Dual-mode digital energy-saving pump and adaptive speed regulation control method

Abstract

The application discloses a dual-mode digital energy-saving pump and a self-adaptive speed regulation control method, belongs to the technical field of water supply equipment and intelligent control, and aims to solve the problems that the existing pump group cannot accurately regulate and control according to differentiated water supply demands of multiple regions, working condition determination is prone to misjudgment, and the energy efficiency of the main and auxiliary pumps is low. First, hydraulic data and historical water consumption data of a water supply network are acquired, grid units are divided, key hydraulic parameters and clustering features are extracted, and target water supply sub-regions are divided in combination with spatial superposition; then, a multi-level working condition label dictionary is constructed based on attributes of the target water supply sub-regions; next, real-time data are collected to match working condition labels and target water consumption demands, and an initial running parameter set of the pump group is generated; finally, parameters are executed and working condition labels are matched in real time, and a fuzzy comprehensive evaluation is used to determine an energy-saving running mode or a dynamic response mode, and parameters are adaptively optimized. The application realizes accurate adaptation of multi-region water supply, takes into account energy saving and response speed, and significantly improves the economy and stability of the water supply system.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of water supply equipment and intelligent control technology, more particularly to a dual-mode digital energy-saving pump and a self-adaptive speed control method. BACKGROUND

[0002] In modern water supply systems, the pump set as the core equipment, its speed control effect affects the water supply quality, energy consumption and stability. With the diversification of water supply scenes, the water demand of different areas such as business, office, residents and emergency is significantly different, and the existing pump set control technology gradually exposes problems. The traditional control mostly adopts global unified regulation, without considering the difference of regional water characteristics, and the situation of redundant water supply in some areas and insufficient pressure in some areas often occurs, which is difficult to balance energy consumption and water supply reliability. The working condition judgment only depends on simple parameter threshold, ignoring parameter change trend and rate, which is easy to misjudge or respond lag, resulting in waste of energy consumption in stable time and water supply fluctuation in sudden change. In addition, the initial parameters are mostly set according to experience or historical data, which is disconnected with real-time demand, and the start-up stage is debugged frequently with high energy consumption; the main and auxiliary pump coordination logic is simple, the auxiliary pump function is not fully played, the main pump is frequently speed-regulated to aggravate the loss, and the long-term water rule is not applied, which is difficult to balance the demand, energy consumption and equipment protection. In summary, the existing technology has shortcomings in regional adaptation, working condition judgment and parameter optimization, and cannot meet the comprehensive needs of modern water supply system in efficiency, stability and energy saving. Therefore, in order to overcome these limitations, the present application provides a dual-mode digital energy-saving pump and a self-adaptive speed control method. SUMMARY

[0003] In view of the shortcomings of the prior art, the purpose of the present application is to provide a dual-mode digital energy-saving pump and a self-adaptive speed control method, which solves the problem of how to accurately match the water demand of different regions for multi-region water supply scenes, accurately judges the water supply working condition to select the adaptive control mode, and optimizes the pump set operation parameters, solves the problems of insufficient pertinence of existing pump set control, low accuracy of working condition judgment and difficulty in balancing energy efficiency and response speed.

[0004] To achieve the above purpose, the present application provides the following technical scheme:

[0005] A self-adaptive speed control method of a dual-mode digital energy-saving pump, comprising:

[0006] Obtaining the water supply pipe network hydraulic data and historical water data of the target water supply area covered by the pump set, dividing the target water supply area into grid units, extracting the key hydraulic parameters and clustering characteristics of each grid unit, dividing the initial clustering group according to the clustering characteristics, and combining the key hydraulic parameters of the grid unit to divide the target water supply sub-area through spatial superposition;

[0007] A multi-level working condition label dictionary is constructed through parameter interval division, trend feature identification, time period clustering analysis and type rule definition based on the functional attributes, historical operation data and regulation and control requirements of the target water supply sub-regions;

[0008] Real-time water consumption data of each target water supply sub-region is collected to construct a real-time working condition label combination for matching the target water consumption requirements of each target water supply sub-region, and an initial operation parameter set of the pump group is generated through multi-pump collaborative load distribution;

[0009] The initial operation parameter set of the pump group is executed, and the basic working condition label and the trend working condition label of each target water supply sub-region are matched in real time, and a fuzzy comprehensive evaluation algorithm is used to quantify the decision index to calculate the mode switching decision value for determining the regulation and control mode, and the initial operation parameter set of the pump group is adaptively optimized according to the regulation and control mode.

[0010] Specifically, the specific steps of dividing the initial clustering group include:

[0011] The water supply network hydraulic data and historical water consumption data of the target water supply region are preprocessed, the initial grid size is set based on the pipe network density distribution of the target water supply region, and adaptive grid unit division is performed;

[0012] The preprocessed water supply network hydraulic data and historical water consumption data are bound according to the coordinates of the grid units to generate a grid data association table;

[0013] According to the water supply network hydraulic data in the grid data association table, a water supply network hydraulic model of the target water supply region is generated, key hydraulic parameters are extracted, and a grid hydraulic parameter reference table is generated;

[0014] Based on the historical water consumption data in the grid data association table, time series analysis is used to extract the clustering characteristics of each grid unit to form a multi-dimensional clustering feature vector of each grid unit, which is used to divide the grid units into initial clustering groups, and an initial clustering result table is constructed.

[0015] Specifically, the specific steps of dividing the target water supply sub-region include:

[0016] A buffer zone is generated for the boundary of each initial clustering group of the initial clustering result table, the grid units with the same key hydraulic parameters as the buffer zone are spatially superimposed, and the spatial overlap degree and the key hydraulic parameter matching degree of the spatially superimposed region are calculated;

[0017] The spatial overlap degree is the ratio of the area of the spatially superimposed region to the area of the initial clustering group;

[0018] The key hydraulic parameter matching degree is measured by the coefficient of variation of the key hydraulic parameters of the grid units in the spatially superimposed region;

[0019] Set a screening threshold, screen the spatial overlap area whose spatial overlap degree and key hydraulic parameter matching degree are greater than the corresponding screening threshold; merge the initial clustering group and the spatial overlap area and smooth the boundary to generate the target water supply sub-region boundary and construct the target water supply sub-region division result table.

[0020] Specifically, the specific steps of constructing the multi-level working condition label dictionary include:

[0021] Obtain the functional attributes of each target water supply sub-region, and extract the key parameter time sequence according to the historical operation data of each target water supply sub-region; perform preprocessing on the extracted key parameter time sequence to generate a standardized key parameter time sequence dataset;

[0022] For the standardized key parameter time sequence dataset, calculate the probability distribution characteristics of each key parameter through statistical analysis, set the parameter interval of each key parameter through threshold division, label the corresponding basic working condition label for each parameter interval, and construct a basic working condition label library;

[0023] Based on the key parameter time sequence corresponding to the basic working condition label, the sliding window algorithm is used to calculate the change rate of each key parameter per unit time, the trend line slope is obtained by trend fitting of the change rate of the key parameter in each sliding window, and the change trend type of the key parameter is judged by combining the preset change rate threshold and the slope threshold. Assign a corresponding trend working condition label to each change trend type, and construct a trend working condition label library.

[0024] Specifically, the specific steps of constructing the multi-level working condition label dictionary further include:

[0025] Based on the key parameters of each target water supply sub-region, calculate the water load curve of each target water supply sub-region, divide the water load curve into typical water time periods, assign a corresponding time period working condition label to each typical water time period, and extract the basic water demand characteristics of each typical water time period to construct a time period working condition label library;

[0026] According to the functional attributes and control priorities of each target water supply sub-region, assign a water supply type working condition label to each target water supply sub-region, and bind each water supply type working condition label with the corresponding control rule to construct a water supply type working condition label library;

[0027] According to the logical levels of the water supply type working condition label, the time period working condition label, the trend working condition label and the basic working condition label, the association rule mining algorithm is used to calculate the association strength between the working condition labels at each level, which is used to screen the typical label combination. Establish the trigger condition and mapping path between the working condition labels at each level to construct a multi-level working condition label dictionary.

[0028] Specifically, the specific steps of matching the target water demand of each target water supply sub-region include:

[0029] From the matched period working condition labels, the historical basic water demand characteristics corresponding to the period working condition labels are extracted, including historical average pressure and historical average load;

[0030] The deviation proportion of the real-time key parameters and the corresponding historical basic water demand characteristics is calculated; based on the historical basic water demand characteristics, the preliminary water demand is generated in combination with the deviation proportion, including the preliminary target pressure and the preliminary target flow;

[0031] The key parameter control range of each target water supply sub-region is obtained, and the preliminary water demand is checked with the corresponding key parameter control range:

[0032] If the preliminary water demand is within the corresponding key parameter control range, the preliminary water demand is taken as the target water demand, otherwise, the preliminary target pressure and the preliminary target flow are modified based on the key parameter control range.

[0033] Specifically, the specific steps of generating the initial operation parameter set of the pump group through multi-pump collaborative load distribution include:

[0034] The target water demand of each target water supply sub-region is summarized to form the total target water demand of the target water supply region, including the total target flow and the reference target pressure;

[0035] The total target flow and the reference target pressure of the target water supply region are associated and matched with the main pump characteristic curve, including the flow-speed curve, the pressure-speed curve and the efficiency-load curve;

[0036] The basic speed interval at which the reference target pressure is output is determined according to the pressure-speed curve of the main pump, and the basic load proportion borne by the main pump in the basic speed interval is obtained in combination with the total target flow;

[0037] According to the main pump basic load proportion and the total target flow, the initial flow distribution value of the main pump is calculated, the initial speed is derived according to the flow-speed curve of the main pump, and the initial output frequency of the main pump is converted to construct the initial operation parameter set of the main pump;

[0038] According to the total target flow and the initial flow distribution value of the main pump, the total load of the auxiliary pump is calculated, and the exclusive load proportion of each auxiliary pump is allocated in combination with the independent target water demand of the target water supply sub-region, which is used to obtain the initial flow distribution value, the initial speed and the corresponding initial output frequency of the auxiliary pump frequency converter of the auxiliary pump in combination with the characteristic curve of each auxiliary pump, to construct the initial operation parameter set of each auxiliary pump.

[0039] Specifically, the specific steps of determining the control mode include:

[0040] According to the parameter interval matching of each key parameter, the basic working condition label is matched, and the change rate of each key parameter in unit time is obtained through the sliding window algorithm to obtain the change trend type of each key parameter, and the trend working condition label is matched;

[0041] The basic working condition label is quantified by coupling the interval coincidence degree and the deviation degree of the normal parameter interval of each key parameter, and the basic fitting degree of the basic working condition label is calculated to construct the basic working condition adaptability index;

[0042] The trend fitting degree is calculated according to the fitting degree of the key parameter change rate and the smoothness threshold and the fluctuation influence weight, and the trend working condition label is quantified to construct the trend working condition stability index;

[0043] The basic working condition adaptability index quantization value and the trend working condition stability index quantization value are substituted into the fuzzy comprehensive evaluation model by combining the type working condition label allocation index weight of the corresponding target water supply sub-region, and the working condition decision value of the target water supply sub-region is obtained by weighted calculation;

[0044] The working condition decision values of all target water supply sub-regions are weighted and summarized according to the importance of the target water supply sub-region to generate the mode switching decision value;

[0045] The mode switching decision value is compared with the preset mode judgment threshold, if the mode switching decision value is greater than the mode judgment threshold, it is determined that the current working condition is adaptive energy-saving operation mode; otherwise, it is determined that the current working condition is adaptive dynamic response mode.

[0046] Specifically, the specific steps of adaptively optimizing the initial operating parameter set of the pump group according to the control mode include:

[0047] If the current working condition is adaptive energy-saving operation mode, the deviation degree of the key parameter of each target water supply sub-region from the normal parameter interval is calculated, and the energy-saving control coefficient is retrieved according to the water supply type working condition label of the target water supply sub-region, which is used to calculate the target water demand correction amount based on the target water demand of the current target water supply sub-region, and the initial operating parameter set of the auxiliary pump of the corresponding target water supply sub-region is updated through the auxiliary pump load redistribution and parameter collaborative adaptation mechanism;

[0048] If the current working condition is adaptive dynamic response mode, the abnormal target water supply sub-region is identified according to the real-time key parameter change rate and the trend working condition label of each target water supply sub-region, and the dynamic response coefficient is retrieved, which is used to calculate the dynamic compensation amount of the target water demand of the abnormal target water supply sub-region based on the current target water demand, and the initial operating parameter set of the pump group of the corresponding target water supply sub-region is updated through the auxiliary pump parameter priority adjustment and main pump collaborative fine-tuning mechanism.

[0049] A dual-mode digital energy-saving pump comprises a data acquisition unit, a central control unit, a pump group execution unit and a data storage unit;

[0050] The data acquisition unit is used for collecting and preprocessing real-time water consumption data of each target water supply sub-region; the central control unit is used for matching real-time working condition labels, determining a control mode, generating and optimizing an initial operation parameter set of a pump group; the pump group execution unit is used for driving the pump group to operate according to the initial operation parameter set of the pump group; and the data storage unit is used for storing basic data, historical data and rule data of the target water supply sub-region.

[0051] The present application has the following advantages:

[0052] The present application realizes accurate division of a target water supply sub-region through grid unit division and spatial superposition, constructs a multi-level working condition label dictionary in combination with attributes of the target water supply sub-region, matches working condition labels and target water consumption demand based on real-time data and generates initial parameters, and then determines a control mode through fuzzy comprehensive evaluation and self-adaptively optimizes parameters, and the corresponding equipment is executed through multi-unit cooperation; the technical scheme effectively solves the problems of poor region adaptability, inaccurate working condition judgment and insufficient parameter optimization in the prior art, can implement differentiated control for different target water supply sub-regions, accurately identifies working conditions to adapt to an energy-saving operation mode or a dynamic response mode, optimizes load distribution and parameter setting of primary and auxiliary pumps, reduces energy waste, guarantees water supply safety in key regions, improves working condition response speed and pump group operation stability, and balances economy, reliability and adaptive flexibility of a water supply system. BRIEF DESCRIPTION OF DRAWINGS

[0053] Figure 1 A flowchart of the adaptive speed control method of the dual-mode digital energy-saving pump of the present application;

[0054] Figure 2 A flowchart of the target water supply sub-region division of the present application;

[0055] Figure 3 A flowchart of the construction of the multi-level working condition label dictionary of the present application;

[0056] Figure 4 A flowchart of the generation of the initial operation parameter set of the pump group of the present application;

[0057] Figure 5 A flowchart of the determination of the control mode of the current working condition of the present application. DETAILED DESCRIPTION

[0058] Please refer to Figure 1 The present embodiment introduces an adaptive speed control method of a dual-mode digital energy-saving pump, which comprises the following steps:

[0059] Step S1: Obtain the water supply pipe network hydraulic data and historical water consumption data of the target water supply area covered by the pump group, set the initial grid size to divide the target water supply area into grid units, construct a pipe network hydraulic model based on the water supply pipe network hydraulic data through a hydraulic simulation software, extract the key hydraulic parameters of each grid unit, extract the clustering features of each grid unit according to the historical water consumption data, perform clustering analysis on the clustering features by using a density clustering algorithm, and divide the target water supply sub-area by using a spatial superposition algorithm in combination with the key hydraulic parameters of the grid units; specifically, the water supply pipe network hydraulic data includes pipe network topology, pipe material type, pipe diameter specification, pipe network node distribution, and pipe laying elevation, which are used to construct a hydraulic model reflecting the physical characteristics and water flow transmission law of the water supply pipe network, and provide basic data for analyzing the hydraulic characteristic differences of different areas; the water consumption data includes time-division water supply flow, pipe network pressure, water consumption duration, and water equipment operation record, which are used to extract feature parameters representing the water consumption behavior law of each area, and provide a basis for judging the consistency of water consumption characteristics; the key hydraulic parameters include pipe network resistance loss, pressure transmission efficiency, water flow response speed, and pipe head loss coefficient along the way, which are used to quantify the differences of different grid units in terms of hydraulic transmission and regulation response, and to determine the core influencing factors of the pump group regulation mechanism; the spatial superposition algorithm refers to superimposing and comparing the water consumption characteristic similar areas obtained by clustering analysis and the key hydraulic parameter similar areas in the spatial level, screening out areas with consistency of water consumption characteristics and uniformity of hydraulic regulation mechanism by calculating the area overlap degree and parameter matching degree, and used to finally determine the target water supply sub-area with clear boundaries and independent regulation conditions.

[0060] In the embodiment, by obtaining the water supply pipe network hydraulic data and historical water consumption data and performing grid division, the complex target water supply area can be decomposed into basic units for quantitative analysis, laying a foundation for subsequent accurate analysis; by constructing a pipe network hydraulic model based on the water supply pipe network hydraulic data and extracting key hydraulic parameters, the essential differences of different grids in terms of hydraulic transmission and regulation response can be accurately reflected, and the analysis of the pump group regulation mechanism can be supported by data; by extracting clustering features from historical water consumption data and performing density clustering analysis, areas with similar water consumption behavior law can be objectively identified, ensuring the scientific nature of water consumption characteristic analysis; by dividing the target water supply sub-area in combination with the key hydraulic parameters through the spatial superposition algorithm, the dual matching of water consumption characteristics and hydraulic regulation mechanism can be realized, the finally divided target water supply sub-area has clear boundaries, and each target water supply sub-area maintains high uniformity in terms of water consumption characteristics and pump group regulation mechanism, providing accurate spatial unit division basis for subsequent development of differentiated adaptive speed control strategies for different target water supply sub-areas, effectively improving the pertinence and effectiveness of pump group regulation.

[0061] Please refer to Figure 2 , preferably, the specific steps of dividing the target water supply sub-area include:

[0062] The water supply network hydraulic data and historical water consumption data of the target water supply area are preprocessed, including data cleaning, integrity checking, missing value processing and time alignment;

[0063] Based on the pipe network density distribution of the target water supply area, an initial grid size is set, and adaptive grid unit division is performed, so that each grid unit contains at least one pipe network node and two or more water appliance interfaces, and the pipe network length covered by a single grid is within a reasonable range;

[0064] The preprocessed water supply network hydraulic data and historical water consumption data are bound according to the coordinates of the grid unit to generate a grid data association table containing grid unit number, hydraulic data field and water consumption data field, so as to realize accurate correspondence between data and spatial unit;

[0065] The water supply network hydraulic data in the grid data association table is imported into the hydraulic simulation software to generate a water supply network hydraulic model of the target water supply area. According to the pipe network topology, the pipe node connection is generated, the preset hydraulic resistance coefficient is matched combined with the pipe material type, and the node elevation parameter is set according to the pipe laying elevation. The steady-state transmission process of water flow under the water supply network hydraulic model is simulated, and the hydraulic calculation results of each grid unit are output. From the calculation results, key hydraulic parameters such as pipe network resistance loss, pressure transmission efficiency, water flow response speed and pipe head loss coefficient along the way are extracted to generate a grid hydraulic parameter comparison table, which quantifies the differences in hydraulic regulation and control mechanism of different grid units.

[0066] Based on the historical water consumption data in the grid data association table, time series analysis is used to extract the clustering characteristics of each grid unit: the daily water consumption load mean, hourly flow fluctuation amplitude and pressure demand standard deviation are calculated by sliding window statistical method; at the same time, combined with the operation record of water equipment, the water consumption period distribution characteristics are extracted by equipment working condition association algorithm. After standardizing the above parameters, a multi-dimensional clustering feature vector of each grid unit is formed. The multi-dimensional clustering feature vectors of all grid units are input into the density clustering algorithm, the elbow rule is used to determine the clustering radius and the minimum sample size, and the grid units with a multi-dimensional clustering feature vector similarity higher than the preset clustering threshold are classified into the same initial clustering group to construct an initial clustering result table containing clustering group number, grid unit list and clustering characteristics.

[0067] A buffer zone is generated for the boundary of each initial clustering group of the initial clustering result table, and then the buffer zone is spatially superimposed with the grid cells consistent with the key hydraulic parameters; the spatial overlap degree and the key hydraulic parameter matching degree of the spatially superimposed region are calculated through spatial analysis, the spatial overlap degree is the ratio of the area of the spatially superimposed region to the area of the initial clustering group, the key hydraulic parameter matching degree is measured by the coefficient of variation of the key hydraulic parameters of the grid cells in the spatially superimposed region, and the smaller the coefficient of variation, the higher the matching degree; a screening threshold is set, including a spatial overlap threshold and a parameter matching degree threshold, and the spatially superimposed region whose spatial overlap degree and key hydraulic parameter matching degree are both greater than the corresponding screening threshold is screened; for the spatially superimposed region whose spatial overlap degree and key hydraulic parameter matching degree are both less than or equal to the corresponding screening threshold, the association relationship with the initial clustering group is removed, only the grid cells in the initial clustering group that do not participate in this type of spatially superimposed region are retained, and then the spatially superimposed region retained after screening is merged with the initial clustering group and the boundary is smoothed to eliminate the boundary jagged defects caused by grid division, and the smooth target water supply sub-region boundary is generated; a target water supply sub-region division result table containing the target water supply sub-region number, spatial coordinate range, core hydraulic parameter and clustering characteristics is constructed, and the target water supply sub-region division is completed.

[0068] Step S2: Based on the functional attributes, historical operation data and regulation requirements of the target water supply sub-region, a multi-level working condition label dictionary including basic working condition labels, trend working condition labels, time period working condition labels and water supply type working condition labels is constructed through parameter interval division, trend feature recognition, time period clustering analysis and type rule definition, and a mapping relationship of each level working condition label is established to form a complete label system. Specifically, the functional attributes include the use function classification of the target water supply sub-region, such as commercial catering, office, public health and fire standby, which is used to clarify the water supply adjustment priority and special requirements of different target water supply sub-regions; the regulation requirements cover control targets such as energy-saving operation, emergency guarantee and pressure stability, which are used to guide the design of regulation strategy associated with labels. The basic working condition label is a label generated by interval division of water consumption data such as pipe network pressure, flow and temperature, which is used to directly reflect the real-time matching state of water supply and water demand; the trend working condition label is a label generated based on the water consumption data change rate and trend recognition result, which is used to represent the dynamic change tendency of water consumption state; the time period working condition label is a label generated after clustering analysis and division of time period on the basis of historical water consumption load curve, which is used to associate the basic water demand characteristics of the corresponding time period; the water supply type working condition label is a label determined according to the functional attributes of the target water supply sub-region, which is used to clarify the overall direction of water supply adjustment of the region; the integration logic of the multi-level working condition label dictionary constrains the filtering range of the time period working condition label under the water supply type label, the time period working condition label limits the parameter benchmark of the bottom layer basic working condition label, the trend working condition label assists the bottom layer basic working condition label in judging abnormal state, and the automatic matching and calling of each level label are realized through the association database to provide standardized label basis for subsequent real-time working condition recognition and regulation decision.

[0069] In the embodiment, the multi-level working condition label dictionary is constructed by collecting the functional attributes and historical operation data of the target water supply sub-region, which can convert complex water supply working conditions into structured label language, realize accurate description and classification of water supply state, combine the basic working condition label with the trend working condition label to reflect real-time supply and demand state and capture parameter change dynamics to provide double basis for working condition abnormality judgment, set the time period working condition label and the water supply type working condition label to make the label system contain time period regularity and reflect regional characteristic differences, enhance the adaptability of the label to different scenes, and establish the hierarchical mapping relationship through the association algorithm to ensure logical coherence between labels and efficient calling, so that the finally formed label dictionary can provide a unified judgment standard for subsequent real-time data matching and mode switching, and significantly improve the standardization and response speed of pump group regulation decision.

[0070] Referring to Figure 3 , preferably, the specific steps of constructing the multi-level working condition label dictionary include:

[0071] obtain the functional attributes of each target water supply sub-region, and extract time series of key parameters such as pipe network pressure, water supply flow and medium temperature according to historical operation data of each target water supply sub-region; the key parameters are core physical quantities in the historical operation data that directly affect pump group regulation and control decisions and can reflect the matching relationship between water supply state and water demand, and are used to provide a data basis for subsequent construction of each level of working condition label, so as to ensure that the label can accurately map the actual operation state of the water supply system and provide a quantitative basis for working condition judgment and control strategy formulation.

[0072] Perform preprocessing operations on the extracted key parameter time series, including removing high-frequency pulse interference in pipe network pressure and water supply flow data using a wavelet filtering algorithm, such as parameter mutations caused by instantaneous start-stop of water equipment, identifying and removing outliers in medium temperature and pipe network pressure through the Laplace criterion, and using timestamp alignment technology to unify key parameter data of different collection frequencies, such as flow data collection frequency and pressure data collection frequency, to the same time dimension, to generate a standardized key parameter time series dataset, ensuring the accuracy, integrity and consistency of the data, and providing a reliable data source for subsequent parameter analysis in the label construction link.

[0073] For the pre-processed key parameter time series dataset, calculate the probability distribution characteristics of each key parameter, such as mean, standard deviation and quartile, combine the functional attributes of each target water supply sub-region and the safety water supply specification, set the parameter interval of each key parameter through threshold division, including the abnormal low interval, warning low interval, normal interval, warning high interval and abnormal high interval; label the corresponding basic working condition label for each parameter interval, such as pipe network pressure normal label, water supply flow warning high label and medium temperature abnormal low label, establish a one-to-one mapping relationship between key parameter value, parameter interval and basic working condition label, generate a basic working condition label library containing label code, parameter type, interval range and label description, and use it to directly reflect the real-time matching state of water supply and water demand.

[0074] Based on the key parameter time series data corresponding to the basic working condition label, a sliding window algorithm is used to set a fixed window length and a sliding step, and the change rate of each key parameter in unit time is calculated, such as the change rate of pipe network pressure and the change rate of water supply flow; the trend of the change rate of the key parameters in each sliding window is fitted by a linear regression algorithm to obtain the slope of the trend line, and the change trend type of the key parameters is judged by combining the preset change rate threshold and the slope threshold, including the steady trend, the slow increasing trend, the rapid increasing trend, the slow sudden drop trend and the rapid sudden drop trend; each change trend type is assigned a corresponding trend working condition label, such as a pressure trend steady label and a flow rapid rise label, a trend working condition label library containing label code, trend type, change rate range and slope range is constructed, and an association rule between the trend working condition label and the key parameter change characteristics is established to represent the dynamic change tendency of water consumption state.

[0075] Based on the key parameters of each target water supply sub-region, the water consumption load curve of each target water supply sub-region is calculated, and the water consumption load curve is divided into typical water consumption periods such as peak period, flat peak period and trough period by time series clustering algorithm; each typical water consumption period is assigned a corresponding period working condition label, such as early peak water label, mid-flat peak water label and night trough water label, and the average load value, load fluctuation amplitude and period duration of each typical water consumption period are extracted as basic water demand characteristics to generate a period working condition label library containing label code, water consumption period range and basic water demand characteristics, which is used to associate the basic water demand characteristics of the corresponding period.

[0076] According to the functional attributes and control priority of each target water supply sub-region, such as the control priority of fire standby area being higher than that of office area, each target water supply sub-region is assigned a unique water supply type working condition label, such as catering type water supply label, office type water supply label and fire standby water supply label; each water supply type working condition label is bound to the corresponding control rule through the rule engine, and the control rule includes key parameter control range and energy saving control parameter, including energy saving control coefficient, energy efficiency optimization benchmark and dynamic response parameter, including dynamic response coefficient, adjustment step threshold and control priority sorting rule, to generate a water supply type working condition label library containing label code, functional attribute, control priority and control rule, which is used to determine the overall direction of water supply regulation in the region.

[0077] According to the logical level of the supply type condition label, the time period condition label, the trend condition label and the basic condition label, the association rule mining algorithm is used to calculate the association strength between the condition labels at each level, and the strong association relationship is screened out by setting the association threshold, such as the dining supply label is often associated with the lunch peak water label and the flow rapid rising label; The trigger condition and mapping path between the condition labels at each level are established, such as when the standby fire supply label is triggered, only the static stable pressure time period condition label and the pressure stable trend label are matched; The above four label libraries and hierarchical mapping relationship are integrated and imported into a relational database to build a complete multi-level condition label dictionary, and the consistency and integrity of the label association are verified by a logical verification algorithm to ensure that the multi-level condition label dictionary can support automatic label matching of real-time data and association calling of control instructions.

[0078] Step S3: Through the distributed sensor network deployed in the target water supply area, real-time water consumption data of each target water supply sub-area is collected, and real-time condition label combination of each target water supply sub-area is constructed through hierarchical progressive label mapping and multi-level condition label dictionary for matching target water demand of each target water supply sub-area, and initial operation parameter set of pump group is generated through multi-pump collaborative load distribution to ensure that the pump group startup state adapts to the current period basic water demand. Specifically, the distributed sensor network deploys pipe network pressure sensors, electromagnetic flowmeters and medium temperature sensors at key monitoring points of target water supply sub-area to collect real-time water consumption data reflecting the water supply state; The hierarchical progressive label mapping is based on the hierarchical logic of the multi-level condition label dictionary, first matches the fixed supply type label according to the properties of the target water supply sub-area, then matches the time period condition label combined with real-time time, then matches the basic condition label through real-time parameter value comparison, and finally matches the trend condition label based on parameter change characteristics, through the progressive association of four layers of labels, it is ensured that the real-time condition label combination constructed can accurately map the current target water demand; When generating the initial operation parameters, the corresponding relationship between the condition and the parameters needs to be established based on the control rules associated with the label combination and the pump group characteristic curve, the abstract demand is converted into concrete operation parameters, and the adaptation of the pump group startup state to the current period basic water demand is realized.

[0079] In this embodiment, the deployment of the distributed sensor network achieves global real-time perception of water demand in each target water supply sub-region, avoiding misjudgment of demand due to incomplete monitoring; the hierarchical progressive label mapping improves the efficiency of label matching and ensures the complete description of the target water demand by following the logical order of the multi-level working condition label dictionary; the initial operating parameters generated based on the label combination enable the pump set to meet the basic water demand without frequent debugging at startup, reducing pressure fluctuations and energy waste during startup, while providing a stable initial state for subsequent dual-mode adaptive speed regulation, further ensuring the stability and economy of the water supply system.

[0080] Referring to Figure 4 , preferably, the specific steps of generating the initial operating parameter set of the pump set include:

[0081] According to the distributed sensor network, real-time water data of each target water supply sub-region is collected and labeled, including target water supply sub-region number marking, data type marking, such as pipe network pressure, water supply flow, medium temperature, and collection time stamp marking, which is used to clearly define the target water supply sub-region, parameter category and time sequence information of the real-time water data, avoiding parameter deviation caused by data and region misplacement;

[0082] According to the number marking of the target water supply sub-region of the real-time water data, the preset water supply type working condition label of the target water supply sub-region is called from the multi-level working condition label dictionary, the water supply type working condition label matching is performed, and the control rules bound by the water supply type working condition label are obtained, the key parameter control range such as pipe network pressure control interval, water supply flow adaptation range and medium temperature safety threshold are obtained, which provides regional characteristic constraint basis for subsequent target water demand determination and parameter calculation.

[0083] According to the data type marking of the real-time water data, the key parameters of the collected real-time water data are identified, and according to the collection time stamp of the key parameters, they are compared with the typical water period range corresponding to the time period working condition label of the target water supply sub-region in the multi-level working condition label dictionary, the current time period working condition label is matched out, and the basic water demand characteristics under the time period working condition label are extracted to generate the target water demand, including target pressure value and target flow value;

[0084] Specifically, the specific steps of generating the target water demand include:

[0085] From the matched period working condition labels, the historical basic water demand characteristics corresponding to the period working condition labels are extracted, including historical average load, historical average pressure, load fluctuation range, pressure fluctuation range and period duration; the extracted historical basic water demand characteristics are converted into standardized data consistent with the unit and magnitude of real-time water data by using data standardization technology, so as to eliminate the differences between historical data and real-time data caused by different collection periods and statistical dimensions.

[0086] The deviation proportion of the real-time key parameters and the corresponding historical basic water demand characteristics is calculated; the preliminary target pressure is obtained by adjusting the historical average pressure based on the deviation proportion of the real-time pressure and the historical average pressure, and the preliminary target flow is obtained by adjusting the historical average load based on the deviation proportion of the real-time flow and the historical average load.

[0087] From the matched supply type working condition label binding control rules, the key parameter control range of each target water supply sub-region is obtained, including the upper and lower limits of pressure control and the upper and lower limits of flow control; the preliminary target pressure and the preliminary target flow are compared and verified with the corresponding key parameter control range: if the preliminary target parameters are all within the key parameter control range, they are directly taken as the target water demand; otherwise, the preliminary target pressure and the preliminary target flow are modified based on the key parameter control range, so that the modified target pressure is strictly between the upper and lower limits of pressure control, and the modified target flow is strictly between the upper and lower limits of flow control, to ensure that the target water demand not only meets the safety water supply constraint, but also fits the supply and demand balance of the actual water use scene;

[0088] According to the key parameter control range matched by the target water supply sub-region and the target water demand, and combined with the characteristic curve of the pump group when it is delivered from the factory, the initial running parameter set of the main pump and each auxiliary pump is generated respectively through multi-pump collaborative load distribution, so as to obtain the initial running parameter set of the pump group. The main pump refers to the core pump body in the pump group that bears the water supply load of the target water supply region and has long-term stable operation capability, which is used to meet the basic water load demand of the target water supply region and keep stable operation in the conventional working condition. The auxiliary pump refers to the collaborative pump body configured for the water load fluctuation characteristics of each target water supply sub-region in the pump group, which is used to supplement the gap in the water supply capacity of the main pump in the target water supply sub-region and adapt to the dynamic water demand of the target water supply sub-region. When the load of the target water supply sub-region rises and the main pump cannot meet the demand in single-pump operation, the auxiliary pump is started, or when the load of the target water supply sub-region decreases and the main pump needs to operate at a reduced load, the auxiliary pump is adjusted in coordination, so as to avoid the increase of energy consumption and equipment wear caused by frequent start-stop or large-scale speed regulation of the main pump.

[0089] Specifically, the specific steps of generating the initial running parameter set of the main pump and each auxiliary pump through multi-pump collaborative load distribution include:

[0090] The target water demand of each target water supply sub-region is aggregated to form the total target water demand of the target water supply region, including the total target flow and the regional benchmark target pressure determined in combination with the pressure constraints of each target water supply sub-region. The characteristic curve data of the main pump and each auxiliary pump in the pump set factory technical data are retrieved, including the flow-speed curve, the pressure-speed curve and the efficiency-load curve. The total target flow and the benchmark target pressure in the overall target water demand of the target water supply region are associated and matched with the characteristic curve of the main pump, the basic speed interval of the main pump when outputting the benchmark target pressure is determined according to the pressure-speed curve of the main pump, and the basic load proportion of the main pump in the basic speed interval is obtained in combination with the total target flow, so as to ensure that the main pump outputs to cover the overall basic water demand of the region and is in a high-efficiency operation state.

[0091] According to the main pump basic load proportion and the total target flow, the initial flow distribution value of the main pump is calculated, the initial speed is derived according to the linear correlation relationship of the flow-speed curve of the main pump with the rated speed as the reference; the consistency of the output pressure at this speed with the regional benchmark target pressure is verified according to the square correlation relationship of the pressure-speed curve, and the speed is fine-tuned until matching when there is a deviation. According to the corresponding relationship between the motor speed and the frequency converter frequency, the initial speed is converted into the initial output frequency of the main pump frequency converter, and the initial running parameter set of the main pump including the initial speed, the initial frequency of the main pump frequency converter, the benchmark target pressure and the initial flow distribution value is integrated and constructed.

[0092] The total load of the auxiliary pump, i.e. the total target flow of the region minus the initial flow of the main pump, is calculated, and the independent target water demand of each target water supply sub-region is retrieved one by one, including the target flow of the target water supply sub-region, the upper and lower limits of pressure control and the load fluctuation characteristics extracted from the time period working condition label. According to the load fluctuation characteristics of the target water supply sub-region, the corresponding auxiliary pump is allocated with a dedicated load proportion, and the target water supply sub-region with a larger load fluctuation amplitude is allocated with a higher proportion to enhance the dynamic response capability. Referring to the main pump parameter calculation logic, the initial flow distribution value, the initial speed and the initial output frequency of the frequency converter of each auxiliary pump are derived in combination with the characteristic curve of each auxiliary pump; the target pressure set value of each auxiliary pump is determined according to the upper and lower limits of pressure control of the target water supply sub-region, so as to ensure the adaptation with the regional benchmark target pressure to avoid pipe network pressure conflict, and the initial running parameter set of each auxiliary pump is formed.

[0093] Step S4: Perform the initial operating parameter set of the pump group, and based on the real-time water consumption data of each target water supply sub-region collected by the distributed sensor network, dynamically match the basic working condition label and the trend working condition label through the multi-level working condition label dictionary to construct a real-time working condition feature vector; adopt a fuzzy comprehensive evaluation algorithm to quantitatively evaluate the real-time working condition feature vector, generate a mode switching decision value, and determine the current working condition adaptation control mode accordingly, and adaptively optimize the initial operating parameter set of the pump group according to the control mode, to realize the precise matching of the pump group operating state and dynamic water demand. Specifically, the control mode includes an energy-saving operation mode and a dynamic response mode: the energy-saving operation mode takes the maximization of the pump group operating efficiency as the core target, and is suitable for stable working conditions and gentle load fluctuations; the dynamic response mode takes the water demand tracking speed as the core target, and is suitable for sudden working conditions and rapid load fluctuations; the initial operating parameter set optimization process needs to combine the control rules associated with the real-time working condition label and the dynamic characteristics model of the pump group to ensure that the optimized parameters not only meet the control range of the key parameters of each target water supply sub-region, but also realize the conflict-free collaborative operation of the main and auxiliary pumps.

[0094] In the present embodiment, through the dynamic matching of the real-time working condition label, the precise perception of the water supply system operating state is realized, providing data basis for mode switching; the application of the fuzzy comprehensive evaluation algorithm improves the scientificity and anti-interference ability of mode judgment, avoiding misjudgment caused by instantaneous parameter fluctuations; the differential control strategy of the dual mode makes the pump group prioritize energy saving in stable working conditions and prioritizes response speed in fluctuating working conditions, balancing system economy and stability; the combination of parameter optimization and constraint verification ensures that the adjusted operating parameters not only meet the real-time demand, but also meet the equipment safety and pipeline constraints, forming a closed-loop control of perception, judgment, control and verification, and significantly improving the dynamic adaptation ability of the water supply system.

[0095] Please refer to Figure 5 , preferentially, the specific steps of determining the control mode of the current working condition include:

[0096] According to the data type mark of the real-time water consumption data, the key parameters directly affecting the pump group control are identified and extracted from the real-time data collected by the distributed sensor network; the basic working condition label parameter interval of the corresponding target water supply sub-region in the multi-level working condition label dictionary is called, the real-time key parameters are compared with the interval, the basic working condition label corresponding to the interval to which the current parameters belong is matched, and the static supply-demand adaptation state is determined; at the same time, a fixed window length and a sliding step are set by using a sliding window algorithm, the change rate of each key parameter in a unit time is calculated, the change trend type of the parameters is judged in combination with the change characteristic threshold of the trend working condition label in the multi-level working condition label dictionary, the corresponding trend working condition label is matched, and the dynamic change tendency is represented.

[0097] The basic matching degree of the basic working condition label and the normal parameter interval of each key parameter is calculated by coupling the interval coincidence degree and the deviation degree, the basic working condition label is quantified, the grading standard is set, the basic matching degree is converted into the basic working condition adaptability index quantization value, and the basic working condition adaptability index reflecting the matching degree of the key parameter and the system safety and stability operation benchmark is constructed. The basic matching degree refers to the spatial coincidence proportion of the actual value of the key parameter and the normal parameter interval, and the comprehensive measurement of the deviation amplitude and direction if the actual value deviates from the normal parameter interval, which is used to accurately quantify the matching degree of the current key parameter state and the system safety and stability operation benchmark requirement, and provides an objective and calculable core basis for the basic working condition adaptability index. Specifically, the interval coincidence degree is calculated first, if the actual value of the key parameter is completely within the normal parameter interval, the interval coincidence degree is set to the highest level; if the actual value is partially within the normal parameter interval, the interval coincidence degree is calculated according to the proportion of the numerical length of the actual value within the normal parameter interval and the total length of the normal parameter interval; then the deviation degree is calculated, if the actual value exceeds the normal parameter interval, the deviation degree is calculated according to the proportion of the difference between the actual value and the boundary value of the normal parameter interval and the total length of the normal interval, and the absolute value of the deviation degree reflects the deviation amplitude; finally, the basic matching degree is calculated by combining the interval coincidence degree and the deviation degree, and the result is limited within a fixed interval.

[0098] According to the matching degree of the key parameter change rate and the stationary feature threshold and the fluctuation influence weight, a trend matching degree is calculated, indexes of each trend working condition label are quantified, and a trend working condition stability index is constructed, which can reflect the dynamic stability level and the response demand urgency of the working condition. The trend matching degree is a comprehensive measure of the closeness of the actual change rate of the key parameter to the stationary feature threshold and the potential influence of the change trend on the system operation stability, which is used to accurately quantify the matching level of the current working condition dynamic change state and the system response demand to fluctuation, and provides an objective and calculable core basis for the trend working condition stability index. Specifically, the change rate matching degree is calculated first: if the actual change rate of the key parameter is lower than the stationary feature threshold, the change rate matching degree is set to the highest level, indicating that the parameter change is close to the stationary operation requirement; if the actual change rate is between the stationary feature threshold and the slow change feature threshold, the change rate matching degree is calculated according to the proportion of the difference between the actual change rate and the stationary feature threshold to the total span of the two threshold intervals, indicating that the parameter change is within the controllable fluctuation range; if the actual change rate is higher than the rapid change feature threshold, the change rate matching degree is set to the lowest level, indicating that the parameter change has deviated significantly from the stationary requirement. Then the fluctuation influence degree is calculated: if the change trend is slow change, the fluctuation influence degree is set to the lowest level, indicating that the influence on the system stability is small; if it is rapid change, the fluctuation influence degree is calculated according to the proportion of the difference between the actual change rate and the rapid change feature threshold to the total span of the rapid change interval, the larger the difference, the higher the fluctuation influence degree, indicating that the potential influence on the system stability is more significant. Finally, the trend matching degree is calculated by combining the change rate matching degree and the fluctuation influence degree, and the result is limited within a fixed interval; the grading standard is set to convert the trend matching degree into the quantized value of the trend working condition stability index, and the index construction is completed.

[0099] The index weight is assigned to the type working condition label corresponding to the target water supply sub-region. The type working condition label includes the priority energy saving class, the priority dynamic response class and the balanced demand class. If the target water supply sub-region is the priority energy saving class, the weight of the basic working condition adaptability index is higher than that of the trend working condition stability index. If it is the priority dynamic response class, the weight of the trend working condition stability index is higher than that of the basic working condition adaptability index. If it is the balanced demand class, the weights of the two indexes are consistent.

[0100] The basic working condition adaptability index quantized value and the trend working condition stability index quantized value are substituted into the fuzzy comprehensive evaluation model, the working condition decision value of the target water supply sub-region is obtained by weighted calculation, the working condition decision values of all target water supply sub-regions are weighted and summed according to the importance of the target water supply sub-region, the mode switching decision value is generated, and the mode switching decision value is compared with the preset mode judgment threshold. If the mode switching decision value is greater than the mode judgment threshold, it is judged that the current working condition is adapted to the energy saving operation mode; otherwise, it is judged that the current working condition is adapted to the dynamic response mode, and the control mode judgment is completed.

[0101] First, the initial operation parameter set of the pump group is adaptively optimized, including the following steps:

[0102] If the current working condition is adapted to the energy-saving operation mode, the deviation degree of the key parameters of each target water supply sub-region from the normal parameter interval is calculated, the energy-saving control coefficient corresponding to the water supply type working condition label is retrieved from the preset control rule according to the water supply type working condition label of the target water supply sub-region, and is used to limit the maximum adjustment range of the correction amount;

[0103] Based on the target water demand of the current target water supply sub-region, the target water demand correction amount is calculated by combining the calculated deviation degree and the energy-saving control coefficient, so as to ensure that the corrected demand meets the safety water supply constraint and compresses the redundancy to adapt to the energy-saving operation target;

[0104] According to the target water demand correction amount of the target water supply sub-region, the initial operation parameter set of the auxiliary pump of the corresponding target water supply sub-region is updated through the auxiliary pump load redistribution and parameter collaborative adaptation mechanism. Specifically, the corrected target water demand of each target water supply sub-region is summarized to determine the total load that the auxiliary pump needs to bear; the load of each auxiliary pump is redistributed according to the proportion of the corrected target water demand and the historical energy efficiency of the auxiliary pump; the speed and the frequency converter frequency are adjusted according to the new load and the auxiliary pump characteristic curve; the new target pressure is determined by referring to the pressure constraint of the target water supply sub-region, and the updated initial operation parameter set of the auxiliary pump is formed after energy efficiency and safety verification.

[0105] If the current working condition is adapted to the dynamic response mode, the abnormal target water supply sub-region is identified and marked as a priority response object according to the real-time key parameter change rate and trend working condition label of each target water supply sub-region; the dynamic response coefficient corresponding to the type is retrieved from the preset control rule according to the water supply type working condition label of the abnormal target water supply sub-region, and is used to determine the sensitivity and response speed of parameter adjustment; the dynamic compensation amount of the target water demand is calculated based on the current target water demand, combined with the key parameter change rate, the trend characteristics and the dynamic response coefficient, to ensure that the compensated demand can quickly match the real-time fluctuation and not exceed the safety operation boundary; the initial operation parameter set of the pump group of the corresponding target water supply sub-region is updated through the auxiliary pump parameter priority adjustment and the main pump collaborative fine adjustment mechanism according to the dynamic compensation amount of the target water demand. Specifically, the speed adjustment step of the auxiliary pump corresponding to the target water supply sub-region marked as the priority response object is increased or the standby auxiliary pump is started according to the dynamic compensation amount, so as to improve the response speed of the flow and the pressure; if the total compensation amount of the target water supply sub-region exceeds the adjustment range of the auxiliary pump, the speed of the main pump is finely adjusted at a small amplitude and a high frequency to collaboratively adapt; the target pressure set value is corrected by referring to the pressure fluctuation allowable range of the target water supply sub-region, and the updated initial operation parameter set of the pump group is formed after dynamic response speed and pipe network stability verification.

[0106] The embodiment also introduces a dual-mode digital energy-saving pump, which comprises a data acquisition unit, a central control unit, a pump group execution unit and a data storage unit, each unit realizes signal interaction through an industrial bus, and the specific structure is as follows:

[0107] The data acquisition unit is used for real-time acquisition and preprocessing of real-time water consumption data of each target water supply sub-region, which comprises a distributed sensor network deployed according to key monitoring points of the target water supply sub-region and a data preprocessing module connected with the network, the distributed sensor network is composed of pipe network pressure sensors, electromagnetic flowmeters and medium temperature sensors with regional number identification, which can respectively collect real-time pressure, flow and temperature signals of the corresponding target water supply sub-region, the data preprocessing module is internally provided with a wavelet filter circuit, an outlier elimination circuit and a time stamp alignment module, which can remove high-frequency pulse interference in sensor acquisition data, filter and eliminate abnormal data, and unify parameters with different acquisition frequencies to the same time dimension, and output standardized real-time data to the central control unit.

[0108] The central control unit as a core control module is signal-connected with the data acquisition unit, the data storage unit and the pump group execution unit, and is internally provided with a working condition label management subunit, a regulation mode determination subunit and a pump group parameter optimization subunit, the working condition label management subunit pre-stores a multi-level working condition label dictionary comprising basic working condition labels, trend working condition labels, time period working condition labels and water supply type working condition labels, can call historical water consumption data and target water supply sub-region attribute data stored in the data storage unit, and matches a current real-time working condition label combination through a preset label mapping logic; the regulation mode determination subunit is internally provided with a fuzzy comprehensive evaluation algorithm module, receives a real-time working condition characteristic vector output by the working condition label management subunit, combines quantization results of a basic working condition adaptability index calculation module and a trend working condition stability index calculation module in the regulation mode determination subunit, and target water supply sub-region regulation priority rules stored in the data storage unit, generates a mode switching decision value, and then determines whether the current water supply working condition is adapted to an energy-saving operation mode or a dynamic response mode; the pump group parameter optimization subunit calls pump group characteristic curves and target water supply sub-region regulation rules stored in the data storage unit according to a mode determination result output by the regulation mode determination subunit, calculates a correction amount or a compensation amount of target water consumption demand, and outputs an optimized initial operation parameter set of the pump group to the pump group execution unit.

[0109] The pump group execution unit is used for executing the initial operation parameter set of the pump group output by the central control unit, and comprises a main pump assembly, an auxiliary pump assembly and a pump group cooperative control module, the main pump assembly is composed of a main pump body, a main pump motor and a main pump frequency converter, the main pump frequency converter is signal connected with the central control unit, can receive a speed regulation instruction to drive the main pump motor to run to meet the basic water load of the target water supply area, and the main pump frequency converter is internally provided with a high-efficiency operation interval monitoring module to ensure that the main pump always operates in an energy-efficient optimal operation range; the auxiliary pump assembly comprises at least two conventional auxiliary pumps and one standby auxiliary pump, each auxiliary pump is provided with an independent motor and a frequency converter, and each auxiliary pump is bound to the corresponding target water supply sub-area through area numbering, the auxiliary pump frequency converter is signal connected with the central control unit, can receive a load distribution instruction and a speed regulation instruction, the conventional auxiliary pump is used for adapting to the load fluctuation of the corresponding target water supply sub-area, and the standby auxiliary pump is started when the load of the corresponding target water supply sub-area exceeds the regulation range of the conventional auxiliary pump or the conventional auxiliary pump fails; the pump group cooperative control module is signal connected with the main pump frequency converter and each auxiliary pump frequency converter respectively, is used for synchronizing the operation parameters of the main pump and the auxiliary pump to avoid pipe network pressure conflict, and simultaneously monitors the operation states of the pumps and feeds back to the central control unit.

[0110] The data storage unit is signal connected with the central control unit in a bidirectional mode, is used for storing the basic data of the target water supply sub-area, including spatial coordinates, functional attributes, control priority, key parameter control range, historical data including historical water consumption data, pump group historical operation parameters, historical working condition label combination, and rule data including multi-level working condition label dictionary, double-mode control rule and pump group characteristic curve data, provides data support for working condition identification, mode determination and parameter optimization of the central control unit.

[0111] Working principle and effects:

[0112] The present application solves the problems of poor region adaptation, working condition misjudgment and parameter optimization lag of the existing pump group control, and realizes efficient adaptation of multi-scene water supply by fusing data mining, labeling management and fuzzy evaluation technology and combining with cooperative execution of the hardware unit.

[0113] Specifically, in the target water supply sub-region division stage, the water supply network hydraulic data and historical water consumption data are obtained, the key hydraulic parameters and clustering features are extracted after adaptive grid division, and the target water supply sub-region with unified characteristics is divided by combining the spatial superposition algorithm, breaking the limitation of traditional "global unified regulation and control", so that the subsequent regulation and control can be targeted to different regions, avoiding energy consumption redundancy in ordinary regions or insufficient supply in key regions; the multi-level working condition label dictionary and initial parameter generation stage is constructed, four types of labels of basic, trend, period and water supply type are generated based on the properties of the target water supply sub-region, the target water consumption demand is determined by matching the labels combined with real-time data, and the initial running parameters are generated through multi-pump collaborative load distribution, solving the extensive problem of setting initial running parameters by experience in traditional way, so that the pump group starts to fit the real-time demand, reducing the pressure fluctuation and energy consumption loss in the starting stage; in the mode determination and parameter optimization stage, the basic working condition adaptability and trend stability indexes are quantified through fuzzy comprehensive evaluation, and the energy-saving operation mode or dynamic response mode is accurately determined, the demand is corrected according to the parameter deviation and energy-saving regulation coefficient in the energy-saving operation mode, and the auxiliary pump load is optimized to compress the redundancy, the auxiliary pump is adjusted to respond to abnormal regional fluctuations in the dynamic response mode, and the main pump is fine-tuned if necessary, avoiding the limitation of traditional single mode, reducing energy consumption when the working condition is stable, responding quickly when the working condition changes, and reducing the equipment loss caused by frequent speed regulation of the main pump.

[0114] In summary, the present application not only realizes the precise differentiated regulation of multi-region water supply and solves the drawbacks of traditional one-size-fits-all regulation, but also improves the accuracy of working condition determination and the flexibility of parameter adaptation, ultimately significantly reduces the energy consumption of the pump group and prolongs the service life of the equipment under the premise of ensuring the safety of water supply in various regions such as business and fire protection, and balances the economy, stability and scene adaptability of the water supply system.

[0115] The above is only the preferred embodiment of the present application, and the protection scope of the present application is not limited to the above-mentioned embodiments, any technical solution falling within the scope of the present application is within the protection scope of the present application. It should be noted that for ordinary technical personnel in the technical field, some improvements and decorations without departing from the principles of the present application are also considered as the protection scope of the present application.

Claims

1. A method of adaptive speed control of a dual mode digital energy efficient pump, characterized in that, The application relates to a water supply system pump group control method and device. The method comprises the following steps: acquiring water supply network hydraulic data and historical water consumption data of a target water supply area covered by a pump group, dividing the target water supply area into grid units, extracting key hydraulic parameters and clustering features of each grid unit, dividing initial clustering groups according to the clustering features, and combining the key hydraulic parameters of the grid units to divide target water supply sub-areas through spatial superposition; based on the functional attributes, historical operation data and regulation and control requirements of the target water supply sub-areas, a multi-level working condition label dictionary is constructed through parameter interval division, trend feature identification, time period clustering analysis and type rule definition; the multi-level working condition label dictionary comprises basic working condition labels, trend working condition labels, time period working condition labels and water supply type working condition labels; real-time water consumption data of each target water supply sub-area is collected to construct a real-time working condition label combination, which is used to match target water consumption requirements of each target water supply sub-area, and an initial operation parameter set of the pump group is generated through multi-pump collaborative load distribution; the initial operation parameter set of the pump group is executed, the basic working condition labels and the trend working condition labels of each target water supply sub-area are matched in real time, and a fuzzy comprehensive evaluation algorithm is adopted to quantize decision indexes to calculate mode switching decision values, which are used to determine the regulation and control mode, and the initial operation parameter set of the pump group is adaptively optimized according to the regulation and control mode; the regulation and control mode comprises an energy-saving operation mode and a dynamic response mode; the specific steps of matching the target water consumption requirements of each target water supply sub-area comprise the following steps: from the matched time period working condition labels, historical basic water consumption requirement features corresponding to the time period working condition labels are extracted, including historical average pressure and historical average load; deviation proportions of real-time key parameters and corresponding historical basic water consumption requirement features are calculated; based on the historical basic water consumption requirement features, preliminary water consumption requirements including preliminary target pressure and preliminary target flow are generated in combination with the deviation proportions; key parameter control ranges of each target water supply sub-area are acquired, and the preliminary water consumption requirements and corresponding key parameter control ranges are verified; if the preliminary water consumption requirements are all within the corresponding key parameter control ranges, the preliminary water consumption requirements are taken as the target water consumption requirements, otherwise, the preliminary target pressure and the preliminary target flow are modified based on the key parameter control ranges; the specific steps of generating the initial operation parameter set of the pump group through multi-pump collaborative load distribution comprise the following steps: target water consumption requirements of each target water supply sub-area are summarized to form total target water consumption requirements of the target water supply area, including total target flow and reference target pressure; the total target flow and the reference target pressure of the target water supply area are matched with a characteristic curve of a main pump, and the characteristic curve comprises a flow-speed curve, a pressure-speed curve and an efficiency-load curve; a basic speed interval of the main pump when outputting the reference target pressure is determined according to the pressure-speed curve of the main pump, and a basic load proportion borne by the main pump in the basic speed interval is acquired in combination with the total target flow; an initial flow distribution value of the main pump is calculated according to the basic load proportion of the main pump and the total target flow, an initial speed of the main pump is derived according to the flow-speed curve of the main pump, and the initial speed is converted into an initial output frequency of the main pump to construct an initial operation parameter set of the main pump. According to the total target flow and the initial flow distribution value of the main pump, the total load of the auxiliary pump is calculated, and the independent target water demand of the target water supply sub-region is combined to distribute the exclusive load proportion of each auxiliary pump, which is used to combine the characteristic curve of each auxiliary pump to obtain the initial flow distribution value, the initial rotating speed of the auxiliary pump and the corresponding initial output frequency of the auxiliary pump frequency converter, and to construct the initial operation parameter set of each auxiliary pump.

2. A self-adapting speed control method for a dual-mode digital energy-efficient pump as claimed in claim 1, characterized in that, The specific steps of the initial clustering group division include: Pretreat the water supply network hydraulic data and historical water consumption data of the target water supply area, set the initial grid size based on the pipe network density distribution of the target water supply area, and perform adaptive grid unit division; Bind the pretreated water supply network hydraulic data and historical water consumption data according to the coordinates of the grid unit to generate a grid data association table; According to the water supply network hydraulic data in the grid data association table, a water supply network hydraulic model of the target water supply area is generated, key hydraulic parameters are extracted, and a grid hydraulic parameter reference table is generated; Based on the historical water consumption data in the grid data association table, the clustering characteristics of each grid unit are extracted by time series analysis to form a multi-dimensional clustering feature vector of each grid unit, which is used to divide the grid unit into an initial clustering group and construct an initial clustering result table.

3. A self-adapting speed control method for a dual-mode digital energy-efficient pump as claimed in claim 2, characterized in that, The specific steps of the target water supply sub-region division include: Generate a buffer zone for the boundary of each initial clustering group in the initial clustering result table, spatially superimpose the grid units with the same key hydraulic parameters in the buffer zone, and calculate the spatial overlap degree and the key hydraulic parameter matching degree of the spatially superimposed region; The spatial overlap degree is the ratio of the area of the spatially superimposed region to the area of the initial clustering group; The key hydraulic parameter matching degree is measured by the coefficient of variation of the key hydraulic parameters of the grid units in the spatially superimposed region; Set a screening threshold, screen the spatially superimposed regions whose spatial overlap degree and key hydraulic parameter matching degree are greater than the corresponding screening threshold, and merge and smooth the boundaries of the initial clustering group and the spatially superimposed region to generate the boundary of the target water supply sub-region and construct a target water supply sub-region division result table.

4. The adaptive speed control method of a dual mode digital energy-efficient pump of claim 1, wherein, The specific steps of constructing the multi-level working condition label dictionary include: Obtain the functional attributes of each target water supply sub-region, and extract the time sequence of key parameters according to the historical operation data of each target water supply sub-region; perform pretreatment on the extracted key parameter time sequence to generate a standardized key parameter time sequence data set; For the standardized key parameter time sequence data set, calculate the probability distribution characteristics of each key parameter through statistical analysis, set the parameter interval of each key parameter through threshold division, and mark the corresponding basic working condition label for each parameter interval to construct a basic working condition label library; Based on the key parameter time sequence corresponding to the basic working condition label, the change rate of each key parameter per unit time is calculated by using the sliding window algorithm, the trend of the change rate of the key parameter in each sliding window is fitted to obtain the trend line slope, and the change trend type of the key parameter is judged by combining the preset change rate threshold and the slope threshold, and the corresponding trend working condition label is assigned to each change trend type to construct a trend working condition label library.

5. A self-adapting speed control method for a dual-mode digital energy-efficient pump as claimed in claim 4, characterized in that, The specific steps of constructing the multi-level working condition label dictionary further include: The water consumption load curve of each target water supply sub-region is calculated based on the key parameters of each target water supply sub-region, the water consumption load curve is divided into typical water consumption time periods, each typical water consumption time period is assigned a corresponding time period working condition label, and the basic water consumption demand characteristics of each typical water consumption time period are extracted to construct a time period working condition label library; Each target water supply sub-region is assigned a water supply type working condition label according to the functional attributes of each target water supply sub-region, and each water supply type working condition label is bound to a corresponding control rule to construct a water supply type working condition label library; According to the logical hierarchy of the water supply type working condition label, the time period working condition label, the trend working condition label and the basic working condition label, the association rule mining algorithm is used to calculate the association strength between the working condition labels at each level, which is used to screen the typical label combination and construct a multi-level working condition label dictionary.

6. The adaptive speed control method of a dual mode digital energy-efficient pump of claim 1, wherein, The specific steps of determining the control mode include: The basic working condition label is matched according to the parameter interval of each key parameter, and the change rate of each key parameter in unit time is obtained by a sliding window algorithm to obtain the change trend type of each key parameter, and the trend working condition label is matched; The basic working condition label is quantified by calculating the basic fitting degree of the basic working condition label and the normal parameter interval of each key parameter through the coupling of interval coincidence degree and deviation degree, and a basic working condition adaptability index is constructed; The trend fitting degree is calculated according to the fitting degree of the key parameter change rate and the smoothness threshold and the fluctuation influence weight, and the trend working condition label is quantified to construct a trend working condition stability index; The basic working condition adaptability index quantization value and the trend working condition stability index quantization value are substituted into the fuzzy comprehensive evaluation model by combining the type working condition label allocation index weight of the corresponding target water supply sub-region, and the working condition decision value of the target water supply sub-region is obtained by weighted calculation; The working condition decision values of all target water supply sub-regions are weighted and summarized according to the importance of the target water supply sub-regions to generate a mode switching decision value; The mode switching decision value is compared with the preset mode determination threshold, if the mode switching decision value is greater than the mode determination threshold, it is determined that the current working condition is adaptive to the energy-saving operation mode; otherwise, it is determined that the current working condition is adaptive to the dynamic response mode.

7. A self-adapting speed control method for a dual-mode digital energy-efficient pump as claimed in claim 6, characterized in that, The specific steps of adaptively optimizing the initial operation parameter set of the pump group according to the control mode include: If the current working condition is adaptive to the energy-saving operation mode, the deviation degree of the key parameters of each target water supply sub-region from the normal parameter interval is calculated, and the energy-saving control coefficient is retrieved according to the water supply type working condition label of the target water supply sub-region, which is used to calculate the target water demand correction amount based on the target water demand of the current target water supply sub-region, and the initial operation parameter set of the auxiliary pump of the corresponding target water supply sub-region is updated through the auxiliary pump load redistribution and parameter collaborative adaptation mechanism. If the current working condition matches the dynamic response mode, the abnormal target water supply sub-area is identified according to the real-time key parameter change rate and the trend working condition label of each target water supply sub-area, and the dynamic response coefficient is called to calculate the dynamic compensation of the target water demand of the abnormal target water supply sub-area based on the current target water demand, and the initial operation parameter set of the pump group of the corresponding target water supply sub-area is updated through the auxiliary pump parameter priority adjustment and the main pump cooperative fine tuning mechanism.

8. A dual mode digital energy efficient pump for implementing the adaptive speed control method of any one of claims 1-7, characterized in that, The system comprises a data acquisition unit, a central control unit, a pump group execution unit and a data storage unit. The data acquisition unit is used for collecting and preprocessing real-time water consumption data of each target water supply sub-area. The central control unit is used for matching real-time working condition labels, determining control modes, generating and optimizing initial operation parameter sets of pump groups; the pump group execution unit is used for driving pump groups to operate according to the initial operation parameter sets of the pump groups; and the data storage unit is used for storing basic data, historical data and rule data of target water supply sub-areas.

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