87 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 belongs to the technical field of rod-pumped well extraction, and particularly provides a rod-pumped well intermittent liquid outlet indicator diagram discrimination method, which comprises the following steps: utilizing a ground indicator diagram acquired in real time to obtain the maximum and minimum load mean values of the ground indicator diagram through a geometrical characteristic method and a standard error method; then the maximum load mean value standard deviation and the minimum load mean value standard deviation of all the sample ground indicator diagrams are obtained respectively, the intermittent liquid outlet well is recognized according to a specific threshold value, continuous and automatic judgment of intermittent liquid outlet of the rod-pumped well is achieved, the oil well intermittent opening intelligent level is effectively improved, and the oil well intermittent opening efficiency is improved. The problems that the workload is large, the efficiency is low, the development requirement of intelligent intermittent pumping of an oil field cannot be met, and the method for determining the intermittent liquid outlet condition of the oil well is high in cost, high in failure rate and incapable of being effectively applied in the existing manual statistical analysis for determining the liquid outlet condition of the oil well are solved. The method meets the development requirement of intelligent intermittent pumping of an oil field, is high in judgment accuracy, reduces the workload, is high in efficiency, is low in cost, and is effectively applied to actual production.

The invention discloses a fault comprehensive diagnosis method and system for an intermittent pumping unit, and the method comprises the steps: dividing a preset monitoring time period through a predetermined intermittent operation cycle of the pumping unit, so as to determine an available electrical parameter and an oil pumping speed parameter; and then conversion calculation is carried out on the reserved electrical parameters and the oil pumping speed parameters to obtain corrected load data and corrected displacement data with higher accuracy, and on this basis, calculation is finally carried out based on a second effective indicator diagram to determine the fault type of the oil pumping unit in the preset monitoring time period, that is, in the whole fault comprehensive diagnosis process, the fault type of the oil pumping unit in the preset monitoring time period is determined. A special load sensor for indicator diagram detection is not needed, the indicator diagram with higher precision is obtained in a parameter conversion mode, the recognition effect is better, the cost is reduced, and the actual influence of the intermittent operation period of the pumping unit on the preset monitoring time period is considered. Good fault diagnosis can be carried out under any condition, and the accuracy of fault identification of the oil pumping unit can be 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 invention discloses a programmable oil well mouth controller based on a 1DCNN indicator diagram recognition model and a control method thereof, and the controller comprises a processor, a data and program memory, a USB interface, a data communication interface, an RS485 bus communication interface, a real-time clock, a ZIGBEE wireless interface, an analog quantity signal input circuit, and a switching value signal input and output circuit. A sound card interface circuit and an audio power amplifier circuit; the control method comprises the steps that load data and displacement data of a wellhead of an oil well are collected in real time according to the operation cycle of the beam-pumping unit, normalization processing is conducted on the data, then interpolation processing is conducted to expand each kind of data to the standard length so as to meet the model requirement, the data are input into a trained 1DCNN network for operation, and then the data are obtained. And outputting the oil well operation condition. The system is high in reliability and convenient to expand; remote data acquisition and control can be realized, and personnel safety can be satisfied.

The invention discloses a multi-source semi-supervised incremental working condition identification method and system for a rod-pumped well, and relates to the technical field of working condition identification of rod-pumped wells. The method is characterized by comprising the following steps: step 1, respectively storing a sample library of an actually measured ground indicator diagram and an actually measured electric indicator diagram; 2, respectively constructing a graph neural network teacher model for two data sources, namely an actually measured ground indicator diagram and an actually measured electric indicator diagram; 3, dynamically fusing the prediction probability of each teacher model; 4, multi-source data distillation learning is carried out; and 5, performing semi-supervised working condition identification by using a label propagation algorithm improved by a logistic regression classifier. According to the multi-source semi-supervised class increment working condition identification method and system for the rod-pumped well, a multi-source fusion technology based on an attention mechanism, class increment learning and semi-supervised learning are simultaneously applied to class increment working condition identification of the rod-pumped well, and a small number of multi-source marking working condition samples are fully utilized; and more efficient, robust and practical rod-pumped well working condition identification is realized by combining a large number of multi-source unknown working condition samples.

The invention discloses a compressor fault judgment method and device, and the method comprises the steps: obtaining the operation parameters of a compressor at a plurality of time points in a preset time period, and obtaining a pressure-time corresponding relation, obtaining a division relationship of a plurality of work cycles in a predetermined time period; according to the pressure-time corresponding relation and the division relation of the multiple work periods, the actual indicator diagram of each work period of the compressor in the preset time period is determined; the actual indicator diagram of each work period is compared with a reference indicator diagram obtained in advance, the state of the compressor is judged according to the comparison result, the state comprises a normal type or a fault type, and the actual indicator diagram of the compressor can be obtained through compressor data obtained in real time; and the real-time working condition of compression is judged by comparing the real-time indicator diagram with the reference indicator diagram, so that the production loss caused by the bumping of the compressor is avoided, the operation cycle of the compressor is prolonged, and the maintenance cost of the compressor is reduced.

The invention discloses an oil pumping unit fault diagnosis method based on multi-modal fusion under a heavy oil working condition, and the method comprises the following steps: collecting operation parameter data of an oil pumping unit, including an indicator diagram, an electric indicator diagram, and structured data such as stroke, stroke frequency, water content, working fluid level and the like, and carrying out the data cleaning; a fusion strategy based on multi-modal interaction is designed, and key features are extracted and subjected to weighted fusion through a cross-modal attention distribution matrix; a dynamic collaborative weight distribution mechanism is introduced, feature integration and complementarity of multi-source data are optimized, and the model constructed by the invention can accurately diagnose the fault type of the oil pumping unit under a complex working condition, improve the diagnosis efficiency and reduce manual intervention. The method has high practical value and economic benefit, and is especially suitable for equipment fault diagnosis scenes under high challenging working conditions such as heavy oil recovery.

1. Name of the design product: Cockpit chart graphical user interface for electronic equipment. 2. Purpose of this design product: This design product is used for an electronic device. 3. The design highlights of this appearance design product lie in the interface content of the graphical user interface. 4. The picture or photo that best illustrates the key points of the design: Main view of Design 1. 5. The contents of other views are conventional designs, omitting the rear view, left view, right view, top view and bottom view of Design 1 and Design 2. 6. Designate Design 1 as the base design. 7. Purpose of the graphical user interface: This graphical user interface is an interactive interface for displaying data of the cockpit retracement trend indicator chart. 8. Human-computer interaction mode of graphical user interface: The main views of Design 1 and Design 2 are the interfaces of the retracement trend indicator chart. Move the mouse up to the chart of the main view of Design 1 to display the interface change status diagram 1 of Design 1. Then click the trend line of the chart to change it to the interface change status diagram 2 of Design 1. Click the fifth small icon in the upper right corner of the chart box of the main view of Design 2 to display the interface change status diagram of Design 2.

The invention relates to the technical field of artificial intelligence, and particularly provides an indicator diagram automatic data enhancement method and device, and the method comprises the following steps: S1, alternately updating the parameters of a target model and an enhancement model based on an automatic enhancement method of teacher knowledge, the enhancement method comprises a color amplification model and a geometric amplification model, the target model is set as a teacher model, and the teacher model is any one model and only needs to be different from the target model; s2, setting a search algorithm and a search space, and updating the structure of the search space by using a color amplification model and a geometric amplification model; s3, generating a semantic auxiliary field; s4, performing image and semantic multi-modal fusion; and S5, preparing a data set. Compared with the prior art, the working condition diagnosis accuracy can be improved while the sample generation efficiency is improved, and the enterprise cost is reduced.

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.

Systems and methods provided herein relate to pump systems. The pump system includes a pump disposed within a well, an actuator operable to move a rod including a ground end coupled to the actuator and a downhole end coupled to the pump, and a controller. The controller is configured to identify a first impulse response and a second impulse response associated with the pump system based on a first model of the pump system. The controller is further configured to generate a second model of the pump system based on the first impulse response and the second impulse response. The controller is further configured to operate the pump system based on a second model.

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.

The invention provides a working fluid level energy calculation method based on an oil well indicator diagram, which comprises the following steps: collecting dynamic data such as polish rod load, displacement, time sequence and motor power of the oil well indicator diagram, and preprocessing the data to generate standardized indicator diagram data; calculating a real-time energy accumulation value of the pump column through a dynamic energy accumulation algorithm; dividing an effective acting period and a non-effective period by adopting a period energy division algorithm; extracting energy characteristic parameters in the effective acting period, wherein the energy characteristic parameters comprise the maximum energy gradient, the effective period duration and the energy fluctuation amplitude; and inputting the energy characteristic parameters into a nonlinear liquid level mapping model, and outputting a working fluid level depth calculation result in combination with a pump efficiency attenuation coefficient. The working fluid level depth can be accurately calculated, and the oil well production efficiency and the management level are improved.

The invention discloses a digital pumping unit monitoring system, which relates to the technical field of pumping units and is characterized in that the initial position of a suspension center is processed through a suspension center initialization unit; the suspension point position is monitored through a suspension point position monitoring unit, and a monitoring result is obtained; then, the indicator diagram unit constructs an indicator diagram of a suspension point, and data cleaning and correction are carried out on the constructed indicator diagram based on a monitoring result; utilizing a feature extraction unit to perform feature extraction and data annotation on the corrected indicator diagram to obtain an annotation data set; based on a data processing unit, processing the vibration frequency data under different fault types to obtain a processing result; and finally, the fault monitoring unit constructs a fault monitoring model, jointly trains the fault monitoring model by using the labeled data set and the processing result, and monitors the oil pumping unit in real time based on the trained fault monitoring model. According to the invention, the efficiency and accuracy of the existing pumping unit monitoring system are improved.

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 discloses a convolution bidirectional time sequence indicator diagram prediction method based on specific data driving, and belongs to the technical field of oil extraction indicator diagram prediction, and the method comprises the following steps: 1, obtaining indicator diagram data, and recording a dynamic corresponding relation between a polished rod load and a suspension center displacement through an indicator diagram; 2, performing data cleaning, data alignment and data normalization on the indicator diagram data, wherein the data obtained after normalization processing is indicator diagram load specific data; step 3, constructing a time sequence sample set based on indicator diagram load specific data; 4, constructing and training a convolution bidirectional time sequence model; and step 5, applying the trained convolution bidirectional time sequence model to prediction of an oil well field oil pumping unit indicator diagram. The method has the capability of accurately predicting various working conditions and even complex combined working conditions, strives for more time for pump repair after pump inspection, and has the characteristics of high calculation speed, low economic cost and high characterization precision.

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.

The invention relates to the technical field of working condition diagnosis, and discloses a rod-pumped well indicator diagram working condition diagnosis method based on geometrical characteristics, which comprises the following steps: S01, constructing a database; step S02, collecting basic data; step S03, acquiring target data; step S04, on the basis of the target data in the step S03, obtaining indicator diagram working condition types corresponding to the successfully matched feature vectors in the database; s05, based on the target data in the step S03, obtaining the working condition type of the classification corresponding to the feature vector; step S06, combining the working condition types in the step S04 and the step S05 to obtain a final working condition diagnosis result; through the step S03, a weight prediction model is adopted to allocate the weights of the feature vectors, so that the importance of different geometric features is reflected, the geometric features are better analyzed, and a foundation is laid for subsequently matching quantifiable feature vectors corresponding to the geometric features.

The invention relates to the technical field of oil exploitation equipment, in particular to an implementation method for constructing an electrical indicator diagram based on electrical parameter data of a pumping unit. The technical problems are as follows: an actual measurement indicator diagram device in the prior art is under the action of periodic alternating load for a long time and is easy to break down; according to the technical scheme, the implementation method for constructing the electrical indicator diagram based on the electrical parameter data of the oil pumping unit comprises the steps that collected power and angular displacement data are substituted into a fitted torque curve formula for calculation, and an indicator diagram is obtained; compared with an actual measurement indicator diagram device in the prior art, which is under the action of periodic alternating load for a long time and is easy to generate faults, the suspension point indicator diagram is indirectly calculated by measuring the electrical parameters of the pumping unit motor and walking beam rotation angle data acquired by the attitude instrument, direct use of the actual measurement device is avoided, and the cost is reduced. Therefore, the efficiency and the accuracy of obtaining the indicator diagram are obviously improved; as the measurement of the electrical parameters and the rotation angle data is relatively stable and reliable, the repeatability of the indicator diagram is also improved.

The invention belongs to the technical field of oil field digitization and oil extraction processes, and particularly provides an oil well intermittent production time adjusting and optimizing method which comprises the following steps: establishing a fault diagnosis simplified model of a sucker-rod pump oil extraction system; solving a recursive format of the indicator diagram of each section of the sucker rod string; calculating to obtain an oil well pump indicator diagram; calculating to obtain a pump fullness degree; and calculating the standard deviation of the pump fullness degree, and adjusting the interval according to the standard deviation of the pump fullness degree. The problems that due to the fact that existing initial intermittent production time adjustment is affected by production factors, intermittent production time adjustment is unreasonable, the oil well productivity cannot be improved, and the energy-saving requirement cannot be met are solved. According to the method, the oil well is always produced under the reasonable intermittent production time adjustment, the development effect is improved, and the energy-saving production requirement is met.

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.

The invention discloses a thin oil abnormal well recognition method and device based on development dynamic data and a readable storage medium. The thin oil abnormal well recognition method comprises the following steps of data collection, wherein production data of a thin oil well, well bore pressure data of the thin oil well and construction data of the thin oil well are collected; the production data comprises automatic indicator diagram data and daily fluid production capacity; the daily fluid production capacity of the thin oil well is pretreated; preprocessing the automatic indicator diagram data of the thin oil well; comparing the preset standard threshold value of the daily liquid production with the fluctuation value of the daily liquid production, and comparing the preset standard threshold value of the automatic indicator diagram with the fluctuation value of the load difference of the current day and the load difference of the last month; and comparing a preset well bore pressure standard threshold value with a pressure value yesterday to obtain a difference value of the pressure value yesterday, and judging whether the well is an abnormal well or not. The abnormal oil well can be quickly and accurately identified, missed judgment and misjudgment are avoided, the identification efficiency is improved, meanwhile, the labor cost and the time cost are reduced, the production cost is reduced, and the economic benefits are improved.

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.