Method and System for Monitoring the Operating State of a Water Pump Unit Based on Digital Twin Technology

Abstract

This application relates to a method and system for monitoring the operating status of a water pump unit based on digital twin technology, belonging to the field of pipeline pump monitoring technology. This method constructs a digital twin model that integrates equipment parameters and environmental sensing data, and combines Internet of Things technology to collect multi-dimensional operating data such as dynamic pressure, flow rate, power quality, and temperature in real time. An innovative multi-dimensional correction algorithm is proposed: the fluid velocity calculation is corrected by the pipe orifice structure parameters, the head calculation is dynamically corrected through the temperature compensation algorithm and the mechanical wear factor, and a harmonic loss calibration mechanism is introduced to dynamically compensate the water pump efficiency. Based on a dual-channel machine learning model, a fault correlation matrix is constructed by improving the weighted K-means algorithm to achieve equipment health status clustering, the efficiency decay trend is predicted by combining with the LSTM network, and the attention mechanism is used to fuse static features and dynamic time series data, and finally an accurate fault diagnosis report and maintenance strategy are generated.

Description

Technical Field

[0001] This application relates to the technical field of pipeline pump monitoring, and more specifically, to a method and system for monitoring the operating status of a water pump unit based on digital twin technology. Background Art

[0002] As the core link of urban water supply, the operating efficiency of water pumps in water treatment plants is directly related to the cost, energy consumption, and water supply quality of water supply. In recent years, with the rapid development of automation technology, sensor technology, and information technology, great progress has been made in the monitoring technology of the operating efficiency of water pumps in water treatment plants. However, there are still significant technical bottlenecks in actual working condition monitoring: ① The traditional monitoring system adopts a distributed architecture, and independent subsystems such as flow meters, electricity meters, and vibration sensors form data islands, resulting in a time series deviation between the pipe loss compensation factor and the motor input power in the calculation of the head; ② Existing digital twin applications mostly focus on single physical field simulation, lacking the ability of electromechanical coupling modeling and unable to analyze the nonlinear influence of harmonic distortion on motor efficiency; ③ The operation and maintenance mode based on threshold alarm is difficult to capture progressive faults such as impeller cavitation and bearing wear.

[0003] The prior art, such as the Chinese patent application with the publication number CN118309644A, discloses a method for monitoring the operating flow of a pipeline pump based on digital twin. The method includes: a data acquisition layer, a communication network layer, a data processing layer, a digital twin model layer, a flow monitoring and prediction layer, a decision support and control layer, and a user interface layer. By constructing a digital twin model of the pipeline pump and combining with the real-time monitoring of the flow rate, the invention realizes the real-time monitoring, intelligent prediction, precise control, and efficient management of the pipeline pump, thus effectively improving the operating efficiency, stability, and economic benefits of the device, and providing strong support for improving the performance and benefits of the pipeline pump system.

[0004] The problems of the above prior art are that through the monitoring of the flow rate, the operating output of the water pump can be obtained, reflecting whether the operating status of the water pump is normal, but the energy consumption data of the water pump is not collected, the input power of the water pump cannot be analyzed and calculated, and the evaluation of the operating efficiency of the water pump and the analysis ability of potential faults are lacking. Therefore, it is difficult to provide data support for effective water pump operation regulation and fault troubleshooting to maintain the water pump in the best efficiency operating state. There is a lack of a dynamic parameter correction mechanism, and the dynamic influence of environmental parameters (such as water temperature, pipeline thermal expansion) and equipment degradation (such as impeller wear) on the monitoring results is not considered. Summary of the Invention

[0005] To solve the above technical problems, the present invention proposes a method and system for monitoring the operating status of a water pump unit based on digital twin technology.

[0006] The technical solution of the present invention is as follows:

[0007] The present invention provides a method for monitoring the operating status of a water pump unit based on digital twin technology, comprising the following steps:

[0008] Construct a digital twin model of the water pump unit, which integrates the equipment parameters and environmental sensing parameters of the water pump unit;

[0009] Obtain the operating status data of the water pump unit in real time through Internet of Things sensors. The operating status data of the water pump unit includes dynamic pressure values, instantaneous flow rates, power quality parameters, and fluid temperature parameters;

[0010] Based on the digital twin model and the real-time operating status data of the water pump unit, dynamically calculate the actual operating efficiency of the water pump unit and perform multi-dimensional corrections;

[0011] Combine the historical operating status data of the water pump unit and the corrected actual operating efficiency, and perform fault diagnosis and efficiency evaluation of the water pump unit through a machine learning model to obtain a fault diagnosis and evaluation report of the water pump unit.

[0012] As a preferred embodiment, the equipment parameters include: a water pump geometric feature parameter group and a metering device feature parameter group, where:

[0013] The water pump geometric feature parameter group includes the inner diameters of the inlet and outlet water pipes of the pump body and the baseline efficiency of the motor;

[0014] The metering device feature parameter group includes the pulse constant of the electric energy meter, the transformation ratio of the voltage transformer, and the installation elevation of the pressure sensor.

[0015] As a preferred embodiment, the multi-dimensional correction includes applying pipe orifice structure parameters to correct the calculation of fluid flow velocity, specifically:

[0016] ;

[0017] ;

[0018] In the formula, is the inlet flow velocity; is the outlet flow velocity; Q is the flow rate; is the inner diameter of the inlet pipe; is the inner diameter of the outlet pipe.

[0019] As a preferred embodiment, the multi-dimensional correction includes adopting a temperature compensation algorithm and a mechanical wear factor to dynamically correct the calculation of the head, specifically:

[0020] ;

[0021] Among them:

[0022] ;

[0023] Wherein, H is the head; is the pressure at the pump outlet; is the pressure at the pump inlet; is the liquid density at the standard operating condition water temperature; g is the acceleration due to gravity; T is the real-time water temperature; is the standard operating condition water temperature; is the pipeline thermal expansion coefficient; is the impeller wear coefficient; is the elevation of the pressure gauge at the pump outlet; is the elevation of the pressure gauge at the pump inlet; is the cumulative operating time of the impeller; is the impeller wear acceleration threshold; is the impeller decay rate.

[0024] As a preferred embodiment, the multi-dimensional correction includes introducing power quality parameters to dynamically calibrate the actual operating efficiency of the pump, including the following process:

[0025] Calculate the real-time effective power of the pump, and the specific formula is:

[0026] ;

[0027] Wherein, is the real-time effective power of the pump; is the liquid density at the real-time water temperature;

[0028] Calculate the real-time input power of the pump, and the specific formula is:

[0029] ;

[0030] Wherein, N is the real-time input power of the pump; is the voltage transformer ratio; is the kWh meter pulse constant; is the time for 10 revolutions of the kWh meter;

[0031] Calculate the comprehensive efficiency of the pump, and the specific formula is:

[0032] ;

[0033] Wherein, is the comprehensive efficiency of the pump;

[0034] Calculate the actual operating efficiency of the pump, and the specific formula is:

[0035] ;

[0036] Wherein, is the actual operating efficiency of the pump; is the motor baseline efficiency; is the real-time power factor; is the rated power factor; is the harmonic loss coefficient; THD is the current waveform distortion rate.

[0037] As a preferred embodiment, the machine learning model adopts a dual-channel heterogeneous architecture, including:

[0038] Feature clustering channel: Generating the clustering centers of the device health status based on the improved weighted K-means algorithm. The objective function of the improved weighted K-means algorithm is:

[0039] ;

[0040] In the formula: J is the objective function of the improved weighted K-means algorithm; C is the total number of clusters; l is the index of the clustering cluster; i is the index of the water pump sample; is the time decay function; is the actual operating efficiency of the i-th water pump; is the clustering center of the actual operating efficiency of the l-th clustering cluster; is the production duration balance coefficient; is the production duration of the i-th water pump; is the clustering center of the production duration of the l-th clustering cluster; m is the total number of fault types; are the fault type indexes, respectively representing the j-th fault type and the q-th fault type; is the element of the fault correlation matrix; is the indication value of whether the i-th water pump has the q-th fault type; is the average occurrence frequency of the q-th fault in the l-th clustering cluster; is the sample set included in the l-th clustering cluster;

[0041] Time series prediction channel: Constructing an efficiency decay trend prediction model using an LSTM network; The objective function of the LSTM network is to design a multi-task time series weighted loss function based on the LSTM cross-entropy loss function, specifically:

[0042] ;

[0043] In the formula, is the multi-task time series weighted loss function; is the classification cross-entropy loss; is the signal-to-noise ratio weight adjustment coefficient; is the phase compensation coefficient; is the dynamic decay weight; is the predicted efficiency value within the time segment t; is the actual efficiency value within the time segment t; is the wear leveling factor; is the KL divergence calculation is the real-time probability distribution of the d-th feature cluster; is the reference probability distribution of the d-th feature cluster; D is the number of feature clusters; d is the index of the feature cluster; is the time window length;

[0044] Cross-channel fusion layer: Through the attention mechanism, it realizes the coupling analysis of the static features of the feature clustering channel and the dynamic time-series features of the time-series prediction channel.

[0045] As a preferred embodiment, the elements of the fault correlation matrix are specifically calculated as follows:

[0046] ;

[0047] In the formula: is the historical number of times that fault type j and fault type q occur simultaneously; is the total number of times that fault type j occurs alone; is the total number of times that fault type q occurs alone; is a constant to prevent the denominator from being 0; .

[0048] On the other hand, the present invention also provides a system for monitoring the operating state of a water pump unit based on digital twin technology, including:

[0049] Digital twin model creation module, which constructs a digital twin model of the water pump unit, and the digital twin model integrates the equipment parameters and environmental sensing parameters of the water pump unit;

[0050] Data acquisition module, which obtains the operating state data of the water pump unit in real time through Internet of Things sensors, and the operating state data of the water pump unit includes dynamic pressure value, instantaneous flow rate, power quality parameters and fluid temperature parameters;

[0051] Efficiency calculation module, which dynamically calculates the actual operating efficiency of the water pump unit based on the digital twin model and the real-time operating state data of the water pump unit, and performs multi-dimensional correction;

[0052] Evaluation and diagnosis module, which combines the historical operating state data of the water pump unit and the corrected actual operating efficiency, and performs fault diagnosis and efficiency evaluation of the water pump unit through a machine learning model to obtain a fault diagnosis and evaluation report of the water pump unit.

[0053] The present invention has the following beneficial effects:

[0054] 1. Dynamically correct the head calculation through the temperature compensation algorithm and the impeller wear index decay model, and combine with the calibration of power quality parameters to significantly improve the calculation accuracy of the effective power and the input power, and reduce the actual efficiency error.

[0055] 2. Adopt the improved weighted K-means algorithm to construct a fault correlation matrix, quantify the probability of fault concurrency, and combine with the LSTM time series prediction model to realize the analysis of equipment degradation trend and early fault warning.

[0056] 3. Based on the digital twin visualization platform, track the efficiency evolution trend, fuse the attention mechanism of static features and dynamic time series, generate targeted maintenance strategies, reduce energy consumption and extend the equipment life.

[0057] 4. Integrate the data of flow meters, pressure gauges, watt-hour meters and environmental sensors to construct a multi-dimensional electromechanical coupling model, break through the limitations of the traditional subsystem data islands, and realize the global perception of the pump operation state.

[0058] 5. The two-channel heterogeneous machine learning model reduces the complexity of LSTM through clustering preprocessing, and combines with the optimization of KL divergence and multi-task loss function to improve the prediction robustness and diagnostic accuracy. Description of the Drawings

[0059] Figure 1 It is a schematic flow chart of the method in the first embodiment;

[0060] Figure 2 It is a schematic diagram of the structure of the pump and the monitoring equipment. Detailed Embodiments

[0061] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative work shall fall within the protection scope of the present invention.

[0062] It should be understood that the step numbers used in the text are only for convenient description and do not limit the execution order of the steps.

[0063] It should be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include the plural forms.

[0064] The terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements and / or components, but do not preclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or their combinations.

[0065] The term "and / or" refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0066] Example 1:

[0067] To make the objectives, technical solutions and advantages of the present invention clearer, the following will combine specific embodiments of the present application and refer to the appended Figure 1 to clearly and completely describe the technical solutions of the present invention.

[0068] To solve the problems of the prior art, the present invention provides a method for monitoring the operating state of a water pump unit based on digital twin technology, including the following steps:

[0069] Construct a digital twin model of the water pump unit, and the digital twin model integrates the equipment parameters and environmental sensing parameters of the water pump unit;

[0070] The equipment parameters include: a water pump geometric feature parameter group and a metering device feature parameter group, where:

[0071] The water pump geometric feature parameter group includes the inner diameters of the inlet and outlet water pipes of the pump body and the baseline efficiency of the motor;

[0072] The metering device feature parameter group includes the pulse constant of the electric energy meter, the transformation ratio of the voltage transformer, and the installation elevation of the pressure sensor.

[0073] Obtain the operating state data of the water pump unit in real time through Internet of Things sensors, and the operating state data of the water pump unit includes dynamic pressure values, instantaneous flow rates, power quality parameters and fluid temperature parameters;

[0074] In this embodiment, the power quality parameters include a time-domain and frequency-domain composite parameter set, specifically including:

[0075] Time-domain parameters: active power fluctuation coefficient, current waveform distortion rate; the active power fluctuation coefficient is used to evaluate power stability.

[0076] Frequency-domain parameters: characteristic harmonic component amplitude spectrum, fundamental power factor.

[0077] Based on the digital twin model and the real-time operating state data of the water pump unit, dynamically calculate the actual operating efficiency of the water pump unit and perform multi-dimensional correction;

[0078] The multi-dimensional correction includes applying the pipe orifice structure parameters to correct the fluid flow velocity calculation, specifically:

[0079] ;

[0080] ;

[0081] In the formula, is the inlet flow velocity; is the outlet flow velocity; Q is the flow rate; is the inner diameter of the inlet pipe; is the inner diameter of the outlet pipe.

[0082] The multi-dimensional correction includes adopting a temperature compensation algorithm and dynamically correcting the head calculation with a mechanical wear factor. Specifically:

[0083] ;

[0084] Among them:

[0085] ;

[0086] In the formula, H is the head; is the pump outlet pressure; is the pump inlet pressure; is the liquid density at the standard operating condition water temperature; g is the acceleration due to gravity; T is the real-time water temperature, obtained through the temperature sensor embedded in the digital twin model; is the standard operating condition water temperature, defaulting to 20°C; is the pipeline thermal expansion coefficient, taking 0.0002 / °C; is the impeller wear coefficient; is the elevation of the pump outlet pressure gauge; is the elevation of the pump inlet pressure gauge; is the cumulative operating time of the impeller; is the impeller wear acceleration threshold, taking 8000 hours; is the impeller attenuation rate, taking 0.003 according to the wear model parameter range of similar mechanical components; by introducing the wear acceleration threshold and the impeller attenuation rate , the defect that the traditional linear model cannot capture the sudden change of equipment performance is solved. For example: when , the impeller wears slowly ; when , the impeller wear rate increases exponentially, quickly approaching 0, which is highly consistent with the three-stage law of real impeller wear (running-in period - stable period - failure period).

[0087] The multi-dimensional correction includes introducing power quality parameters to dynamically calibrate the actual operating efficiency of the pump, including the following process:

[0088] Calculate the real-time effective power of the water pump. The specific formula is:

[0089] ;

[0090] In the formula, is the real-time effective power of the water pump; is the liquid density at the real-time water temperature;

[0091] Calculate the real-time input power of the water pump. The specific formula is:

[0092] ;

[0093] In the formula, N is the real-time input power of the water pump; is the transformation ratio of the voltage transformer; is the pulse constant of the electric energy meter; is the time for 10 revolutions of the electric energy meter;

[0094] Calculate the comprehensive efficiency of the water pump. The specific formula is:

[0095] ;

[0096] In the formula, is the comprehensive efficiency of the water pump;

[0097] Calculate the actual operating efficiency of the water pump. The specific formula is:

[0098] ;

[0099] In the formula, is the actual operating efficiency of the water pump; is the baseline efficiency of the motor; is the real-time power factor, measured embeddedly by the electric energy meter; is the rated power factor, obtained from the motor nameplate; is the harmonic loss coefficient, calibrated through the amplitude spectrum of the characteristic harmonic components; THD is the current waveform distortion rate, obtained through the harmonic analysis of the electric energy meter.

[0100] Combine the historical operating status data of the water pump unit and the corrected actual operating efficiency, and perform fault diagnosis and efficiency evaluation on the water pump unit through a machine learning model to obtain a fault diagnosis and evaluation report of the water pump unit.

[0101] The machine learning model adopts a dual-channel heterogeneous architecture, including:

[0102] Feature clustering channel: Generate the clustering center of the device health status based on the improved weighted K-means algorithm;

[0103] Time series prediction channel: Build an efficiency decay trend prediction model using the LSTM network;

[0104] Cross-channel fusion layer: The coupling analysis of the static features of the feature clustering channel and the dynamic time-series features of the time-series prediction channel is realized through the attention mechanism.

[0105] In this embodiment, the feature clustering channel: generates the clustering centers of the device health status based on the improved weighted K-means algorithm, which specifically includes the following processes:

[0106] Collect the operation data of the historical water pump throughout its life cycle, including: water pump model, actual operation efficiency of the water pump throughout its life cycle, water pump failure type and corresponding energy consumption, flow rate, and head;

[0107] Classify the water pumps according to different failure types. The failure types are divided into m categories. Then, the failure situation of the water pump is represented by the following formula:

[0108] F ;

[0109] In the formula, F is the failure situation of the water pump; if the water pump has the jth failure, then , otherwise 0;

[0110] Calculate the clustering center of each classification cluster; the specific calculation formula is:

[0111] ;

[0112] ;

[0113] ;

[0114] In the formula: is the clustering center of the production duration of the lth clustering cluster; is the clustering center of the actual operation efficiency of the lth family cluster; is the jth component of the failure type clustering center; is the production duration of the ith water pump; is the actual operation efficiency of the ith water pump; is the ith water pump regarding the jth failure type; is the number of water pumps in the lth category; is the set belonging to the lth category of water pumps; ;

[0115] The objective function of the improved weighted K-means algorithm in the feature clustering channel is:

[0116] ;

[0117] Where: J is the objective function of the improved weighted K-means algorithm; C is the total number of clusters; l is the l-th cluster; i is the pump sample index, representing the i-th pump; is the time decay function; is the actual operating efficiency of the i-th pump; is the clustering center of the actual operating efficiency of the l-th cluster; is the production duration balance coefficient; is the production duration of the i-th pump; is the clustering center of the production duration of the l-th cluster; m is the total number of fault types; is the fault type index, representing the j-th and q-th fault types respectively; is the fault correlation matrix element; is the indication value of whether the i-th pump has the q-th fault type, 1 for yes and 0 for no; is the average occurrence frequency of the q-th fault in the l-th cluster; is the sample set included in the l-th cluster;

[0118] Among them, the fault correlation matrix element, the specific calculation formula is:

[0119] ;

[0120] In the formula: is the historical number of times that fault type j and fault type q occur simultaneously; is the total number of times that fault type j occurs alone; is the total number of times that fault type q occurs alone; is a very small constant to prevent the denominator from being 0; ;

[0121] Among them, the time decay function, which decreases with the increase of production time, the specific calculation formula is:

[0122] ;

[0123] In the formula: is the decay rate parameter.

[0124] Through iterative calculation, the objective function J is minimized to obtain a reasonable clustering of the pump production duration, the actual operating efficiency of the pump, and the pump fault conditions.

[0125] Time series prediction channel: An efficiency decay trend prediction model is constructed using an LSTM network; specifically including the following steps:

[0126] Arrange the data of each classification set of the clustered pumps in chronological order, and divide the data points of each set into a training set and a test set;

[0127] Feature extraction is performed on each classification in chronological order to obtain feature clusters. The specific formula is as follows:

[0128] ;

[0129] In the formula, is the feature row vector of the q-th row in the p-th set; is the average value of the actual operating efficiency of the water pump in the p-th set; is the standard deviation of the actual operating efficiency in the p-th set; is the average value of the commissioning time of the water pump in the p-th set; is the standard deviation of the commissioning time of the water pump in the p-th set; is the failure frequency of m different failure types in the p-th set;

[0130] The efficiency decay trend prediction model is constructed by using the training set to train the long short-term memory neural network (LSTM) of the time series prediction channel. The objective function of the LSTM network is to design a multi-task time series weighted loss function based on the LSTM cross-entropy loss function. Specifically:

[0131] ;

[0132] In the formula, is the multi-task time series weighted loss function; is the classification cross-entropy loss; is the signal-to-noise ratio weight adjustment coefficient, ; is the phase compensation coefficient, ; is the dynamic decay weight; is the predicted efficiency value within the time segment t; is the actual efficiency value within the time segment t; is the wear leveling factor, = ; is the KL divergence calculation is the real-time probability distribution of the d-th feature cluster; is the reference probability distribution of the d-th feature cluster; D is the number of feature clusters; d is the index of the feature cluster; is the time window length;

[0133] Among them, the dynamic decay weight assigns higher weights to recent time steps. The specific calculation formula is as follows:

[0134] ;

[0135] In the formula: is the decay rate;

[0136] The iterative training of the multi-task time series weighted loss function reaches a preset threshold or the maximum number of iterations, and a trained efficiency decay trend prediction model is obtained.

[0137] Cross-channel fusion layer: Through the attention mechanism, the coupling analysis of the static features of the feature clustering channel and the dynamic time series features of the time series prediction channel is realized. Among them, the attention coupling function is:

[0138] ;

[0139] ;

[0140] In the formula: Attention mechanism weight; is the time series expression form of the actual operating efficiency of the water pump; is the cumulative operating time of the impeller, that is, the time series variable; is the time-varying expansion of the time series data of the fault type q; is the predicted value of the actual operating efficiency of the water pump obtained after the coupling analysis of the static features and the dynamic time series features; is the time series value of the impeller wear coefficient; is the time series value of the real-time power factor; is the time series value of the current waveform distortion rate.

[0141] Based on the fault diagnosis and efficiency evaluation results of the machine learning model, the visual tracking of the water pump efficiency evolution trend is realized through the digital twin visualization platform, and a decision support report including optimization suggestions and maintenance strategies is generated.

[0142] Embodiment 2:

[0143] This embodiment provides a system for monitoring the operating state of a water pump unit based on digital twin technology, including:

[0144] Digital twin model creation module, which constructs a digital twin model of the water pump unit, and the digital twin model integrates the equipment parameters and environmental sensing parameters of the water pump unit;

[0145] Data acquisition module, which obtains the operating state data of the water pump unit in real time through Internet of Things sensors, and the operating state data of the water pump unit includes dynamic pressure value, instantaneous flow rate, power quality parameters and fluid temperature parameters;

[0146] Efficiency calculation module, which dynamically calculates the actual operating efficiency of the water pump unit based on the digital twin model and the real-time operating state data of the water pump unit, and performs multi-dimensional correction;

[0147] The evaluation and diagnosis module combines the historical operating state data of the water pump unit and the corrected actual operating efficiency, and conducts fault diagnosis and efficiency evaluation of the water pump unit through a machine learning model to obtain a fault diagnosis and evaluation report of the water pump unit.

[0148] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent the situation of A existing alone, A and B existing simultaneously, and B existing alone. Where A and B can be singular or plural. The character " / " generally represents an "or" relationship between the associated objects before and after. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0149] Those of ordinary skill in the art can realize that the various units and algorithm steps described in the embodiments disclosed herein can be implemented by a combination of electronic hardware, computer software, and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present application.

[0150] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0151] In several embodiments provided by the present application, if any function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (hereinafter referred to as ROM), random access memory (hereinafter referred to as RAM), magnetic disks, or optical discs that can store program codes.

[0152] The above are only embodiments of the present invention, and do not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied to other related technical fields, shall be similarly included in the patent protection scope of the present invention.

Claims

1. A method for monitoring the operating status of a pump unit based on digital twin technology, characterized in that, Including the following steps: Construct a digital twin model of the water pump unit, and the digital twin model integrates the equipment parameters and environmental sensing parameters of the water pump unit; Obtain the operation status data of the water pump unit in real time through Internet of Things sensors, and the operation status data of the water pump unit includes dynamic pressure value, instantaneous flow rate, power quality parameters and fluid temperature parameters; Based on the digital twin model and the real-time operation status data of the water pump unit, dynamically calculate the actual operation efficiency of the water pump unit and perform multi-dimensional correction; Combining the historical operation status data of the water pump unit and the corrected actual operation efficiency, perform fault diagnosis and efficiency evaluation of the water pump unit through a machine learning model to obtain a fault diagnosis and evaluation report of the water pump unit; The power quality parameters specifically include: current waveform distortion rate, characteristic harmonic component amplitude spectrum and fundamental power factor; The multi-dimensional correction includes correcting the fluid flow velocity by applying the pipe orifice structure parameters, dynamically correcting the head by using the temperature compensation algorithm and mechanical wear factor, and introducing the power quality parameters to dynamically calibrate the actual operation efficiency of the water pump; Dynamically correcting the head by using the temperature compensation algorithm and mechanical wear factor, specifically: ； Where: ； Where, H is the head; is the pressure at the pump outlet; is the pressure at the pump inlet; is the liquid density at the standard operating condition water temperature; g is the acceleration due to gravity; T is the real-time water temperature; is the standard operating condition water temperature; is the coefficient of thermal expansion of the pipeline; is the impeller wear coefficient; is the elevation of the pressure gauge at the pump outlet; is the elevation of the pressure gauge at the pump inlet; is the cumulative operating time of the impeller; is the impeller wear acceleration threshold; is the impeller decay rate.

2. The method for monitoring the operating state of a water pump unit based on digital twin technology according to claim 1, characterized in that: The equipment parameters include: a water pump geometric feature parameter group and a metering device feature parameter group, where: The water pump geometric feature parameter group includes the inner diameters of the inlet and outlet water pipes of the pump body and the baseline efficiency of the motor; The metering device feature parameter group includes the pulse constant of the electric energy meter, the transformation ratio of the voltage transformer, and the installation elevation of the pressure sensor.

3. The method for monitoring the operating status of a water pump unit based on digital twin technology according to claim 1, characterized in that: Correcting the fluid flow velocity by applying the pipe orifice structure parameters, specifically: ； ； In the formula, is the inlet velocity; is the outlet velocity; Q is the flow rate; is the inner diameter of the inlet pipe; is the inner diameter of the outlet pipe.

4. The method for monitoring the operating status of a water pump unit based on digital twin technology according to claim 1, characterized in that: Introducing the power quality parameters to dynamically calibrate the actual operation efficiency of the water pump, and calculating the actual operation efficiency of the water pump. The specific formula is: ； Wherein, is the actual operating efficiency of the water pump; is the comprehensive efficiency of the water pump; is the baseline efficiency of the motor; is the real-time power factor; is the rated power factor; is the harmonic loss coefficient; THD is the current waveform distortion rate.

5. The method for monitoring the operating state of a water pump unit based on digital twin technology according to claim 1, wherein: The machine learning model adopts a dual-channel heterogeneous architecture, including: Feature clustering channel: Generate equipment health status clustering centers based on the improved weighted K-means algorithm; the objective function of the improved weighted K-means algorithm is: ； Where: J is the objective function of the improved weighted K-means algorithm; C is the total number of clusters; l is the index of the i-th cluster; i is the index of the pump sample; is the time decay function; is the actual operating efficiency of the i-th pump; is the clustering center of the actual operating efficiency of the l-th cluster; is the production duration balance coefficient; is the production duration of the i-th pump; is the clustering center of the production duration of the l-th cluster; m is the total number of failure types; are the failure type indices, representing the j-th and q-th failure types respectively; is the element of the failure correlation matrix; is the indication value of whether the i-th pump has the q-th failure type; is the average occurrence frequency of the q-th failure in the l-th cluster; is the sample set included in the l-th cluster; Time series prediction channel: Construct an efficiency decay trend prediction model by using an LSTM network; the objective function of the LSTM network is to design a multi-task time series weighted loss function based on the LSTM cross-entropy loss function, specifically: ； Wherein, is the multi-task time-series weighted loss function; is the categorical cross-entropy loss; is the signal-to-noise ratio weight adjustment coefficient; is the phase compensation coefficient; is the dynamic attenuation weight; is the predicted efficiency value within the time segment t; is the actual efficiency value within the time segment t; is the wear leveling factor; is the KL divergence calculation; is the real-time probability distribution of the d-th feature cluster; is the reference probability distribution of the d-th feature cluster; D is the number of feature clusters; d is the index of the feature cluster; is the time window length; Cross-channel fusion layer: Realize the coupled analysis of the static features of the feature clustering channel and the dynamic time series features of the time series prediction channel through the attention mechanism.

6. The method for monitoring the operating state of a water pump unit based on digital twin technology according to claim 5, characterized in that: The specific calculation formula of the fault correlation matrix element is: ； Wherein: is the historical number of times when failure type j and failure type q occur simultaneously; is the total number of times when failure type j occurs alone; is the total number of times when failure type q occurs alone; is a constant to prevent the denominator from being 0; .

7. A system for monitoring the efficiency and faults of a water pump based on digital twin technology, characterized in that, For implementing any of the methods described in claims 1-6, including: Digital twin model creation module, which constructs a digital twin model of the water pump unit, and the digital twin model integrates the equipment parameters and environmental sensing parameters of the water pump unit; Data acquisition module, which obtains the operation status data of the water pump unit in real time through Internet of Things sensors, and the operation status data of the water pump unit includes dynamic pressure value, instantaneous flow rate, power quality parameters and fluid temperature parameters; Efficiency calculation module, which dynamically calculates the actual operation efficiency of the water pump unit based on the digital twin model and the real-time operation status data of the water pump unit, and performs multi-dimensional correction; The evaluation and diagnosis module combines the historical operation status data of the water pump unit and the corrected actual operation efficiency, and conducts fault diagnosis and efficiency evaluation of the water pump unit through a machine learning model to obtain a fault diagnosis and evaluation report of the water pump unit.

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