2402results about "Pump testing" patented technology

The invention relates to the technical field of multi-sensor fusion, in particular to a high-reliability multi-sensor fusion pump station monitoring system and an intelligent early warning control method, and the method comprises the steps: 1, carrying out the synchronous fusion processing of data based on the sampling frequency difference of multi-source heterogeneous sensors, so as to guarantee the consistency of time axes; 2, calculating the consistency difference of the data of each sensor according to the synchronized data, and dynamically shielding an abnormal data source to avoid false alarm interference; 3, real-time operation state modeling is carried out based on the data change trend after dynamic shielding; and 4, accurate early warning and decision optimization are realized. According to the high-reliability multi-sensor fusion pump station monitoring system and the intelligent early warning control method, data synchronous fusion processing is performed based on the sampling frequency difference of the multi-source heterogeneous sensors, so that the consistency of a time axis is ensured, and the problem of influence of asynchronous data on the state judgment accuracy is effectively solved.

The invention provides a water pump residual life prediction method based on a large model, and the method comprises the following steps: S1, collecting the multi-source heterogeneous data of the operation of a water pump in real time through a vibration sensor, a temperature sensor, a pressure sensor and a monitoring unit, the temperature sensor monitors temperature gradient changes of the bearing and the sealing cavity, the pressure sensor records inlet and outlet pressure fluctuation characteristics, and the monitoring unit extracts three-phase current harmonic components of the motor; s2, carrying out lightweight preprocessing on the multi-source heterogeneous original sensing data at an edge computing node, wherein the lightweight preprocessing comprises vibration signal noise reduction processing based on wavelet transform, temperature and pressure data calibration normalization of load segments according to working conditions, and transient abnormal data flow filtering through a sliding time window; and S3, inputting the preprocessed data stream into a cloud large model platform, and analyzing the long-period dependency relationship of the vibration signals through a Transform encoder in a time sequence feature extraction module.

The invention relates to the technical field of vibration monitoring, in particular to a slurry pump motion monitoring management system based on data analysis, which comprises a multi-source acquisition module, a period judgment module, a trend focusing module and a dynamic management and control module. According to the method, vibration data are collected through a three-axis acceleration sensor, a fundamental frequency amplitude is extracted through FFT, time-frequency characteristics and periodic distribution are fused in combination with an extreme value interval sequence, the data representation dimension is enhanced, the rising slope is processed through moving average, the periodic variation is calculated, random noise is restrained, and trend continuity is enhanced. Monotonicity test is combined with dynamic threshold judgment to improve anomaly recognition sensitivity; a sliding window is used for segmenting multi-cycle data; a sudden change interval is positioned based on a pressure amplification rate second derivative; the limitation of a frequency domain on transient feature recognition is broken through, a pressure amplification rate three-dimensional vector is constructed, cosine similarity is calculated, and a working condition difference degree is quantified to dynamically bind a maintenance instruction; and closed-loop feedback is formed to improve the early warning and decision matching degree.

The invention relates to the technical field of pump station energy-saving optimization, in particular to a pump station multi-target energy-saving optimization control method and system for self-calibration of a pump efficiency curve, and the method comprises the following steps: arranging a flowmeter, a pressure sensor and an electric energy metering device at key operation parts of a pump station, and collecting the flow, lift and power operation data of a pump set in real time; calculating pump efficiency characteristic points based on flow, lift and power data collected in real time; the characteristic points are compared with a designed rated pump efficiency curve, and if the deviation of the corresponding efficiency value exceeds a preset threshold value, the pump efficiency curve is corrected through a parameter fitting method so as to obtain a corrected pump efficiency curve reflecting the current working condition. According to the method, the flow, lift and power operation data of the pump set are collected in real time, the pump efficiency characteristic points are calculated and compared with the designed rated pump efficiency curve, the pump efficiency curve is dynamically corrected through a parameter fitting method, and the consistency of the pump efficiency model and the actual working condition is achieved.

In order to improve the stability and reliability of the micro diaphragm pump, the invention provides the micro diaphragm pump with the columnar bubble and liquid monitoring function. The channel is formed in the pump cavity, the valve body is installed in the channel, the valve body is composed of the upper layer, the middle layer and the friction layer, the sensing part is arranged on the middle layer, the sensing area is formed through cooperation of the friction layer and the sensing part, and then columnar bubbles and liquid in the micro diaphragm pump are monitored. The device is small in size, high in energy density, high in controllability and capable of monitoring columnar bubbles and liquid.

The invention relates to the technical field of test bed operation monitoring, in particular to a fuel pump test bed operation monitoring method and system. The method comprises the following steps: acquiring monitoring time sequence data of operation of a fuel pump test bed, performing feature extraction, constructing a coupling prediction model of a space-time diagram attention network-unscented Kalman filter, and predicting a process noise covariance matrix and a measurement noise covariance matrix according to the coupling prediction model, and performing state estimation by using an unscented Kalman filter to obtain vector posterior probability distribution, constructing a fault evolution trajectory manifold, calculating a mahalanobis distance between the vector posterior probability distribution and the fault evolution trajectory manifold, and taking the mahalanobis distance as a monitoring index. According to the scheme of the invention, the deep features which can better reflect the inherent nonlinear and complex dynamic characteristics of the system can be extracted from the multi-source data, the estimation accuracy of the filter on the potential health state of the system is improved, and the defects that model parameters are fixed and gradual change faults are difficult to capture in a traditional method are overcome.

Systems and methods of operating a fracturing operation include a platform having an upper surface and a channel, wherein the platform is configured to receive a carrier mounted with a pumping unit; and a conduit configured to transfer fluid to or from a manifold system, the conduit having a first end configured to be connected to the pumping unit. The channel may be configured to receive the conduit when the carrier is at least partially received on the platform. A guide member may be configured to guide the conduit into the channel and include a rounded portion and an abutment surface, wherein the conduit is configured to extend at last partially around the rounded portion, and the carrier is configured to engage the abutment surface. The systems and methods may also include a controller configured to control a braking system of the carrier when the pumping unit is actuated.

A method of calibrating a pump system can include providing a pump, calibrating a pump input with an input device connected to the pump, and calibrating a pump output with an output device connected to the pump. Calibrating the pump input can include selecting a first input signal value, generating a transmitted input signal corresponding to the first input signal value, and setting a nominative pump input value to the first input signal value. Calibrating the pump output can include receiving a received output signal outputted from the pump, and setting a nominative pump output value to the first input signal value.

The invention discloses a pneumatic diaphragm pump control method and system for an emulsion matrix transport vehicle, and relates to the technical field of intelligent fluid conveying control. By means of integrating temperature monitoring and adjustment, viscosity prediction and parameter setting, multi-parameter coupling optimization and the like, accurate control and dynamic optimization of key parameters in the emulsion matrix transportation process are achieved, temperature distribution in the storage tank can be monitored and adjusted in real time, pumping parameters are dynamically adjusted according to the viscosity of the matrix, and the pumping efficiency is improved. The pumping pressure, the flow and the vibration frequency are coordinated through a multi-parameter optimization algorithm, so that the problems of low conveying efficiency and high equipment failure rate of a traditional rotor pump are solved, the conveying efficiency and the construction quality of an emulsion matrix are improved, the energy consumption and the maintenance cost are reduced, the reliability of equipment is remarkably improved, and the service life of the equipment is remarkably prolonged. And powerful guarantee is provided for road construction and maintenance.

The invention belongs to the technical field of intelligent control of electric submersible pumps, and discloses a ground control system and control method of an electric submersible pump. A sensor module of the system is used for collecting operation data of the electric submersible pump in real time; the intelligent calculation module is used for analyzing and calculating the collected real-time operation data of the electric submersible pump to obtain optimized control parameters; the control execution module is used for generating a corresponding instruction according to the optimized parameters and accurately adjusting the steering, rotating speed and stroke parameters of the servo motor; the real-time monitoring and feedback optimization module is used for monitoring the system state after execution of the control execution module in real time and judging whether system operation is abnormal or not according to a real-time monitoring result; and the alarm optimization adjustment module is used for dynamically optimizing the control strategy of the electric submersible pump based on the parameter abnormal condition fed back by the real-time monitoring module after the abnormal state of the system is detected. The control efficiency of the electric submersible pump is effectively improved, and the problem that in the prior art, a control method is low in efficiency is solved.

The invention provides a large reciprocating compressor continuous stepless regulation system and method based on intelligent control, and the method comprises the steps: building a continuous stepless regulation model according to a load change trend and process fluctuation characteristics, and determining an initial parameter set of a regulation instruction through a preset load range regulation threshold value and a dynamic response speed requirement; according to the non-equilibrium stress adjustment scheme, dynamically adjusting operation parameters of a compressor execution mechanism, monitoring the abrasion state of key components through a real-time feedback control technology, and determining operation data of the components after stress equilibrium; and the operation data of the parts after stress balance are obtained, the operation parameters of the compressor are subjected to secondary calibration through an intelligent control system in combination with an energy efficiency improvement target, and the optimized optimal pressure ratio state parameters are obtained.

The disclosure relates to the technical field of air pumps, and particularly relates to an air pump having a reminding function. The air pump includes a pump cylinder provided with a first hollow cavity and a push rod assembly that is pullable. The push rod assembly is movably inserted into the pump cylinder. The air pump further includes an air pressure sensor and a reminding mechanism. The air pressure sensor and the reminding mechanism are both mounted in the pump cylinder or the push rod assembly. The air pressure sensor is connected with the reminding mechanism. An objective of the disclosure is to solve a problem that an existing manual air pump cannot give a reminder when air extraction is completed, and provide an air pump having a reminding function when air extraction is completed.

The invention discloses a self-encoder-based water pump operation abnormal working condition identification system and method. The system comprises a water pump monitoring data acquisition module, a data preprocessing feature extraction module, a self-encoder training module and an abnormal data monitoring analysis module. The water pump monitoring data acquisition module is connected with the data preprocessing feature extraction module, the data preprocessing feature extraction module is connected with the auto-encoder training module, and the auto-encoder training module is connected with the abnormal data monitoring analysis module; the problems that comprehensive state prediction evaluation is not considered, the maintenance efficiency is low and the stability is limited in the prior art are solved.

The invention discloses a dynamic target pressure generation method based on multi-source pressure characteristics and an energy efficiency curve, and belongs to the technical field of energy-saving control of industrial air compression systems. According to the method, the minimum value, the maximum value, the average value and the mode of tail end pressure are counted, the dynamic range of gas consumption requirements is accurately represented, and the dynamic target pressure is generated. The air compressor energy efficiency curve library is set, the optimal continuous pressure interval is screened instead of single-point optimization, the operation stability and the energy efficiency advantage are considered, finally, the target pressure of each air compressor is distributed through the multi-target optimization algorithm, energy consumption waste caused by pressure overlapping is avoided, and the energy efficiency of the air compressor is improved. The global energy efficiency is optimal, and the problems that due to frequent loading and unloading of the air compressors of an existing air compression station, energy consumption is wasted, the tail end air pressure requirement is lower than the air compressor set value, and the redundancy of the pressure set value is too high when the multiple air compressors operate in parallel are solved.

The invention discloses a drilling pump fluid end anomaly detection method based on a valve impact phase index. The method comprises the steps that firstly, a signal decomposition method is adopted to extract valve impact components in vibration signals; then, extracting an impact peak value in each period, and taking the impact peak value as a valve impact phase of the corresponding period; the relative phase is calculated according to the phase data between the adjacent cylinder bodies; further calculating a variance index and a mean value index of the relative phase to represent a phase distribution characteristic; and finally, an anomaly correlation matrix is constructed based on the indexes, and anomaly detection and fault cylinder positioning identification are realized in combination with a preset judgment rule.

The invention relates to the technical field of power consumption management, in particular to a pump station unit energy consumption real-time analysis system which comprises a load feature extraction module, a frequency stability recognition module, a start-stop window recognition module, a voltage response regulation and control module and a signal filtering module. According to the method, a sliding trend sequence is constructed through load state data and current increment information, slope abrupt change points are identified, trend direction classification is carried out, accurate identification of a frequency operation stability section is realized in combination with frequency variation amplitude, and a regulation and control time window is constructed through trend consistency judgment of energy consumption and flow in the same time domain. A voltage input strategy is set according to the trend change rate, a mapping table is formed, signal filtering and recovery are achieved through trend continuity judgment in confidence evaluation of energy consumption labels, and the behavior segmentation precision, the adaptability of electric energy input regulation and control and the continuity and controllability of overall operation state recognition in the energy consumption monitoring process are effectively enhanced.

The invention relates to the technical field of equipment running state monitoring, in particular to a gas compressor running state monitoring method and system.The method comprises the steps that a real-time monitoring data sequence of a gas compressor under each preset dimension in the current compression period is obtained, and the real-time monitoring data sequence of the gas compressor under each preset dimension in the current compression period is obtained for any sampling moment of the current compression period; obtaining a basic weight of each preset dimension at any sampling moment according to a preset number of groups of historical data; performing stage division on the current compression period to obtain a stage weight set of each stage, and according to the stage weight set, adjusting the basic weight of each preset dimension at any sampling moment to obtain a dynamic weight; performing weighting processing on the real-time monitoring data of each preset dimension by using the dynamic weight to obtain an equipment health index at any sampling moment; and according to the change rate of the equipment health indexes at the adjacent sampling moments in the current compression period, abnormal early warning is performed on the gas compressor, so that the operation state of the gas compressor is monitored more accurately.

The invention discloses a flow control method of a gas compressor, which relates to the technical field of flow control of gas compressors, and comprises the following steps of: deploying a sensor in the gas compressor, monitoring various parameters of gas flow in real time, and providing gas flow monitoring data for the whole system; performing preprocessing operation on the original data acquired from the sensor; dividing the gas flow data into a plurality of monitoring windows according to the time length, and for each monitoring window, extracting an index of potential gas reverse flow from the preprocessed data by adopting a feature engineering technology as a feature vector to represent a potential risk of gas flow reverse flow. The air flow parameters are monitored in real time, data preprocessing is carried out, the feature engineering technology and the deep learning model are combined, the potential risk in the air flow is accurately predicted, the rotating speed of the compressor and the opening degree of the air inlet valve are adjusted in time when the risk occurs, and therefore effective control over reverse flowing of the air flow is achieved; and long-term operation of the compressor in a safe and efficient state is ensured.

The invention relates to an oil-proof emulsification control method and system for an air compressor of an urban rail vehicle, and solves the problem of lubricating oil emulsification caused by insufficient working efficiency of the air compressor and environment humidity change by constructing a comprehensive control system comprising the compressor, an oil-proof emulsification device and a controller. The system monitors lubricating oil temperature and environment humidity in real time through a temperature sensor and a humidity sensor; the working rate of the air compressor is accurately calculated, wherein the vehicle operation time t2 takes the operation speed larger than 10 km / h as the calculation starting point; the controller comprehensively judges whether the oil-proof emulsification device is started or not based on temperature, humidity and working rate data, and the control strategy comprises the step of dynamically adjusting the working rate judgment node according to the real-time temperature and humidity. According to the method, accurate and reliable control over the oil-proof emulsification process is achieved, emulsification of the lubricating oil is effectively prevented, and energy consumption and maintenance cost can be reduced.

The invention discloses a fully-mechanized coal mining liquid supply system speed regulation method based on model prediction and multi-objective optimization, and belongs to the technical field of fully-mechanized coal mining liquid supply control, and the method comprises the following steps: S1, collecting the real-time state information of a pump station; s2, according to the real-time state information of the pump station, utilizing the prediction model to predict the outlet pressure of the pump station; s3, constructing a multi-objective optimization function, and S4, controlling the pump station outlet pressure according to the predicted pump station outlet pressure and the multi-objective optimization function. According to the method, the health of the pump machine is guaranteed, and the service life of the pump machine can be effectively prolonged, the maintenance cost is reduced and the reliability and economy of the system are improved by monitoring the health state of the pump machine in real time and taking the health state into an optimization target.

The invention relates to the technical field of air compressor optimization, and discloses an intelligent air compressor optimization control method and system based on an IOE control system.The intelligent air compressor optimization control system comprises a collection unit, a preprocessing unit, an analysis unit and an optimization unit, rich operation data such as air inlet pressure, flow, exhaust pressure and temperature are comprehensively collected in real time, denoising is conducted through a moving average method, and data accuracy is ensured; normal, overload and abnormal fluctuation modes are clearly divided, accurate judgment is performed by means of a preset interval and reasonable calculation, and powerful support is provided for control decision making; in a normal mode, intelligent regulation is performed according to a parameter average value, and operation efficiency and stability are improved; the valve opening degree is adjusted according to the deviation degree in the overload mode, the load is effectively reduced, and equipment is protected; in the abnormal fluctuation mode, a PID control algorithm is used, the opening degree, the cooling amount and the like of the valve are flexibly adjusted according to deviation and the change rate, and parameters are rapidly recovered to be stable. The fault rate and the maintenance cost can be greatly reduced, and the working efficiency is remarkably improved.

The utility model discloses a gas loss detection device and a compressor, and aims to solve the technical problems that the detection of the hydrogen leakage rate of the compressor is inaccurate, and leaked hydrogen is not recycled. The gas loss detection device comprises a recovery pipeline, a compression detection mechanism, a pneumatic control valve and a high-pressure separator are arranged on the recovery pipeline, the compression detection mechanism is communicated with an isolation cavity of a compressor, and hydrogen in the isolation cavity enters a hydrogen buffer tank of the compression detection mechanism to be stored and compressed; after being purified by the high-pressure separator, the hydrogen enters a gas inlet pipeline of the compressor to be reused, so that the waste of hydrogen is avoided and the cost is reduced; and a pressure gauge of the compression detection mechanism detects the pressure of the buffer tank, records the relationship between pressure change and time at a control end, and calculates the leakage rate of the compressor so as to judge the sealing performance of the compressor cylinder.

The invention relates to the technical field of reciprocation pump control, in particular to a closed-loop control motor-driven ball screw reciprocation pump and a control method, and the closed-loop control motor-driven ball screw reciprocation pump is mainly characterized in that an outlet of a fluid end module is connected with a pressure sensor; the pressure sensor, the motor controller, the rotary transformer and the position sensor are electrically connected with the central controller, the central controller is used for outputting a control signal enabling the driving motor to rotate to the motor controller, and the motor controller is used for controlling the driving motor to operate. According to the scheme, a brake is removed, the defects caused by the brake are avoided, in the prior art, no matter whether a brake pad is abraded or the inertia problem of the brake exists, a brake system cannot be achieved in the industry of the reciprocating pump at all, and in the reciprocating pump, the brake pad is not abraded. The speed, the position and the thrust of the reciprocating pump device are accurately controlled in a full-digital manner through the central controller, so that the ball screw reciprocating pump with the structure achieves the purpose of industrial use in the industry.

The invention discloses a fracturing equipment state monitoring and fault diagnosis system and method, and belongs to the technical field of oil and gas field fracturing equipment intelligence. The invention aims to solve the problems that in the prior art, monitoring depends on a single signal, fault early warning lags behind, and the misjudgment rate is high. The method comprises the following steps: collecting multi-source operation data of the fracturing pump in real time; establishing a theoretical pressure indicator diagram, and comparing the theoretical pressure indicator diagram with an actual indicator diagram generated by real-time data to realize first-stage fault judgment; an improved wavelet threshold noise reduction method is adopted to process the signals, and time domain and frequency domain features are extracted; and inputting the processed feature data into the combined diagnosis model for second-stage fault judgment. The joint diagnosis model combines a principal component analysis (PCA) model used for uncalibrated data anomaly detection and a BP neural network model used for calibrated data fault classification. Through deep fusion of the mechanism model and the data driving model, environmental interference is effectively resisted, and the diagnosis accuracy and the operation and maintenance efficiency are remarkably improved.

A steam single-screw compressor testing system is characterized in that the steam single-screw compressor testing system is composed of a steam pocket (5) and a liquid-gas separator (3), an electric heater (12) and an inverted-T-shaped steam distribution mixer (13) are installed in the steam pocket (5), and the steam pocket (5) is communicated with a steam single-screw compressor (1) through a steam return pipeline (6); the steam single-screw compressor (1) is communicated with a gas inlet of the liquid-gas separator (3) through a high-pressure steam outlet pipeline (2), high-pressure steam separated by the liquid-gas separator (3) is communicated with a gas inlet of the inverted-T-shaped steam distribution mixer (13) installed in the steam pocket through a low-pressure steam pipeline (4), and the bottom of the inverted-T-shaped steam distribution mixer (13) is located below the waterline of the steam pocket (5). And small holes are fully distributed, so that high-temperature steam and water in the steam pocket are uniformly mixed for heat transfer, and heat energy is recycled. The air conditioner is simple in structure, the compressor is started without electric heating, and energy and water resources are saved.

The invention relates to the technical field of industrial compressed air system intelligent control, in particular to a digital air compression station intelligent control method and system based on AI self-learning, and the system comprises a data collection layer, an AI prediction layer, an optimization control layer and an execution layer. The data acquisition layer realizes accurate acquisition and preprocessing of multi-dimensional data through a three-stage high-precision sensor network and an edge computing node; the AI prediction layer adopts an XGBoost and LSTM mixed model to be combined with an online learning mechanism to realize accurate prediction of the gas consumption demand and the energy efficiency ratio within 1-24 hours; the optimization control layer completes air compressor operation combination optimization and pressure dynamic control based on an improved NSGA-II algorithm and self-adaptive PID adjustment; the execution layer realizes control instruction landing through frequency converter and cluster cooperative scheduling. Meanwhile, an equipment health degree monitoring module, a multi-source data fusion correction module and a waste heat-gas consumption collaborative optimization module are innovatively introduced, and the problems that a traditional air compression station is low in energy efficiency, poor in self-adaption, insufficient in prediction precision and lack of equipment management are solved.

The invention discloses an air compressor control method and device, a controller and a storage medium. According to the specific implementation scheme, the method comprises the steps that under the condition that the internal pressure of the air compressor meets the use requirement of the vehicle, a temperature signal, working time and the rotating speed of the air compressor corresponding to the air compressor are determined; inputting the temperature signal, the working time and the rotating speed of the air compressor into a water content model to obtain the water content of the engine oil of the air compressor; and according to the water content of the engine oil of the air compressor, the operation state of the air compressor is controlled. The temperature signal, the working time and the rotating speed of the air compressor are input into the water content model, the water content of the engine oil of the air compressor is calculated, the water content of the engine oil of the air compressor is monitored in real time, the running state of the air compressor is controlled through the water content of the engine oil of the air compressor, and the problem that the water content of the engine oil of the air compressor cannot be actively controlled in the prior art is solved. The problem of engine oil emulsification in the operation process of the air compressor is solved, and resource waste is reduced.

The invention provides an oilless air compressor operation fault detection method, electronic equipment and a storage medium, and the method comprises the steps: determining an operation data list corresponding to each type of data sensor according to a plurality of operation data collected by a plurality of types of data sensors of an oilless air compressor in a target time period, obtaining a prediction data list corresponding to the target data list; according to the matching degree of the prediction data list and the target data list, determining an adjustment confidence coefficient corresponding to the target data list; according to the operation data lists corresponding to the plurality of types of data sensors, obtaining the key confidence degree of the anomaly detection result of each type of data sensor; according to the method, the corresponding operation detection result of the oilless air compressor in the target time period is determined according to the sum of the multiple key confidence coefficients, the initial confidence coefficient and the multiple adjustment confidence coefficients, and the determined operation state is more accurate by conducting comprehensive judgment on the combined data among the multiple types of data sensors of the oilless air compressor.

The invention discloses an energy-saving control system and method based on air compressor operation state analysis, and relates to the technical field of air compressor energy-saving control. The system comprises the following components: a data acquisition module, a data processing module, a virtual twin model construction module, an energy-saving control strategy test module, a scheme evaluation and optimization module and an actual control execution module. According to the method, the virtual twin model of the air compressor system is constructed, the operation states under different energy-saving control strategies are simulated in the meta-cosmic space, the energy-saving effects of various strategies can be rapidly and accurately evaluated, the energy-saving control strategies are continuously optimized through the scheme evaluation and optimization module, and the energy-saving effect of the air compressor system is improved. And finally, the optimal energy-saving control strategy is determined and applied to an actual control system, the energy-saving effect and the operation efficiency of the air compressor are improved, and the energy consumption and the operation cost are reduced.

According to the variable-speed water pump energy efficiency evaluation and diagnosis method and system, the problem that an existing energy efficiency model is difficult to provide an evaluation and diagnosis reference for water pump operation conditions in water pump speed change and complex industrial scenes is solved. The method comprises the steps of obtaining different-rotating-speed operation data of the variable-speed water pump under the normal operation condition; establishing a variable-speed water pump energy efficiency dynamic prediction reference model; determining a variable-speed water pump energy efficiency evaluation diagnosis reference; determining variable-speed water pump energy efficiency evaluation diagnosis indexes and threshold values; acquiring water pump operation real-time data, predicting water pump reference energy efficiency according to the water pump energy efficiency dynamic prediction reference model, calculating water pump actual energy efficiency according to water pump actual power consumption data, and evaluating and diagnosing the water pump operation energy efficiency condition according to a water pump energy efficiency evaluation diagnosis threshold value. The system comprises a water pump operation data acquisition module, a water pump energy efficiency prediction reference and correction model construction module, a water pump energy efficiency evaluation and diagnosis reference determination module, a water pump energy efficiency evaluation and diagnosis index and threshold module and a water pump energy efficiency prediction and diagnosis module.