Natural gas production and operation strategy recommendation method and system

Abstract

The invention relates to the field of natural gas production and the technical field of new-generation information, in particular to a natural gas production and operation strategy recommendation method and system. According to the method, natural gas production equipment, market price fluctuation and weather data are collected and preprocessed in real time, data cleaning, noise reduction and feature extraction are carried out, and feature vectors used for model training are generated. The system adopts an online learning algorithm to carry out incremental training and self-adaptive adjustment on the model, an optimized production and operation strategy is generated, the strategy execution effect is monitored in real time through a closed-loop feedback mechanism, the model is adjusted based on feedback, and continuous optimization of the strategy is ensured. The natural gas production efficiency and the market response capability are effectively improved.

Description

Technical Field

[0001] The present invention relates to the fields of natural gas production, operation and management and new generation information technology, and in particular to a natural gas production and operation strategy recommendation method and system. Background Art

[0002] In the process of natural gas production and operation, traditional decision-making methods often rely on experience and static data, which makes it difficult to effectively respond to the real-time status changes of production equipment, market price fluctuations, and the impact of external factors such as weather. This decision-making method based on static and offline data has many disadvantages, such as delayed decision-making, low resource utilization, and insufficient responsiveness to market changes, which can easily lead to a decline in production efficiency and loss of economic benefits.

[0003] With the popularization of Internet of Things technology and the maturity of big data analysis methods, more and more real-time data can be collected and used, such as sensor data from production equipment, price fluctuation data from market trading platforms, and weather forecast data from meteorological services. These data provide new opportunities for optimizing natural gas production and operation. However, due to the multi-source, real-time and complexity of these data, how to effectively integrate them and transform them into specific production and operation strategies is still a technical problem that needs to be solved urgently.

[0004] Existing technical solutions have deficiencies in real-time data processing and the integration of multi-source data. They can usually only process a single type of data or rely on post-analysis, making it difficult to form a unified, dynamically optimized production and operation strategy. In addition, the lack of an effective feedback mechanism and adaptive adjustment function results in the system being unable to adjust and optimize strategies in a timely manner when faced with sudden situations such as drastic market fluctuations or extreme weather, affecting production continuity and economic benefits. Therefore, developing a system that can integrate multi-source data, generate and optimize production and operation strategies in real time is of great significance to improving the efficiency of natural gas production and operation and market responsiveness. Summary of the invention

[0005] The present invention provides a natural gas production and operation strategy recommendation method and system to solve the problem of how to integrate real-time data of natural gas production equipment, market price fluctuation information and weather forecast data to generate optimized production and operation strategies, thereby improving the efficiency of natural gas production and market responsiveness.

[0006] In order to solve the above problems, the present invention provides a natural gas production and operation strategy recommendation method and system, including: Real-time collection and preprocessing of multi-dimensional data from natural gas production equipment, market price fluctuation information and weather data, and data cleaning, noise reduction and feature extraction; Performing stream computing analysis based on the data and generating a combined feature vector for model training; Use online learning algorithms to incrementally train and adaptively adjust the model to generate optimized production and operation strategies; Through a closed-loop feedback mechanism, the effectiveness of strategy execution is monitored in real time, and the model is retrained and adjusted based on feedback to continuously optimize production and operation strategies.

[0007] Furthermore, the method further comprises: Real-time collection of sensor data from natural gas production equipment, including pressure, temperature, flow, etc.; Collect market price fluctuation information and weather data, and pre-process and time-synchronize the data.

[0008] Furthermore, the preprocessing in the method also includes: data cleaning, removing abnormal values ​​and missing values, and performing noise reduction on the sensor data through a filtering algorithm.

[0009] Furthermore, the method also includes: utilizing a stream computing framework to aggregate preprocessed data in real time and extract key features; performing complex event processing on multi-source data to identify abnormal events that affect production and operations.

[0010] Furthermore, the method also includes analyzing the sensor data and market data in different time windows through sliding window technology to generate feature sequences for subsequent models.

[0011] Furthermore, the method also includes: performing incremental training on the model based on the preprocessed multidimensional data, analyzing the sensitivity of the input features to the model output through the Jacobian matrix, and adjusting the model parameters.

[0012] Furthermore, during the model training process, a Hessian matrix is ​​used to adjust the model parameter update step size to increase the model convergence speed.

[0013] Furthermore, the method also includes: generating production and operation strategies based on real-time data, including production adjustment strategies, equipment maintenance plans and market transaction strategies.

[0014] Furthermore, the production regulation strategy is generated according to the changes in real-time pressure and flow data to optimize the natural gas production.

[0015] Furthermore, the equipment maintenance plan generates equipment maintenance or adjustment suggestions based on abnormal fluctuations in temperature and pressure data.

[0016] The key innovative features of the present invention include: (1) Real-time collection and intelligent scheduling of multi-dimensional data: The system can collect and process multi-dimensional data from production equipment, market and weather in real time to ensure the timeliness and accuracy of the data.

[0017] (2) Real-time adjustment of adaptive models: The system uses an online learning algorithm to perform incremental training and adaptive adjustments to the model based on real-time data changes to ensure the accuracy and robustness of the model.

[0018] (3) Closed-loop feedback mechanism: The system monitors and adjusts the effectiveness of strategy execution through a closed-loop feedback mechanism, continuously optimizes production and operation strategies, and improves the system's adaptability and decision-making accuracy.

[0019] The following are its main beneficial effects: (1) Improving the accuracy and timeliness of strategy generation: By collecting and processing multi-source data from production equipment, market, and weather in real time, the present invention can quickly respond to data changes and generate more accurate production and operation strategies, thus avoiding decision-making errors caused by data lag in traditional methods.

[0020] (2) Enhance the adaptive capability of the system: The present invention introduces an adaptive adjustment mechanism that can automatically adjust model parameters and strategy generation rules based on real-time feedback, thereby maintaining the effectiveness and economic benefits of the strategy in the face of sudden situations such as drastic market fluctuations or extreme weather.

[0021] (3) Improving resource utilization and production efficiency: By optimizing multiple links such as production regulation, equipment maintenance and market transactions, the present invention can maximize resource utilization, reduce unnecessary energy consumption and production costs, and improve overall production efficiency and market competitiveness.

[0022] (4) Providing a closed-loop optimization mechanism: The closed-loop feedback management mechanism in the present invention monitors the execution effect of the strategy in real time, automatically retrains the model and adjusts the strategy, ensures the continuous optimization of the production and operation strategy, adapts to the dynamically changing environment, and further improves the overall efficiency of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 It is a flow chart of a natural gas production and operation strategy recommendation method and system provided by an embodiment of the present invention.

[0024] Figure 2 The present invention is a system flow chart of a natural gas production and operation strategy recommendation method. DETAILED DESCRIPTION

[0025] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0026] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in the various ways described without departing from the spirit or scope of the present application. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0027] Reference Figure 1 , is a flow chart of a natural gas production and operation strategy recommendation method and system according to an embodiment of the present invention. The natural gas production and operation strategy recommendation method and system may at least include steps S100-S400: S100: perform data collection and preliminary processing near the data source, data cleaning, noise reduction and preliminary feature extraction.

[0028] S200: Perform further streaming processing and intelligent scheduling on the pre-processed data on the central server.

[0029] S300, combining the output of the central processing module, performs real-time training and adaptive adjustment of the model.

[0030] S400. Monitor and adjust the execution effect of the strategy through a closed-loop feedback mechanism.

[0031] A natural gas production and operation strategy recommendation method and system provided in an embodiment of the present invention are mainly used to solve the technical problem solved by the present invention, which is "how to integrate the real-time data of natural gas production equipment, market price fluctuation information and weather forecast data to generate optimized production and operation strategies, thereby improving the efficiency of natural gas production and market responsiveness."

[0032] The present invention discloses a method and system for recommending natural gas production and operation strategies. The method includes: collecting multidimensional data from natural gas production equipment, market and weather, and performing preprocessing and feature extraction; real-time processing and intelligent scheduling of data through stream computing; incremental training and adaptive adjustment of models using online learning algorithms; generating optimal production and operation strategies based on processed data, and continuously monitoring and optimizing strategy execution effects through a closed-loop feedback mechanism. The system can improve the efficiency and market responsiveness of natural gas production, and realize precise and intelligent strategy recommendations.

[0033] S100, data collection and preliminary processing, data cleaning, noise reduction and preliminary feature extraction are performed near the data source. Step S100 includes steps S110-S140: S110, collect data from multiple data sources in real time, including sensor data of production equipment, market price fluctuation information, weather forecast, etc. The system can handle multi-dimensional and multi-format data input. Specifically: At the natural gas production site, the sensors monitor the key parameters of the production equipment in real time. The sensors are installed on natural gas wellheads, pipelines, compression stations and other equipment, and are responsible for collecting core production data such as pressure, temperature, flow, gas composition, etc. The data is usually transmitted to the edge computing device at a high frequency. When the sensor captures physical changes in the production environment, the data is immediately recorded and transmitted to the edge computing node. The edge device receives the data through a wired or wireless network to ensure real-time and accuracy. Due to the high-dimensional characteristics of the data, the system needs to synchronize multi-dimensional data.

[0034] Furthermore, the price fluctuation information of the natural gas market is also one of the important input sources of the data acquisition module. The data comes from the market trading platform or third-party data provider, and is usually released at a higher frequency (such as updated every minute or every hour), reflecting the market supply and demand dynamics and price change trends. Market price data is obtained from a remote server through an API interface or a dedicated data service, and the data format may include JSON, XML or CSV files. The edge computing device receives the data in real time and parses out key indicators (such as spot prices, futures prices, trading volume, etc.), and synchronizes it with the collection of other data sources. Considering that market data may come from different time zones or have time differences, the system automatically standardizes the timestamp to ensure data consistency.

[0035] Furthermore, the weather conditions have a significant impact on natural gas production and consumption, especially extreme weather (such as blizzards, extreme high temperatures) will affect the operating status of production equipment and market demand. Therefore, the system also needs to collect weather forecast data in real time. Weather data usually comes from the Meteorological Bureau or commercial meteorological service providers. Edge computing devices obtain real-time weather information through API interfaces, including indicators such as temperature, humidity, wind speed, and precipitation. The frequency of weather data collection can be set to update every hour or more frequently according to demand, and the system synchronizes the data with production sensor data and market data. Due to the high spatial resolution of weather data (such as regional weather forecasts), the system will accurately match the geographical location of the natural gas production site when collecting.

[0036] Furthermore, the data acquisition module not only has to process data from different sources, but also needs to deal with the multi-dimensional and multi-format challenges of the data. In order to ensure that the data can be effectively used by subsequent modules, the system will unify the format, time synchronization and dimension integration of the data during the data acquisition process. After receiving various types of data, the system first performs format conversion to unify the data into an internally processable format (such as parsing XML and JSON data into structured tabular data). Next, the system synchronizes the data from different data sources to ensure that the timestamps of different data are consistent so that accurate time series comparisons can be performed in subsequent analysis. Finally, the system integrates multi-dimensional data (such as pressure and temperature data from sensors, market prices, and weather data) to generate input data with a high amount of information for use by real-time model training and adaptive adjustment modules.

[0037] S120: Clean the collected data to remove noise and outliers to ensure the accuracy and reliability of the data. Specifically: During the natural gas production process, the high-frequency data collected by the sensor may contain some invalid data points, such as missing data caused by sensor failure, abnormal spikes, or incomplete data records. Invalid data points will affect the accuracy of model training and decision-making, so they must be cleaned up first. The system performs a preliminary scan of the collected sensor data to identify abnormal values ​​or missing values. For example, the normal operating range of a pressure sensor is 0 to 100MPa. If the pressure value recorded at a certain moment suddenly exceeds this range, it is identified as an abnormal value. Similarly, if a data point contains only part of the necessary information (such as only a timestamp but no actual value), the system will mark it as an invalid data point and delete it. For market price data and weather data, the system will identify and delete blank records or duplicate records caused by data source failures or transmission errors to ensure that the data processed subsequently is complete and valid.

[0038] Furthermore, errors in sensors or data transmission processes may sometimes result in inaccurate data records. The system first identifies data points that may have errors, such as sudden changes in the value of a pressure sensor in a short period of time (such as an instantaneous drop from 50MPa to 0MPa) or obvious drift of a temperature sensor. For the abnormal fluctuations, the system will repair them through interpolation methods, such as replacing the abnormal value with the average of the two normal data points before and after. If the frequency of occurrence of erroneous data points is high, the system will also use historical data models or rule bases to repair them to ensure that the repaired data can accurately reflect the actual situation. For example, in market price data, if the fluctuation range of a certain price is significantly beyond the normal range, the system will use the price trends before and after the moment for regression analysis and correction.

[0039] Furthermore, in the harsh environment of natural gas production equipment, sensors may be affected by electromagnetic interference, mechanical vibration, etc., resulting in noise in the data. Although the noise signal does not represent the actual production status, it will interfere with data analysis and model prediction. Therefore, the system needs to filter the noise signal. The system uses filtering algorithms (such as Kalman filtering and low-pass filtering) to filter out high-frequency noise in sensor data. For example, in the pressure sensor data, if high-frequency fluctuations are detected and the fluctuations are inconsistent with the normal operation logic of the equipment, the system will smooth it through a filter. For short-term sharp fluctuations in market price data, if the fluctuations are identified as market noise (such as abnormal data caused by trading system errors or extreme market conditions), the system will filter the noise by setting thresholds and sliding averages. Similarly, if there are unreasonable mutations in weather data (such as temperature changes of dozens of degrees in an instant), the system will treat them as noise and filter them.

[0040] S130: Use signal processing technology to reduce noise in the data, eliminate random interference and noise in the data, and ensure the data quality for subsequent analysis and model training. Specifically: At the natural gas production site, the data collected by the sensor in real time (such as pressure, temperature, flow, etc.) may be interfered by environmental noise, such as electromagnetic interference, mechanical vibration, etc. The interference will introduce high-frequency noise into the data, affecting the smoothness and accuracy of the data. Filters (such as low-pass filters, high-pass filters or band-pass filters) are commonly used signal processing tools to reduce the high-frequency noise. After receiving the real-time sensor data, the system first applies a low-pass filter to eliminate the high-frequency noise. For example, for the data of the pressure sensor, if the system detects frequent high-frequency fluctuations in a short period of time, the fluctuations may be caused by environmental noise rather than actual physical phenomena. The system will smooth it through a low-pass filter and retain the low-frequency components in the data, that is, the part related to the actual pressure change. For different types of sensor data, the system will select appropriate filter parameters to ensure the optimization of the filtering effect, which can eliminate noise without losing key signals.

[0041] Furthermore, moving average is a common smoothing technique used to reduce random fluctuations in data and highlight long-term trends. For market price data and weather data, such data usually have a certain volatility, especially in a short period of time, there may be non-continuous and drastic changes (such as a jump or drop in market prices), and the changes may not represent actual trends, but short-term market noise or small fluctuations in the data collection process. The system applies a moving average algorithm to the data and averages the data within a certain time window to smooth out short-term fluctuations. For example, market price data is updated once a minute, and the system can select a suitable time window (such as 5 minutes or 10 minutes) and calculate the average price within this time window to filter out drastic fluctuations in the short term. At the same time, weather data can also be processed in a similar way to eliminate sudden changes on a small time scale to ensure that the data is more consistent and representative.

[0042] Furthermore, for complex multi-dimensional data (such as a data set that combines pressure, temperature, market prices, and weather information), noise reduction requires more than simple filtering and moving averages, and may require more advanced signal processing techniques such as wavelet denoising or signal reconstruction. The method can remove more complex noise patterns while retaining the main features of the data. For highly complex and high-dimensional data, the system uses wavelet denoising technology to decompose the signal into different frequency bands and then remove the high-frequency noise components. The system then recombines the main components of the data through signal reconstruction technology to generate a refined data set with minimized noise and maintaining the characteristics of the original signal. This process is particularly suitable for multivariate data such as wind speed and precipitation in weather forecast data, as well as market price forecast data that requires higher accuracy.

[0043] In some cases, the characteristics of the noise may change over time, such as fluctuations in sensor noise levels due to changes in environmental conditions. To this end, the system can use adaptive noise reduction technology to adjust the noise reduction parameters in real time to adapt to changes in noise characteristics in different time periods. The system dynamically adjusts the parameters of the filter or the time window of the moving average by monitoring the noise level of the data in real time. If the system detects an increase in noise levels (for example, a sudden increase in mechanical vibrations around the sensor), the adaptive noise reduction module automatically adjusts the filter cutoff frequency or the width of the moving average window to filter the noise more effectively. This adaptive mechanism ensures that the system maintains high data quality in the described environment and provides stable and reliable input for subsequent analysis and decision-making.

[0044] S140, extract key features from the original data, which will serve as an important data basis for subsequent model input. Specifically: During the natural gas production process, the raw data collected by sensors include various physical parameters such as pressure, temperature, flow, and gas composition. Some of the parameters directly reflect the operating status of the production equipment, while others may serve as auxiliary information. The feature extraction module must first identify the key indicators and extract them as the main features. The system receives multi-dimensional sensor data from equipment such as wellheads, pipelines, and compression stations in real time. First, the system automatically identifies core parameters such as pressure, temperature, and flow based on a pre-defined feature list. The parameters directly affect production efficiency and safety, and are therefore extracted first. At the same time, the system can also dynamically adjust the feature list, automatically increase or decrease feature extraction based on changes in real-time data (such as fluctuations in gas composition), and ensure that the extracted features are always closely related to the actual situation of the production environment.

[0045] The key features are the core data elements extracted from multi-source data that can significantly affect the optimization and decision-making process of natural gas production and operation strategies. These features reflect important variables of production equipment, market dynamics and external environment (such as weather) through data processing, feature extraction and combined analysis, and play a core role in model training and optimization decision-making. Specifically, key features include but are not limited to the following types: 1. Production equipment characteristics: Real-time operating data from natural gas production equipment, such as wellhead pressure, temperature, flow, etc. By monitoring and processing these sensor data, the system can extract core features that can characterize the operating status of production equipment, such as pressure change rate, correlation between temperature and pressure, etc. These features can reflect the load condition and potential failure risk of the equipment, and are key inputs for optimizing production strategies.

[0046] 2. Market volatility characteristics: The core features extracted by the system from market price fluctuation data include spot price volatility, futures price change trends, and trading volume fluctuations. These features are used to predict changes in market supply and demand, thereby generating production and trading strategies that adapt to market changes. Market volatility characteristics directly affect the formulation of production and marketing decisions.

[0047] 3. Weather risk characteristics: Weather data includes parameters such as temperature, humidity, precipitation, and wind speed. The system extracts key characteristics related to production and market demand from these data, such as extreme weather index and the risk probability of weather affecting equipment operation. These characteristics help the system adjust production before extreme weather arrives to ensure production safety and market supply and demand balance.

[0048] Key feature extraction process The extraction of key features is based on efficient processing and intelligent analysis of multi-source data. First, the data preprocessing module cleans and standardizes different data sources to ensure data quality and consistency. Then, the system extracts features and merges and analyzes multi-dimensional data. The specific steps include: 1. Data preprocessing: Unify the format, filter noise and process outliers of data from production, market and weather to ensure data availability.

[0049] 2. Feature extraction algorithm: Through machine learning and statistical analysis methods, the variables that have the greatest impact on production decisions are identified from the cleaned data. For example, for production equipment data, the system uses time series analysis to extract the working status characteristics of the equipment, such as the changing trend of equipment pressure.

[0050] 3. Feature fusion: Fusion analysis of features from different data sources to generate more representative combined features. For example, by combining market price fluctuation features with weather features, a comprehensive market demand forecasting indicator can be generated.

[0051] Furthermore, in addition to the directly extracted physical parameters, the feature extraction module also needs to calculate some derived features from the basic data, and the derived features can usually provide more valuable decision-making information. For example, the changing trend of pressure and temperature, the volatility of flow rate, the rate of change of gas composition, etc. After extracting the original data such as pressure and temperature, the system further calculates the changing trend of the parameters. For example, by calculating the average rate of change of pressure within a certain time window, the system can identify potential abnormalities in the production process (such as possible leaks or blockages). Similarly, the rate of change of temperature, the volatility of flow rate, etc. are also calculated and extracted. The derived features are generated by complex mathematical formulas and time series analysis techniques. The system will dynamically adjust the size of the calculation window and the parameters of the calculation formula according to different production stages and environmental conditions to ensure the accuracy and applicability of the derived features.

[0052] Furthermore, natural gas production and operation not only depends on the physical parameters on site, but also needs to be combined with external data such as market price fluctuations and weather conditions. Therefore, the feature extraction module needs to extract fusion features from the multi-source data to help the model understand the current production and operation environment more comprehensively. The system continuously obtains market price data (such as spot prices, futures prices) and weather data (such as temperature, humidity, wind speed) through the API interface. Based on the data, the system will calculate fusion features such as "the correlation between market price fluctuations and changes in production pressure" and "the impact coefficient of weather conditions on natural gas demand". For example, when the market price fluctuates violently, the system will use the price volatility as a feature, perform correlation analysis with the production pressure data, and extract comprehensive features that can reflect the impact of market supply and demand on production operations. The fusion features are generated through cross-domain data fusion technology, which can help the model more accurately predict changes in market demand and optimize production strategies.

[0053] Furthermore, in order to ensure the equal contribution of different features in the model and prevent deviations in model training due to different dimensions, the system standardizes and normalizes the extracted features. In this way, all features are mapped to the same scale for easy model processing and analysis. The system first performs statistical analysis on each extracted feature and calculates its mean, standard deviation and other statistics. Next, the system standardizes the features (i.e., subtracts the mean and divides by the standard deviation) or normalizes them as needed (i.e., maps the feature values ​​to the range of 0-1). For example, pressure features and market price features may have different magnitudes and fluctuation ranges. The system maps them to the same magnitude through standardization to ensure that their contributions to model training are not unbalanced due to numerical differences.

[0054] In the present invention, multi-source data refers to real-time data from multiple different data sources, including sensor data of natural gas production equipment, market price fluctuation data and weather forecast data. The data sources provide information on the production environment, market demand and the impact of the external environment on natural gas production. Multi-source data is collected in real time by the edge data acquisition module, and its consistency and integrity are ensured through data cleaning, noise reduction and time synchronization.

[0055] Merge analysis refers to integrating pre-processed data from different data sources into a unified feature vector and analyzing it in the central processing module. In the process, the central processing module uses stream computing technology to perform real-time aggregation and complex event processing on the multi-source data, and extracts key features for model training. Specifically, the central processing module analyzes sensor data, market price data, and weather data in different time windows through sliding windows and event detection mechanisms to identify the potential impact of production anomalies, price fluctuations, and weather changes on production and operations.

[0056] The output of the central processing module is multi-dimensional feature data after feature extraction, which is directly input into the model for training and optimization. The model processes the data through an online learning algorithm to generate the optimal strategy that adapts to current production and market conditions. The logical relationship between the central processing module and the model is that the central processing module provides the model with multi-source feature data updated in real time, and the model uses the features to generate and adjust strategies, forming a closed-loop adaptive strategy optimization system.

[0057] S200, further stream processing and intelligent scheduling are performed on the pre-processed data on the central server. Step S200 at least includes steps S210-S240: S210: Use the stream computing framework to process the pre-processed data transmitted from the edge in real time. Specifically: The data transmitted from the edge computing device includes sensor data from the natural gas production site, market price fluctuation data, and weather forecast data. The data has been pre-processed and cleaned, but when entering the stream computing framework, further real-time access and preliminary filtering are still required to ensure the integrity and consistency of the data. The system uses Apache Kafka as the input pipeline of the data stream to access the data stream from the edge computing device in real time. The data stream is divided into different topics or channels, for example, one channel for sensor data and another channel for market data. Kafka is responsible for transmitting the data in an orderly manner to Apache Flink for processing. Before the data enters Flink, the system performs preliminary filtering to remove duplicate data or remaining abnormal data to ensure that each data can be effectively processed. The filtering criteria are based on predefined rules, such as timestamp validity check and data type integrity verification.

[0058] Furthermore, in order to extract useful information from streaming data, the system needs to perform real-time aggregation and window processing on the data. Window processing can group data by time or event, so as to better analyze the trends and changes of the data. In Flink, the system uses sliding window or tumbling window technology to group the incoming data stream. For example, for sensor data, the system can set a time window (such as 5 minutes). Within this time window, the system will aggregate data such as pressure and temperature, and calculate statistics such as average, maximum, and minimum. At the same time, market price data can also be aggregated according to the time window to calculate the price fluctuation range and trading volume within a period of time. The aggregated data can more accurately reflect the overall trend over a period of time, providing a basis for subsequent model analysis.

[0059] Furthermore, in the natural gas production and operation environment, a single data stream often cannot fully describe the state of the system. The system needs to use complex event processing (CEP) technology to combine multiple data streams for analysis to identify possible abnormal events or key decision points. In Flink, the system uses the CEP engine to merge and analyze data streams from different channels (such as sensor data streams, market data streams, and weather data streams). For example, the system can define a rule that when it detects that the pressure value of the pressure sensor suddenly increases and the market price fluctuates significantly during the same period, the system can identify that this may be due to an increase in production pressure caused by tight market supply and demand. This multi-stream merging process can help the system identify potential risks or opportunities in advance and adjust production strategies in a timely manner.

[0060] Furthermore, after data aggregation and event processing, the system needs to analyze the data in real time and generate key features that can be used for model input. The features include the operating status of production equipment, the fluctuation trend of market prices, and the impact of weather on production. The system uses Flink's real-time computing capabilities to conduct in-depth analysis of the aggregated data. For example, while calculating pressure and temperature, the system also calculates the changing trend and correlation of the parameters to generate features such as "pressure-temperature correlation coefficient". At the same time, the system also analyzes the fluctuation pattern of market prices and generates features such as "price volatility" and "market supply and demand balance index". For weather data, the system generates "extreme weather index" or "weather impact coefficient on demand". The features are then transmitted to the real-time model training and adaptive adjustment module for model training and adjustment.

[0061] S220: While processing data, the system intelligently and dynamically allocates computing tasks to the most appropriate computing nodes or servers based on the current load and resource availability. Specifically: During the operation of the system, real-time data streams generated by different data sources (such as sensor data, market price data, and weather data) may cause dynamic changes in computing load. The intelligent scheduling module first needs to monitor and evaluate the current computing tasks and resource utilization in real time. The system collects key indicators such as CPU usage, memory usage, and network bandwidth usage of each computing node in real time through built-in monitoring tools. The indicators are used to evaluate the current load of each computing node. For example, when the system receives high-frequency sensor data (such as thousands of pressure and temperature data per second), some computing nodes may tend to be saturated due to high load. At this time, the intelligent scheduling module will immediately detect the high load state of the node and record indicators such as the task queue length and the average processing time of the task as the basis for subsequent scheduling decisions.

[0062] Furthermore, different data processing tasks have different priorities in the system. For example, real-time sensor data related to safe production may have a higher priority, while the processing of certain market data may be relatively delayed. The intelligent scheduling module needs to dynamically adjust the priority of the task according to the nature of the task and the current system status. The system prioritizes each task according to its importance and timeliness. For example, sensor data may be marked as "high priority", while weather data may be marked as "medium priority". When market price data fluctuates violently, the system may also temporarily upgrade it to "high priority". Based on the priority labels, the intelligent scheduling module gives priority to high-priority tasks when resources are tight, ensuring that critical tasks can be processed in the shortest time, thereby ensuring the real-time responsiveness of the system.

[0063] Furthermore, based on real-time monitoring and priority adjustment, the intelligent scheduling module needs to dynamically allocate the computing tasks of the current system to the most suitable computing nodes or servers. The scheduling algorithm takes into account factors such as the real-time requirements of the task, the current load of each node, the availability of computing resources, and network latency. The system uses scheduling algorithms (such as scheduling strategies based on genetic algorithms or reinforcement learning) to evaluate the processing capabilities and current load conditions of each computing node. Then, the system assigns new tasks to nodes with light loads, sufficient resources, and low network latency. For example, if the system detects that the CPU utilization of a node is low and the network latency is within an acceptable range, new sensor data processing tasks will be assigned to this node first. The scheduling algorithm also regularly re-evaluates and adjusts task allocation to ensure load balancing and optimal resource utilization of each node. For sudden large-scale data flows (such as large data updates caused by drastic market price fluctuations), the system will adopt a load balancing strategy to distribute tasks to multiple nodes for processing to avoid single-point overload.

[0064] Furthermore, in order to maximize the resource utilization of the system, the intelligent scheduling module needs to continuously optimize resource allocation and perform task migration when necessary, that is, migrate some assigned tasks from a heavily loaded node to another lightly loaded node. When the system monitors that the load of a node continues to increase and exceeds the preset safety threshold, the intelligent scheduling module triggers the task migration mechanism. This mechanism analyzes which tasks can be safely migrated (such as non-critical tasks that occupy higher resources for a short time) and migrates them to other nodes with lighter loads. At the same time, the system evaluates the network overhead and possible delay effects of the migration to ensure that task migration does not cause a decrease in the overall performance of the system. Through this dynamic resource scheduling and task migration, the system can still maintain efficient and stable operation under high load conditions.

[0065] S230: dynamically adjust the task allocation of each node according to the current workload of the system to avoid excessive concentration of computing tasks on a certain node and prevent performance bottlenecks. Specifically: In the natural gas production and operation strategy recommendation system, the data sources (such as sensor data, market price data, and weather data) generate a large amount of data streams in real time. The data needs to be processed immediately to ensure the system's response speed and the accuracy of decision-making. The dynamic load balancing module first needs to continuously monitor the real-time workload of each computing node. The system forms a global view of the current system workload by collecting key indicators such as CPU utilization, memory occupancy, and network bandwidth utilization of each computing node in real time. For high-frequency sensor data (such as pressure and temperature data updated multiple times per second), the system can quickly capture the instantaneous high load caused by the data stream. If the load of a node exceeds the preset threshold (for example, the CPU utilization exceeds 85%), the system will trigger the load balancing mechanism to prevent the node from overloading and causing performance degradation.

[0066] Furthermore, when the system detects that the load of certain nodes exceeds the set safety threshold, the dynamic load balancing module needs to immediately reallocate some tasks to nodes with lighter loads. This process must be done quickly and seamlessly to ensure the continuity and timeliness of data processing. The system uses intelligent scheduling algorithms (such as scheduling strategies based on load prediction) to transfer some tasks on the current heavily loaded nodes to other nodes with lighter loads. The system also considers factors such as network latency and data transmission speed to ensure that task migration does not lead to a decrease in overall processing efficiency.

[0067] Furthermore, in the dynamic load balancing process, the system not only needs to handle instantaneous load peaks, but also needs to optimize the global load distribution through long-term strategy adjustments to prevent certain nodes from being in a high-load state for a long time and affecting the stability of the system. The system uses historical load data and trend analysis to dynamically adjust the load balancing strategy. For example, if the system finds that certain nodes are often in a high-load state during certain periods of time (such as market trading peaks), the system will pre-adjust the task allocation strategy and pre-allocate some tasks during the said period to other nodes to avoid sudden load shocks during peak periods. At the same time, the system will dynamically adjust the task allocation frequency and data flow processing priority according to the current available resources of each node to ensure that the resource utilization of each node remains within a reasonable range.

[0068] Furthermore, during the operation of the system, the system may face performance bottlenecks due to a sudden increase in data traffic or resource failures at certain nodes. The dynamic load balancing module needs to have the ability to quickly avoid performance bottlenecks to ensure the high availability of the system. When the system detects that a node is approaching a performance bottleneck (such as memory usage approaching 100% or network bandwidth approaching saturation), the system will quickly reduce the load on the node and assign new tasks to other nodes. At the same time, the system will temporarily increase the resource allocation limit of other nodes and increase their processing power to take over tasks migrated from bottleneck nodes. In addition, the system will also start a failover mechanism to ensure that in extreme cases, the system can quickly transfer critical tasks to backup nodes to prevent service interruptions.

[0069] S240: Optimize the data processing flow to reduce processing delays and waste of computing resources. Specifically: In the natural gas production and operation strategy recommendation system, from sensor data, market price data to weather data, the multi-source data streams need to pass through multiple processing nodes. The primary task of data stream optimization is to design and optimize the processing path of the data stream to reduce unnecessary delays and redundancy in data transmission. The system first analyzes the entire data stream path from the data source to the final decision output. Through a stream computing framework (such as Apache Flink or Apache Kafka), the system optimizes the data transmission path and reduces the number of processing times at intermediate nodes. For example, after the sensor data is initially processed by the edge computing device, it is directly transmitted to the central processing node for further analysis without passing through irrelevant intermediate nodes. The design of this optimized path makes the entire process of data flow from acquisition to decision-making more efficient and reduces unnecessary delays, especially in the case of high-frequency data updates (such as collecting and processing thousands of data per second).

[0070] Furthermore, in data flow optimization, parallel processing is a key means to improve processing efficiency. By splitting data processing tasks into multiple parallel processing flows, the system can process multiple data streams at the same time, thereby greatly reducing processing time. The system assigns different data streams to different computing nodes for parallel processing based on the type and priority of the data stream. For example, pressure sensor data, temperature data, market price data, and weather data can be processed in parallel on different nodes, and then merged at the central node. This parallel processing mechanism not only improves the efficiency of data processing, but also ensures that the system can respond in a timely manner without performance bottlenecks during peak data traffic periods (such as when market prices fluctuate sharply or extreme weather warnings occur).

[0071] Furthermore, in order to ensure real-time processing of data streams, the system needs to dynamically adjust the processing flow according to the current task load and the priority of the data stream, and give priority to key data streams to ensure the real-time responsiveness of the system. The system sets different processing priorities according to the timeliness and importance of the data. For example, sensor data related to safe production will be given priority, while market price data and weather data will dynamically adjust their processing priorities according to the current market or meteorological conditions. When the system detects drastic fluctuations in market prices or extreme weather is approaching, it will immediately increase the priority of the data stream to ensure that they can be processed in the shortest time, thereby affecting the system's decision-making. This real-time dynamic adjustment mechanism ensures that the system can flexibly respond to emergencies, reduce delays, and improve the timeliness and accuracy of decision-making.

[0072] During the data flow optimization process, the system also needs to optimize the use of memory and cache, reduce unnecessary memory usage, and avoid processing delays caused by memory bottlenecks.

[0073] S300: Combine the output of the central processing module to perform real-time training and adaptive adjustment of the model. Step S300 at least includes steps S310-S340: S310: After receiving the real-time processed data, the system uses the online learning algorithm to perform incremental training on the existing model. Specifically: ①Definition and symbolic representation of input data In the natural gas production and management system, the following input data symbols are used: P t : Wellhead pressure data at time t (unit: MPa) T t : Wellhead temperature data at time t (unit: °C) Q t : Natural gas flow data at time t (unit: cubic meters / hour) M t : Market price fluctuation data at time t (unit: yuan / cubic meter) W t : Weather data at time t, including temperature, humidity, wind speed and other dimensions (unit: combined data vector) X t =[P t ,T t ,Q t ,M t ,W t ]: Combined feature vector, representing all input data at time t.

[0074] ②Construction of original model algorithm formula

[0075] Among them, Θ t is the model parameter vector at time t; α, β, γ are the learning rate coefficients of the model, which control the contribution of each part to the overall update; is the loss function L for the model parameter Θ at the previous moment t-1 The gradient of the model error indicates the gradient descent direction; J t is the Jacobian matrix, which represents the sensitivity of each input feature to the loss function (partial derivative matrix); is the Hessian matrix, which is a second-order derivative matrix used to describe the curvature of the loss function and help determine the adaptability of the step size. is the trace of the Hessian matrix, which is used to represent the curvature effect of the model in high-dimensional space; It is a high-order topological correction term, which is used to correct the topological relationship between historical parameters of the model to ensure the continuity of the topological structure during the model parameter update process.

[0076] ③ The operation process of the original algorithm, which performs real-time updates and adaptive adjustments of the model through the following steps: A. Real-time data input and preprocessing. The system receives the latest sensor data P t ,T t ,Q t 、Market price dataM t and weather data t After that, the data is integrated into a combined feature vector X t Before being transmitted to the model, the data is normalized and feature extracted so that each input data has the same dimension.

[0077] B. Gradient update and sensitivity analysis. The system first calculates the gradient of the loss function L relative to the current model parameters. Next, use the Jacobian matrix J t Perform sensitivity analysis on each input feature to identify the features that have the greatest impact on the model output.

[0078] C. Curvature correction and topology optimization. In order to improve the convergence speed and stability of the model, the system calculates the Hessian matrix And extract its trace , to adjust the update step size of the model parameters. In addition, the system uses the topology correction term Correct historical changes in model parameters to avoid excessive parameter jumps.

[0079] D. Incremental update of model parameters. The system uses the above formula to perform incremental update of parameters △Θ t , which is added to the current model parameters Θ t-1 Generate updated model parameters Θt =θ t-1 +△Θ t This update process can adapt to dynamic changes in data in real time without retraining the model from scratch.

[0080] E. Model performance verification and adaptive adjustment. After each update, the system uses the latest validation data to evaluate the model performance. If the model performs poorly, the system will fine-tune it by adjusting the learning rate coefficients α, β, γ or introducing more historical data to ensure the accuracy and robustness of the model in a real-time environment.

[0081] S320: The system automatically adjusts the structure and parameters of the model according to the changes in real-time data. Specifically: ①Assume that the initial state of the model is represented by the parameter vector θ0.

[0082] ② Functional representation of adaptive adjustment. The system defines the adaptive adjustment process of the model through functional analysis methods. Set a functional F, which reflects the dependence of model parameters on input data:

[0083] Among them, θ(t) is the model parameter vector at time t; L is the Lagrangian function, which represents the loss function of the model and reflects the error between the model prediction and the actual data.

[0084] ③ Adaptive adjustment of high-order partial derivatives. In order to enable the model to adapt to data changes in real time, the system calculates the high-order partial derivatives of the model loss function L with respect to the parameter θ and makes adjustments based on this. The update formula for the adjusted model parameter θ(t) is:

[0085] Among them, η is the learning rate, which controls the step size of parameter adjustment; κ is the second-order term coefficient, which reflects the sensitivity to higher-order changes; and are the first and second order partial derivatives of F with respect to the parameter θ, respectively.

[0086] ④ Topology adjustment and model structure optimization. In some cases, the system requires a larger-scale structural adjustment. At this time, the system uses the concept of topology to adaptively adjust the structure of the model. Define the topological structure T of the model and dynamically adjust its topological characteristics according to the input data:

[0087] Where G is the graph representation of the model, nodes represent features, and edges represent the connections between features; T(G) is the current topological structure; is the rate of change of pressure data; δ is the preset change threshold. When the change exceeds the threshold, the system will trigger structural adjustment and adjust the model structure to T´(G).

[0088] ⑤ Feedback mechanism and iterative optimization. The system monitors the performance of the model after adaptive adjustment through the feedback mechanism, and continuously fine-tunes the parameters through iterative optimization. Define a feedback functional G to represent the model performance feedback:

[0089] in, is the actual market price data; is the market price predicted by the model; the system uses the value of the feedback functional to dynamically adjust parameters such as η and κ to ensure that the model is gradually optimized during the adaptive adjustment process.

[0090] S330. Regularly evaluate the prediction accuracy and robustness of the model, identify possible deviations or failures, and correct the model through automatic calibration mechanisms. Specifically: ① Definition of input data and model prediction error. Assume that the output of the model at time t is , the actual observed value is Y t . Prediction error e t Defined as:

[0091] Among them, e t Represents the prediction error of the model at time t.

[0092] ② Error function and robustness evaluation. In order to evaluate the robustness of the model, an error functional is introduced and robustness function , the function is defined as:

[0093]

[0094] Where Ω represents the state space of the natural gas production system, x represents the position vector in the space, and the error functional By integrating the entire state space, the global error of the system is quantified; the robustness function It represents the sensitivity of the error to time changes. This function can be used to determine the stability of the model in the face of drastic changes.

[0095] ③ Mathematical implementation of the automatic calibration mechanism. According to the results of the system error and robustness analysis, the automatic calibration mechanism will adjust the model parameters θ tTo minimize the error and enhance the robustness. The optimization algorithm based on the Lagrange multiplier method is used, and the Lagrange function is defined as:

[0096] where λ is the Lagrange multiplier, is the preset robustness threshold. During calibration, the following optimization problem is solved:

[0097] By solving this set of equations, the optimized model parameters are obtained ; This process ensures that the calibrated model can maintain high robustness while minimizing errors, that is, it can still work stably when the external environment changes.

[0098] ④ Topology adjustment and model structure optimization. During the calibration process, if the error or robustness problem is caused by the limitations of the model structure, the system will consider adjusting the topology structure. The topology graph G(V,E) of the defined model, where V is a set of nodes (such as different features or levels) and E is a set of connection weights. The objective function T(G) of topology adjustment is defined as:

[0099] Among them, k ij is the error coupling coefficient between nodes, w ij is the connection weight and β is the regularization parameter.

[0100] By optimizing T(G), the updated topological structure G*(V*,E*) can be obtained, thereby improving the global performance of the model. The optimization process uses topological theory to adjust the overall structure of the model to make it more suitable for current data and environmental requirements.

[0101] ⑤ Verification and long-term optimization after calibration. Finally, the model calibrated through the above steps will be verified on actual data. The system will calculate new error functionals and robustness functions, and make further adjustments or confirmations based on the results.

[0102] The long-term optimization part uses an iterative update strategy:

[0103] Where η is the learning rate. This iterative strategy ensures that the model can be continuously optimized in the long term, adapt to new data changes, and trigger topology adjustment or parameter calibration again when necessary.

[0104] S340: This module can also integrate multiple different models and dynamically select or weight the output of the models to generate the best policy recommendations. Specifically: ① Mathematical basis of multi-model integration and selection. In order to reflect originality in multi-model integration and selection, the following formula is designed: Multiple regression model: used to capture the linear relationship between multiple variables:

[0105] Among them, X k (t i ) represents the kth input variable (such as ), β k is the regression coefficient, is the error term.

[0106] Neural network model: used to process complex nonlinear features, applying higher-order function partial derivative formulas and complex analysis:

[0107] Among them, σ(·) is the activation function, w j ,w jk is the network weight, b j , b0 is the bias term, and f(·) is the nonlinear activation function of the output layer.

[0108] Topology optimization model: Use topology methods to optimize the combination of outputs from different models:

[0109] Among them, Ω is the parameter space, It is a combined weight function that is adjusted based on the dynamic changes of the input data.

[0110] ② Dynamic weighting and selection of model outputs. The system dynamically weights or selects model outputs through the following formula to generate the best strategy recommendations: Weighted average strategy:

[0111] Among them, Y k (t i ) are the outputs of different models (such as Y R (t i ),Y N (t i ),Y T (t i )), α k (t i ) is the dynamic weight updated according to real-time data, and the weight update formula is:

[0112] Among them, e k (t i) is the error of the kth model, and η is the learning rate.

[0113] Application of Bellman-Ford algorithm: used to deal with path optimization problems in dynamic weighted selection:

[0114] Among them, c k (t i ) is the path weight, which is dynamically adjusted according to historical data performance.

[0115] ④ Model selection and strategy generation. Based on the output of each model, the system generates the final strategy recommendation Y(t i ) and apply it to production or market decisions. Fluctuating weather data When possible extreme weather is displayed, the system may rely more on the nonlinear processing capabilities of the neural network model, dynamically increase its weight, and generate strategies to cope with complex environments.

[0116] ⑤ Long-term evaluation and model optimization. The system continuously monitors the performance of multi-model integration and adjusts the parameters and weights in the formula based on long-term real-time data to maintain long-term stability and effectiveness. The system analyzes the performance of the model in different scenarios, adjusts the learning rate η in the weight update formula or updates the combined weight function in the topology optimization model , ensuring the robustness and prediction accuracy of the integrated model in dynamic environments.

[0117] The output results of the data processing and analysis module (S300) directly affect the working process of the optimization algorithm and decision model (S400). S300 generates a structured data set through efficient processing and intelligent analysis of multi-source data, which will be used for subsequent strategy optimization. Specifically, the workflow of S300 includes the following steps: 1. Data preprocessing and feature extraction: S300 performs data cleaning, denoising, outlier processing and data format standardization from multi-source data (including sensor data from natural gas production equipment, market fluctuation data and weather data) to ensure that all data can be processed uniformly. Furthermore, the system extracts key physical features based on sensor data (such as pressure, temperature, flow, etc.), extracts price change rate and trading volume fluctuation range based on market price fluctuation data, and extracts key risk indicators such as extreme weather conditions based on weather data.

[0118] 2. Data merging and integration: The merging and analysis step in S300 integrates the features of data from different sources. For example, the system synchronizes different data sources based on timestamps and calculates comprehensive features based on the relationship between production data and market fluctuations and weather risks, such as "the correlation coefficient between pressure changes and price fluctuations" or "the probability of the impact of weather risk index on production safety". The features play a key role in the subsequent strategy generation process.

[0119] 3. Data windowing and time series processing: Production data, market data and weather data have time series characteristics. During the processing, the S300 will decompose the data into several time windows (for example, 5-minute and 1-hour windows), and perform aggregation and statistical processing within the time windows to generate time series data. The sequence data provides a reliable basis for subsequent trend prediction and model training.

[0120] Further, after completing the processing of S300, all generated data features will be passed as input to the optimization algorithm and decision model module (S400). Each model in S400 will perform strategy optimization based on the input data.

[0121] The optimization algorithm and decision model of S400 are not processed separately, but directly depend on the output results of S300. In S400, the optimization algorithm receives the structured data transmitted from S300 and performs the following processing: 1. Model training and real-time correction: The optimization algorithm and decision model of S400 will use the time series data generated from S300 and its associated feature sets as training data. Specifically, it includes training production forecasting models and market response models through machine learning and deep learning algorithms. For example, in natural gas production, combined with the feature of "correlation between production pressure and market price change rate" provided in S300, the training model predicts future production pressure changes and market demand fluctuations. In this process, all time series data and multi-source fusion features processed in S300 are important inputs for model training, and the prediction model is corrected through dynamic adjustment of real-time data.

[0122] 2. Multi-objective optimization and strategy generation: Based on the comprehensive data output by S300, S400 generates production strategies and market trading strategies through a multi-objective optimization algorithm. Specifically, the system evaluates each strategy by combining multiple objectives such as the operating efficiency of production equipment, production safety, and market returns through a weighted model. For example, the system predicts the maximum operating capacity of production equipment through physical parameters such as pressure, temperature, and flow extracted by S300, and then compares it with the market price forecast results to generate the optimal production and sales plan suitable for current production conditions and market conditions. The fusion features of the time series and multi-source data generated in S300 directly affect the optimization algorithm of S400, ensuring that the generated strategy is based on the results of real-time and high-dimensional feature analysis.

[0123] 3. Feedback mechanism and dynamic adjustment: The optimization process of S400 not only relies on the input of S300, but also continuously adjusts through the closed-loop feedback system. After the actual execution of the strategy, the system will continue to monitor the operating data of the production equipment and the market transaction data, and feed the real-time data back to S300 for reprocessing. This feedback data will also be re-entered into the decision model in S400 to further optimize the strategy.

[0124] S300 and S400 have achieved a close logical connection through the processing of multi-source data, extraction of time series features, model training and optimization strategy generation. The data and features processed in S300 are directly used as inputs of S400, ensuring the rationality, effectiveness and real-time nature of strategy generation.

[0125] S400: Monitor and adjust the execution effect of the strategy through a closed-loop feedback mechanism. Step S400 at least includes steps S410-S440: S410: Based on the real-time adjusted model output data, the system automatically generates the optimal production and operation strategy. Specifically: ① Real-time data input and model output analysis. During the strategy generation process, the system first receives real-time input from different data sources, and analyzes the data in combination with the model output results to provide basic information for strategy formulation. The system dynamically adjusts the strategy generation rules based on the model output results and real-time input data to ensure that the strategy can effectively respond to the current production and market environment. The strategy generation rules cover the following aspects: Production adjustment: according to wellhead pressure and natural gas flow Based on the data, the system determines the stability of current production and capacity utilization and generates a production adjustment strategy. For example, when the system detects that the changing trend of pressure and flow may lead to overload, it will recommend reducing the production speed or adjusting the output to avoid equipment overload.

[0126] Equipment maintenance schedule: combined with temperature and pressure data The system predicts the possible failure risk of the equipment and generates a maintenance scheduling strategy. For example, if the system finds that the temperature and pressure at a certain moment are significantly deviated from the normal range, the system will generate a recommendation for immediate shutdown maintenance or adjustment of equipment operating parameters.

[0127] Market trading strategy: according to market price fluctuations and weather data , the system generates a market trading strategy. If the market price shows an upcoming upward trend, the system may recommend increasing production and selling inventory at the right time; on the contrary, if the price is predicted to fall, it may recommend reducing production or delaying sales.

[0128] ③Mathematical optimization process of strategy generation During the strategy generation process, the system uses mathematical optimization methods to evaluate and select the strategy options to ensure that the generated strategy is optimal under multi-dimensional objectives. This optimization process involves the following key steps: Objective function setting: The system sets the objective function of multi-objective optimization, taking into account multiple factors such as output maximization, cost minimization, and market revenue maximization. Assume that the objective function is:

[0129] Among them, w1, w2, and w3 are the weights of each goal, and the system adjusts them according to the current environment and policy priorities.

[0130] S420: The system transmits the generated strategy to the decision maker or the automated execution system through the interface. Specifically: After the strategy generation is completed, the system first needs to pass the strategy to the relevant decision makers or automated systems in an operational form. This process involves converting complex strategies into concise and clear instructions and generating corresponding decision interfaces. The system formats the output results in the strategy generation module, such as production adjustment instructions, equipment maintenance plans, market trading strategies, etc., into text or graphical interfaces that are easy to understand and operate. The instructions are based on core data input, and the decision interface is presented to users, such as production schedulers, equipment maintenance personnel, or market analysts, through the API interface provided by the system. The interface can include detailed strategy descriptions, recommended operating steps, expected results, and necessary adjustment options.

[0131] Strategy application of automated execution system. When the strategy is recommended for the automated execution system, the strategy instructions can be directly converted into execution operations and applied to actual production operations, equipment control or market transactions. Based on the generated strategy, the system sends the instructions to the relevant automated equipment or system through the control interface. For example: Production scheduling: The system receives and interprets real-time pressure and flow data and automatically adjusts the production line output settings. For example, when the pressure exceeds the safety threshold, the system automatically reduces the output to ensure equipment safety.

[0132] Equipment control: Based on the temperature and equipment operating status, the system automatically performs equipment maintenance operations, such as adjusting the temperature, starting backup equipment, or performing equipment maintenance. For example, when an abnormal temperature rise is detected, the system immediately shuts down the machine and notifies the maintenance team.

[0133] Market transactions: The system combines market prices and weather data to automatically execute market transaction strategies, such as adjusting the timing and price of natural gas sales. When market prices rise, the system will automatically instruct the inventory management system to release more inventory and sell it; when prices fall, sales may be delayed until prices rise again.

[0134] Real-time monitoring and feedback of strategy execution. During the strategy execution process, the system needs to continuously monitor the execution effect of the strategy and make necessary adjustments based on real-time feedback to ensure the effectiveness of the strategy. The system monitors various key indicators in real time during execution, such as pressure, temperature, market price changes, etc. of production equipment. Once it is detected that the actual execution effect is significantly different from the expected (such as the pressure has not been effectively reduced or the market price fluctuations are beyond the forecast range), the system will immediately trigger the feedback mechanism to adjust the strategy or execution method. For example, the system may further reduce production, increase maintenance frequency, or modify the trading plan to ensure the best effect.

[0135] Manual intervention and strategy adjustment by decision makers. Although the system can automatically execute most strategies, some complex situations may require manual intervention by decision makers. The system provides decision makers with the necessary tools and information so that they can adjust or intervene in strategy execution when necessary. When unforeseen abnormal situations occur during strategy execution (for example, equipment failure or extreme market changes), the system issues warnings through the decision interface and provides real-time data and adjustment suggestions. Decision makers can adjust parameters, modify strategies or directly intervene in operations through the interface, such as temporarily shutting down certain production equipment, increasing market supply or adjusting sales prices. The system combines the information of manual adjustments with the automatically executed strategies and dynamically updates the subsequent execution plan.

[0136] The system will also conduct long-term tracking and analysis of the effects after execution, combining historical data and execution feedback to optimize the future strategy generation and execution process. The system analyzes the historical data of strategy execution to evaluate the success rate of the strategy, problems in execution, and room for improvement. For example, the system may analyze the deviation between market trading strategies and actual returns over a period of time, or the impact of equipment maintenance strategies on reducing downtime. Based on the analysis results, the system will update the rules and optimization algorithms in the strategy generation module to improve the accuracy and execution efficiency of future strategies.

[0137] S430: The system continuously monitors the effectiveness of strategy execution and evaluates strategy execution through feedback data. Specifically: Closed-loop feedback management: The system continuously monitors the effectiveness of strategy execution and evaluates the implementation of the strategy through feedback data (such as the comparison of actual production data with expected output). If the strategy effect is found to be inconsistent with expectations, the system will automatically feedback to the real-time model training and adaptive adjustment module, triggering the model to be readjusted, forming a closed-loop optimization mechanism.

[0138] In the natural gas production and operation strategy recommendation system, the closed-loop feedback management module plays a key role. It monitors the actual execution data in real time and compares the data with the model output when the strategy is generated. If the actual output is found to be significantly different from the expected, the system will immediately start the feedback mechanism.

[0139] First, the system compares the collected real-time data with the strategy execution target and analyzes the reasons for the deviation. For example, the system may find that the actual natural gas production is lower than expected because the wellhead pressure fluctuation exceeds the model prediction range or the market price changes suddenly. At this time, the system feeds back the deviation to the real-time model training and adaptive adjustment module.

[0140] In the feedback mechanism, the system uses the deviation data to retrain and adjust the model. For example, in response to the pressure fluctuations found, the system may adjust the model's sensitivity to wellhead pressure, or modify the weight of the market price's impact on the strategy. In this way, the model can quickly adapt to new environmental conditions, thereby improving the accuracy of subsequent strategy generation.

[0141] Finally, the adjusted model generates a new strategy and is put into execution again. The system continues to monitor the execution effect of the new strategy. If a discrepancy is found again, the closed-loop feedback mechanism will be triggered again to ensure continuous optimization during strategy generation and execution. This closed-loop feedback management ensures that the natural gas production and operation strategy recommendation system can maintain high efficiency, accuracy and adaptability in a dynamic production environment, enabling production and operation to better cope with complex and changing market and environmental conditions.

[0142] S440: The system evaluates the effect of each execution strategy, stores historical strategies and their results, and uses the data to further optimize the future strategy generation process. Specifically: During the natural gas production and operation process, the system will continuously collect and monitor the actual effect data of the strategy execution. The data directly reflects the execution of the current strategy, and the system will compare the data with the expected results when the strategy was generated. For example, a strategy is expected to increase the natural gas production to a certain level under specific pressure and temperature conditions, and the system will monitor whether the actual production data reaches the expected value.

[0143] The system conducts quantitative analysis of the difference between the execution effect and the expected result. This process includes: Error analysis: Calculate the error between the actual output and the strategy expectation. Influencing factor analysis: The system analyzes the cause of the error, which may involve multiple influencing factors. For example, abnormal fluctuations in wellhead pressure may cause production to be lower than expected, or a sudden drop in market prices may cause the market trading strategy to fail to execute as expected.

[0144] Historical data storage and review. In order to optimize future strategies, the system stores the results and related data of each strategy execution in the historical database. The data includes: detailed description of the execution strategy: such as strategy content, execution time, and related conditions (such as pressure, temperature, etc.). Execution effect: including actual output, market performance, etc. Evaluation results: such as error size, possible causes of error, etc.

[0145] Through long-term accumulated data, the system can review and analyze the performance of strategies under similar conditions and identify the characteristics of efficient and inefficient strategies. Based on the analysis of historical data and the evaluation of current strategy performance, the system will optimize the strategy generation process, which includes: Model parameter adjustment: If the system finds that the strategy effect deviates greatly from expectations under certain specific conditions (such as high market volatility or extreme weather), the system will adjust the model parameters. For example, increase sensitivity to market price fluctuations, or change the response strategy to wellhead pressure. Strategy rule optimization: The system will adjust or update the rules for strategy generation. For example, after discovering that the impact of weather conditions on production is greater than expected, the system may increase the weight of weather data in strategy generation, or introduce more complex models to deal with the impact of weather on production. Testing and application of new strategies: The optimized strategy will first be run in a simulated environment or small-scale test to ensure that the optimization is effective before large-scale application. This process ensures that new strategies are verified before implementation, reducing risks in actual operations.

[0146] While implementing the optimization strategy, the system continues to conduct real-time monitoring and effect evaluation, and feeds back the execution effect of the new strategy to the system, forming a closed-loop optimization process. Through this cycle, the system can continuously adjust and improve the strategy generation and execution process to ensure efficient operation in a dynamic market and production environment.

[0147] Another implementation method, Figure 2 As shown, it is a structural block diagram of a natural gas production and operation strategy recommendation method and system according to an embodiment of the present application. Figure 2 As shown, the system may include: Data acquisition unit: used to obtain multi-dimensional data from natural gas production equipment, market platforms and weather forecast systems in real time, including but not limited to pressure, temperature, flow, market price, weather conditions, etc. This unit ensures the timeliness and accuracy of multi-source data through technical means such as sensors and API interfaces.

[0148] Data preprocessing unit: used to clean and reduce noise of collected data, remove outliers and missing values, and handle data inconsistency. This unit uses preprocessing techniques such as filtering algorithms, moving averages, and feature extraction to convert raw data into a format that is easy to analyze in order to improve data quality.

[0149] Stream computing unit: used for real-time stream processing and aggregation analysis of pre-processed data. Through the stream computing framework (such as Apache Flink or Kafka), this unit can quickly process large-scale data streams, extract key features, identify complex events and generate real-time analysis results, ensuring efficient and timely data processing.

[0150] Model training and adaptive adjustment unit: Use online learning algorithms to incrementally train system models and dynamically adjust model parameters based on real-time data to ensure that the model can adapt to data changes and continuously optimize. This unit uses technologies such as reinforcement learning and deep learning to improve the model's prediction accuracy and the effectiveness of strategy generation.

[0151] Strategy generation unit: Generates the optimal production and operation strategy based on the data processed by the model, including production adjustment, equipment maintenance arrangement, market transaction strategy, etc. This unit uses optimization algorithms to comprehensively consider multiple goals (such as production efficiency, cost, market benefits) to generate strategies, and ensures the optimality of the strategy through a mathematical optimization process.

[0152] Execution and monitoring unit: used to convert the generated strategy into specific operation instructions, pass them to related devices or systems through the control interface and execute them. At the same time, the unit continuously monitors the execution effect of the strategy and adjusts the strategy based on real-time feedback to ensure that the implementation of the strategy meets the expected goals.

[0153] Feedback and optimization unit: used to analyze the effect of strategy execution, compare actual execution data with expectations, identify deviations and provide feedback. This unit triggers model retraining and adjustment through a closed-loop feedback mechanism to ensure continuous optimization and adaptability of system strategies.

[0154] The key innovative features of the present invention include: (1) Real-time collection and intelligent scheduling of multi-dimensional data: The system can collect and process multi-dimensional data from production equipment, market and weather in real time to ensure the timeliness and accuracy of the data.

[0155] (2) Real-time adjustment of adaptive models: The system uses an online learning algorithm to perform incremental training and adaptive adjustments to the model based on real-time data changes to ensure the accuracy and robustness of the model.

[0156] (3) Closed-loop feedback mechanism: The system monitors and adjusts the effectiveness of strategy execution through a closed-loop feedback mechanism, continuously optimizes production and operation strategies, and improves the system's adaptability and decision-making accuracy.

[0157] The following are its main beneficial effects: (1) Improving the accuracy and timeliness of strategy generation: By collecting and processing multi-source data from production equipment, market, and weather in real time, the present invention can quickly respond to data changes and generate more accurate production and operation strategies, thus avoiding decision-making errors caused by data lag in traditional methods.

[0158] (2) Enhance the adaptive capability of the system: The present invention introduces an adaptive adjustment mechanism that can automatically adjust model parameters and strategy generation rules based on real-time feedback, thereby maintaining the effectiveness and economic benefits of the strategy in the face of sudden situations such as drastic market fluctuations or extreme weather.

[0159] (3) Improving resource utilization and production efficiency: By optimizing multiple links such as production regulation, equipment maintenance and market transactions, the present invention can maximize resource utilization, reduce unnecessary energy consumption and production costs, and improve overall production efficiency and market competitiveness.

[0160] (4) Providing a closed-loop optimization mechanism: The closed-loop feedback management mechanism in the present invention monitors the execution effect of the strategy in real time, automatically retrains the model and adjusts the strategy, ensures the continuous optimization of the production and operation strategy, adapts to the dynamically changing environment, and further improves the overall efficiency of the system.

[0161] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of the changes or substitutions within the technical scope disclosed in the present application, and the changes or substitutions should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A natural gas production and operation strategy recommendation method, characterized in that: The following steps are involved: S100, real-time collection and pre-processing of multi-dimensional data from natural gas production equipment, market price fluctuation information and weather data, and data cleaning, noise reduction and feature extraction; S200, performing stream computing analysis based on the data, and generating a combined feature vector for model training; S300, using online learning algorithms to incrementally train and adaptively adjust the model to generate optimized production and operation strategies; S400, monitors the effectiveness of strategy execution in real time through a closed-loop feedback mechanism, retrains and adjusts the model based on feedback, and continuously optimizes production and operation strategies.

2. The natural gas production and operation strategy recommendation method according to claim 1, characterized in that: Step S100 includes: S110, real-time collection of sensor data from natural gas production equipment, including pressure, temperature, and flow; S120: Collect market price fluctuation information and weather data, and pre-process and time-synchronize the data.

3. The natural gas production and operation strategy recommendation method according to claim 2, characterized in that: The preprocessing in S120 includes data cleaning, removing outliers and missing values, and performing noise reduction on the sensor data through a filtering algorithm.

4. The natural gas production and operation strategy recommendation method according to claim 1, characterized in that: Step S200 includes: S210, using the stream computing framework to aggregate the preprocessed data in real time and extract key features; S220. Perform complex event processing on multi-source data to identify abnormal events that affect production and operations.

5. The natural gas production and operation strategy recommendation method according to claim 4, characterized in that: The S220 analyzes the sensor data and market data in different time windows through the sliding window technology to generate a feature sequence for subsequent models.

6. The natural gas production and operation strategy recommendation method according to claim 1, characterized in that: Step S300 includes: The model is incrementally trained based on the preprocessed multidimensional data, the sensitivity of the input features to the model output is analyzed through the Jacobian matrix, and the model parameters are adjusted.

7. The natural gas production and operation strategy recommendation method according to claim 6, characterized in that: During the model training process, the Hessian matrix is ​​used to adjust the model parameter update step size to improve the model convergence speed.

8. The natural gas production and operation strategy recommendation method according to claim 1, characterized in that: Step S400 includes: Generate production and operation strategies based on real-time data, including production adjustment strategies, equipment maintenance plans, and market transaction strategies.

9. The natural gas production and operation strategy recommendation method according to claim 8, characterized in that: The production regulation strategy is generated according to the changes in real-time pressure and flow data and is used to optimize natural gas production.

10. The natural gas production and operation strategy recommendation method according to claim 8, characterized in that: The equipment maintenance plan generates equipment maintenance or adjustment suggestions based on abnormal fluctuations in temperature and pressure data.

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