consumption E i It has a nonlinear relationship with the distribution flow rate and is calculated as follows:

[0082]

[0083] Among them, a i ,b i ,c i is the energy consumption coefficient of water pump i;

[0084] The life weighting term σ i It is used to dynamically reflect the risk level of water pump life consumption, and is calculated as follows:

[0085] σ i =1+α·exp(-β·L i )

[0086] Among them, α, β are adjustment coefficients, which are used to control the extent of risk increase; λ is the life protection weight, which balances energy consumption optimization and life optimization; when L i When σ is the lowest, i If the maximum value is reached, the flow distribution of the current water pump i will be reduced to protect its life.

[0087] The beneficial technical effects of the present invention are at least as follows:

[0088] The present invention adopts edge computing technology to transform traditional centralized control into a distributed architecture. An independent edge node is deployed on each water pump, which is responsible for local data collection and preliminary calculation, and dynamically adjusts the operating parameters under the guidance of the central node. The distributed architecture significantly reduces data transmission delay, improves system response speed, and enhances the robustness and fault tolerance of the system, solving the problems of insufficient real-time performance and single-point failure risk of traditional centralized control.

[0089] In view of the shortcomings of water pump life management, this invention introduces fuzzy logic algorithm to dynamically adjust the start and stop priority and operation frequency of water pumps according to real-time data such as pressure fluctuation, start and stop frequency and current life status. Fuzzy control can effectively balance the load of each water pump and avoid overuse of certain equipment, thereby extending the overall service life of the system and solving the problem that traditional methods cannot dynamically adapt to life management.

[0090] This paper proposes a dual-objective optimization algorithm that comprehensively considers the energy consumption characteristics and life status of the water pump, and achieves the coordinated improvement of energy efficiency optimization and life balance through real-time load distribution adjustment. In the short term, real-time energy consumption optimization is achieved through edge nodes, and in the long term, global data is integrated through federated learning through central nodes to further optimize the overall system performance, solving the problem of separation of energy efficiency optimization and life management.

[0091] This invention achieves an overall breakthrough from system architecture to control algorithm through the combination of edge computing and fuzzy control. Edge computing improves real-time performance and reliability, fuzzy control provides dynamic life optimization capabilities, and dual-objective optimization achieves coordinated management of energy efficiency and life. These innovations complement each other and constitute a complete solution, providing a new technical path for the efficient and reliable operation of parallel water pumps. BRIEF DESCRIPTION OF THE DRAWINGS

[0092] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0093] Figure 1 This is a flow chart of the intelligent algorithm optimization combination and control method of parallel water pumps of the present invention. DETAILED DESCRIPTION

[0094] Embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and cannot be understood as limiting the present invention.

[0095] like Figure 1 As shown, an embodiment of the present invention provides an intelligent algorithm optimization combination and control method for parallel water pumps, the method comprising:

[0096] S1. Configure sensors and computing modules on the edge nodes of each water pump to collect and preprocess the operating data of each water pump in real time to generate a unified real-time feature vector and time series feature; the operating data includes the flow rate Q of the water pump during operation. i 、Pressure Pi , Power consumption E i 、N number of starts and stops i and the current load demand D.

[0097] Specifically, sensors and computing modules are configured on the edge nodes of each water pump to collect real-time data including the flow rate (Q i , unit: cubic meter / second), pressure (P i , unit: Pascal), power consumption (E i , unit: Watt), start and stop times (N i , unit: times) and current load demand (D, unit: cubic meters / second).

[0098] Among them, the flow Q i The pressure is accurately measured by ultrasonic or electromagnetic flowmeter, sampling once per second. i The pressure value of the water pump outlet is collected through the pressure sensor, and the sample is taken once per second. i The real-time power consumption of the pump is calculated through the power metering module. i Based on the accumulation of the pump start and stop signals (high level / low level), the start and stop frequency per minute is recorded. The current load demand D is provided by the system demand forecasting module and updated every minute.

[0099] Furthermore, according to the characteristics of data from different sources, the following strategies are used for preprocessing:

[0100] Denoising: For Q i and P i For signals with equal continuous values, a sliding window smoothing algorithm is used to calculate the mean value every 5 sampling points to suppress random noise. The formula is as follows:

[0101]

[0102] in, is the smoothed value at time t, w is the window size (value 5), X i [k] is the original sampling data.

[0103] Normalization: In order to facilitate the subsequent life evaluation, Q i , P i and E i Normalized to [0,1] according to their respective historical extreme value ranges:

[0104]

[0105] in, and The variables X are i The historical minimum and maximum values.

[0106] Each preprocessed variable is constructed into a real-time feature vector of the water pump:

[0107]

[0108] in, Indicates the normalized value of the flow rate. Indicates the normalized value of pressure. Represents the normalized value of power consumption. N i It represents the number of starts and stops per unit time, and does not need to be normalized. D represents the load demand of the system, which is directly used as input.

[0109] Furthermore, in order to extract the historical trend of the water pump operation, a feature sequence window is generated in real time every minute, with a length of the past 5 minutes of data. The form of each feature vector is:

[0110]

[0111] This window is used to capture short-term operational changes and provide input to the life assessment module.

[0112] Furthermore, edge node upload rules:

[0113] Each edge node sends the latest feature vector window every minute Upload to the central node for model training and global optimization. When the set threshold value (0.8) is exceeded, the high-frequency upload mechanism is triggered to send all historical feature data to the central node.

[0114] The high-frequency upload mechanism specifically includes:

[0115] The edge node establishes a communication connection with the central node through wired or wireless means. Under the high-frequency upload mechanism, this connection may require higher bandwidth and lower latency to ensure real-time transmission of data;

[0116] Once the connection is established, the edge node will send data to the central node at a higher frequency. This high-frequency transmission can ensure that the data can quickly reach the central node in abnormal situations for subsequent analysis and processing;

[0117] After receiving the data, the central node will confirm and verify it to ensure the integrity and accuracy of the data; if the data is incorrect or lost, the central node may send a feedback message to the edge node, requesting it to resend or supplement the data.

[0118] S2. Dynamically calculate the life score of the water pump based on the real-time feature vector to quantify the remaining life.

[0119] Specifically, define a pump life state model The model predicts the life score L of the water pump based on the feature sequence window i (The range is [0,1], 1 means full life, 0 means life is exhausted). The model design combines the start-stop loss of the pump mechanical components with the long-term impact of load fluctuations. The formula is as follows:

[0120]

[0121] Among them, β0 represents the initialization bias term, which represents the initial life of the equipment (such as L for a new water pump). i =1). j Represents the weight of the time series feature, reflecting the contribution of different features to life prediction, obtained by model training. i represents the cumulative fatigue factor, describing the cumulative loss effect of high load and frequent start-stop, and is defined as:

[0122]

[0123] Where ΔP i [k] is the pressure fluctuation amplitude at the kth moment in the window (unit: Pascal), N i [k] is the number of starts and stops, α k is the time decay weight (e.g. α k =0.8 k-1 ).

[0124] In the actual operation of the system, the edge node of each pump inputs its characteristic window in real time To life model And calculate the current life score L i . Generate life state based on calculation results:

[0125]

[0126] Life state includes: Represents the time series feature window, providing short-term pump operation trend information. i Represents the life score, which is used to quantify the remaining life of the current pump.

[0127] Furthermore, in order to improve the robustness of the model to abnormal data, a dynamic anomaly detection mechanism is designed:

[0128] If the difference between two consecutive calculated lifespan scores exceeds a threshold value δ (eg, δ=0.1), an abnormal flag is recorded and an automatic verification mechanism is triggered.

[0129] The automatic verification process includes:

[0130] Complete Feature Window Upload to the central node. ;

[0131] The central node is retrained using the latest data The parameters β0,β j ,γ, avoids the error expansion caused by long-term drift of the model.

[0132] Furthermore, the edge node updates the life state State every minute. i , and serves as a direct input for the next step of pump start and stop optimization to adjust the priority and scheduling strategy of the pump.

[0133] S3. Design a dynamic start-stop solution based on a fuzzy logic algorithm according to the remaining life to balance the load demand and the life distribution of the water pump.

[0134] Specifically, the fuzzy control design selects three input variables and calculates the output variable priority according to the actual working conditions of the water pump system. i :

[0135] Input variables:

[0136] Lifespan score L i : The current life status of the water pump, L i The lower the value, the lower the priority.

[0137] Pressure fluctuations The difference between the current pressure and the average pressure in the window indicates the impact of load fluctuations on the operation of the pump.

[0138] Start and stop frequency The difference between the current start-stop times and the historical average value within the window is used to reflect the wear status of mechanical components.

[0139] Output variables:

[0140] Priority i : The range is [0,1], indicating the start and stop priority of the water pump. The higher the value, the higher the priority.

[0141] Design pressure sensitivity and start-stop wear compensation

[0142] In view of the special needs of pump start-stop optimization, the following innovative items are designed to enhance the adaptability of the model:

[0143] Pressure sensitivity term ψ i : Used to reduce the priority when the load fluctuates violently, to avoid excessive participation of the pump in the adjustment of unstable loads:

[0144] ψ i =λ1·|ΔP i | 2

[0145] Where λ1 is the pressure fluctuation sensitivity factor (typical value λ1 = 0.1), |ΔP i | 2 Used to amplify the suppressive effect of high pressure fluctuations on priority.

[0146] Start-stop wear compensation termξ i : Used to dynamically adjust the priority and reduce the running opportunities of pumps with too high start and stop frequency:

[0147] ξ i =λ2·max(0,ΔN i )

[0148] Wherein, λ2 is the start-stop wear adjustment coefficient (typical value λ2=0.05).

[0149] Furthermore, considering the above factors, the priority i Calculated by the following formula:

[0150] Priority i =μ L ·L i -ψ i -ξ i

[0151] Among them, μ L is the weight of the life score (typical value μ L =1.0), directly reflecting the main influence of life span on priority. i It is a pressure sensitive item and suppresses excessive burden of fluctuation on the water pump. i To compensate for the start and stop wear, the pump with a high start and stop frequency is restricted from continuing to run.

[0152] Furthermore, the priority is calculated i Finally, all water pumps are sorted by priority, and the load is distributed one by one from high to low until the current load demand D (unit: cubic meters / second) of the system is met. If the system load demand decreases, the water pumps are stopped one by one from low to high according to the priority until the load meets the minimum requirement.

[0153] It can be understood. Example: Assume that the system currently has 3 water pumps, whose life scores are [0.9, 0.7, 0.6], and the pressure fluctuation ΔP i is [0.05, 0.1, 0.02], start-stop frequency ΔN i is [0,2,1], and the current load demand D=70 (unit: cubic meters / second).

[0154] Calculate the priority of each pump:

[0155] Priority1=μL ·0.9-ψ1-ξ1=1.0·0.9-0.1·(0.05) 2 -0=0.89975.

[0156] Priority2=μ L ·0.7-ψ2-ξ2=1.0·0.7-0.1·(0.1) 2 -0.05·2=0.685.

[0157] Priority3=μ L ·0.6-ψ3-ξ3=1.0·0.6-0.1·(0.02) 2 -0.05·1=0.59596.

[0158] Sort by priority i =[Pump1:0.89975,Pump2:0.685,Pump3:0.59596]. When distributing the load, the first two pumps are started first to meet the load requirement of D=70, and the third pump is stopped.

[0159] Furthermore, the generated optimization plan is: Schedule = {Pump1: on, Pump2: on, Pump3: off}, and the plan update cycle is 1 minute.

[0160] S4. Based on federated learning, optimize the remaining life of the water pump from a global perspective, and dynamically optimize and adjust the operation strategy based on the start-stop scheme.

[0161] Specifically, in order to reflect the characteristics of the patent, an optimization objective function that comprehensively considers energy consumption and life loss is designed, which is defined as follows:

[0162]

[0163] Where n represents the total number of pumps. i It represents the flow rate ratio of pump i, which is calculated as:

[0164]

[0165] Among them, Q i is the flow rate allocated to pump i (unit: cubic meters per second), D is the total load demand of the system, is the maximum flow capacity of pump i.

[0166] E i It represents the energy consumption of pump i in watts (W), which has a nonlinear relationship with the distribution flow rate and is usually fitted by an empirical formula:

[0167]

[0168] Among them, a i ,b i ,c i is the energy consumption coefficient of pump i, which depends on its design characteristics. i represents the life score of pump i, which is obtained from the life model in step 2. Updated in real time. i It represents the innovative life weighted item, dynamically reflects the risk level of water pump life consumption, and is defined as:

[0169] σ i =1+α·exp(-β·L i )

[0170] Understandably, when L i When σ is low, i The rapid increase forces the optimization algorithm to reduce the flow distribution of the pump and protect its life.

[0171] Among them, α, β are adjustment coefficients (such as α = 2, β = 5), which are used to control the extent of risk increase. λ represents the life protection weight, balancing energy consumption optimization and life optimization, and the typical value λ∈[0.1,1.0].

[0172] Furthermore, the optimization problem needs to satisfy the following constraints:

[0173] The total flow distribution meets the system load requirements:

[0174]

[0175] The flow distribution of each pump does not exceed its maximum capacity:

[0176]

[0177] A deactivated pump does not distribute flow: If the Pump i :off, then Q i =0.

[0178] Furthermore, the distributed gradient descent method is used to optimize the objective function J. The algorithm steps are as follows:

[0179] Initialization: by priority i Sort the pumps and allocate flow to high-priority pumps first, and the initial allocation satisfies the constraints.

[0180] Gradient calculation: Calculate the gradient of each pump for the objective function J

[0181]

[0182] in, is the gradient of the impact of flow on the life score, which is determined by the life model The parameters in are calculated.

[0183] Update flow distribution: Use gradient descent to adjust the flow distribution of each pump:

[0184]

[0185] Among them, α is the step size factor to ensure the stability of the optimization process (such as α = 0.01).

[0186] Iteration termination condition: When the flow adjustment of all pumps meets the constraint conditions and the change of the objective function ΔJ is less than the set threshold (such as ΔJ<10 -3 ), the iteration stops.

[0187] Furthermore, after the optimization is completed, the optimal flow distribution plan FlowPlan = {Q1, Q2, ..., Q n}Satisfy the constraints and minimize the objective function J.

[0188] Updated start-stop optimization plan Schedule ′ By checking the flow rate Q i Determine: If Q i =0, then Pump i :off.

[0189] Example: Assume that the system has 3 pumps and their initial states are as follows:

[0190] Lifespan score L i =[0.9,0.7,0.5], maximum flow The current load demand D=70.

[0191] Initial flow distribution: Q1=40, Q2=30, Q3=0.

[0192] Through iterative optimization, the final flow distribution result is FlowPlan = {35,35,0}, corresponding to the start-stop plan Schedule ′ ={Pump1:on,Pump2:on,Pump3:off}.

[0193] Furthermore, the optimized traffic distribution Q is collected i And energy consumption data E i , as a sample to update the life model The central node periodically integrates the data of all edge nodes, uses federated learning technology to update the global optimization model, and sends it to each edge node to achieve long-term performance improvement.

[0194] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0195] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a division of logical functions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the device or unit can be electrical, mechanical or other forms.

[0196] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server or network device, etc.) to perform all or part of the steps of the methods described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, and other media that can store program codes.

[0197] Although embodiments of the present invention have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the claims and their equivalents.

Claims

1. A parallel water pump intelligent algorithm optimization combination and control method, characterized in that: The method comprises the following steps: S1. Configure sensors and computing modules on the edge nodes of each water pump to collect and preprocess the operating data of each water pump in real time to generate a unified real-time feature vector and time series feature; the operating data includes the flow rate Q of the water pump during operation. i 、Pressure P i , Power consumption E i 、N number of starts and stops i and the current load demand D; The time series feature extraction is as follows: Generate a feature sequence window in real time every minute Extract the historical trend of the water pump operation, the length of which is the data of the past 5 minutes, as shown below: Among them, X i [t-4] represents the feature vector of the window at time t-4, X i [t-3] represents the feature vector of the window at time t-3, X i [t-2] represents the feature vector of the window at time t-2, X i [t-1] represents the feature vector of the window at time t-1, X i [t] represents the feature vector of the window at time t; The edge node upload rules are as follows: Each edge node sends the latest feature vector window every minute Upload to the central node for model training and global optimization; however, when abnormal fluctuations are detected, each edge node triggers a high-frequency upload mechanism to send all historical feature data to the central node; S2. Dynamically calculate the life status of the water pump based on the real-time feature vector and quantify the remaining life; S3. Designing a dynamic start-stop scheme based on a fuzzy logic algorithm according to the life state to balance the load demand and the life distribution of the water pump; S4. Based on federated learning, optimize the remaining life of the water pump from a global perspective, and dynamically optimize and adjust the operation strategy based on the start-stop scheme; The design of a dynamic start-stop scheme based on a fuzzy logic algorithm according to the life state to balance load demand and pump life distribution specifically includes: Design the input variables and output variables of the fuzzy logic algorithm; wherein the input variables are life score, pressure fluctuation and start-stop frequency, and the output variable is priority; In view of the special needs of water pump start-stop optimization, combined with fuzzy logic algorithm, pressure sensitive items and start-stop wear compensation items are designed; wherein, the pressure sensitive item is used to reduce the priority when the load fluctuates violently to avoid excessive participation of the water pump in the adjustment of unstable load; the start-stop wear compensation item is used to dynamically adjust the priority to reduce the operation opportunity of the water pump with too high start-stop frequency; the pressure sensitive item is expressed as follows: ψ i =λ1·|ΔP i | 2 Where λ1 is the pressure fluctuation sensitivity factor, |ΔP i | 2 Used to amplify the suppressive effect of high pressure fluctuations on priority; The start-stop wear compensation term is expressed as follows: x i =λ2·max(0,ΔN i Among them, λ2 is the start-stop wear adjustment coefficient; The input and output variables are combined with the pressure sensitive term and the start-stop wear compensation term to express as follows: Priority i =μ L ·L i -ψ i -x i Among them, L i is the lifespan score, μ L is the weight of the lifespan score, which directly reflects the main impact of lifespan on priority; ψ i It is a pressure sensitive item and suppresses excessive burden of fluctuation on the water pump. i This is a start-stop wear compensation item that limits the continued operation of pumps with high start-stop frequencies; Calculate Priority i After that, all water pumps are sorted by priority, and the load is distributed one by one from high to low until the current load demand D of the system is met; if the load demand is reduced to the minimum, the water pumps are stopped one by one from the lowest to the highest priority until the load meets the minimum requirement; Finally, the optimization plan Schedule is generated.

2. According to claim 1, a parallel water pump intelligent algorithm optimization combination and control method is characterized in that: The pre-processing comprises: For flow rate Q i and flow rate P i A continuous value signal is used, and a sliding window smoothing algorithm is used to calculate the mean value every 5 sampling points to suppress random noise; the sliding window smoothing algorithm is expressed as follows: in, is the smoothed value at time t, w is the window size, X i [k] is the original sampling data; The flow rate Q i , flow rate P i and energy consumption E i Normalized to |0,1] according to their respective historical extreme value ranges, expressed as follows: in, and The variables X are i The historical minimum and maximum values.

3. According to claim 2, a parallel water pump intelligent algorithm optimization combination and control method is characterized in that: The real-time feature vector X i It is expressed as follows: in, Indicates the normalized value of flow; represents the normalized value of pressure; Represents the normalized value of power consumption; N i It indicates the number of starts and stops per unit time; D indicates the load demand.

4. The intelligent algorithm optimization combination and control method for parallel water pumps according to claim 1 is characterized in that: The S2 specifically includes: Constructing a pump life state model Predict the life score L of the water pump based on the feature sequence window i , where the lifespan score L i The range is [0,1], 1 means the life is complete, 0 means the life is exhausted; the water pump life state model It is expressed as follows: Among them, β0 represents the initialization bias term, which represents the initial life of the device; β j It represents the weight of time series characteristics, reflecting the contribution of different characteristics to life prediction; γ is the weight coefficient of cumulative fatigue factor; j represents time j; φ i It represents the cumulative fatigue factor, which describes the cumulative loss effect of high load and frequent start and stop; When the pump is actually running, the edge node of each pump inputs its characteristic window in real time. To life model And calculate the current life score L i , Generate life state State based on the calculation results i , which is expressed as follows: in, Represents the time series feature window, providing short-term pump operation trend information; L i Represents the life score, which is used to quantify the remaining life of the current water pump; A dynamic anomaly detection mechanism is designed to improve the robustness of the water pump life status model to abnormal data.

5. The intelligent algorithm optimization combination and control method for parallel water pumps according to claim 4 is characterized in that: The cumulative fatigue factor is expressed as follows: Among them, ΔP i [k] is the pressure fluctuation amplitude at the kth moment in the window, N i [k] is the number of starts and stops, α k is the time decay weight.

6. The intelligent algorithm optimization combination and control method for parallel water pumps according to claim 4 is characterized in that: The dynamic anomaly detection mechanism specifically includes: If the difference between the lifespan scores calculated twice in a row exceeds the threshold δ, an abnormal flag is recorded and an automatic verification mechanism is triggered; wherein the automatic verification mechanism specifically includes: Complete Feature Window Upload to the central node. The central node is retrained using the latest collected pump operation status data The parameters β0,β j ,γ, avoids the error expansion caused by long-term drift of the model.

7. The intelligent algorithm optimization combination and control method for parallel water pumps according to claim 1 is characterized in that: If the load demand is reduced to the minimum, the water pumps are stopped one by one from the lowest to the highest priority until the load meets the minimum requirement. The generated optimization plan is: Schedule = {Pump1: on, Pump2: on, Pump3: off}, and the plan update cycle is 1 minute.

8. The intelligent algorithm optimization combination and control method for parallel water pumps according to claim 1 is characterized in that: The S4 specifically includes: Design an optimization objective function J that comprehensively considers energy consumption and life loss; wherein the optimization objective function J satisfies the following constraints: A. The total flow distribution meets the system load requirement D, which is expressed as follows: B. Flow distribution Q of each water pump i Not exceeding its maximum capacity, expressed as follows: in, Indicates the maximum flow distribution of each pump; C. The deactivated pump does not distribute flow: If the Pump i :off, then Q i =0; The objective function J is optimized using the distributed gradient descent method; After the optimization is completed, the optimal flow distribution plan FlowPlan = {Q1, Q2, ..., Q n }Satisfy the constraints and minimize the objective function J, and update the start-stop optimization plan Schedule ′ By checking the flow rate Q i Determine: If Q i =0, then Pump i :off; Collect optimized traffic distribution Q i And energy consumption data E i , as the calculation of the sample updated lifetime score to adapt it to the latest operating conditions.

9. The intelligent algorithm optimization combination and control method for parallel water pumps according to claim 8 is characterized in that: The optimization objective function J is defined as follows: Where n represents the total number of pumps, η i Indicates the flow rate ratio of pump i, E i represents energy consumption, σ i represents the life weighting term, L i represents the lifespan score; The distributed gradient descent method is used to optimize the objective function J, specifically: By priority i Sort the pumps, prioritize the flow to the high-priority pumps, and ensure that the initial allocation meets the constraints. Calculate the gradient of each pump for the objective function J. The calculation is as follows: in, is the gradient of the effect of flow rate on the life score; At the same time, the gradient descent method is used to adjust the flow distribution of each water pump, as shown below: Among them, α is the step size factor, which ensures the stability of the optimization process; When the flow adjustment of all water pumps meets the constraint conditions and the objective function change ΔJ is less than the set threshold, the iteration is stopped.

10. The intelligent algorithm optimization combination and control method for parallel water pumps according to claim 9 is characterized in that: The flow rate ratio η of the water pump i i , calculated as follows: Among them, Q i is the flow rate allocated to pump i, D is the total load demand of the system, is the maximum flow capacity of pump i; The energy consumption E i It has a nonlinear relationship with the distribution flow rate and is calculated as follows: Among them, a i ,b i ,c i is the energy consumption coefficient of water pump i; The life weighting term σ i It is used to dynamically reflect the risk level of water pump life consumption, and is calculated as follows: s i =1+α·exp(-β·L i ) Among them, α, β are adjustment coefficients, which are used to control the extent of risk increase; λ is the life protection weight, which balances energy consumption optimization and life optimization; when L i When σ is the lowest, i If the maximum value is reached, the flow distribution of the current water pump i will be reduced to protect its life.

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