3406results 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 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.

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 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 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 discloses an immersed server operation control method and system, and relates to the technical field of immersed cooling, and the method comprises the steps: judging whether a liquid level is higher than a preset starting liquid level, and activating a cooling liquid circulation system to confirm liquid flow; the liquid inlet speed of the cooling liquid is optimized according to feedback of the flow velocity sensor, and the stable state of the liquid level in the server cabinet is monitored; whether cooling liquid leaks or not is judged based on liquid level acceleration and flow velocity deviation, when the liquid level is insufficient, an alarm is triggered, and the liquid level and circulation time are automatically adjusted. According to the method, the automation level and the operation safety of the system are improved, sufficient supply of the cooling liquid under the high-load condition of the server is ensured, the downtime caused by leakage of the cooling liquid is shortened, the long-term operation cost of the system is reduced through timely maintenance notification, and the service life of the system is prolonged by automatically adjusting the liquid level and the circulation time. And quick recovery of the system when leakage occurs is ensured, so that the reliability and the fault recovery capability of the system are improved.

The invention discloses a plunger pump fault diagnosis method and device, a medium and equipment. The method comprises the following steps: acquiring a pressure signal and an original vibration signal of the plunger pump; acquiring data in different states; obtaining frequency domain information based on continuous wavelet transform; performing multi-source data fusion; acquiring a global dependency relationship between the input and the output based on an encoder; and the fault of the plunger pump is diagnosed. By introducing a multi-source data fusion method, time domain and frequency domain signals collected by different types of sensors are comprehensively processed, so that information of different data sources is effectively integrated, and the accuracy and comprehensiveness of fault diagnosis are improved; important features in fault signals are automatically recognized and weighted through the Transform model, the extraction precision of fault features is improved, the model can effectively learn the fault features in a few-sample environment by optimizing a network structure and training parameters in the training process, and the diagnosis precision is remarkably improved.

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 invention relates to the technical field of voiceprint detection, and discloses an abnormal voiceprint detection system and method based on a pump characteristic spectrum, and the system comprises a collection module which sets a sampling rate, generates a voiceprint index for a pump according to an analysis result, and determines an abnormal signal; the processing module recognizes the voiceprint indexes, obtains the characteristic frequency spectrum of the pump machine based on the processing result, determines the abnormal position of the pump machine, and determines the abnormal type of the pump machine according to the abnormal voiceprint characteristics; and the verification module compares the abnormal voiceprint characteristics with historical voiceprint data, determines the similarity of abnormal voiceprints, re-determines the abnormal type of the pump machine by adopting a clustering algorithm when the abnormal type is judged to be inaccurate, and determines the abnormal prediction type of the pump machine based on a convolutional neural network model when the abnormal position is located on the wall surface of the pump machine. Verifying whether the exception type is accurate according to the exception prediction type; and the early warning module determines a graded alarm according to the abnormal position and the verified abnormal type. The method has the characteristics of good detection and analysis.

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 provides a real-time monitoring method and system for the running health state of a pump set. The method comprises the steps of obtaining vibration data of a load end bearing seat in real time when a pump set operates; performing time-frequency feature transformation on the vibration data to obtain time-frequency feature data; using the time-frequency characteristic data to train an auto-encoder to obtain an auto-encoder model based on reconstruction; the time-frequency feature data after mask processing and the rebuilding-based auto-encoder model obtained through training are used for training a rebuilding generation task-based auto-encoder to obtain an anomaly detection model; inputting vibration data in an early health state as a sample into the anomaly detection model, and calculating an obtained anomaly score to obtain a health degree reference; and real-time vibration data is subjected to time-frequency feature processing and then input into the anomaly detection model to obtain an anomaly score, and the current health degree value of the pump set is obtained. The operation state of the pump set can be monitored in real time for a long time, and the manual inspection cost and the serious equipment fault occurrence risk are reduced.

The invention provides a self-adaptive volume-variable intelligent air compressor and a control system thereof, and relates to the technical field of air compressors. The control system comprises a multi-source heterogeneous sensing fusion module, a dynamic manifold optimization control module, a quantum probability fault pre-judgment module, a cross-domain energy cooperative scheduling module, a distributed self-organizing communication module and a self-adaptive meta-evolution module. And a grey Markov chain model and a wavelet decomposition-support vector machine are combined to accurately predict the real-time electricity price and the factory gas consumption load. And according to the predictions, the operation mode of the air compressor is intelligently adjusted through dynamic game equilibrium and a multi-target differential evolution algorithm. In the low ebb period of the electricity price, the operation load is automatically increased by 30%-40%, and meanwhile the gas storage capacity is increased by 25%-35%; and in the peak period of electricity price, stored gas is reasonably released, and the operation load is reduced by 20%-30%. And energy waste caused by unreasonable operation is effectively avoided.

The invention relates to the technical field of lubricating oil monitoring, in particular to a compressor lubricating oil online monitoring vibration diagnosis method and system, and the method comprises the steps: obtaining vibration signal data of a compressor bearing, a connecting rod mechanism and a piston assembly and lubricating oil medium parameters, arranging the data according to a time sequence, and generating a lubrication working condition time sequence signal data set; according to the method, through time sequence matching of vibration signal data and lubricating oil state parameters, a lubricating working condition time sequence signal data set is established, and in combination with time window division, the bearing vibration frequency offset, the piston transverse impact amplitude and the connecting rod torsional vibration change rate are extracted; and calculating the viscosity drop rate, the temperature change range and the pollutant particle growth rate of the lubricating oil, screening the correlation trend of the bearing vibration frequency offset before and after the change of the lubricating state, and enhancing the lubricating state recognition capability.

The invention discloses a pressure fluctuation suppression method and device for an emulsion pump station, and relates to the technical field of hydraulic transmission, and the method comprises the steps: monitoring the pressure data of a preset position in real time by deploying a pressure sensor of the emulsion pump station, and obtaining a pressure sequence set of a pump outlet, an equipment inlet and a main supply pipeline; inputting the sequence set into a BP neural network model, predicting and outputting the pressure distribution of the main supply pipeline of the next monitoring node, and calculating the pressure deviation; if the pressure deviation is smaller than the preset fluctuation scale, the deviation is eliminated, valve opening and flow proportion optimization analysis is conducted according to pressure distribution, and an adjusting scheme is output; and according to the adjustment scheme, executing optimization control of the emulsion pump station of the next monitoring node. The technical problems of unstable working of the pump station and equipment damage caused by pressure fluctuation in the emulsion pump station are solved, and the technical effects of improving the operation stability of the pump station, reducing equipment abrasion and controlling hysteresis quality through real-time monitoring and optimal adjustment are achieved.

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 an air compressor fault monitoring and operation and maintenance management method and system based on the AI technology, and relates to the technical field of air compressors, the key points of the scheme are as follows: based on the operation data of an air compressor, reading pressure and temperature data to carry out data standardization processing, and utilizing a CNN network to carry out feature extraction on the pressure and temperature data; and identifying an abnormal fluctuation mode of sudden increase or sudden decrease, and monitoring the change trend of the abnormal mode to obtain abnormal mode tracking information. The method is used for carrying out continuous monitoring and feature extraction on pressure and temperature data, an abnormal fluctuation mode can be effectively recognized, the development trend of the abnormal fluctuation mode can be predicted, short-term data abnormity can be captured through application of the long-term and short-term memory network, long-term data changes can be analyzed, prediction of potential risks in the future is provided, and the method is suitable for popularization and application. The accuracy and timeliness of fault prediction are improved, so that a maintenance team can carry out intervention before the problem develops to a serious fault, and the downtime caused by the fault is remarkably reduced.

A high-pressure fuel injection pump testing device comprises a machine table, a motor capable of moving left and right is installed on the machine table, a supporting plate capable of being adjusted up and down is arranged on one side of the motor, a fuel injection pump is placed on the supporting plate, a pressing plate is arranged on the supporting plate, the fuel injection pump is pressed by the pressing plate, and a first connector is connected to a shaft extension of the motor. A first connector is connected to a cam shaft of the fuel injection pump, a second connector is connected to a cam shaft of the fuel injection pump, the first connector and the second connector are detachably connected, supporting baffles are connected to the two sides of the supporting plate, an adjusting base is connected to the supporting baffles, a thermosensitive probe and a displacement sensor are installed on the adjusting base, and the thermosensitive probe and the displacement sensor face the position of a plunger in the fuel injection pump. Compared with the prior art, according to the testing device for the high-pressure fuel injection pump, the fuel injection pump is quickly connected with the motor through the connector, the thermosensitive probe and the displacement sensor on the supporting plate are used for measuring temperature change and deformation and providing testing data, and support is provided for improvement of materials of a shell of the high-pressure fuel injection pump.

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.