19037results about "Pump control" patented technology

A portable fan includes: an airflow portion and a display screen. The airflow portion includes an air outlet, the air outlet is disposed surrounding a periphery of the display screen. The display screen has an outer surface at least partially curved; and / or the display screen is configured to display at least one of: a remaining battery power level, a current air speed, a battery power level during charging, and an operating state.

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 discloses a water supply pipe network water hammer control method and system based on multifunctional module fusion, and relates to the technical field of data identification. A time sequence diagram neural network prediction and traceability module which realizes water hammer risk prediction and propagation path traceability based on a causal constraint graph neural network model; the reinforcement learning intervention decision module is used for generating an active intervention strategy through a reinforcement learning agent, forming closed-loop control, abstracting a water supply pipe network into a graph structure, and modeling in combination with a time sequence, so that a propagation path of pressure waves can be comprehensively reflected, a blind area of traditional local modeling is overcome, and the comprehensiveness and accuracy of water hammer event detection are improved; and a causal analysis result is input as an adjacent matrix, so that the interference of irrelevant edges on prediction in topology is effectively eliminated, and the learning efficiency and causal traceability of the model are improved.

The invention belongs to the field of industrial automation and intelligent manufacturing, particularly provides a metallurgical fan multi-physics field coupling and dynamic performance optimization method based on digital twinning, and aims to solve the technical problems of performance optimization and fault prediction of a metallurgical fan under complex operation conditions. The method comprises the following steps: acquiring multi-physical field data such as fluid mechanics, thermodynamics and structural mechanics through a distributed sensing network and a real-time data acquisition technology, and performing cleaning, denoising and feature extraction on the data; the method comprises the following steps: constructing a high-precision multi-physics field coupling digital twin model based on a geometric structure and material characteristics of a metallurgical fan, and carrying out model calibration in combination with historical data and real-time sensing data; the dynamic performance of the draught fan under different operation conditions is simulated through the co-simulation technology, the operation parameters of the draught fan are dynamically adjusted through a data-driven optimization algorithm, real-time optimization of the performance is achieved, and the purposes of improving the operation efficiency, reducing energy consumption and prolonging the service life of equipment are achieved.

The invention discloses a digital twin enabling intelligent pump station preventive operation and maintenance system. Comprising a dynamic twin construction module, a multi-source heterogeneous multi-modal data acquisition module, an edge computing and cloud collaboration module, an equipment health degree evaluation module, a predictive maintenance decision module, a cross-system data fusion module, a self-evolution knowledge graph module, an intelligent diagnosis and early warning module, a self-adaptive maintenance decision module and a man-machine collaboration interaction module. And the dynamic twin construction module comprises a physical-virtual synchronous calibration mechanism and an equipment degradation parameter dynamic updating mechanism. According to the method, the limitation problem of a traditional static model is solved, the method can adapt to nonlinear changes under complex working conditions, the comprehensive judgment and prediction capability of the system on the equipment state can be enhanced, the energy utilization efficiency is improved, the energy consumption is reduced, the decision and verification mechanism is perfected, and the data acquisition and processing problem is improved; the problems of timeliness and flexibility of the model are solved, and the computing architecture and the response capability are optimized.

The invention provides a compressor control method and system. The method belongs to the technical field of compressor control, and comprises the following steps: collecting multi-dimensional data in the operation process of a compressor in real time through a multi-parameter sensor network; preprocessing the collected multi-dimensional data, and constructing a data set; learning and optimizing a compressor control strategy based on historical data and a current environment state through a reinforcement learning model based on a deep neural network; continuous iterative updating is carried out through analog simulation and online learning; and on the basis of the adaptive neural network model, the running state and performance parameters of the compressor in a future period of time are predicted in real time, and the network structure and parameters are dynamically adjusted according to the current working condition and historical data of the compressor. Multi-dimensional data in the operation process of the compressor are collected in real time through a multi-parameter sensor network. The data are preprocessed, including denoising, missing value filling, abnormal value detection and the like, so that the data quality is ensured.

Apparatus and methods are described including placing, into a subject's body, a blood-pump tube, with an impeller disposed within the blood-pump tube. At least one blood-pressure-measurement element extends to at least an outer surface of the blood-pump tube, such that the distal end of the blood-pressure-measurement element is in direct fluid communication with a bloodstream of the subject outside the blood-pump tube at a location that is proximal to blood-inlet opening(s) defined by the blood-pump tube. Blood is pumped through the blood-pump tube, using the impeller. Pressure of the bloodstream of the subject outside the blood-pump tube is measured by measuring blood pressure at the distal end of the left-ventricular blood-pressure-measurement element. Other applications are also described.

The invention provides a tunnel fan group cooperative control dynamic optimization method and system. According to the method, dynamic data of vehicles in a tunnel are tracked through the Leiyu fusion technology, and a multi-dimensional game parameter set is constructed in combination with edge computing node analysis data and environment monitoring indexes. Based on a dynamic game theory model, fan cooperative response intensity is dynamically adjusted, smoke exhaust efficiency and evacuation channel wind speed constraint are balanced, fan control priority is calculated, differential rotating speed and deflection angle control is driven, and game strategy weight is iteratively optimized after environment feedback data is generated. And realizing rolling time domain control until the vehicle movement and the environment index reach a stable interval. According to the technical scheme provided by the invention, dynamic collaborative optimization of tunnel smoke exhaust and evacuation safety is realized, and the ventilation response efficiency and the evacuation channel safety in an emergency scene are improved.

A portable fan with predictable battery life and a method for predicting battery life thereof. The portable fan includes a fan body, where a circuit board assembly and a storage battery are installed in a shell of the fan body, a microprocessor and a current collector are encapsulated in the circuit board assembly, a load and a display device are provided on the fan body, and the display device includes a function lighting display area and a battery life display area. According to the portable fan, the display device can be used to indicate a working mode that the fan body is currently turned on and the working battery life that the storage battery can provide under the corresponding working mode. An intuitive and standard time unit is used for indication, so that a clear visual perception effect is achieved.

An engine for aeromechanical instability abatement includes a sensor configured to capture data from rotating blades of the engine system, a airflow effector device, and an engine controller. The engine controller is configured to control the airflow effector device according to a nominal schedule, detect, based on a signal from the sensor indicating a vibration amplitude of the rotating blades within a frequency band, an incipient instability condition, in response to the incipient instability condition being present, determine a modified control parameter for at least one of the airflow effector device, and control the airflow effector device according to the modified control parameter, deviating from the nominal schedule.

The invention discloses a dredge pump control system and method for river regulation, and relates to the technical field of dredge pump control. The dredge pump control system comprises a sensing module, a centralized control module, an energy-saving execution module and a control parameter recording module; the sensing module is arranged on a multi-element sensor at a key part of a dredge pump for acquiring data, and the data is pre-processed by a microprocessor and then transmitted to the centralized control module at a high speed by a wireless transmission module, so that an accurate basis is provided for system control; according to the invention, by integrating multiple sensors and an advanced control algorithm, the dredging efficiency and accuracy of the dredge pump of the dredger are obviously improved; the sensing module can monitor key parameters of the dredge pump in real time, such as suction pressure, discharge flow, pump shaft temperature and sediment concentration, and it is ensured that the running state of the dredge pump is comprehensively known; and the centralized control module dynamically optimizes a control sequence by using a model predictive control and fuzzy logic control algorithm so as to adapt to variable working conditions.

The invention relates to the technical field of environment perception, and discloses an optimization method and device for a multi-climate self-adaptive environment perception cooling fan system, and the method comprises the steps: collecting multi-point temperature, humidity and air pressure data, and carrying out the filtering processing, and obtaining an environment state vector; executing mathematical modeling of the multi-degree-of-freedom cooling system according to the environment state vector to obtain a linearized state space model; performing temperature gradient estimation and humidity compensation based on the linearized state space model to obtain an adaptive control law; inputting the adaptive control law into a model-free adaptive prediction controller to obtain a rolling optimization control sequence; weight self-adaptive adjustment and heat dissipation efficiency hierarchical prediction control are carried out on the rolling optimization control sequence based on climate conditions, fan rotating speed control and wind direction adjustment execution signals are generated, the problem that a traditional heat dissipation system is not sensitive to space temperature distribution and humidity changes is solved, and the heat dissipation control precision under different humidity conditions is improved.

The invention relates to the technical field of water pump energy-saving control, and discloses a water pump energy-saving control system based on an intelligent data model, which comprises a data sensing module, a modal analysis module and the like. The data sensing module collects flow, lift and power data and is provided with a dynamic monitoring window; the modal analysis module divides operation modes and generates an energy efficiency characteristic matrix; the rule construction module extracts a core energy efficiency factor and constructs an energy-saving regulation and control strategy; the priority scheduling module analyzes the control priority matrix and evaluates an energy efficiency deviation index; a mapping calibration module fits the optimal energy efficiency reference curve to generate an energy efficiency deviation gradient map; the control execution module generates a regulation and control instruction set according to the map. The system realizes fine management and energy efficiency optimization of water pump operation through multi-mode energy efficiency analysis and dynamic regulation, solves the problems of poor adaptability of traditional control working conditions, low energy-saving efficiency and the like, is suitable for water pump energy-saving control in the fields of industry, municipal administration and the like, and is accurate and efficient.

The invention relates to the technical field of high-pressure dredge pump simulation calculation, and discloses a construction and application method of a high-pressure efficient dredge pump simulation calculation model. A model is established based on dynamic fluid-solid coupling parameters (mud pulsating pressure, impeller dynamic stress and the like), soil multiphase rheological parameters (particle phase volume fraction, viscous resistance coefficient and the like) and pipeline transient resistance parameters (transient pressure drop gradient and the like), and factors such as rotating speed-power matching and the like are incorporated to calculate an operation envelope and a critical failure threshold. A multi-physics field coupling analysis system comprising parameter configuration, dynamic simulation (integrating a flow field and a structural vibration solver) and a safety evaluation module is developed, after actually measured data and self-defined parameters are input, dynamic flow velocity distribution, pressure pulsation characteristics and the like are obtained through fluid-solid bidirectional coupling calculation, and an optimal configuration scheme is generated. The system supports database management, multi-rheological model matching and three-dimensional visualization, the simulation precision and the engineering efficiency are improved, and support is provided for performance optimization and safety evaluation of the high-pressure working condition.

The invention relates to the technical field of water pump control, and discloses a water pump energy-saving optimization control method and system. The method comprises the steps of obtaining water pump outlet pressure, pipe network tail end flow and water pump motor power monitoring signals; calculating a flow-pressure propagation time compensation coefficient and a pressure fluctuation compensation value to eliminate the influence of signal propagation lag and amplitude fluctuation; extracting an operation condition feature vector by combining the motor power frequency domain energy distribution feature and the pressure fluctuation compensation value, and matching the operation condition feature vector with an energy-saving parameter mapping rule base to generate an energy-saving parameter combination; correcting a water pump efficiency reference curve in real time based on the combination, outputting an efficiency attenuation factor, and generating a control deviation value in combination with a target water supply demand; according to the control deviation value, the water pump rotating speed adjusting quantity and a start-stop priority sequence are calculated, and the priority sequence is optimized in combination with historical operation data to generate a machine number control instruction; and finally synthesizing a control signal to drive an execution mechanism.

The invention relates to the technical field of vacuum extraction control, and discloses a multi-stage vacuum extraction control system which comprises a central processing unit, a multi-stage vacuum pump set control module, a vacuum degree dynamic monitoring module and the like. The multi-stage vacuum pump set control module coordinates all stages of pumps to work according to a preset vacuum degree target value, and the vacuum degree dynamic monitoring module collects pressure data in real time and judges abnormity. The pressure gradient adjusting module adjusts the extraction rate of the adjacent pump to maintain the vacuum gradient stable when the pressure is abnormal, and the abnormity response module controls starting and stopping or rate correction of the pump according to the abnormity level. In addition, the system further has the functions of vacuum pump efficiency evaluation, pressure feedback compensation, leakage detection, redundancy switching, historical data backtracking, collaborative optimization and the like. The system can accurately control the vacuum degree, efficiently deal with abnormities and realize equipment state monitoring and optimization, and is widely applied to the fields with high requirements on the vacuum degree, such as semiconductor manufacturing and vacuum coating.

The invention relates to the technical field of industrial equipment health monitoring and fault diagnosis, and discloses a centrifugal pump intelligent detection method and system based on multi-source data. The centrifugal pump intelligent detection method based on the multi-source data comprises the steps that centrifugal pump multi-source heterogeneous data are collected and preprocessed; extracting multi-scale features and constructing an optimal feature subset through hierarchical feature selection; constructing a multi-level dynamic Bayesian network to establish a probabilistic reasoning framework; calculating a feature credibility weight based on the signal quality, the feature stability and the diagnosis correlation; executing multi-source evidence fusion and Bayesian state reasoning; confidence self-calibration and parameter self-adaptive adjustment are realized through historical diagnosis result feedback; the method can effectively cope with the complex and changeable environment of an industrial site, can still keep stable diagnosis performance under the condition that the sensor part loses efficacy or the data quality is uneven, improves the accuracy and reliability of fault diagnosis, and provides powerful technical support for predictive maintenance of industrial equipment.

A pump system includes a housing defining a first internal volume and a second internal volume, a first piston positioned to separate the first internal volume into a first chamber and a second chamber, a second piston positioned to separate the second internal volume into a third chamber and a fourth chamber, a first inlet check valve configured to permit fluid flow into the first chamber, a second inlet check valve configured to permit fluid flow into the third chamber, a directional control valve (DCV) repositionable between (a) a first position where the DCV is configured to fluidly couple the second chamber to a high pressure fluid source and (b) a second position where the DCV is configured to fluidly couple the fourth chamber to the high pressure fluid source, and a relief valve configured to supply a fluid to the DCV through an orifice to move the DCV.

The invention relates to a method and system for monitoring the running state of a water pump unit based on the digital twin technology, and belongs to the technical field of pipeline pump monitoring. The method comprises the steps that a digital twin model fusing equipment parameters and environment sensing data is constructed, and multi-dimensional running data such as dynamic pressure, flow, electric energy quality and temperature are collected in real time in combination with the Internet of Things technology; a multi-dimensional correction algorithm is innovatively provided, fluid flow velocity calculation is corrected through pipe opening structure parameters, lift calculation is dynamically corrected through a temperature compensation algorithm and a mechanical wear factor, and dynamic compensation is conducted on the water pump efficiency by introducing a harmonic loss calibration mechanism. Based on a two-channel machine learning model, a fault incidence matrix is constructed by improving a weighted K-means algorithm to realize equipment health state clustering, an efficiency attenuation trend is predicted in combination with an LSTM network, static characteristics and dynamic time sequence data are fused by using an attention mechanism, and finally an accurate fault diagnosis report and a maintenance strategy are generated.

The invention relates to the technical field of monitoring and early warning, and discloses an automatic monitoring and early warning method and system for a pump room. The method comprises the following steps: collecting hydraulic pulsation, cavitation acoustic emission and bearing temperature rise data through multiple sensors; a pump efficiency attenuation vector is obtained based on an environment adaptive algorithm optimization feature; predicting the health state of the equipment by using a hydraulic model; carrying out deep learning classification by adopting a fault propagation network; and four-stage grading early warning is realized by combining water inflow-pump efficiency coupling judgment. According to the method and the device, the technical problem that the gradual change fault and the multi-parameter coupling fault mode of the pump room equipment under the underground complex working condition cannot be accurately identified in the prior art is solved, and the accuracy of fault prediction and the timeliness of early warning of the underground drainage pump room are improved.

The invention discloses an energy efficiency control method for a cooling fan of an industrial electric cabinet, and the method comprises the steps: firstly collecting temperature, humidity, equipment current / voltage and other multi-mode sensing data, carrying out the PCA algorithm dimension reduction and feature extraction, combining with Granger causality test, thermodynamic principle and machine learning algorithm, and carrying out the building and fusion, thereby obtaining a cooling efficiency model. Thirdly, preprocessing current and voltage data of the equipment, training and optimizing an LSTM model to predict load power, and calculating a fan rotating speed control value through a heat dissipation efficiency model; then, a PWM speed regulation signal is generated by adopting a fuzzy PID control algorithm, and a pulse width modulation and smooth speed regulation mixed strategy is applied to the duty ratio; and finally, energy efficiency indexes are collected to compare and update the weight of the LSTM model, knowledge distillation is introduced to optimize new model training, and self-adaptive adjustment of the rotating speed of the fan is achieved. The rotating speed of the fan can be accurately adjusted, energy consumption is reduced, the energy utilization efficiency is improved, performance reduction of equipment due to overheating is effectively avoided, and the service life of the equipment is prolonged.

The shutdown of a compressor (10) installed in a refrigerant circuit (2) in air conditioning or refrigeration system is controlled so as to prevent unpowered reverse rotation of the compressor. Prior to terminating electric power to the compressor drive motor (24), the pressure within the system is relieved and substantially equalized across the compressor, thereby eliminating the possibility of unpowered reverse rotation of the compressor at shutdown. Pressure relief and equalization may be achieved by reducing the operating speed of the compressor to a low forward speed for a period of time prior to deenergizing the compressor drive motor. Pressure equalization may also be achieved by driving the compressor in reverse rotation prior to deenergizing the drive motor.

The invention relates to the technical field of intelligent control, and discloses an intelligent adjusting method and device for a cooling fan intelligent control system resistant to a sand and dust environment, and the method comprises the steps: carrying out the multi-working-condition data collection of a cooling test platform of a cooling fan, and obtaining a sand and dust environment sample library; performing quantitative modeling analysis on dust accumulation dynamic characteristics and an air duct blockage formation mechanism based on the sand and dust environment sample library to obtain a thermal equivalent model; performing feature extraction and integration based on the thermal equivalent model to obtain a multi-dimensional dust environment heat dissipation feature vector; the multi-dimensional sand and dust environment heat dissipation feature vectors are input into a gradient lifting learning model with a multi-attraction mechanism to be processed, and fan cooperative control parameters adapting to the sand and dust environment are obtained.According to the method, the working state of a heat dissipation system in the sand and dust environment is comprehensively represented, the problem that description of traditional features in the sand and dust environment is insufficient is solved, and the method is suitable for being applied to the sand and dust environment. Therefore, the adaptive capacity and the reliability of the cooling fan in a complex sand and dust environment are improved.

The invention discloses an industrial cloud platform compressor air system management and control system based on an Internet of Things gateway. The data acquisition unit acquires operation data of the compressor through a sensor, and transmits the operation data to the Internet of Things gateway unit after preprocessing the operation data. And the Internet of Things gateway unit performs end-to-end encryption processing, equipment identity verification and dynamic access control on the data. And after receiving and decrypting the data, the industrial cloud platform digital twin unit constructs and updates a digital twin model based on real-time data, extracts key performance indexes by using a stream computing technology, and stores the key performance indexes in a time sequence database to support analysis and optimization. And the intelligent diagnosis optimization unit performs fault diagnosis in combination with the key performance indexes and high-order spectrum analysis, and optimizes operation parameters of the compressor through a reinforcement learning algorithm. The distributed autonomous control unit adjusts a real-time operation state based on optimization parameters, realizes load adaptive balance and energy efficiency optimization, starts a local autonomous control strategy when communication is interrupted, and ensures safe and efficient operation of the system.

The invention relates to a self-adaptive pressure regulation vacuum pump closed-loop control system and method. The system comprises a model building module which is used for acquiring real-time data flow of a vacuum pump and building a nonlinear dynamic model to obtain a pressure-flow-power feature mapping relation; the predictive analysis module calculates a pressure-flow predictive value based on the relationship and compares the pressure-flow predictive value with the real-time power data for analysis. And generating a parameter adjustment instruction once the deviation value exceeds a preset threshold value. And the control scheme generation module corrects the control parameters according to the parameter adjustment instruction and generates an optimized power adjustment scheme. And if the data is abnormal, generating a state update value. And the control scheme updating module fuses the state updating value and the pressure-flow prediction value to obtain an optimized model parameter so as to adjust a weight coefficient of an adaptive control strategy and generate a control scheme. The system effectively improves the accuracy of vacuum pump pressure control, greatly enhances the ability of the system to deal with abnormal conditions and equipment performance changes, and ensures long-term stable operation of the vacuum pump.

The invention relates to the field of mine ventilation, in particular to an intelligent adjusting method and system for mine ventilation. The method comprises the following steps: constructing a dynamic topology model taking a roadway as an edge and taking a fan and an air door as nodes based on a mine CAD drawing; acquiring the actual wind pressure and the actual wind volume of each roadway branch in real time, performing inversion to obtain the current wind resistance, and updating the wind resistance attribute of the edge in the dynamic topology model by using the current wind resistance; calculating the optimal air volume of the roadway branch by using the updated dynamic topology model; based on the optimal air volume, target regulation and control parameters of the draught fan and the air door are obtained through solving by means of a particle swarm algorithm; and according to the target regulation and control parameters, the fan and the air door are adjusted through a fuzzy PID algorithm. The problem that in the prior art, mine ventilation adjustment is not intelligent and safe enough is solved, the theoretical optimal air volume is obtained through deep analysis of the mine, then ventilation adjustment is conducted step by step, and the scientificity and efficiency of ventilation adjustment are improved.

The invention relates to the technical field of mine ventilation control, in particular to a mine fan ventilation control method and system based on digital twinning. The method comprises the following steps: carrying out data preprocessing on acquired mine monitoring data; performing feature extraction on the preprocessed mine monitoring data; utilizing a random forest algorithm to predict the relationship between the data characteristics and the fan air volume; a digital twinborn model is used for resolving the relation between the data characteristics and the fan air volume, and a fan ventilation control strategy is obtained; and generating a PLC control instruction based on the fan ventilation control strategy. And through multi-source data fusion and deep analysis, the real-time ventilation requirement of the mine can be accurately grasped. Based on random forest algorithm prediction and digital twinborn model calculation, dynamic and accurate adjustment of the air volume of the draught fan is achieved, it is ensured that the concentration of harmful gas in all underground areas is maintained within a safety threshold value, the oxygen content is sufficient, an ideal environment is created for personnel operation and equipment operation, and the safety and reliability of a ventilation system are remarkably improved.

The invention relates to the technical field of industrial monitoring, in particular to a fan running state comprehensive monitoring system and a noise and current detection method thereof, and the system comprises a noise data acquisition module, a vibration noise correlation analysis module, a running current acquisition module, an abnormal current detection module, an equipment fault diagnosis module and a performance degradation prediction module. According to the method, the sound data are collected, the frequency and amplitude changes are analyzed, the types of various sound sources are recognized, abnormity caused by mechanical parts is found in the early stage, association analysis of vibration data and sound data is combined, understanding of the physical state of the fan is enhanced, and by monitoring current data and the fan blade rotating speed in real time, the fan blade rotating speed can be accurately detected. Abnormal detection of the current data and evaluation of the current stability are realized, a data basis is provided for fault diagnosis, the operation efficiency and the performance degradation trend of the fan are evaluated in combination with the current data, the service life and the maintenance period of equipment are effectively predicted, and the operation strategy and the maintenance plan of the fan are optimized.

The invention relates to the technical field of mine safety, and discloses a coal mine underground gas monitoring method and system. The method comprises the following steps: acquiring methane absorption intensity data by adopting a tunable diode laser absorption spectrum, converting the methane absorption intensity data into gas concentration based on a Lambert-Beer law, performing temperature and humidity compensation, calculating a transmission priority according to concentration deviation to form a data packet, and establishing a spatial interpolation model by utilizing multipoint data to predict gas concentration distribution. Calculating a risk assessment index based on the predicted distribution, automatically adjusting the rotating speed of the ventilator, and controlling the electrical equipment to be powered off. According to the method, the technical problems of low detection precision, incomplete monitoring coverage, low data transmission efficiency and lack of prediction and early warning capabilities in the existing underground coal mine gas monitoring method are solved. And the accuracy of gas concentration detection and the intelligent level of gas monitoring are improved.

The invention relates to the technical field of water pump control, and provides a water supply and drainage pump set assembly and a digital control system, which realize self-adaptive control under a complex working condition by fusing multi-source data such as pressure, flow and temperature, break through the dependence of traditional control on a single parameter, and improve the response capability of the control system to water fluctuation and equipment state change. Based on a long-short-term memory network trained by historical multi-source data, a control system can predict a pressure set value in advance, the pressure set value is solved through a particle swarm algorithm, and weight fusion is performed according to the predicted pressure set value and the solved pressure set value to generate an adjustment instruction, so that the starting and stopping times of equipment and mechanical wear are reduced, and the service life of the equipment is prolonged. The maintenance cost is reduced, and water resource waste is avoided through accurate control; and meanwhile, through a redundancy design and a fault-tolerant mechanism, the control system can still operate stably under the scenes of sensor faults, equipment abnormity and the like.