46 results about "Indicator diagram" patented technology

The invention relates to the technical field of oil well fault monitoring, and discloses an oil well indicator diagram real-time fault prediction method and system. The method comprises the following steps: acquiring an oil well sensor data stream, buffering and checking data integrity through a sliding window, and aligning multi-channel sensor data by applying a dynamic time warping algorithm to generate a standardized data stream; extracting time domain features based on the data stream, and comparing the time domain features with a historical feature library after principal component analysis dimension reduction to generate a feature difference index; triggering a multi-level threshold strategy according to the difference index, collecting an incremental training data set, finely tuning the model by adopting an elastic weight preserving algorithm, and generating a hot switching ready model; after the model is loaded, a fault probability value is generated through GPU accelerated reasoning, and an early warning event with a timestamp is generated; and finally analyzing the message into an early warning protocol message edge for transmission, and dynamically optimizing system resources based on logs. According to the method, the delay problem of high-frequency data flow is effectively solved, and the fault prediction accuracy and the system response speed are remarkably improved.

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

The invention discloses a multi-dimensional index calculation method based on a graph database. The method comprises the following steps: defining an index graph storage model based on the graph database; registering a parameterized calculation task corresponding to the index in a built-in calculation framework of a graph database; when a user query request arrives, submitting a query transaction through a long connection channel established by driving the graph database, and directly accessing local storage data by a calculation framework to complete real-time calculation and return a result; when the original data in the graph database is changed, a database trigger captures a change event and pulls up the calculation task in the same process; user query triggering and data updating triggering share the same set of calculation task and graph traversal logic, and storage and calculation integrated real-time multi-dimensional index calculation and updating are achieved. The multi-dimensional index calculation method based on the graph database provided by the invention only depends on the graph database and its own calculation framework, simplifies a data processing link, and improves the index output efficiency.

The invention discloses an oil well intelligent monitoring and remote control system, which belongs to the technical field of oil well monitoring and control, and is characterized by comprising the following steps: acquiring oil well production data, and obtaining a real-time indicator diagram and indicator diagram characteristics according to a preset electrical parameter inverse demonstration indicator diagram model; sending a preset security perturbation instruction sequence and obtaining response data corresponding to the security perturbation instruction sequence; obtaining core mechanism parameters of the oil pumping unit system based on the response data and a preset safety perturbation instruction sequence; obtaining a digital shadow model according to the core mechanism parameters and the indicator diagram characteristics; according to the method, core mechanism parameters are identified through anti-demonstration indicator diagram characteristics of an electric parameter anti-demonstration indicator diagram model and a safety perturbation instruction, and closed-loop intelligent control from oil well state monitoring to liquid production flow regulation and control is formed through the digital shadow model. And the accuracy of oil well flow control, the safety of system operation and the energy efficiency management level are improved.

The invention provides an oil well load indicator diagram prediction method and system, and the method comprises the steps: synchronously collecting an initial electrical parameter data set, a load data set, an accelerometer data set and a barometer data set of an oil well, the initial electrical parameter data set comprising an active power data unit, and then obtaining an active power data segment and a load data segment; obtaining a time lag based on the two, and further obtaining a final electrical parameter data segment; acquiring a reference displacement sequence through the accelerometer data set and the barometer data set; obtaining a dynamic stroke length based on the reference displacement sequence and the load data segment, and further obtaining a final displacement sequence; and constructing a prediction neural network, and obtaining an oil well load indicator diagram based on the prediction neural network and the real-time electrical parameter data segment. According to the technical scheme, the indicator diagram is obtained through continuous and high-frequency real-time electric parameter data segments, motion period information loss caused by low-frequency data collection is avoided, accumulative errors are avoided, and the accuracy of the indicator diagram is ensured.

The present application relates to a frequency and voltage coordination optimization method for beam pumping system based on dynamometer card, which converts the traditional dynamometer card into a P-T dynamometer card of load and time, and obtains the mapping relationship between the P-T dynamometer card and the electric diagram by comparing the P-T dynamometer card and the electric diagram in the same time domain and the same period, relying on the mathematical relationship between the equivalent load torque of the polished rod and the equivalent driving torque of the motor. The method converts the electric diagram features into P-T dynamometer card features, takes the mapping feature points of the P-T dynamometer card as input, relies on deep learning to output the optimal P-T dynamometer card load, and finally obtains the corresponding optimal frequency and optimal voltage according to the mapping model processing data, to establish a flexible intelligent coordination optimization model for the frequency and voltage of the beam pumping system based on the dynamometer card. The present application avoids the application of electric diagram acquisition equipment, improves the energy saving effect and the anti-theft performance, and can ensure the real-time flexible intelligent coordination optimization of the frequency and voltage of the beam pumping system.

The embodiment of the invention provides an index map processing method and a related device, and the method comprises the steps: obtaining a triggering sequence and first coordinate data of a first index node; determining a second index node associated with the first index node according to the trigger sequence, and determining a connection position of the first index node and the second index node according to the trigger sequence and the first coordinate data; the first index node and the second index node are connected according to the connection position to obtain an index map, and the index map corresponds to a plurality of index data tables; and based on the first coordinate data and the second coordinate data of the connection position, performing updating processing on the index data table corresponding to the first coordinate data and the index data table corresponding to the second coordinate data. By adopting the embodiment of the invention, the data volume for storing the index atlas can be reduced.

The invention belongs to the technical field of oil field automation, and particularly relates to a model training method, a fault processing method, equipment and a storage medium. Determining an initial sample data set according to an original oil pumping unit indicator diagram, generating a second sample indicator diagram of a target category by using an image generation model according to a first sample indicator diagram corresponding to the target category in the initial sample data set for the target category with insufficient data volume, and performing data enhancement on the first sample indicator diagram corresponding to the target category, and screening a first target sample indicator diagram based on the second sample indicator diagram and the third sample indicator diagram, supplementing the first target sample indicator diagram into the initial sample data set to obtain a sample data set, and then training the first initial model by using the sample data set to obtain a first indicator diagram classification model. By means of the mode, the problem of sample imbalance is solved, and the classification model obtained through training is closer to the spectrum.

This invention relates to the field of computer data processing and data visualization technology, specifically a personalized report configuration platform system, comprising: a data virtualization module: establishing physical data source mapping and constructing atomic data pools; an indicator graph construction module: generating atomic indicator dependency graphs, storing logical operation relationships rather than calculation results; a context-aware module: capturing environmental parameters such as tenant roles and time windows; a dynamic compilation engine: injecting environmental parameters into the graph and instantiating and parsing logical nodes to generate structured query instructions; and a rendering and compositing module: executing instructions to obtain result data and mapping it to view components. This invention achieves dynamic instantiation of logic defined once in multiple places, avoiding hard-coding development and improving the maintainability and response speed of the system.

The invention relates to a structure-semantic double-graph collaborative SAR image quality evaluation method, which comprises the following steps of separating an SAR image, and constructing a double-channel quality measurement set for independent modeling; indexes are obtained, correlation calculation is carried out, an index map is constructed, and each node corresponds to one index; on the basis of the index map, the dependency relationship between indexes is calculated from the structure dimension and the semantic dimension through self-supervised learning, the dependency relationship of the two dimensions is integrated, and an image level score based on the integration result is output; and constructing a measurement index shared with system analysis and a measurement index special for information retrieval, and carrying out quality measurement evaluation: after extracting a structure sub-graph, integrating an IR special index which is not contained in SAR prior into a global graph. The method is superior to a reference model in the aspects of recognition accuracy, recall rate, precision and efficiency, and a solution with high universality and good interpretability is provided for cross-modal image quality evaluation.

The invention provides a real-time classification method and system for working condition data, a storage medium and a computer. The method comprises the following steps: preprocessing sampling data obtained by continuously sampling an oil well to obtain an operation cycle of the oil well; the sampling frequency is adjusted according to the operation cycle of the oil well, secondary sampling is conducted on the oil well through the adjusted sampling frequency, and obtained secondary sampling data is compressed; performing indicator diagram classification coding based on the past indicator diagram classification data to obtain a coding table, and training and simulating a neural network by using the past indicator diagram classification data to obtain a neural network model; and carrying out data processing on the secondary sampling data by utilizing a neural network model so as to realize real-time classification of the working condition data of the oil well. The machine learning based on the neural network algorithm can be used for analyzing and identifying the wellhead working condition, data acquisition is quantized, a simplified neural network is constructed, and a foundation is laid for improvement of an oil extraction working condition identification technology.

The invention relates to the technical field of oil and gas exploitation, in particular to a rod-pumped well working condition diagnosis and identification method and device based on edge intelligence. At the edge end, a deep neural network inference engine MNN is constructed to train a rod-pumped well model, rod-pumped well production abnormal working condition data is collected, and an abnormal working condition indicator diagram data set is obtained after cleaning, noise processing and normalization; then, combining with a rod-pumped well production monitoring video stream feature sequence to form a rod-pumped well abnormal working condition and production monitoring video image combined data set, fusing with an abnormal working condition alarm information sequence obtained through language model preprocessing, and jointly reasoning time sequence data images, video stream data and texts; and an iterative multi-modal modulation neural network model is used as an optimal generalization model, and a prediction result is output. According to the method, the diagnosis real-time performance is improved, the diagnosis accuracy of the abnormal working condition of the rod-pumped well is improved, and meanwhile the production stability of the rod-pumped well is improved.

This invention relates to the field of intelligent fault diagnosis technology, and provides an intelligent fault diagnosis method for reciprocating compressors based on multi-feature fusion, comprising: acquiring dynamometer card data during the operation of the reciprocating compressor and preprocessing it; extracting multi-dimensional features with clear physical meaning from the dynamometer card, including energy, dynamic, geometric, loss, and proportional features; fusing the multi-dimensional features based on an adaptive weight allocation strategy to calculate a comprehensive fault score; combining feature combinations and weight configurations to match a fault mode library and identify the fault type; and determining the fault level based on the comprehensive fault score using fuzzy boundary processing and a dynamic threshold adaptive update mechanism. This method, by constructing a multi-dimensional feature space and integrating physical mechanisms and intelligent algorithms, achieves high-precision, multi-type, and refined fault diagnosis for reciprocating compressors, significantly improving diagnostic accuracy and reducing the risk of false alarms and missed alarms.

1. The name of the design product: graphical user interface for displaying outbound call indicators of electronic equipment. 2. The use of the design product: an electronic device. 3. The design points of the design product: in the graphical user interface. 4. The picture or photo that best indicates the design points: front view. 5. The use of the graphical user interface: for displaying outbound call indicators in hourly intervals. 6. The human-computer interaction mode of the graphical user interface: the main view interface is the initial interface, in the main view, the interface displays the latest time period hour report key indicators, management indicators, indicator trend charts, etc. When the user clicks "10:00" below "hourly report", the main view changes to the interface change state graph, in which the interface displays the currently selected time period hour report key indicators, management indicators, indicator trend charts, etc.

The application discloses a fracturing equipment state monitoring and fault diagnosis system and method, and belongs to the intelligent technology field of oil and gas field fracturing equipment. The application aims to solve the problems of single signal monitoring, lagging fault early warning and high misjudgment rate in the prior art. The method comprises the following steps: collecting multi-source operation data of a fracturing pump in real time; establishing a theoretical pressure indicator diagram, and comparing the theoretical pressure indicator diagram with an actual indicator diagram generated by real-time data to realize first-stage fault judgment; processing signals by using an improved wavelet threshold denoising method, and extracting time domain and frequency domain features; and inputting the processed feature data into a joint diagnosis model to realize second-stage fault judgment. The joint diagnosis model combines a principal component analysis (PCA) model for uncalibrated data anomaly detection and a BP neural network model for calibrated data fault classification. Through deep fusion of a mechanism model and a data-driven model, the application effectively resists environmental interference, and significantly improves diagnosis accuracy and operation and maintenance efficiency.

This application relates to an operator performance optimization method, apparatus, computer device, computer-readable storage medium, and computer program product. The method includes: acquiring the original operator code; executing the original operator code and acquiring performance indicator text data output during the execution of the original operator code; generating a graphical performance analysis view corresponding to the original operator code based on the performance indicator text data; acquiring images of the graphical performance analysis view based on performance bottleneck indicators; fusing and analyzing the performance indicator text data and performance indicator image data using a pre-set visual language model to obtain performance bottleneck diagnostic data for the original operator code; analyzing the performance bottleneck diagnostic data using multiple pre-set expert agents; and optimizing the original operator code based on code optimization suggestions. This method can improve the efficiency and effectiveness of code optimization.

The application discloses a kind of fault detection method, device, computer equipment and computer readable storage medium, applied to reliability field, the method comprises: obtaining the structural parameter of compressor, process parameter and pressure change, will be according to structural parameter and process parameter, generate first pressure-gas volume diagram;According to structural parameter and pressure change, generate second pressure-gas volume diagram;Then, first and second pressure-gas volume diagram are superimposed, obtain third pressure-gas volume diagram;Then, third pressure-gas volume diagram is carried out logarithmic coordinate transformation and natural coordinate transformation, obtain fourth pressure-gas volume diagram;Finally, the fault prediction result of fourth pressure-gas volume diagram is obtained using the preset convolutional neural network model.Therefore, the difference between the indicator diagram under the theoretical condition and the indicator diagram under the actual condition can be obviously reflected, so that the model can accurately predict whether the compressor is faulty and the corresponding fault category.

This invention discloses a multi-dimensional indicator calculation method based on a graph database, comprising: defining an indicator graph storage model based on the graph database; registering parameterized calculation tasks corresponding to the indicators in the built-in computing framework of the graph database; when a user query request arrives, submitting the query transaction through a long connection channel established by the graph database driver, and the computing framework directly accessing local storage data to complete real-time calculation and return the results; when the original data in the graph database changes, the database trigger captures the change event and launches the calculation task in the same process; the user query trigger and the data update trigger share the same set of calculation tasks and graph traversal logic, realizing real-time multi-dimensional indicator calculation and updating with integrated storage and computation. The multi-dimensional indicator calculation method based on a graph database provided in this application only relies on the graph database and its own computing framework, simplifying the data processing chain and improving the efficiency of indicator output.

The application discloses a gas well plunger gas lift drainage gas indicator diagram drawing method and application thereof, wherein the method comprises the following steps: drawing a first straight line segment according to the relationship between pressure and displacement when the plunger moves downward in the gas column; drawing a second straight line segment according to the relationship between pressure and displacement when the plunger falls in the liquid column; drawing a third straight line segment according to the jump drop value of the pressure on the plunger when the well is opened; drawing a fourth straight line segment according to the relationship between pressure and displacement when the plunger discharges the gas column in the upward process; drawing a fifth straight line segment according to the relationship between pressure and displacement when the plunger discharges the liquid column in the upward process; and drawing a sixth straight line segment according to the pressure drop value of the plunger after reaching the wellhead. The indicator diagram drawn by the application effectively guides real-time analysis of the plunger gas lift process, and plays an important role in plunger operation condition diagnosis, prediction, drainage capacity measurement and system optimization.

The present application belongs to the technical field of fault detection of beam pumping unit, and particularly relates to a beam pumping unit crank pin bearing fault diagnosis method. The method comprises: obtaining the horse head displacement curve and the indicator diagram of the beam pumping unit, and determining the crank pin bearing fault of the pumping unit when the horse head displacement curve and the indicator diagram in a certain stroke satisfy any of the following conditions: burrs appear in the corresponding points of the first half of the horse head displacement curve in the horse head upward process, and the loading line in the indicator diagram is twisted; burrs appear in the corresponding points of the first half of the horse head displacement curve in the downward process of the horse head, and the unloading line in the indicator diagram is twisted; burrs appear in the corresponding points of the first half in the upward process of the horse head and in the corresponding points of the first half in the downward process of the horse head, and the loading line and the unloading line in the indicator diagram are twisted. The method does not need to be inspected by a person on the site, can simply, quickly and timely find the crank pin bearing fault of the pumping unit, and ensures the normal operation of the pumping unit.

The present application relates to the technical field of oil well fault monitoring, and discloses an oil well indicator diagram real-time fault prediction method and system. The method comprises: obtaining an oil well sensor data stream, verifying data integrity through a sliding window buffer, applying a dynamic time warping algorithm to align multi-channel sensor data to generate a standardized data stream; extracting time domain features based on the data stream, comparing the features with a historical feature library after dimensionality reduction by principal component analysis to generate a feature difference index; triggering a multi-level threshold strategy according to the difference index, collecting an incremental training data set, fine-tuning the model using an elastic weight maintenance algorithm and generating a hot switching ready model; loading the model and generating a fault probability value through GPU accelerated inference, generating a timestamped early warning event; finally, analyzing the early warning protocol message edge transmission, and dynamically optimizing system resources based on logs. The present application effectively overcomes the delay problem under high-frequency data flow, significantly improving the accuracy of fault prediction and the system response speed.

The application provides a cloud edge joint control system for intelligent variable-speed driving of a pumping unit and a control method thereof, and belongs to the technical field of pumping unit monitoring. According to the indicator diagram obtained through electrical parameter inversion, the application performs working condition diagnosis, automatically judges the continuous pumping mode and the intermittent pumping mode according to the working condition diagnosis result and the environmental temperature, automatically adjusts the pumping frequency in the continuous pumping mode, automatically starts and stops in the intermittent pumping mode, realizes the balance between oil well supply and discharge with low energy consumption, and the whole working mode is completely self-adaptive to the change of the oil well working condition, thereby improving the oil well management efficiency and reducing the production energy consumption.

The invention discloses a controllable rod-pumped well analysis method and device, a medium and equipment, and the method comprises the steps: segmenting an indicator diagram into a plurality of slices according to displacement based on a slice indicator diagram extraction method, and extracting the features of each slice; generating a decision tree model based on a model distillation method of random forest gradual coarsening; in the random forest distillation process, a sample supplementing method is adopted to reduce the variance in the model thickening process; and performing model training based on a strong control rule. According to the method, the indicator diagram is segmented into the slices, and the features of each slice are extracted, so that expert experience description is facilitated, and a large number of specialists do not need to label samples; a decision tree model is provided for expert rule summarization through a model distillation method of gradually coarsening a random forest, so that low expert knowledge quality during cold start is avoided; model training is carried out through a strong control rule, expert rules are further refined and improved, and controllability and interpretability of expert knowledge can be effectively achieved.

The sucker-rod pump underground health diagnosis disc comprises a main disc and an auxiliary disc which are coaxially and rotationally connected, the first main disc face of the main disc is provided with an indicator diagram area, an indicator diagram classification area and a first analysis numbering area, and the second main disc face of the main disc is provided with a first indicator diagram analysis area, a second analysis numbering area and a first measure numbering area. The first indicator diagram analysis area, the second analysis numbering area and the first measure numbering area are all annular areas and are sequentially and correspondingly arranged from outside to inside, and a second indicator diagram analysis area, a third analysis numbering area, a second measure numbering area, a measure area and a first transparent area are arranged on the first auxiliary disc face of the auxiliary disc. The sum of the number type of the third analysis number area and the number type of the second analysis number area is equal to the number type of the first analysis number area. According to the method and the system, technicians can quickly diagnose the rod-pumped well, accurately identify potential faults and take correct and effective measures in time, so that continuous development of the faults is avoided, and the possibility of accidents is reduced.

The application discloses a comprehensive working condition diagnosis method based on electric parameter and dynamometer card information fusion, and based on dictionary coding of current state of an oil pumping well motor as "0: stable, 1: rising, 2: falling", the working condition is classified as follows: A class which can use electric parameter data to quickly diagnose specific working condition; B class and C class which can use electric parameter data to quickly diagnose specific working condition category but cannot determine specific working condition, B class such as down pump (B1) and insufficient liquid supply (B2) working condition with current coding all as "0-02"; C class such as fixed valve leakage (C1) and pipe leakage (C2) working condition with current coding all as "2-0"; and D class which cannot use current change to represent working condition. The algorithm fully gives play to the advantages of electric parameter identification and dynamometer card identification method, has higher accuracy than traditional electric parameter identification method, and has better identification accuracy and speed than traditional dynamometer card identification method.

The invention discloses a mechanical production well working fluid level prediction method. The method comprises the following steps that oil well static data, working fluid level measurement data and oil well real-time collection data are obtained; the oil well static data, the working fluid level measurement data and the oil well real-time collection data are preprocessed; electric parameter characteristic parameters and indicator diagram characteristic parameters are obtained through calculation based on the preprocessed oil well static data and the preprocessed oil well real-time collected data; performing dimensionless processing on the electrical parameter characteristic parameters and the indicator diagram characteristic parameters to obtain dimensionless characteristic parameters; calculating a Pearson's correlation coefficient and a Spearman correlation coefficient of each dimensionless characteristic parameter, and screening through the Pearson's correlation coefficient and the Spearman correlation coefficient to obtain a working fluid level correlation characteristic parameter; and processing the working fluid level related characteristic parameters through the working fluid level online prediction model to obtain a working fluid level prediction value of the mechanical production well. According to the method for predicting the working fluid level of the mechanical production well, the working fluid level prediction precision is improved, and subtle changes of the production process can be sensed in an all-weather, all-parameter and whole-process mode.

1. Name of this design product: Graphical user interface for large-scale information comparison for electronic devices. 2. Purpose of this design product: for use in an electronic device. 3. The key design point of this design product lies in the graphical user interface. 4. The picture or photo that best illustrates the key points of the design: Design 1 interface change state diagram 3. 5. The hardware design part is conventional, omitting the rear view, left view, right view, top view and bottom view. 6. Designate Design 1 as the base design. 7. Uses of Graphical User Interface: Graphical User Interface is used by the user to compare large amounts of information. The main view of Design 1 is the data information of the single customer group insight report; click "Compare Insights" in the main view of Design 1 to obtain Design 1 interface change status diagram 1; after selecting the customer group to be compared in Design 1 interface change status diagram 1, click "Go to Compare" to obtain Design 1 interface change status diagram 2; after filling in the comparative insight form in Design 1 interface change status diagram 2, click "OK" to obtain Design 1 interface change status diagram 3; in Design 1 interface change status diagram 3, scroll horizontally to compare the same indicators of different customer groups to obtain Design 1 interface change status diagram 4. When the information scrolls horizontally, the title name of each indicator is fixed, and the chart information of each indicator participates in the scrolling. The interaction method and jump relationship from the main view of Design 2 to the interface change state diagram 3 of Design 2 are the same as the interaction method and jump relationship from the main view of Design 1 to the interface change state diagram 3 of Design 1; by scrolling information vertically in the interface change state diagram 3 of Design 2, the interface change state diagram 4 of Design 2 is obtained. When the information scrolls vertically, the customer group identification information is fixed at the top, and the indicator information of each customer group scrolls vertically. The interaction method and jump relationship from the Design 3 main view to the Design 3 interface change state diagram 3 are the same as the interaction method and jump relationship from the Design 1 main view to the Design 1 interface change state diagram 3; click the "Edit" button or "Add Insight" in the insight view in the Design 3 interface change state diagram 3 to edit the insight view information and obtain the Design 3 interface change state diagram 4.

The invention relates to the technical field of intelligent fault diagnosis, and provides a reciprocating compressor indicator diagram intelligent fault diagnosis method based on multi-feature fusion, and the method comprises the steps: obtaining indicator diagram data in the operation process of a reciprocating compressor, and carrying out the preprocessing; extracting multi-dimensional features with clear physical significance from the indicator diagram, wherein the multi-dimensional features comprise energy, dynamic, geometric, loss and proportion features; fusing the multi-dimensional features based on an adaptive weight distribution strategy, and calculating a fault comprehensive score; combining feature combination and weight configuration, matching a fault mode library, and identifying a fault type; and based on the fault comprehensive score, a fuzzy boundary processing and dynamic threshold adaptive updating mechanism is adopted to determine the fault level. According to the method, by constructing the multi-dimensional feature space and fusing a physical mechanism and an intelligent algorithm, high-precision, multi-type and refined diagnosis of faults of the reciprocating compressor is achieved, the diagnosis accuracy is remarkably improved, and the misinformation and missing report risks are reduced.

The invention provides a small sample class increment non-analytic indicator diagram fault diagnosis method based on serialization and comparative learning. The method comprises the following steps of non-analytic indicator diagram serialization; comparative learning pre-training; carrying out incremental learning on small sample classes; performing fault diagnosis; the invention further provides a small sample class increment non-analytic indicator diagram fault diagnosis system based on serialization and comparative learning, wherein the method is applied to the small sample class increment non-analytic indicator diagram fault diagnosis system. According to the small sample class increment non-analytic indicator diagram fault diagnosis system based on serialization and comparative learning and the diagnosis method thereof, the information density of the non-analytic indicator diagram is improved through a serialization method, and efficient and stable dynamic fault diagnosis is achieved in combination with comparative learning pre-training, a lightweight adapter, sample replay and an NME classifier.

The application discloses a kind of self-adaptive control methods of oil production machine non-stop interval pumping, the method uses deep learning model to identify the indicator diagram of oil production machine, and marks the well of insufficient liquid supply and waxing.For the marked well, the liquid supply degree is calculated and compared with the threshold value to determine whether non-stop interval pumping control is needed.Time series indicator diagram is used to train neural network, predict the liquid supply degree change rate, and adaptively modify the non-stop interval pumping operation time to avoid the problems of long stop time of traditional interval pumping, easy waxing, pump jamming and safety hazards, etc., so as to establish a more reasonable oil production machine interval pumping work system to achieve the purpose of energy saving and efficiency increasing.