Extensible water pump digital twin construction method

Abstract

The invention discloses an extensible water pump digital twinborn body construction method, and particularly relates to the field of digital twinborn, and the method comprises the steps: S1, constructing a physical model, S2, collecting water pump performance data, S3, preprocessing the water pump performance data, S4, constructing a water pump digital twinborn model, S5, applying the water pump digital twinborn model, and S6, optimizing a water pump digital twinborn body. The water pump digital twinborn model is constructed according to multiple dimensions of water pump flow data, water pump pressure data and water pump operation data, the model is continuously improved according to the actual operation condition of the water pump, the expandability of the water pump digital twinborn body is achieved, the water pump digital twinborn model can also conduct safety monitoring on a water resource allocation project, and the water resource allocation project safety is improved. And different running conditions of the water pump are judged, and different response results are made, so that the engineering safety is further improved.

Description

Technical Field

[0001] The present invention relates to the field of digital twin technology, and more specifically, to a scalable method for constructing a digital twin of a water pump. Background Art

[0002] As the core and key of the entire water resources allocation project, water pumps play a vital role in water conservancy projects. In recent years, digital twin technology has received widespread attention and attention, and has been applied to varying degrees in various industries and fields. However, it has just started in the water conservancy industry, and its continued development still faces many key technologies that need to be solved urgently. How to combine digital twin technology with classical hydrodynamics theory to provide decision-making support for project operation and management is an important issue that needs to be solved in the "New Era Ecological Smart Water Conservancy Project".

[0003] The existing water pump safety monitoring methods for water conservancy projects generally include data collection and transmission steps, data processing and analysis steps, and monitoring stations and control centers. The data collection and transmission steps are composed of data collectors and transmission equipment, which are responsible for collecting data from various monitoring points and transmitting and storing them; the data processing and analysis steps include data processing software, data analysis algorithms, data models and visualization tools, etc., which process, organize and analyze the collected hydrological data; the monitoring stations and control centers are the installation points of various monitoring equipment and provide real-time monitoring data. The control center is the core of data collection, processing, analysis and report generation, which is responsible for receiving and managing data from the monitoring stations and providing corresponding hydrological information and services to relevant departments and users.

[0004] However, there are still some shortcomings in its actual use. For example, in the data collection and transmission steps, since the water pump is in a complex and changeable natural environment, the uncertainty of extreme weather and climate increases the difficulty of monitoring and the inaccuracy of data; manual observation and simple sensor monitoring are often used when collecting data, which cannot provide real-time and accurate data. The limitations of sensor technology also limit the accuracy and scope of monitoring, making it impossible to discover some potential safety hazards in time; when analyzing data, there are obstacles to data sharing and communication due to different data collection departments, which makes it difficult to integrate and utilize data. Summary of the invention

[0005] In order to overcome the above-mentioned defects of the prior art, an embodiment of the present invention provides a scalable method for constructing a digital twin of a water pump, which solves the problems raised in the above-mentioned background technology through the following scheme.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] S1: Construct physical model: Construct physical model according to the water pumps required for water resource allocation project;

[0008] S2: Collecting water pump performance data: Based on the physical model constructed in S1, collect water pump performance data for the water resource allocation project, the water pump performance data including water pump flow data, water pump pressure data and water pump operation data;

[0009] S3: Preprocessing water pump performance data: Preprocessing the water pump performance data collected by S2, including data cleaning, data correction, filtering and feature extraction;

[0010] S4: Build a digital twin model of the water pump: Based on the water pump performance data and physical model preprocessed by S3, establish a water pump performance intelligent analysis model, and build a digital twin model of the water pump according to the water pump performance intelligent analysis model;

[0011] S5: Apply the water pump digital twin model: Based on the water pump digital twin model constructed in S4, apply the water pump digital twin model and perform visualization;

[0012] S6: Optimize the digital twin of the water pump: Based on the water pump performance data set, the water pump performance intelligent analysis model and the water pump operation status, the water pump digital twin is optimized using modularization.

[0013] Preferably, the S2 includes a water pump flow data acquisition unit, a water pump pressure data acquisition unit and a water pump operation data acquisition unit. The water pump flow data acquisition unit is used to collect water pump flow data, including inlet flow, outlet flow, volume flow and mass flow; the water pump pressure data acquisition unit is used to collect water pump pressure data, including inlet pressure, outlet pressure and head; the water pump operation data acquisition unit is used to collect water pump operation data, including the historical operation time, historical maintenance time, water pump active power and shaft power of the water pump.

[0014] Preferably, the preprocessing automatically screens and removes abnormal data in the water pump performance data, and uses data cleaning technology to correct the data; data cleaning refers to discovering and correcting identifiable errors in the data, including checking data consistency, processing invalid values ​​and missing values, filtering is to remove noise and interference in the water pump performance data in the virtual body, and feature extraction is to extract information from the water pump performance data collected in S2, including but not limited to the following features: average value: calculate the average historical maintenance time and the average historical operating time over a period of time, reflecting the operating status of the water pump; maximum and minimum values: calculate the maximum outlet pressure and inlet pressure, minimum outlet pressure and inlet pressure over a period of time, reflecting the extreme working state of the water pump; percentage: calculate the percentage of the water pump, hydraulic power and volume over a period of time, reflecting the efficiency of the water pump.

[0015] Preferably, the water pump digital twin model is simulated based on the preprocessed water pump performance data, creates a virtual modeling corresponding to the physical world, establishes a water pump performance intelligent analysis model, and establishes a water pump performance intelligent analysis model based on the water pump knowledge graph. The water pump performance intelligent analysis model includes but is not limited to a water pump flow digital model, a water pump pressure digital model, and a water pump operation digital model. The water pump flow digital model is used to call the water pump flow data in the water pump performance data set, and analyze the water pump flow data to obtain the water pump flow stability evaluation coefficient. The specific analysis formula of the water pump flow stability evaluation coefficient is:

[0016]

[0017] Where Y represents the pump flow stability assessment coefficient, Co represents the pump outlet flow, Ci represents the pump inlet flow, Cv represents the pump volume flow, Cm represents the pump mass flow, Cv represents the pump 预 Indicates the volume of liquid passing through the pump outlet per preset unit time, Cm 预 It indicates the mass of liquid passing through the pump outlet in a preset unit time; the pump pressure digital model is used to call the pump pressure data in the pump performance data set and analyze the pump pressure data to obtain the pump pressure safety assessment coefficient. The specific analysis formula of the pump pressure safety assessment coefficient is:

[0018]

[0019] Where Z represents the water pump pressure safety assessment coefficient, Lc represents the water pump head, Lc 理 Indicates the theoretical head of the water pump, Po indicates the outlet pressure of the water pump, Pi indicates the inlet pressure of the water pump, Po min Indicates the minimum outlet pressure, Po max Indicates the maximum value of the outlet pressure; the water pump operation digital model is used to call the water pump operation data in the water pump performance data set, and analyze the water pump operation data to obtain the water pump operation status evaluation coefficient. The specific analysis formula of the water pump operation status evaluation coefficient is:

[0020]

[0021] Among them, Eh represents the active power of the pump, E represents the shaft power, Eh / E represents the efficiency of the pump, Tp represents the historical operating time of the pump, Tg represents the historical maintenance time of the pump, u1 and u2 are the quadratic coefficient and linear coefficient of the historical maintenance time, u1>0, u2>0; based on the intelligent analysis model of pump performance, the pump flow stability assessment coefficient, the pump pressure safety assessment coefficient and the pump operation status assessment coefficient are integrated, connected to the pump digital twin model, and the physical behavior of the pump is simulated.

[0022] Preferably, the water pump knowledge graph is based on the preprocessed water pump performance data, and integrates the preprocessed water pump flow data, water pump pressure data and water pump operation data to obtain a complete water pump performance data set; according to the water pump performance data set, the water pump flow data and water pump pressure data are classified based on the water pump operation data to obtain the analysis points corresponding to the water pump performance data set, and the analysis points corresponding to the water pump performance data set are the water pump knowledge fields corresponding to the water pump performance data set, and the water pump knowledge fields include but are not limited to the water pump flow analysis field, the water pump pressure analysis field and the water pump operation analysis field, and different water pump knowledge graphs are constructed according to different water pump knowledge fields.

[0023] Preferably, the S5 combines the digital twin model of the water pump to determine the operating status of the water pump and visualize the water pump. The operating status of the water pump includes a green zone, a yellow zone and a red zone. If the operating status of the water pump enters the green zone, it means that the water pump is in a normal and safe operating state, the system is operating normally, and no additional monitoring or intervention is required; if the operating status of the water pump enters the yellow zone, it means that the water pump is in a potential risk or slightly abnormal operating state, the system is about to have problems or some minor abnormalities already exist. At this time, an early warning prompt is issued through the twin platform so that the operator can pay attention in time and take necessary preventive measures; if the operating status of the water pump enters the red zone, it means that the water pump is in a serious abnormal or dangerous operating state, the system has problems or is about to have a serious failure. At this time, the twin platform prompts to deal with it according to the plan to ensure the safety and stability of the system; the plan uses the plan model to formulate corresponding disposal plans according to the reasons for the operation entering the "red zone", such as cavitation, pressure pulsation, hump and overwater level. The method for judging whether the water pump enters the red zone is as follows:

[0024] D1: Obtain real-time perception data from physical entities, including monitoring systems and data processing systems. Real-time perception data includes collected and pre-processed pump performance data;

[0025] D2: In the virtual body, first, the performance curve is digitized into a digital model; second, based on the real-time sensing data obtained or the corresponding data manually input, the decision support algorithm is started to calculate the unit number and corresponding speed to be operated, and the calculation results are output to the database;

[0026] D3: Synchronously display the virtual operating point trajectory and the real-time operating point on the virtual screen;

[0027] D4: The virtual operating point is corrected based on historical big data;

[0028] D5: When the operating point enters the "red zone", a corresponding disposal plan should be proposed.

[0029] Preferably, the visualization processing includes a user interface layer and a system service layer. Through the user interface and using front-end development technology, measurement point management, instrument management, three-dimensional view and monitoring data query functions are realized, and an interactive interface is set to allow users to interact with the digital twin model of the water pump, such as adjusting parameters and viewing historical data. Public service functions are provided through the system service layer, and detailed data reports and analysis reports are generated for use by managers.

[0030] Preferably, the S6 adopts modularization to optimize the digital twin of the water pump based on the water pump performance data set, the water pump performance intelligent analysis model and the water pump operation status. The modularization includes a data module, a model module and an application module. The data module, the model module and the application module are connected and communicated by means of reserved interface slots. The reserved interface slots adopt a microservice architecture or a plug-in architecture so that the mechanism model can be added to the digital twin when needed. The architecture supports dynamic loading and unloading of modules to adapt to different application scenarios and requirements; the data module is used to collect water pump performance data and continuously integrate the water pump performance data set, establish a real-time mapping mechanism, synchronize and update the data of the physical entity and the water pump digital twin in real time, and provide communication with other The module uses an interface for data exchange. Through the reserved interface slot, new sensors or data sources are added to obtain new perception data. The newly added perception data is automatically integrated into the water pump performance data set in the data module to update the water pump digital twin in real time. The model module optimizes the water pump performance intelligent analysis model through the water pump performance data set. According to actual needs and data changes, the model parameters are adjusted, the structure is optimized, and retrained. The water pump performance intelligent analysis model includes but is not limited to the water pump flow digital model, the water pump pressure digital model, and the water pump operation digital model. The rationality of the water pump digital twin is calculated based on the water pump flow digital model, the water pump pressure digital model, the water pump operation digital model, and the updated mechanism model. The specific calculation formula is:

[0031]

[0032] Where P represents the rationality of the water pump digital twin, Y represents the water pump flow stability assessment coefficient, Z represents the water pump pressure safety assessment coefficient, T represents the water pump operation status assessment coefficient, λ1, λ2 and λ3 are the proportional coefficients corresponding to different assessment dimensions, λ1>0, λ2>0, λ3>0, φ is the rationality adjustment parameter of the updated mechanism model, and the rationality P of the water pump digital twin is compared with the preset rationality P of the water pump digital twin. yuMake a comparison to determine whether the digital twin of the water pump is reasonable, and issue an early warning for unreasonable digital twins of the water pump and further optimize and update them; the data model also provides an interface for model calling and data transmission with other modules, and updates or replaces the mechanism model in the existing model module through the reserved interface slot to adapt to new physical entities and changing environmental conditions. The updated mechanism model is seamlessly integrated into the digital twin of the water pump; the application module is based on the data module and the model module, and monitors, warns, makes decisions and diagnoses faults on the use of the water pump according to the digital twin of the water pump. The behavior parameters of the digital twin of the water pump are driven by three methods: static values, simulation values ​​and mechanism model data. Among them, the static value is used to initialize the behavior parameters of the digital twin of the water pump as the initial parameter, the simulation value is used when the digital twin of the water pump lacks specific data, and the mechanism model data is mostly used to drive simulation. The behavior parameters of the digital twin of the water pump include water discharge flow control of the water pump, water head control of the water pump, and start and stop of impeller rotation. It interacts with users according to different behavior parameters, applies scenarios under different behavior parameters, and provides an interface for integration with other systems or modules.

[0033] Technical effects and advantages of the present invention:

[0034] 1. Provide a foundation for the water pump digital twin model by constructing a physical model, collect water pump performance data based on the physical model, and clean, denoise and normalize the collected data to eliminate outliers and noise preprocessing in the data, improve the quality and reliability of the data, and provide an important basis for the subsequent construction of the water pump digital twin model; construct a water pump digital twin model in multiple dimensions of water pump flow data, water pump pressure data and water pump operation data, and continuously improve the model according to the actual operation of the water pump, so that the water pump equipment can be monitored in real time in multiple dimensions, abnormal conditions can be discovered in time, the risk of failure can be predicted, and maintenance suggestions can be given, which can significantly reduce the downtime of failures, improve the stability and reliability of the equipment, analyze and predict the operating status of the water pump, thereby optimizing equipment operation and reducing energy consumption costs;

[0035] 2. Based on the water pump digital twin model, the water pump digital twin model is displayed to managers in an intuitive and vivid way through three-dimensional visualization technology, which helps managers better understand the operating status, performance parameters and potential problems of the water pump. It adopts modular design and open data interfaces to facilitate subsequent functional expansion and system integration, and realize the scalability of the water pump digital twin model. The water pump digital twin model can also conduct safety monitoring of water resource allocation projects, judge different operating conditions of water pumps, and make different response results to further improve the safety of the project. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1It is a schematic diagram of the overall structure of the present invention.

[0037] Figure 2 It is a schematic diagram of the red zone judgment of the water pump operating status of the present invention. DETAILED DESCRIPTION

[0038] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0039] As attached Figure 1 A scalable water pump digital twin construction method is shown, comprising the following steps: S1 to S6.

[0040] S1: Construct physical model: Construct the physical model according to the water pumps required for the water resources configuration project.

[0041] S2: Collecting water pump performance data: Based on the physical model constructed in S1, water pump performance data is collected for the water resource allocation project, wherein the water pump performance data includes water pump flow data, water pump pressure data and water pump operation data.

[0042] In this embodiment, it should be specifically explained that S2 includes a water pump flow data acquisition unit, a water pump pressure data acquisition unit and a water pump operation data acquisition unit. The water pump flow data acquisition unit is used to collect water pump flow data, including inlet flow, outlet flow, volume flow and mass flow; the water pump pressure data acquisition unit is used to collect water pump pressure data, including inlet pressure, outlet pressure and head; the water pump operation data acquisition unit is used to collect water pump operation data, including the historical operation time of the water pump, historical maintenance time, water pump active power and shaft power.

[0043] Furthermore, the outlet flow and inlet flow of the water pump are measured in real time by installing a flow volume flow meter and a flow sensor at the outlet and inlet of the water pump respectively. The volume flow rate is the volume of liquid passing through the outlet of the water pump per unit time, and the mass flow rate is the mass of liquid passing through the outlet of the water pump per unit time; the outlet pressure of the water pump is the pressure value at the outlet of the water pump, which represents the water flow pressure output by the water pump; the inlet pressure of the water pump is the pressure value at the inlet of the water pump, which represents the water flow pressure input by the water pump. A pressure gauge is connected to the inlet or outlet of the water pump for measurement. The head is the capacity required for the water pump to pump water from a low place to a high place, and is an important indicator for measuring the ability of the water pump to lift liquid.

[0044] S3: Preprocessing water pump performance data: Preprocessing the water pump performance data collected by S2, including data cleaning, data correction, filtering and feature extraction.

[0045] In this embodiment, it should be specifically explained that the preprocessing automatically screens and removes abnormal data in the water pump performance data, and uses data cleaning technology to correct the data; data cleaning refers to discovering and correcting identifiable errors in the data, including checking data consistency, processing invalid values ​​and missing values, filtering is to remove noise and interference in the water pump performance data in the virtual body, and feature extraction is to extract information from the water pump performance data collected in S2, including but not limited to the following features: average value: calculate the average historical maintenance time and the average historical operating time over a period of time to reflect the operating status of the water pump; maximum and minimum values: calculate the maximum outlet pressure and inlet pressure, minimum outlet pressure and inlet pressure over a period of time to reflect the extreme working state of the water pump; percentage: calculate the percentage of the water pump, hydraulic power and volume over a period of time to reflect the efficiency of the water pump.

[0046] S4: Build a digital twin model of the water pump: Establish a water pump performance intelligent analysis model based on the water pump performance data and physical model preprocessed by S3, and build a water pump digital twin model based on the water pump performance intelligent analysis model.

[0047] In this embodiment, it should be specifically explained that the water pump digital twin model is simulated based on the preprocessed water pump performance data, creates a virtual modeling corresponding to the physical world, establishes a water pump performance intelligent analysis model, and establishes a water pump performance intelligent analysis model based on the water pump knowledge graph. The water pump performance intelligent analysis model includes but is not limited to a water pump flow digital model, a water pump pressure digital model, and a water pump operation digital model. The water pump flow digital model is used to call the water pump flow data in the water pump performance data set, and analyze the water pump flow data to obtain the water pump flow stability evaluation coefficient. The specific analysis formula of the water pump flow stability evaluation coefficient is:

[0048]

[0049] Where Y is the water pump flow stability evaluation coefficient, Co is the water pump outlet flow, Ci is the water pump inlet flow, Co / Ci is the water pump volumetric efficiency, Cv is the volume flow of the water pump, Cm is the mass flow of the water pump, Cv is the 预 Indicates the volume of liquid passing through the pump outlet per preset unit time, Cm 预 It indicates the mass of liquid passing through the pump outlet in a preset unit time; the pump pressure digital model is used to call the pump pressure data in the pump performance data set and analyze the pump pressure data to obtain the pump pressure safety assessment coefficient. The specific analysis formula of the pump pressure safety assessment coefficient is:

[0050]

[0051] Where Z represents the water pump pressure safety assessment coefficient, Lc represents the water pump head, Lc 理 Indicates the theoretical head of the pump, Lc / Lc 理 Indicates the hydraulic efficiency of the pump, Po indicates the outlet pressure of the pump, Pi indicates the inlet pressure of the pump, Po min Indicates the minimum outlet pressure, Po max Indicates the maximum value of the outlet pressure; the water pump operation digital model is used to call the water pump operation data in the water pump performance data set, and analyze the water pump operation data to obtain the water pump operation status evaluation coefficient. The specific analysis formula of the water pump operation status evaluation coefficient is:

[0052]

[0053] Wherein, Eh represents the active power of the water pump, E represents the shaft power, Eh / E represents the efficiency of the water pump, Tp represents the historical operation time of the water pump, Tg represents the historical maintenance time of the water pump, u1 and u2 are the quadratic term coefficient and the linear term coefficient of the historical maintenance time, u1>0, u2>0;

[0054] Based on the intelligent analysis model of water pump performance, the water pump flow stability assessment coefficient, the water pump pressure safety assessment coefficient and the water pump operation status assessment coefficient are integrated, connected to the water pump digital twin model, and the physical behavior of the water pump is simulated; the water pump knowledge graph is based on the preprocessed water pump performance data, and the preprocessed water pump flow data, water pump pressure data and water pump operation data are integrated to obtain a complete water pump performance data set; according to the water pump performance data set, the water pump flow data and water pump pressure data are classified based on the water pump operation data to obtain the analysis points corresponding to the water pump performance data set. The analysis points corresponding to the water pump performance data set are the water pump knowledge fields corresponding to the water pump performance data set. The water pump knowledge fields include but are not limited to the water pump flow analysis field, the water pump pressure analysis field and the water pump operation analysis field. Different water pump knowledge graphs are constructed according to different water pump knowledge fields.

[0055] S5: Apply the water pump digital twin model: Based on the water pump digital twin model constructed in S4, apply the water pump digital twin model and perform visualization.

[0056] In this embodiment, it should be specifically explained that S5 combines the digital twin model of the water pump to determine the operating status of the water pump and visualize the water pump. The operating status of the water pump includes green zone, yellow zone and red zone. If the operating status of the water pump enters the green zone, it means that the water pump is in a normal and safe operating state, the system is operating normally, and no additional monitoring or intervention is required; if the operating status of the water pump enters the yellow zone, it means that the water pump is in a potential risk or slightly abnormal operating state, the system is about to have problems or some minor abnormalities already exist. At this time, an early warning prompt is issued through the twin platform so that the operator can pay attention in time and take necessary preventive measures; if the operating status of the water pump enters the red zone, it means that the water pump is in a serious abnormal or dangerous operating state, the system has problems or is about to have a serious failure. At this time, the twin platform prompts to deal with it according to the plan to ensure the safety and stability of the system; the plan uses the plan model to formulate corresponding disposal plans according to the reasons for entering the "red zone", such as cavitation, pressure pulsation, hump and overwater level. The method for judging whether the water pump enters the red zone is as follows:

[0057] A1: Obtain real-time perception data from physical entities. Physical entities include monitoring systems and data processing systems. Real-time perception data includes collected and pre-processed pump performance data.

[0058] A2: In the virtual body, first, the performance curve is digitized into a digital model; second, based on the real-time sensing data obtained or the corresponding data manually input, the decision support algorithm is started to calculate the unit number and corresponding speed to be operated, and the calculation results are output to the database;

[0059] A3: Synchronously display the virtual operating point trajectory and the real-time operating point on the virtual screen;

[0060] A4: The virtual operating point is corrected based on historical big data;

[0061] A5: When the operating point enters the "red zone", a corresponding disposal plan should be proposed.

[0062] Furthermore, the visualization processing includes a user interface layer and a system service layer. Through the user interface and using front-end development technology, the measurement point management, instrument management, three-dimensional view and monitoring data query functions are realized, and an interactive interface is set up to allow users to interact with the digital twin model of the water pump, such as adjusting parameters and viewing historical data. The system service layer provides public service functions and generates detailed data reports and analysis reports for use by managers.

[0063] S6: Optimize the digital twin of the water pump: Based on the water pump performance data set, the water pump performance intelligent analysis model and the water pump operation status, the water pump digital twin is optimized using modularization.

[0064] In this embodiment, it should be specifically explained that S6 adopts modularization to optimize the digital twin of the water pump based on the water pump performance data set, the water pump performance intelligent analysis model and the water pump operation status. The modularization includes a data module, a model module and an application module. The data module, the model module and the application module are connected and communicated by means of reserved interface slots. The reserved interface slots adopt a microservice architecture or a plug-in architecture so that the mechanism model can be added to the digital twin when needed. The architecture supports dynamic loading and unloading of modules to adapt to different application scenarios and requirements; the data module is used to collect water pump performance data and continuously integrate the water pump performance data set, and provide an interface for data exchange with other modules. Through the reserved interface slot, new sensors or data sources are added to obtain new perception data. The newly added perception data is automatically integrated into the water pump performance data set in the data module, and the water pump digital twin is updated in real time; the model module optimizes the water pump performance intelligent analysis model through the water pump performance data set, and adjusts the parameters, optimizes the structure and retrains the model according to actual needs and data changes. The water pump performance intelligent analysis model includes but is not limited to the water pump flow digital model, the water pump pressure digital model and the water pump operation digital model, and the rationality of the water pump digital twin is calculated based on the water pump flow digital model, the water pump pressure digital model, the water pump operation digital model and the updated mechanism model. The specific calculation formula is:

[0065]

[0066] Where P represents the rationality of the water pump digital twin, Y represents the water pump flow stability assessment coefficient, Z represents the water pump pressure safety assessment coefficient, T represents the water pump operation status assessment coefficient, λ1, λ2 and λ3 are the proportional coefficients corresponding to different assessment dimensions, λ1>0, λ2>0, λ3>0, φ is the rationality adjustment parameter of the updated mechanism model, and the rationality P of the water pump digital twin is compared with the preset rationality P of the water pump digital twin. yuMake a comparison to determine whether the digital twin of the water pump is reasonable, and issue an early warning for unreasonable digital twins of the water pump and further optimize and update them; the data model also provides an interface for model calling and data transmission with other modules, and updates or replaces the mechanism model in the existing model module through the reserved interface slot to adapt to new physical entities and changing environmental conditions. The updated mechanism model is seamlessly integrated into the digital twin of the water pump; the application module is based on the data module and the model module, and monitors, warns, makes decisions and diagnoses faults on the use of the water pump according to the digital twin of the water pump. The behavior parameters of the digital twin of the water pump are driven by three methods: static values, simulation values ​​and mechanism model data. Among them, the static value is used to initialize the behavior parameters of the digital twin of the water pump as the initial parameter, the simulation value is used when the digital twin of the water pump lacks specific data, and the mechanism model data is mostly used to drive simulation. The behavior parameters of the digital twin of the water pump include water discharge flow control of the water pump, water head control of the water pump, and start and stop of impeller rotation. It interacts with users according to different behavior parameters, applies scenarios under different behavior parameters, and provides an interface for integration with other systems or modules.

[0067] The present invention provides a foundation for the water pump digital twin model by constructing a physical model, and collects water pump performance data based on the physical model, and cleans, denoises and normalizes the collected data, eliminates outliers and noise preprocessing in the data, improves the quality and reliability of the data, and also provides an important basis for the subsequent construction of the water pump digital twin model; by constructing the water pump digital twin model in multiple dimensions of water pump flow data, water pump pressure data and water pump operation data, and continuously improving the model according to the actual operation of the water pump, it allows real-time monitoring of multiple dimensions of the water pump equipment, timely detection of abnormal conditions, prediction of failure risks, and giving maintenance suggestions, which can significantly reduce failure downtime and provide Improve the stability and reliability of equipment, analyze and predict the operating status of the water pump, so as to optimize equipment operation and reduce energy consumption costs; based on the water pump digital twin model, through 3D visualization technology, the water pump digital twin model is displayed to management personnel in an intuitive and vivid way, which helps managers to better understand the operating status, performance parameters and potential problems of the water pump. It adopts modular design and open data interfaces to facilitate subsequent functional expansion and system integration, and realize the scalability of the water pump digital twin model; the water pump digital twin model can also conduct safety monitoring of water resource allocation projects, judge different operating conditions of water pumps, and make different responses to further improve the safety of the project.

[0068] Secondly: In the drawings of the embodiments disclosed in the present invention, only the structures related to the embodiments disclosed in the present invention are involved, and other structures can refer to the general design. In the absence of conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0069] Finally: The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A scalable method for constructing a digital twin of a water pump, characterized in that: include: S1: Construct physical model: Construct physical model according to the water pumps required for water resource allocation project; S2: Collecting water pump performance data: Based on the physical model constructed in S1, collect water pump performance data for the water resource allocation project, the water pump performance data including water pump flow data, water pump pressure data and water pump operation data; S3: Preprocessing water pump performance data: Preprocessing the water pump performance data collected by S2, including data cleaning, data correction, filtering and feature extraction; S4: Build a digital twin model of the water pump: Based on the water pump performance data and physical model preprocessed by S3, establish a water pump performance intelligent analysis model, and build a digital twin model of the water pump according to the water pump performance intelligent analysis model; S5: Apply the water pump digital twin model: Based on the water pump digital twin model constructed in S4, apply the water pump digital twin model and perform visualization; S6: Optimize the digital twin of the water pump: Based on the water pump performance data set, the water pump performance intelligent analysis model and the water pump operation status, the water pump digital twin is optimized using modularization.

2. A scalable water pump digital twin construction method according to claim 1, characterized in that: The S2 includes a water pump flow data acquisition unit, a water pump pressure data acquisition unit and a water pump operation data acquisition unit, and the water pump flow data acquisition unit is used to collect water pump flow data, including inlet flow, outlet flow, volume flow and mass flow; The water pump pressure data acquisition unit is used to collect water pump pressure data, including inlet pressure, outlet pressure and head; the water pump operation data acquisition unit is used to collect water pump operation data, including the water pump's historical operation time, historical maintenance time, water pump active power and shaft power.

3. A scalable water pump digital twin construction method according to claim 1, characterized in that: The preprocessing automatically screens and removes abnormal data in the water pump performance data, and uses data cleaning technology to correct the data; data cleaning refers to discovering and correcting identifiable errors in the data, including checking data consistency, processing invalid values ​​and missing values, filtering is to remove noise and interference in the water pump performance data in the virtual body, and feature extraction is to extract information from the water pump performance data collected in S2, including but not limited to the following features: average value: calculate the average historical maintenance time and average historical operation time over a period of time to reflect the operating status of the water pump; maximum and minimum values: calculate the maximum outlet pressure and inlet pressure, minimum outlet pressure and inlet pressure over a period of time to reflect the extreme working state of the water pump; Percentage: Calculate the percentage of the pump, hydraulic power and volume over a period of time to reflect the efficiency of the pump.

4. A scalable water pump digital twin construction method according to claim 1, characterized in that: The water pump digital twin model is simulated based on the preprocessed water pump performance data, creates a virtual modeling corresponding to the physical world, establishes a water pump performance intelligent analysis model, and establishes a water pump performance intelligent analysis model based on the water pump knowledge graph. The water pump performance intelligent analysis model includes but is not limited to a water pump flow digital model, a water pump pressure digital model, and a water pump operation digital model. The water pump flow digital model is used to call the water pump flow data in the water pump performance data set and analyze the water pump flow data to obtain the water pump flow stability evaluation coefficient. The specific analysis formula of the water pump flow stability evaluation coefficient is: Where Y is the water pump flow stability evaluation coefficient, Co is the water pump outlet flow, Ci is the water pump inlet flow, Co / Ci is the water pump volumetric efficiency, Cv is the volume flow of the water pump, Cm is the mass flow of the water pump, Cv is the 预 Indicates the volume of liquid passing through the pump outlet per preset unit time, Cm 预 Indicates the mass of liquid passing through the pump outlet within a preset unit time; The water pump pressure digital model is used to call the water pump pressure data in the water pump performance data set and analyze the water pump pressure data to obtain the water pump pressure safety assessment coefficient. The specific analysis formula of the water pump pressure safety assessment coefficient is: Where Z represents the water pump pressure safety assessment coefficient, Lc represents the water pump head, Lc 理 Indicates the theoretical head of the pump, Lc / Lc 理 Indicates the hydraulic efficiency of the pump, Po indicates the outlet pressure of the pump, Pi indicates the inlet pressure of the pump, Po min Indicates the minimum outlet pressure, Po max Indicates the maximum outlet pressure; The water pump operation digital model is used to call the water pump operation data in the water pump performance data set and analyze the water pump operation data to obtain the water pump operation status evaluation coefficient. The specific analysis formula of the water pump operation status evaluation coefficient is: Wherein, T represents the evaluation coefficient of the pump operation status, Eh represents the active power of the pump, E represents the shaft power, Eh / E represents the efficiency of the pump, Tp represents the historical operation time of the pump, Tg represents the historical maintenance time of the pump, u1 and u2 are the quadratic term coefficient and the linear term coefficient of the historical maintenance time, u1>0, u2>0; Based on the intelligent analysis model of water pump performance, the water pump flow stability assessment coefficient, the water pump pressure safety assessment coefficient and the water pump operation status assessment coefficient are integrated, connected to the water pump digital twin model, and the physical behavior of the water pump is simulated.

5. A scalable water pump digital twin construction method according to claim 4, characterized in that: The water pump knowledge graph is based on the preprocessed water pump performance data, and integrates the preprocessed water pump flow data, water pump pressure data and water pump operation data to obtain a complete water pump performance data set; according to the water pump performance data set, the water pump flow data and water pump pressure data are classified based on the water pump operation data to obtain the analysis points corresponding to the water pump performance data set. The analysis points corresponding to the water pump performance data set are the water pump knowledge fields corresponding to the water pump performance data set. The water pump knowledge fields include but are not limited to the water pump flow analysis field, the water pump pressure analysis field and the water pump operation analysis field. Different water pump knowledge graphs are constructed according to different water pump knowledge fields.

6. The scalable water pump digital twin construction method according to claim 1, characterized in that: The S5 combines the digital twin model of the water pump to determine the operating status of the water pump and visualize the water pump. The operating status of the water pump includes green zone, yellow zone and red zone. If the operating status of the water pump enters the green zone, it means that the water pump is in a normal and safe operating state, and the system is operating normally without additional monitoring or intervention; if the operating status of the water pump enters the yellow zone, it means that the water pump is in a potential risk or slightly abnormal operating state, and the system is about to have problems or some minor abnormalities already exist. At this time, an early warning prompt is issued through the twin platform so that the operator can pay attention in time and take necessary preventive measures; if the operating status of the water pump enters the red zone, it means that the water pump is in a serious abnormal or dangerous operating state, and the system has problems or is about to have a serious failure. At this time, the twin platform prompts to deal with it according to the plan to ensure the safety and stability of the system.

7. A scalable water pump digital twin construction method according to claim 6, characterized in that: The visualization processing includes a user interface layer and a system service layer. Through the user interface, front-end development technology is used to realize measurement point management, instrument management, three-dimensional view and monitoring data query functions, and an interactive interface is set to allow users to interact with the digital twin model of the water pump, such as adjusting parameters and viewing historical data. Public service functions are provided through the system service layer, and detailed data reports and analysis reports are generated for use by managers.

8. The scalable water pump digital twin construction method according to claim 1, characterized in that: The S6 optimizes the digital twin of the water pump by modularization based on the water pump performance data set, the water pump performance intelligent analysis model and the water pump operation status. The modularization includes a data module, a model module and an application module. The data module, the model module and the application module are connected and communicated by means of reserved interface slots. The reserved interface slots adopt a microservice architecture or a plug-in architecture so that the mechanism model can be added to the digital twin when needed. The architecture supports dynamic loading and unloading of modules to adapt to different application scenarios and requirements; The data module is used to collect pump performance data and continuously integrate pump performance data sets, establish a real-time mapping mechanism, synchronize and update data between physical entities and pump digital twins in real time, and provide an interface for data exchange with other modules. By reserving interface slots, new sensors or data sources are added to obtain new perception data. The newly added perception data is automatically integrated into the pump performance data set in the data module to update the pump digital twin in real time. The model module optimizes the pump performance intelligent analysis model through the pump performance data set, adjusts parameters, optimizes the structure and retrains the model according to actual needs and data changes. The pump performance intelligent analysis model includes but is not limited to the pump flow digital model, the pump pressure digital model and the pump operation digital model, and calculates the rationality of the pump digital twin based on the pump flow digital model, the pump pressure digital model, the pump operation digital model and the updated mechanism model. The specific calculation formula is: Where P represents the rationality of the water pump digital twin, Y represents the water pump flow stability assessment coefficient, Z represents the water pump pressure safety assessment coefficient, T represents the water pump operation status assessment coefficient, λ1, λ2 and λ3 are the proportional coefficients corresponding to different assessment dimensions, λ1>0, λ2>0, λ3>0, φ is the rationality adjustment parameter of the updated mechanism model, and the rationality P of the water pump digital twin is compared with the preset rationality P of the water pump digital twin. yu Make a comparison to determine whether the digital twin of the water pump is reasonable, issue an early warning for unreasonable digital twins of the water pump, and further optimize and update them; the data model also provides an interface for model calling and data transmission with other modules, and updates or replaces the mechanism model in the existing model module through the reserved interface slot to adapt to new physical entities and changing environmental conditions. The updated mechanism model is seamlessly integrated into the digital twin of the water pump; The application module is based on the data module and the model module. It monitors, warns, makes decisions and diagnoses faults on the use of the water pump according to the water pump digital twin. It drives the behavioral parameters of the water pump digital twin through three methods: static values, simulation values ​​and mechanism model data. Among them, the static value is used to initialize the behavioral parameters of the water pump digital twin as the initial parameter. The simulation value is used when the water pump digital twin lacks specific data. The mechanism model data is mostly used to drive simulation. The behavioral parameters of the water pump digital twin include water pump outlet flow control, water pump head control and impeller rotation start and stop. It interacts with users according to different behavioral parameters, applies scenarios under different behavioral parameters, and provides an interface for integration with other systems or modules.

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