72 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 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.

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.

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.

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.

The present invention discloses a fusion inference recognition method for pumping working conditions based on the fusion similarity of indicator diagrams, which includes the following processes: (1) Obtaining, annotating and classifying, and training the indicator diagrams. (2) Extracting features from each graph in the sample set using the trained Resnet50 and saving the feature vectors; (3) Obtaining the load and displacement data collected by the dynamometer of the pumping well to be detected and drawing the indicator diagram; (4) Making a fusion inference judgment on the pumping working conditions based on the fusion similarity of the indicator diagrams; (5) Sending the working condition judgment result to the upper computer for display. A fusion similarity calculation method is proposed. The similarities obtained based on the Pearson correlation coefficient algorithm and the local hashing algorithm are fused according to certain steps, giving full play to the advantages of both to obtain a more reasonable similarity between the graph to be measured and the sample graph, and ultimately effectively improving the recognition rate.

The invention discloses a common parameter neural network for analyzing the electrical parameter characteristics of a pumping unit, and the common parameter neural network consists of an input layer, four convolution layers, a leveling operation layer, five full connection layers and an output layer, the input layer is used for receiving the production parameters of the input active power, the rod length, the rod diameter, the pump diameter and the model, and the output layer is used for outputting the production parameters of the input active power, the rod length, the rod diameter, the pump diameter and the model; according to the method, a common parameter neural network composed of pumping unit motor power, sucker rod weight and oil well pump diameter can be utilized to more truly fit factors influencing a calculation result, the factors are substituted into the calculation process of data such as an indicator diagram, a working fluid level and liquid production capacity, important pumping unit production data such as the indicator diagram and working conditions are converted in real time, and the calculation result is more accurate. And main production data such as the liquid production capacity and the working fluid level are calculated, important oil pumping unit production data such as indicator diagrams and working conditions are converted in real time through the electric parameters of the oil pumping unit well, the main production data such as the liquid production capacity and the working fluid level are calculated, and the purpose of simplifying the daily collection data type and data size of the oil pumping unit well is achieved.

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 invention provides an intelligent oil well paraffin precipitation prediction method based on LSTM. The method comprises the steps that 1, an oil well time sequence data sequence used for training a model is acquired; 2, performing data preprocessing operation to obtain a training data set; 3, carrying out feature selection on the preprocessed data, and selecting features which have great influence on the area of an oil well indicator diagram; 4, constructing and optimizing an oil well paraffin precipitation prediction model based on the LSTM network; and step 5, performing prediction by using the optimized LSTM model, and performing anti-normalization operation on the predicted data to obtain predicted real data. The intelligent oil well paraffin precipitation prediction method based on the LSTM overcomes the problem that correlation between oil well paraffin precipitation and many features and the time sequence of the oil well paraffin precipitation are not considered in a traditional method, meanwhile, the LSTM model is optimized through the grey wolf algorithm, the oil well paraffin precipitation prediction method is high in prediction precision and robustness, and the method is suitable for being used for oil well paraffin precipitation prediction. The defects in the field in the prior art are overcome.