175 results about "Control chart" patented technology

The invention discloses a pig breeding intelligent management and control system based on multi-modal data monitoring, and belongs to the technical field of pig breeding management, and the system comprises a data collection and individual recognition module which comprises an RFID ear tag, an inspection robot, an environment sensor and an intelligent feeder, and is used for determining the identity information of a pig individual based on the data collected by the RFID ear tag and the inspection robot, associating the collected multi-modal data of the live pigs in the pig house, and uploading the multi-modal data to edge computing equipment through an MQTT protocol for preprocessing; the feature extraction module extracts physiological and behavior features of live pigs by using a multi-modal deep learning fusion model; the key breeding index analysis module constructs a live pig health index and an environment quality index; the abnormity early warning module adopts a CUSUM control chart to accumulate index deviation and respond to abnormity; the management regulation and control model constructs a Markov decision process by minimizing energy consumption and maximizing health indexes, intelligently adjusts the rotating speed of a fan and a feeding strategy, and realizes precise environmental control and feeding optimization.

Disclosed herein are system, method, and computer program product embodiments for detecting anomalies during software testing. The methods include generating a plurality of test reports for the software program by executing one or more test cases on a plurality of versions of the software program, generating a control chart based on the plurality of test reports, generating an alert when at least one testing characteristic includes an anomaly over the plurality of versions of the software program as determined based on the control chart. The control chart includes a plot associated with at least one testing characteristic of the software program, and a historical context associated with execution of the one or more test cases on the plurality of versions of the software program.

The invention discloses an oil and gas pipeline safety assessment method and system based on stress monitoring and intelligent prediction, and a storage medium, relates to the field of oil and gas pipeline safety monitoring and assessment, and aims to solve the problem that an oil and gas pipeline safety monitoring and assessment method based on stress monitoring and intelligent prediction is lacked in the prior art. And the potential safety hazard of the pipeline is difficult to identify effectively. Comprising the following steps: S100, performing numerical simulation on stress distribution of a pipeline under different working conditions by adopting finite element analysis, and determining a stress sensitive part of the pipeline; s200, a stress sensor collects stress data of a stress sensitive part of the pipeline in real time; s300, an XLSTM model is constructed, the model adopts a gating mechanism and residual connection, hyper-parameters of the model are optimized by adopting a Hemma optimization algorithm, and a stress trend is predicted based on the optimized model; and S400, carrying out stress abnormity early warning by adopting an exponential weighted moving average control chart, introducing a self-adaptive threshold adjustment mechanism, carrying out abnormity detection on real-time stress data, and identifying potential safety risks.

Disclosed is an explainable method for monitoring a state of a generator of a wind turbine generator system on the basis of a spatio-temporal graph. The method includes: S1: acquiring data collected by a supervisory control and data acquisition (SCADA) system; S2: carrying out data understanding on the SCADA data, selecting features associated with the generator, and carrying out data preparation on the selected feature data, and obtaining valid data; S3: embedding the SCADA data, and forming a directed spatio-temporal graph data sequence; and S4: carrying out modeling of a normal behavior model of the generator on the constructed directed spatio-temporal graph data sequence, computing a full-graph-level residual and a node-level residual, computing a residual through an exponentially weighted moving average (EWMA) control chart method, carrying out full-graph-level state monitoring on the generator, forming a fault information transmission chain relation, and enhancing explainability and robustness of a monitoring result.

The invention discloses a flexible circuit board production quality monitoring method and system based on data feedback, and the method comprises the steps: collecting the physical parameters of development etching, drilling and copper plating processes, such as line width, line distance, aperture, roundness and copper thickness, and workshop temperature and humidity and dust concentration data; generating a single-variable control chart for a single-process physical parameter, generating a multivariable T2 control chart for a multi-coupling parameter process, and calculating a process stability index and a comprehensive fluctuation index; inputting the abnormal data into a BP neural network, identifying an abnormal mode type and outputting characteristic parameters; generating a parameter correction instruction according to the abnormal mode and the characteristic parameters, and adjusting equipment parameters in real time; recalculating the process capability index based on the adjusted data, and triggering secondary feedback if the process capability index does not reach the standard; and dynamically adjusting the threshold value of the control chart according to the standard deviation and the mean value of the process capability indexes of the continuous batches. Production key parameters are comprehensively covered, abnormity is found in time, accurate and comprehensive detection is achieved, real-time adjustment is achieved, stability and controllability are ensured, and the product quality is improved.

The invention relates to the field of fan operation monitoring, in particular to a dishwasher fan operation state real-time monitoring method and system.The method comprises the steps that a current stabilization sequence and a stabilization starting point moment of a fan are obtained, and a stable current mean value and a stable current standard deviation are calculated; calculating a load intensity index of the fan; fitting according to a least square method to obtain an adjustment index, and calculating an equivalent wind resistance coefficient based on the load intensity index and the adjustment index; acquiring a center line, an upper limit and a lower limit of the control chart, and calculating a current correction value at a target moment by using EWMA; generating and sending an alarm signal in response to that the current correction value is less than the lower limit or greater than the upper limit; and in response to that the current correction value is not greater than the upper limit and not less than the lower limit, calculating the fault probability of the fan to complete monitoring. According to the technical scheme, the precision of the fan monitoring result can be improved.

The invention discloses an image generation method and device and electronic equipment, and belongs to the technical field of computer vision and image processing. The method comprises the steps of inputting feature information of a target building into a multi-modal big language model, and performing cross-modal reasoning based on the feature information through the multi-modal big language model to obtain key environment semantic information of the target building; constructing a space block model of the target building by utilizing the key environment semantic information; generating a target space condition control chart according to the space body block model and target observation configuration selected by a user; wherein the target space condition control chart is a visual angle screenshot, corresponding to the target observation configuration, of the space block model; inputting the key environment semantic information and the target space condition control chart into a condition diffusion model to obtain an initial view angle image, corresponding to the target observation configuration, of the target building; and determining a target view angle image of the target building according to the initial view angle image. According to the invention, the image generation effect can be improved.

The invention relates to a wind power plant state monitoring technology, in particular to a multi-unit joint monitoring model based on spatial-temporal correlation, which comprises the following steps: S1, performing cross reference on multiple units to eliminate spatial correlation; s2, vector autoregression prediction is carried out, and time correlation is eliminated; and S3, monitoring a multivariable statistical process based on a residual vector. Aiming at the problem that modeling is interfered by spatial-temporal correlation of wind power plant-level multi-unit state data, the invention provides a multi-unit joint monitoring model, fault feature extraction is realized through multi-unit cross reference, and the spatial non-stationary trend among wind power plant units is eliminated; based on a vector autoregression (VAR) model, carrying out joint modeling on the data of the multiple units, eliminating time autocorrelation among the multiple units, and obtaining a stable and independent residual vector; and a multivariable residual control chart is established, the state of the wind power plant is jointly monitored, and a fault unit is accurately identified. The model guarantees the operation reliability of the wind turbine generator, and effectively reduces the operation and maintenance cost of a wind power plant.

The invention relates to the technical field of data processing, in particular to a pharmaceutical production line state monitoring method and system. The method comprises the following steps: acquiring humidity data of each area of the pharmaceutical production line at each moment; according to the performance of the overall humidity data at each moment in the Shewhart control chart, the initial abnormal degree of the overall humidity data at each moment is obtained, the humidity data of all the areas at each moment are clustered, and the humidity non-uniformity of each humidity cluster combination at each moment is obtained; the method comprises the steps of obtaining humidity non-uniformity of the whole humidity data at each moment, obtaining the measurement authenticity of the whole humidity data at each moment based on the humidity non-uniformity, correcting the initial abnormal degree according to the measurement authenticity, obtaining the final abnormal degree of the whole humidity data at each moment, and judging the abnormal condition of the state of the pharmaceutical production line based on the final abnormal degree. According to the invention, the accuracy of anomaly detection is improved.

The present invention discloses a method, system, and storage medium for oil and gas pipeline safety assessment based on stress monitoring and intelligent prediction, which relates to the field of oil and gas pipeline safety monitoring and assessment. The method aims to address the problem in the prior art of lacking a method for oil and gas pipeline safety monitoring and assessment based on stress monitoring and intelligent prediction, making it difficult to effectively identify potential safety hazards in pipelines. The method comprises: S100, using finite element analysis to perform numerical simulations on the stress distribution of pipelines under different working conditions to determine stress-sensitive locations in the pipeline; S200, using stress sensors to collect stress data from stress-sensitive locations in the pipeline in real time; S300, constructing an XLSTM model, which uses a gating mechanism and residual connections, and uses a Hippo optimization algorithm to optimize the model's hyperparameters, and predicting stress trends based on the optimized model; S400, using an exponentially weighted moving average control chart for stress anomaly warning, introducing an adaptive threshold adjustment mechanism, performing anomaly detection on real-time stress data, and identifying potential safety risks.

The invention relates to a method for crosstalk modeling in a multi-mirror arrangement (31) with displaceable individual mirrors (32), comprising the following steps: providing a multi-mirror arrangement (32) with N displaceable individual mirrors (32) and a control device (35) for positioning the individual mirrors (32), specifying a control pattern (34) for positioning at least a subset of the individual mirrors (32*), positioning the individual mirrors (32*) by controlling them with the control pattern (34), detecting the actual positions of the individual mirrors (32*) actually resulting from controlling with the control pattern (34), determining a coefficient matrix with coefficients for quantifying crosstalk behavior, adapting the control device (35),wherein the control pattern serves as a predictor of a training data set (33) for a neural network for predicting a relationship between a control pattern (34) and the resulting actual positions of the individual mirrors (32), and wherein the coefficients of the determined coefficient matrix serve as targets of the training data set.

The invention discloses a road structure performance catastrophe early warning method and system. The method comprises the following steps: S1, synchronously acquiring data by using a three-dimensional ground penetrating radar and a deflectometer; s2, introducing a dielectric constant correction signal, quantifying a volume defect, and taking the volume defect as a physical constraint inversion mechanical parameter; s3, fusing the multi-dimensional parameters to construct a damage potential index, and predicting the residual life by using a graph space-time neural network; and S4, on-line mutation detection is carried out based on a Bayesian and cumulative sum control chart algorithm, and the weight of the model is updated through a treatment feedback closed loop. According to the method, physical-data dual-drive modeling is adopted, the problems of one-sided detection means, poor model interpretability and parameter solidification are solved, and accurate evaluation of road performance degradation and continuous evolution of the model are achieved.

The invention relates to the technical field of industrial equipment monitoring, in particular to an MVR (mechanical vapor recompression) evaporator operation monitoring method for industrial wastewater treatment, which is capable of immunizing normal working condition amplitude interference and dynamically fusing a multi-sensor waveform mode, and comprises the following steps: S1, obtaining a normalized mode sequence of each sensor; s2, calculating a mode offset score between the normalized mode sequence and each corresponding health mode template; s3, performing weighted fusion on the mode offset score of each sensor based on the dynamic sensitivity weight to obtain a self-adaptive blockage index; and S4, analyzing a continuous deviation trend of the adaptive blockage index relative to a preset healthy baseline based on the cumulative sum control chart so as to monitor the running state of the MVR evaporator. According to the invention, the monitoring sensitivity and decision reliability of early-stage collaborative abnormity of blockage can be improved.

The invention discloses a transformer substation equipment defect image generation method and system based on two-stage denoising, belongs to the technical field of computer vision and image generation, and solves the problem that in the prior art, the geometry and contour of equipment cannot be still kept in a steady state under the condition that an inference end does not depend on a control chart. The method comprises the following steps: acquiring normal samples and defect samples of a substation scene target defect category, extracting structure priori in the normal samples, and selecting a diffusion base model at the same time; in the first stage, a diffusion model base and a lorabase model are fused to generate latent variables with residual noise, and in the second stage, a diffusion base model, the lorabase model and a loradeject model are fused to continue decoding the latent variables with the residual noise to generate defect images; and based on the training set B and a defect image training discrimination model, discriminating the defect image, and screening generated defect data which cannot be distinguished from real distribution, namely generating a final substation equipment defect image. The method is used for generating the transformer substation equipment defect image with two-stage denoising.

The present invention discloses an online fault monitoring method for a wind turbine, belonging to the technical field of online fault detection of wind power generation units, which specifically integrates fractional-order extended dispersion entropy and cumulative sum control chart, and includes the following steps: collecting historical vibration signals of core components of the wind turbine; using fractional-order extended dispersion entropy to capture deep dynamic information changes in the component vibration signals; detecting possible abnormal conditions in the process of dynamic information changes with the help of the cumulative sum control chart and sending out alarm information; comparing the detection results with the actual operation conditions of the wind turbine to determine the detection performance of the method. The present invention provides a method that can accurately and quickly detect abnormal conditions of the wind turbine and send out alarms, which can be simply and economically realized, provides a strong basis for discovering faults of the wind turbine and carrying out maintenance, and ensures the reliable and stable operation of the wind turbine.

The invention discloses a cross-domain data collaborative management method based on edge computing. The method comprises the following steps: S1, acquiring data to form a time series data set; s2, extracting features of time sequence data at edge nodes, and generating a state vector sequence; s3, inputting the state vector sequence into an edge decision network for weighted aggregation to generate a collaborative state sequence; s4, detecting a behavior change point of the collaborative state sequence by adopting a cumulative sum control chart method, and constructing a state mapping sequence; s5, based on the state mapping sequence, constructing a region index set through discrete wavelet transform; s6, inputting the regional index set into an edge decision network to generate a management instruction sequence; and S7, executing the management instruction sequence at the edge node, and updating the edge decision network based on an execution result. According to the method, multiple intelligent algorithms are fused to realize data collaborative management of cross-domain edge nodes, and the response efficiency and decision-making ability in an edge environment are improved.

The invention discloses an abnormal behavior identification method based on deep learning and big data analysis, and relates to the technical field of network abnormal behavior identification, and the method comprises the following steps: 1, monitoring a data flow in real time, calculating a threshold value through a dynamic threshold value method, and supplementing noise and an abnormal value processing mechanism; 2, selecting two different types of anomaly detection models of a density-based local outlier factor and a clustering-based K-means, analyzing data from different angles, and verifying a parameter selection and adjustment strategy; according to the method, the data flow is monitored in real time through a dynamic threshold method and a control chart technology, data changes can be adapted, and the real-time performance and accuracy of anomaly detection are improved. Two different types of anomaly detection models, namely a density-based local outlier factor and a clustering-based K-means, are adopted, data are analyzed from different angles, and multi-model integration is realized. And detection results are fused by adopting weighted fusion, so that the effective utilization rate of the features and the detection accuracy can be improved.

The invention relates to the field of industrial equipment anomaly detection, in particular to a slaughter line anomaly identification method and system based on digital twinning, and the method comprises the steps: carrying out the operation data collection and health reference data collection of a splitting saw in a slaughter line through a data collection unit and a digital twinning interface unit; performing digital twinborn reference correction and accumulation on instantaneous current data of a driving unit and performing control chart anomaly detection processing to obtain a preliminary anomaly judgment result of the operation state of the splitting saw; carrying out fusion evaluation processing on the physical characteristic data and the health reference data of the splitting saw to obtain an abnormal source tendency discrimination factor; the preliminary abnormity judgment result and the abnormity source tendency judgment factor are comprehensively analyzed to obtain the abnormity identification result of the current processing period of the splitting saw, so that the equipment abnormity identification accuracy in the slaughter line is improved.

According to the temperature control and SPC statistical system for continuous cyclic cap making, temperature detection focuses on a threaded area on the side edge of a bottle cap, and a temperature data-bottle cap-mold cavity number association is established in combination with a groove of a forming device and double proximity switches; an upper die and lower die two-way independent cooling loop is adopted, the on-off proportion of an electromagnetic valve is controlled and adjusted according to the periodic circulation time proportion, and pressure balance is achieved in cooperation with a cold water bypass electromagnetic valve. An SPC statistical method is introduced, temperature fluctuation and mold cavity consistency are monitored through a control chart after data preprocessing, graded early warning is generated, and cooling parameters are dynamically optimized through a waterway optimization closed-loop unit. Accurate temperature control, abnormity traceability and mold cooling dynamic regulation and control of the key area of the bottle cap are achieved, the product percent of pass and production stability are improved, and energy consumption and maintenance cost are reduced.

The present invention relates to the field of industrial equipment anomaly detection, and in particular to a slaughter line anomaly identification method and system based on digital twins. The method comprises: collecting operating data and health reference data of a splitting saw in a slaughter line through a data acquisition unit and a digital twin interface unit; obtaining a preliminary abnormality judgment result of the operating state of the splitting saw by performing digital twin reference correction and cumulative control chart anomaly detection processing on the instantaneous current data of the driving unit; obtaining an abnormality source tendency discriminant factor by fusing and evaluating the physical feature data and health reference data of the splitting saw; obtaining an abnormality identification result of the current processing cycle of the splitting saw by performing a comprehensive analysis of the preliminary abnormality judgment result and the abnormality source tendency discriminant factor, thereby improving the accuracy of abnormality identification of equipment in the slaughter line.

The application provides an anti-noise signal periodicity anomaly monitoring method and system which overcomes correlation false positives, comprising: a collection step: collecting data signals every preset time interval; a processing step: dividing the collected data signals according to a preset rule and performing feature extraction; a conversion step: performing orthogonal conversion on the extracted features to eliminate the correlation between the features; a monitoring step: processing the converted data to form an MO control chart, and monitoring the MO control chart; and if abnormal data appears, reporting. The MO test based on the EnNRC features can maintain high test accuracy under strong background noise and has stronger anti-noise interference ability.

The present invention provides a remote sensing detection method for sand dune movement and speed variation, which can be applied to the field of remote sensing image processing technology. The method includes: extracting dune edges from dune remote sensing observation time-series images to obtain a dune edge image sequence; preprocessing the dune edge image sequence to obtain a sand ridge line image sequence; constructing a velocity vector field from the sand ridge line image data of two adjacent frames in the sand ridge line image sequence using a dense optical flow method to obtain a space-time cube array of the sand ridge line movement velocity field; performing pixel-by-pixel time-series analysis on the space-time cube array of the sand ridge line movement velocity field using an exponentially weighted moving average control chart to obtain a time-series rate anomaly judgment result; and obtaining a dune movement and speed variation detection result by superimposing the time-series rate anomaly judgment result onto the dune remote sensing observation time-series images for identification. The present invention also provides a sand dune movement and speed variation remote sensing detection system, electronic equipment, and storage medium.

The invention provides a self-starting control chart sequence rebound parameter updating method based on warning limit triggering, and relates to the field of statistical process control. Different from a traditional product quality characteristic distribution model based on normal distribution, the method mainly takes product quality monitoring in a high-quality manufacturing process as a target, modeling is performed on an event occurrence interval variable Xt in the process by using gamma distribution, a pivot vector # imgabs0 # is constructed based on the modeling, and mapping from the Xt to self-starting event occurrence interval data St is completed. Further constructing a monitoring statistic Qt of the self-starting control chart by using an exponential weighted moving average method, dynamically switching a parameter updating process based on a time sequence and a sequence bounce according to position information of the Qt in a safety domain, a warning domain and an out-of-control domain of the control chart, delaying the progress that an out-of-control sample flows into parameter estimation, and calculating the out-of-control sample according to the parameter updating process. Data pollution caused by out-of-control samples to parameter estimation is reduced, and the monitoring efficiency of the self-starting control chart is improved.

1. Name of the Design Product: Electronic Device with a Graphical User Interface for Controlling a UPS. 2. Use of the Design Product: For an electronic device. 3. Design Key Points of the Design Product: Lies in the interface content of the graphical user interface. 4. Picture or Photograph that Best Illustrates the Design Key Points: Front View. 5. Use of the Graphical User Interface: To display, interact, operate, and / or control information, data, and / or parameters of an uninterruptible power supply system. 6. Human-Computer Interaction Method of the Graphical User Interface: The front view shows the overview information of the uninterruptible power supply system. When the user clicks the "Meters" button above the front view, the interface will jump to the change state diagram to implement the meter function. 7. Other Situations Requiring Explanation: Other views of this design are conventional designs, and other views are omitted.

The application discloses a flexible circuit board production quality monitoring method and system based on data feedback, collects physical parameters such as line width, line spacing, hole diameter, roundness and copper thickness of developing etching, drilling and copper plating processes, and workshop temperature and humidity and dust concentration data, generates a single variable control chart for single process physical parameters, generates a multivariate T 2 Control chart for multiple coupling parameter processes, calculates process stability index and comprehensive fluctuation index, inputs abnormal data into a BP neural network, identifies abnormal mode types and outputs characteristic parameters, generates parameter correction instructions according to the abnormal mode and the characteristic parameters, adjusts equipment parameters in real time, recalculates the process capability index based on the adjusted data, triggers secondary feedback if the process capability index does not meet the standard, and dynamically adjusts the control chart threshold value according to the standard deviation and mean value of the process capability index of continuous batches. The application comprehensively covers key production parameters, discovers abnormalities in time, accurately and comprehensively detects, adjusts in real time to ensure stability and controllability, and improves product quality.

The embodiment of the application provides a label verification method and device, an electronic device and a storage medium, and belongs to the technical field of financial technology. The method comprises the following steps: obtaining a data label to be verified; classifying the data label, and screening out a target data label of a numerical type; inputting the target data label into a preset decile label model in batches to generate a verification result of the target data label; generating a data control chart based on the verification result; and performing rationality judgment on the data distribution of the target data label according to the data control chart and a preset threshold range to obtain a judgment result, wherein the judgment result comprises whether the target data label is an abnormal data label. Based on this, the embodiment of the application can quickly, efficiently and accurately verify customer labels, so that insurance business personnel can analyze the characteristics of customers from multiple angles, select customers according to the labels, and thus realize accurate customer touch and provide more refined operation targets for customers.

The application discloses a beat control method and device, a linear motor equipment and an industrial automation system, and belongs to the technical field of automation control. The method comprises the following steps: after starting a program corresponding to a beat control graph element, attribute parameters of the beat control graph element are acquired; the attribute parameters are set through an interactive interface, and the attribute parameters are determined according to operation equipment beat information and operation equipment distribution information; arrival position information and cooperation time interval information corresponding to at least one working procedure are determined according to the attribute parameters, so that a mover component is controlled to move to a position indicated by the arrival position information within a time interval indicated by the cooperation time interval information. Compared with the prior art, the beat control strategy is adjusted by modifying codes, and the method has the advantages of being more intuitive and convenient to operate, high in debugging efficiency and strong in universality.

The invention discloses a tobacco process whole flow monitoring method, device and equipment, a medium and a product. The method comprises the steps that in a data processing platform, a central database is connected, to-be-analyzed data are obtained, and the to-be-analyzed data are acquired in real time through Internet of Things equipment of a plurality of to-be-monitored nodes deployed in the whole process of the tobacco technology; performing abnormal data monitoring on the to-be-analyzed data through a control chart theory or a machine learning algorithm, determining an abnormal data monitoring result, and visually displaying a related monitoring chart of the to-be-analyzed data; under the condition that the abnormal data monitoring result indicates that the abnormal data does not exist, optimizing the set process parameters corresponding to the to-be-analyzed data through a deep learning algorithm in combination with a feedback controller, and continuing the process flow through the optimized set process parameters; and when the abnormal data monitoring result indicates that the abnormal data exists, performing abnormal data early warning. According to the scheme, high efficiency and stable quality of related production processes of the tobacco process can be ensured.

The invention discloses a big data enabled smart factory full-process online monitoring and early warning system, and belongs to the technical field of industrial internet and intelligent manufacturing. According to the system, aiming at the problems of data fusion bottleneck, computing architecture limitation, intelligent analysis defects and the like of a traditional factory monitoring system, full-process monitoring and early warning are realized through a multi-source heterogeneous data fusion module, an edge-cloud collaborative architecture, an intelligent monitoring engine, a multi-mode early warning module and the like. The system adopts InfluxDB to realize millisecond-level data acquisition, utilizes a compression function to reduce data transmission quantity, combines a Spark framework to drive a three-dimensional digital twinning board, integrates an EWMA control chart and an LSTM neural network to carry out multi-modal early warning, realizes fault diagnosis and energy consumption anomaly detection through GCN, NMF and DTW, optimizes an early warning decision by means of a random forest, and realizes fault diagnosis and energy consumption anomaly detection. And the system is in butt joint with an industrial internet platform through a standardized API interface. The system can break through the bottleneck of data fusion, reduces the transmission and storage cost, improves the monitoring and early warning accuracy, and is used for various industrial scenes.

The invention provides an electric submersible screw pump rotating speed adjusting method and system considering crude oil viscosity. The method comprises the following steps: calculating the power consumption per ton of liquid per hundred meters by utilizing the working fluid level depth, the oil pressure, the casing pressure, the pump depth, the daily power consumption of a single well and the daily produced liquid; determining the maximum reasonable submergence under the predefined crude oil viscosity and the minimum reasonable submergence under the predefined viscosity according to the correlation between the data of the hectometer ton liquid power consumption and the submergence data, and drawing a block critical submergence control chart; and searching a block critical submergence control chart, and adjusting the rotating speed of the electric submersible screw pump according to whether the actual submergence is between the maximum reasonable submergence and the minimum reasonable submergence. According to the scheme provided by the invention, the comprehensive influence of the crude oil viscosity on the electric submersible screw pump, shaft flow and other systems can be considered, the critical submergence control chart is established, different submergence is recommended for oil wells with different crude oil viscosities, differential supply and production balance is realized, the overall lifting efficiency of a block is improved, and benefit development is realized.